(upbeat music) >> Hey everyone, welcome back to theCUBE live in Miami on the floor of IFS Unleashed. I'm your host, Lisa Martin. Had some great conversations. Have more great conversations coming your way. I have two guests joining me. Please welcome Martin Schirmer, the President of Enterprise Service Management, IFS Assyst. And Parminder Khosa, the Senior IT Manager at Parexel. Guys, it's great to have you on the program. >> Lovely to be here. >> It's good to be here. >> Martin, talk to me a little bit... tell the audience a little bit about Assyst so that that get that context before we start asking questions. >> Yeah. Absolutely. So IFS Assyst is a recent acquisition. It's an acquisition we made about a year ago. And fundamentally, it's a platform that takes care of IT service management, enterprise service management and IT operations management. So think of it, of managing sort of the ERP for IT and then broadening that out into the sort of enterprise where you're driving enterprise use cases for all lines of businesses like HR, finance, facilities, so on and so forth. >> Got it. And then Parminder, give the audience just a little bit of a flavor of Parexel, who you guys are, what you do. >> Sure. >> Maybe the impact that you make. >> Yeah, so Parexel is a clinical research organization. And what that means is that we manage drug trials for big pharmaceutical companies. So we're a big company. We're 25,000 people. We have offices in 150 locations all the way from Japan and the east through to the West Coast of the USA. >> Big company. >> Yeah, we are. We are a lot of people. >> And let's start chatting now Martin with some of the questions that you have so we get the understanding of how IFS and Parexel are working together. >> Yeah. Absolutely. I suppose... I mean the first thing is and thank you for traveling here all the way from the UK. (Lisa chuckles) Appreciate it and great energy and vibe. So just what the first question I had really was, you're customer of ours for the last 15 years plus. Maybe just give the audience a bit of context into your journey and how you've evolved from the sort of early years to where you're going into the future. >> Sure. So our history, I was part of a company that Parexel acquired that was already using Assyst. And as Parexel acquired us, they were in the process of also buying Assyst. So it became a kind of natural fit where I carried on with Assyst. And we started relatively small, sort of just the service desktop. And throughout the ongoing 15 years or so, we've just grown and expanded into kind of being a critical tool for Parexel right now. >> Okay, that's fantastic. I mean part of that journey, I know you started in sort of the more they call a ticketing space or IT service management space. Expand a little bit how you've expanded out of that and really moved into the enterprise. >> Sure. So yeah. So when we first rolled Assyst out, it was as I say, purely IT. And eventually we reached out to other business units to say asking questions like, Are you managing your workload through email? Are you managing your workload through Excel spreadsheets? In which case, if you are, we've got a solution for you that will make it a much better experience for your customers. They're all internal. It'll make it much easier for you because you will have official tracking going on through our system. I'll make it better for your management because we can drive metrics from all of the data that we're getting. So if you imagine finance we're getting, kind of 200 miles a day because of the size of our company. And they were just working through them one by one responding, and they becomes just a mess. So we developed forms for them to say, "Okay, Larry raise all your requests here. We will pick it up. We will manage it. We will communicate with you. And once the piece of work that you've asked for is done, we will let you know." And as we go through that process, we'll make it better for us because as I say we're getting those metrics. And we'll make it better for you because we can spot where our gaps are. If a request is taking three days, and of that three days, two days is waiting for someone on our end to respond to you or is waiting for us waiting for a customer to respond, we can iron those out and make it a much better experience for everyone. >> That's fantastic. It's really music to my ears because we always pushing the industry to say move away from just the IT side and really get into the enterprise. And it sounds like you've really gotten a lot of sort of productivity and efficiency gains out of that. >> Definitely, definitely. And it becomes kind of a happy circle. So the finance guys will work with the procurement guys. And they also look... Well, we're doing all of our work through Assyst now. So procurement's a little turnaround. So, well we're using this big spreadsheet to manage all of ours. Can we do the same? And they'll reach out to us and we'll say, "Of course we can. What is your process?" For example, they will say, okay, if someone asks for a new laptop, we need to get the approval from their line manager, from the supplier. We need to do our own internal work and then we will send it out. So imagine if you're doing that in a an email chain. It just becomes chaos. >> Yeah. >> So we will build all of that out for them. And then procurement will talk to HR and it just becomes a snowball. And before you know it, we are doing about 4,000 tickets per day in our Assyst system. And of those, 50% perhaps maybe more than 50% now will be non IT related. >> Oh, that's fantastic. Really music to my ears. And it really breaking down the boundaries or silos within an organization. It's really good. Let the teams work together. Right? >> Definitely. And that's one of the key things that we've learned is that we have to engage completely with our business partners. And our business partners are becoming more and more IT literate as well. So for example, we had a recent big HR solution provided to us. And as part of that, we know there are going to be questions, and queries and perhaps even issues to do with our HR system. So we have to work with us guys, the Assyst front end, the IT HR guys who look after the databases, all of the technology in the background. Then there'll be IT HR who are Workday experts. And then kind of not necessarily at the bottom of the chain will be the HR people themselves who are in their own way, experts in their area, experts in IT in a certain way. So all of those people have to work together. We become the front end, but we have to work with all of those parts of the business. >> That's really great. It's basically what you just said is taking business, IT processes and underpinning solutions. Effectively digital transformation, right? >> Exactly. Yeah. So HR is a great example. They used to have paper flying around with leave request, with sickness requests, with all of those kind of issues. And you said, well if you have an issue with your HR system, you can't raise a leave request, or you can't raise a sickness request, tell us. We will take care of it. We will fix it for you. We will give you the instructions. And we will get rid of all of that paper. >> That's brilliant. Just sort of turning the attention. And all of that, how do you drive the sort of, we'll talk about the autonomous enterprise. How do you drive automation in that process? >> Yeah. Of course, we have to map all of those processes out. Because we're not the experts in HR or procurement or whatever the business area may be. We have to really dig into their work methods, their working areas. What is necessary for them? What is a must have? What is a like to have? What is we don't really need? So we really drive into that processes. Once we've got those, we will automate them. We will build them out in Assyst with the process designer. It's very intuitive now. The latest version is really good to work with. We will do some pretty clever stuff in there. We'll say, okay the manager approval. If the manager is not there, then escalate it to the next person. Then we go to HR and say, okay HR have taken two days to do this. We're not particularly okay with that. So we will escalate it to the next person. And all of that process is completely automated, completely in Assyst. >> Brilliant. I mean obviously, we have a codeless workflow engine with a designer. And if you look at one of the trends from post covid is a war in talent in particular developers. The IDC says there's going to be around 4 million shortage of developers. What is your view on, how easy... Do I need developers? Is it easy, is it difficult to do these workflow extensions and automations? >> Definitely not, no. So the two key areas that you mentioned that with the customizer to develop the forms to make them available to our end users, drag and drop. Really easy to do. You can put some nice filters in there. You can put some nice variables in there. You can drive intelligent drive the forms from there as well. So if option A is correct, then don't show me option B, show me option C. And all of that is codeless, entirely codeless. I don't need to type any code. And when we move on to a process designer that hooks in nicely with the form customizer because we can say, "Okay, if option B on that form is selected, then runs this process." And all of that process is entirely codeless as well. Drag and drop. Creates some tasks. Create some decisions. >> Fantastic. >> Brilliant. >> Sounds really good. Switching gears a little bit. You spoke about experience, and that's also obviously very topical post, well, Covid becoming a remote workforce. Clearly, we need to be digitally connected to our business and organization because the hybrid workforce, as we all know, is here to stay. And that employee experience is fundamental because it is their sort of channel to the engagement of the organization. Of course, that has retention impacts and productivity impact. So just from your perspective, how was Covid, from your perspective, and how easy or difficult was it to get your employees engaged and productive and working? >> Yeah. And for us, it's a double edged sword Covid was. Because of the nature of our business. We do covid stuff. We do drug stuff. So we may have issues with some trials that are related to that. So we need to escalate those. We need to be aware of them and move them to the top of the chain as soon as possible. And then Assyst becomes a source of truth. Everybody knows that if I've got an issue with the current environment that we're living in, I can raise it in Assyst. And everybody knows that's where that information is. There's no need to have huge conference calls or huge email chains to try and follow those around. So with our Assyst platform, with our employees as well, everybody knew that this is where the source of truth was. We didn't have any dropouts. We didn't have any concerns with our system or performance. We knew it was there. We had to do some work like, as I say, around covid issues just to make sure they get pushed up to the top of the chain. But otherwise, we were fine. And great credit to our IT operations team as well who managed that pretty much seamlessly. >> That's brilliant. That's good news. >> Yeah. >> It really is. Just taking a little bit further and talking a little bit about what next. My team has been, I know, talking to your team about the whole area of asset management. Maybe talk to us a little bit about that journey. >> Sure, sure. So we're an ITOM customer as well. So all of our hardware data is stored within the ITOM platform. So we've pushed out the agents to all of our end user machines, so 25,000 agents. And we're in the process of integrating that into our Assyst platform to make that the single source of truth. And that part of that we're working on the software asset management side as well. So we've got a really good idea of where our software assets are. It comes to all license auditing, we know exactly how much we've got there. And the more complex side of it is of course server. So software management management as well. So we're in the process of getting all of that data as well. So once we've done all that, there is other all as the next step. The next step will be to perhaps do monitoring or pushing out software using the ITOM platform and getting rid of some of the disparate systems that we have right now. >> Well that's good news. And I think I saw a study. I think, every single person as an employee carries around 15 or 20 assets with him at any one time. Be it from a PC, phone, physical software licenses, so on and so forth. In that context, I can imagine the business case around it. >> Definitely. Yeah. And every, again, we map every user to their assets and (indistinct) their assets. And again Assyst as a source of truth for that. So if you want to look at my record, so, all right. Pam's got a laptop. He's got a mobile phone. We're thinking about giving him a tablet, but we'll find out. That he's in the process of getting a tablet as well. So I can have a look at my user record and know exactly what I've got with all of the asset tags and the various links that it has to the software pieces so it becomes a big tree of my assets. >> That's wonderful. Just the question I had was, we spoke about breaking down silos and the enterprise use cases and the effect that has. Do you envisage that Assyst can really get to being enterprisewide as, when I say enterprisewide, everybody in the organization effectively using this tool as their sort of source of experience, and level of automation of process? >> Definitely, definitely. As I say, we're getting... We're really pushing to get to that. As I say, 4,000 tickets a day with a user base of 25,000 kind of means that everybody will interact with the system perhaps every two weeks or so. So we're getting to that point and with the new functionality that's coming out with the Assyst product, with the team's integration, and the bot and everything that will bring to us because we are a big. We use teams. We use bots. We use that kind of technology. It will just fit in seamlessly. And trying to break down the silos, as I say finance, procurement, all of the big beasts within our company already are using the Assyst tool. And we want to bring in more and more of those processes as we mature. >> Brilliant. I think Omnichannel's critical. We want to connect from any device from anywhere. It's just the way we work. So I think that's critical. Teams is of course a a tool that most of us have become too familiar with. >> Yup. (chuckles) >> To be fair. (chuckles) It's better to be here in person finally, right? >> Yeah. >> So I think, that's all exciting news. And it's really fantastic. >> Great. >> So I suppose maybe in the time that we have left, what's next? >> What's next for us is that we're in the process of migrating our solution to the cloud, to the IFS cloud. That will open up a huge new user base for us. If we think all of our customers, all of our people who work on studies will have the ability to connect to Assyst and ask questions. That's a lot of it is just ask a question, or raise an issue or ask for something. So we're talking, it could be expanded by hundreds of thousands of new users that will meet more people on the backend to manage those requests as well. So yeah. It's just going to get bigger and bigger. And as you say, with the CMDB work that we're doing as well, that's another big ongoing stream for us. >> It's great because as you know, with Assyst we have a disruptive licensing model. >> Yeah. >> We have a t-shirt size pricing. All you can need based a number of employees. So there's no barriers to entry for you. >> There really is. And that really helps us because as I said initially, particularly when finance came on board and now they're expanding, there is no cost implication for it. The more that we use it, the better it is for. The more bang for buck that we get. >> Yep. That's our mantra. Enterprise users, right? For the price of a cup of coffee, for the price of a user. That's our mantra. >> I love it. You guys have done such a great job of articulating the synergies in the relationship that IFS Assyst has with Paraxel. You talked about the great outcomes that you're achieving. And it's all about Martin, I know, from IFS Assyst perspective, it's all about helping customers achieve those outcomes and those moments of service that are so critical to your customers on the other end staying with you, doing more business. Whether it's the end user customer, whether it's the actual employee. You talked a lot about the customer experience, the employee experience, and what you guys are doing together to enable that. And I always think that the employee experience and the customer experience are like this. They're inextricably linked. You can't, you shouldn't. Otherwise you're going to have problems. >> Yeah, no, absolutely. And there's actually a study on that saying that, 70% of customers generally don't feel they get what they want from organizations. >> 70. Wow! >> And if you take that one step further to what you said, the interconnectivity between customer employee, employee shops on Amazon, right? It's on those websites. So you can't be rolling out and digitally connect to the employee with something that is clunky and has the wrong experience. Like I said, it really affects that level of engagement the employee has with the company which happens to be largely these days remote. >> It does. Last question Martin, is for you. Talk to us about what's next for IFS Assyst. Obviously, we're back in person. There's a lot of momentum about the company. I was talking with Darren, the growth and first half was great. He kind of gave us some teaser about second half, but what's next from your perspective? >> Yeah. So what's next for us is achieving our goal. We are here to disrupt the industry. It's an industry that's dominated by one player and a fair amount of legacy players. We've disrupted the business model as I've told you. We here to do more because it's a simple thing. And that's the word simple. We want to keep things simple. We're going to keep engineering and driving our product forward, right? We've made sure that our platform is up there with the best. Yeah. We've just been certified by pink. Pink is a verification of ITIL four they call it. So it's a body. And the top level is you can get 20 out of 20. We got 17 out of 20. There's only one other vendor that has more than us and it's only by little. And after it's a big white space, the next one is 14. So we on the right track. We are going to of course drive and capture the market. So watch this space. We here to grow. >> We will watch this space. Congratulations on being that disrupter. >> Thank you. >> Parminder great work with what you guys are doing. You did a great job of articulating, as I said, the customers tour here. We appreciate your insights, your time. >> Thank you very much. >> Pleasure. >> All right, my pleasure. >> Thank you. For my guests, I'm Lisa Martin. You're watching The Cube live from Miami on the show floor of IFS Unleashed. We'll be back after a short break.

(upbeat music) >> Hi, everyone. Welcome to today's session of theCUBE presentation of AWS Startup Showcase, the new breakthrough in DevOps, data analytics, cloud management tools, featuring OpsRamp for the cloud management migration track. I'm John Furrier, your hosts of theCUBE Today, we're joined by Jordan Sheer, vice president of corporate marketing and Michael Fisher, director of product management in OpsRamp. Gentlemen, thank you for joining us today for this topic of challenges of delivering availability for the modern enterprise. >> Thanks, John. >> Yeah, thanks for having us. >> Hey, so first of all, I have to congratulate you guys on the successful launch and growth of your company. You've been in the middle of the action of all this DevOps, microservices, cloud scale, and availability is the hottest topic right now. IT Ops, AI Ops, whoever you want to look at it, IT is automating a way in a lot of value. You guys are in the middle of it. Congratulations on that, and congratulations on being featured. Take a minute to explain what you guys do. What's the strategy? What's the vision? What's the platform. >> Yeah, I'll take that one. So I would just kind of take a step back and we look at the broader landscape of the ecosystem of tools that all sits in. There's a lot of promises and a lot of whats and features and functionality that are being announced. Three pillars of durability and all these tools are really trying to solve a fundamental problem we see in the market and this problem transcends the classic IT ops and it's really front and center, even in this modern DevOps market, this is the problem of availability. And so when we talk about availability, we don't just mean the four nines for an uptime metric, availability to the modern enterprise, is really about an application doing what it needs to do to serve the users in a way that works for the business. And I always like to have a classic example of an e-commerce site, right? So maybe you can get to an e-commerce sites online, but you can't add an item to a cart, right? Well, you can't do something that is a meaningful transaction for the business. And because of that, that experience is not available to you as a user and it's not available to the business because it didn't result in a positive outcome. So the promise of OpsRamp is really around this availability concept and the way we rationalize this as a three pillar formats. And so we think the three pillars of availability are the ability to observe data, this is the first piece of it all. And from a problem perspective, what we're really trying to say is do we have the right data at any given point in time to accurately diagnose, assess, and troubleshoot application behavior? And we see it as a huge problem with a lot of enterprises, because data that can be often siloed, too many tools, many teams, and each one has a slightly different understanding of application health. For example, the DevOps team may have a instance of Prometheus or they may have some other monitoring tool, or the IT team may have their own set, right? But when you have that kind of segmented view of the world, you're not really having the data in a central place to understand availability at the most holistic level, which is really from an end-user to that middleware, to the databases, to underlying microservices, which are really providing the end-user experience. So that observed problem is that first thing OpsRamp tries to solve. Secondly, this is the analyze phase, right? So analyze to us means are we giving the proper intelligence on top of the data to drive meaningful insights to this operator and user? And the promise here is that can we understand that baseline performance and potentially even mitigate future instance from happening? How often do we hear a cloud provider going down or some SaaS provider going down because of some microservice migration issue or some third party application or networking they're relying on? I can think of dozens on my head. So that's kind of the second piece. And then lastly is around this act. This is an area of a lot of investment for ops because we think this is the final pillar for nailing this availability problem. Because again, IT teams are not getting larger, they're getting smaller, right? Everyone's trying to do more with less. And so from a platform perspective, how do we enable teams to focus on the most business critical tasks, which are your cloud migrations, adopting microservices to run your modern applications, innovative projects. These are the things that IT and DevOps teams are tasked with. And maintaining availability is not something people want to do, that should be automated. And so when you think of automation, this is a big piece for us. So again, the key problem is how can we enable these IT or DevOps teams to focus on those business critical things, and automate it with the rest. And so this is the OpsRamp's three pillars of availability. >> John: Talk about the platform, if you don't mind. I know you've got a slide on this. I want to jump into it because this comes up a lot, availability's not just throughout uptime, because you know, uptime, five nine reliability is an old school concept. Now you have different kinds of services that might be up but slow, would cause some problems, as applications and this modern era have all these new sets of services. Can you go through and talked about the platform? >> Yeah, absolutely. So OpsRamp has a very... We address this availability problem pretty holistically, like I mentioned. From a platform perspective, there that two core lines that are comprising a product. One is this hybrid monitoring piece. This is that data layer. And the next one is event management, it's more of the we'll talk about that analysis. And so we treat the monitor as a direct feed into this event management. We're layering that on top, or layering machine learning and AI to augment the insights derived from that first pillar. And so this is where we see a really interesting intersection of data science and monitoring tools. We invest a lot in this area because there's a lot of meaningful problems to solve. In particular alert fatigue, or potentially root cause analysis, things that can take an operator or a developer a long time to do on their own, OpsRamp tries to augment that knowledge of your systems and applications so that you can get to the bottom of things faster and get on with your day. And so it's not just for the major outages, it's not just for the things that are on Twitter or CNN that's for daily things that can just distract you from the ability to do your job, which is to be a core innovator for a business. >> I will really say John, that we are already seeing some couple things here. Number one, we're already actually seeing fundamental transformations in the marketplace. Customers who have seen reduction in alert volumes of up to 95% in some cases, which is as you can imagine, that's completely transformational for these businesses. And number two, I think one of the promises of hybrid of observability working in tandem with event and incident management is the idea of finding unknown unknowns within your organization and being able to act upon them. All too many times nowadays, monitoring tools are there to just surface issues that you may know that you're looking for and then help you find it and then take action on them. But I think the idea of OpsRamp is that we really using that big data platform that Michael talks about is to really surface all the issues that you might not be able to see, identify the root cause, and then take action on those root causes. So in our world, application availability is a much more proactive activity where the IT operations team can actually be proactive about these incidents and then take action on them. >> Yes. Jordan, if you don't mind, I'm following up on that real quick. Talk about the difference uptime versus availability, because something could be up and reliable but not available and its services get flaky. Things may look like they're up and running. Can you just unpack that a little? >> So to me, I mean the really key aspect of availability that I think the old definition of uptime doesn't address is performance. That something can be up, but not performing, but still not really be available. And his e-commerce example, I think is a great one. Let's take, for example, you get on Amazon, right? The Amazon e-commerce experience is always available. And what that means is that at any given moment, when I want to click through the e-commerce experience, it performs. It's available. It's always there and I can buy it at any given time. If there's a latency issue, if the application has a lag, if it takes 30 seconds to really perform an activity on that application, in the alternative definition, that's not available anymore. Even though the application may be up, it's not performing, it's not providing a frictionless end customer experience, and it's not driving the business forward, and therefore it's not available. The definition of availability in OpsRamp is creating a meaningful customer experience that actually drives the business forward. So in that definition, if a service is up but it's latent, but it's not providing excellent customer experience that the business wants to promise to its end-user, it's not available. So that's really how we're redefining this whole notion of availability and we're urging our customers and people in the marketplace to do the same. Ask yourself the hard question, is your application available or is it just up? >> Yeah, and I think that the confluence of the business logic around what the outcome is, and I think this is the classic cliche, "Oh, it's all about outcomes." Here, you're saying that the outcome can be factored into the policy of the tech, meaning this is the experience we want for our users, our customers, and this is what we determined as acceptable and excellent. That's the new metric, so that's the new definition. You can almost flip the script. It feels like it's being flipped around. Is that the right way to think about it? >> Well, yeah, I think that's actually absolutely correct that an application needs to be business aware, especially in the modern day because all of the businesses that we work with, their applications are really the stock and trade of the business. And so if you create an application that is not business aware, that is just there for its own sake or is not performing according to the revenue goals or the targets of the business, then it's no longer available. >> I mean, it could be little things. It could be like an interface on the UI, it could be something really small or a microservice that's not getting to the database in time or some backup or some sort of high availability. Really interesting things could happen with microservices and DevOps, can you guys share some examples of what people might fall into from a trap standpoint or just from a bad architecture? What are some of the things that they might see in their environment that would say that they need help? >> Yeah, I can probably take that one. So there's a lot of, I call them symptoms of a bad availability experience. And I wouldn't even say it's a pure microservice specific thing. I would say it's really any application that's end-user phasing. I see similar pitfalls. One is a networking issue. I see the number one thing usually with these kinds of issues that networking or config changes that can cause environments to go down. And so when we talk to organizations get to the bottom of this is usually a config wasn't thought through thoroughly, or it was a QAed, they didn't have the proper controls in place. I would say that's probably the number one reasons I see applications go unavailable. I think that's some majority of DevOps teams that can empathize with that is someone did something and I didn't know, and it caused some applications servers go down and it causes cascading event of issues. That's like modern paradigm of issues. On old school days, it's a layer zero issue, someone unplugged something. Well, modern times it's someone pushed something I don't have an idea of what we're doing opposing a downstream effect it would have been and therefore my application went unavailable. So that's again, probably the number one pitfall. And again, I think the hardest problem in microservices still around networking, right? Enterprise level networking and connecting that with many data center applications. For example, Kubernetes, which is the provider or the opera orchestrator of any microservice is still getting to the level, many organizations are still getting a level of comfort with trusting production applications to run on it because one is a skill gap. There's not many large organizations have a huge Kubernetes application team, usually they're fairly small agile units. And so with that, there's a skill gaps, right? How do you network in Kubernetes? How do you persist in storage? How to make sure that your application has the proper security built into it, right? Because that these are all legacy problems kind of catching up with the modern environments, because just because you're modernizing, it doesn't mean these old problems go away. It just take a different form. >> Yeah. That's a great point. Modernization. You guys, can you guys talk about this modern application movement in context to how DevOps has risen really into providing value there? Certainly with cloud scale and how companies are dealing with the old legacy model of centralized IT or security teams who slow things down? Because one of the things that we're seeing in this market is speed, faster developer time to market, time to value. Especially if you're an e-commerce site, you're seeing potentially real-time impact. So you have the speed game on the application side that's actually good, being slowed down by lack of automation or just slow response to a policy or a change or an incident. I mean, this seems to be a big discussion. Can you guys share your thoughts on this and your reaction to that? >> I can tell you that one of the places that we are displacing, one of the markets that we are displacing is the legacy ITOM market, because it can't provide the speed that you're talking about, John. I think about a couple of specific examples. I won't necessarily name the providers, but there are several legacy item providers that for example, require an appliance. They require an appliance for you to administer IT operations management services. And that in and of itself is a much slower way of deploying item. Number two, they require this customized proof of value, proof of concept operation, where companies, enterprise organizations need to orchestrate the customization of the item platform for their use. You buy separate management packs that would integrate with different existing applications on your stack. To us, that's too slow. It means you have to make a bunch of decisions upfront about your item practice and then live with those decisions for years to come, especially with software licenses. So by even moving that entire operation to SaaS, which is what the OpsRamp platform has done, has accelerated the ability to drive availability for applications. Number two, and I'd like to pitch this over to Michael, because I think this is really fundamental to how OpsRamp is driving availability, is the use of artificial intelligence. So when we think about being proactive and we think about moving more quickly, it takes machine learning to do a lot of that work to be able to monitor alert streams and alert floods, especially with the smaller scale down IT teams that Michael has mentioned before. You need to harness the power of artificial intelligence to do some of that work. So those are two key ways that I see the platform driving additional speed, especially in a DevOps environment. And I'd love to hear as well from Michael, additional enhancements. >> Michael, if you don't mind, I'll add one thing. First of all, great call out there, Jordan. Yeah. So the legacy slow down, it's like say appliance or whatever that also impacts potentially the headroom on automation. So if you could also talk about the AI machine learning, AI piece, as well as how that impacts automation, because the end of the day automation is going to have to be lock step in with the AI. >> Yeah. And this kind of goes back to that OpsRamp three pillars of availability, right? So that's the what we do, but again, it's all goes back to the availability problem. But we see that observe, analyze, and act as a seamless flow, right? To have it under the same group or the same tent provides tremendous opportunity and value for our DevOps or IT Ops teams that trust the OpsRamp platform because I'm a big believer that garbage in, garbage out. Having the monitoring data in native or having this data native to your tool provides a lot of meaningful value for customers because they have their monitoring data, which is coming from the OpsRamp tool. They have the intelligence, which is being provided by their ops cube machine learning. And they have our process automation and workflow to feed off that directly. And so when I think of this modernization problem, I really think about modern DevOps teams and the problems they face, which is around doing more with less, that's kind of the paradigm of many teams, each one is trying to learn, how do I do security for Kubernetes? How do I observe my security in the Kubernetes' cluster? How do I make sure my CI/CD pipeline is set up in such a way that I don't need to monitor it, or I don't need to give it attention? And so having a really seamless flow from that observe, analyze, act enables those problems to be solved in a much more seamless way that I don't see many legacy providers be able to keep up with. >> Awesome. Jordan, if you don't mind, I'd love to get your definition of what modern availability means. >> Yeah. So, you know, as I've gone through a little bit previously, so modern availability to me is availability uptime. It's also performance, right? Is the app location marks set down by both the application team, but also by the business. And number three is it business aware. So a truly modern available application is being able, is driving an excellent customer experience according to the product roadmap, but it's also doing it in a way that moves the business forward. Right? And if your applications today are not meeting those benchmarks, if they're performing but they're not driving the business forward, if they're not performing, if they're not up, if they don't meet any one of those three core tenants, they're not truly available. And I think that what's most impactful to me about what the platform, what OpsRamp in particular does in today's environment is operating under that modern definition of available is more difficult than ever. It is more difficult because we are living in a hybrid, distributed, multi-cloud world with tons of software vendors that are being sold into these organizations today that are promising similar results. So when you're an IT operator, how do you drive availability in light of that kind of environment? You have reduced budget. You have greater complexity, you have more tools than ever, and yet your software is more impactful to the bottom line than ever before. It's in this environment that we took a hard look at what's going on in the world, and we say these operators need help driving availability. That's the germination of the OpsRamp platform. >> That's a great point. We're going to come into the culture. And the second Emily Freeman's keynote about the revolution in DevOps talks about this, multiple personas and multiple tools that drive specialism, specialties that actually don't help in the modern era. So I'm going to hold that for a second. We'll come to the cultural question in a minute. Michael, if you don't mind to pivot off that definition, what are the metrics? With all those tools out there, all these new things, what are the new metrics for modern availability? It's more than MTTR. >> Yeah. This whole metrics that I think people spend a lot of time on, I think it's actually people thinking in the wrong direction if you ask me. So I've seen a lot of work. People say that the red metrics, that rate error duration or its views, utilization, saturation errors, or it's these other more contrived application metrics. I think they're looking at a piece of the stack, they're not looking at the right things. Even things like mean time to resolve and critical and server response time, mean time to tech, those are all downstream indicators. I like to look at much more proactive signals. So things like app deck score, your application index, or application performance index, these are things that are much more end-user facing or even things like NPS score, right? This has never really been a classic metric for these operations teams, but what a NPS score shows you is are your users happy using your applications? Is your experience giving what they expect it to be? And usually when you ask these two questions, even if you ask the DevOps team do you know what your Atlas score is? And you use NPS score, but what are those, right? Because it's just never been in that conversation. Those have been more maybe on the business side or maybe on the product management side. But I think that as organizations modernize, we see a much more homogenous group forming among these DevOps and product units to answer these kinds of questions. That's something we focus a lot on OpsRamp it's not seeing the silo of DevOps product or Ops. We're each thinking of how do you have a better NPS and how do we drive a better app decks? Because those are our leading indicators of whether or not our applications available. >> So I want to ask you guys both before, again, back to the own cultural question I really want to get into, but from a customer standpoint, they're being bombarded with sales folks, "Hey, buy my tool. I got some monitoring over a year. I got AI ops. I got observability." I mean, there's a zillion venture back companies that just do observability, just monitoring, just AI Ops. As the modern error is here, what's going on in the psychology of the customer because they want to like clear the noise. We saw it in cybersecurity years ago. Right? They buy everything, and next thing you know, they're going to fog of tools. What's the current state of the customer? What do they need right now as to be positioned for the automation, for the edge, all these cool cloud-scale next gen opportunities? >> Yeah. So in my mind, it's basically three things, right? Customers, number one, they want a vision. They want a vision that understands their position in the enterprise organization and what the vision for application development is going to be moving forward. Number two, they don't want to be sold anymore. You're absolutely right. It's harder and harder to make a traditional enterprise sale nowadays. It's because there's a million vendors. They're just like us. They're trying to get people on the phone and it can be tough out there. And number three, they want to be able to validate on their own with their own time. So in light of that, we've introduced a free trial of our cloud monitoring. It's a lightweight version of the OpsRamp platform, but it is a hundred percent free right now. It is available for two weeks with an unlimited number of users and resource count. And you come in and you can get started on your own using preloaded infrastructure from us if you want, or you could bring your own infrastructure. And we can tell you that customers who onboard through the free trial can see insights on their infrastructure within 20 minutes of onboarding. And that experience in and of itself is a differentiator and it allows our customers to buy on their own terms and timelines. >> Sure. And that's a great point. We brought this up last quarter in the showcase, one of the VCs brought up and says he was an old school VC, kind of still in the game, but he was saying in the old days in shelf where you didn't know if it was going to be successful until like downstream, now it's SaaS. If a customer doesn't see the value immediately. It's there. I mean, there's no hiding. You cannot hide from the truth of value here in the modern era. That's a huge impact on how customers now are evaluating and making decisions. >> Absolutely. And you know, I don't think any customer out there wants to read it on the white paper on the state of enterprise IT anymore. We recognize that and so we are hyper-focused on driving value for our customers and prospects as fast as possible, and still providing them the control that they need to make decisions on their own terms. >> Michael, I've got to ask you, since you have the keys to the kingdom on the product management side, what's the priorities on your side for customers, obviously the pressure's there, you guys are doing great, customers try it out for free. They can get, see the value and then double down on it. That's the cloud way. That's what's DevOps all about. You have to prioritize the key things, what's going on with your world. >> Yeah. And I would say of course prod has their own perspective on this. Our number one goal right now is to accelerate that time to value. And so when we look at one who we're targeting, right? So there's DevOps user, this modern application of operator, what are their core concerns in the world? One is, again, that data problem. Are we bringing the right type of data to solve meaningful problems? And two, are we making insights out of that? So from my priority's perspective, we're really driving more focus on this time to value problem and reduced time to there's some key value metrics we have and I'll go to that, but it's all an effort to make sure that when they hit our platform and they use our platform, we're showing them their return on investment as fast as possible. And so, what a return on investment means (indistinct) can slightly vary, but we try to narrow focus on our key target persona and market and focused on them. So right now it definitely is on that modern DevOps team enterprise, looking to provide modern application availability. >> Awesome. Hey guys, for the last two minutes, I'd love to shift now to the culture. So Jordan, you mentioned that appliance, the item example, which is I think indicative of many scenarios in the legacy old world, old guard school, where there's a cultural shift where some people are pissed off, they're going to go and they slowing things down, right? So you see people that are unhappy, the sites having performance of an e-commerce sites, having five second delays or some impact to the business, and the developers are moving fast with DevOps. The DevOps has risen up now where it's driving the agenda. Kind of impacting the old school departments, whether it's security or IT, central groups that are responding in days and weeks to requests, not minutes. This is a huge cultural thing. What's your thoughts on this? >> I absolutely think it's true. I think the reason were options differ slightly on that is we do see the rise of DevOps culture and how it starts to take control and rest the customer experience back from the legacy providers within the organization, but we still see that there's value in having a foot in the old and a foot in the new, and it's why that term hybrid, we talked about hybrid observability is really important to us. It's true, DevOps culture has a lot of great reasons why it's taken over, right? Increases in speed, increases in quality, increases in innovation, all of that. And yet the enterprise is still heavily invested in the old way. And so what they are looking for is a platform to get them from the old way to the new way fast. And that's where we really shine. We say we can enable, we can work with the existing tool set that you have, and we can move you even more in the future of this new definition of availability. And we can get you that DevOps state of play even quicker. And so you don't have to make a heavy lift and you don't have to take a big gamble right now. You can still provide this kind of slow moving migration plan that you need to feel comfortable, and it doesn't force you to throw away a bunch of stuff. >> And if you guys can comment on whole day two operations, that's where the whole ops reliability thing comes in, right? This is kind of where we're at right now, Dev and Ops. Ops really driving the quality and reliability, availability and your definition. This is key, right? This is where we're starting to see the materialization of DevOps. >> It's why we have guys like Michael Fisher who are really driving our agenda forward, right? Because I think he represents the vision of the future that we all want to get to. And the platform that the product team in OpsRamp is building is there, right? But we also want to provide a path for day two, right? There are still some companies are living in day one and they want to get to day two. And so that's where we drive out here. >> And Michael, the platform with the things like containers really helps people get there. They don't have to kill the old to bring in the new, they can coexist. Can you quickly comment your reaction to that? >> Yeah, absolutely. And I talked to a lot of, I won't name any but large scale web companies, and they're actually balancing this today. They have some infrastructure or applications running on bare metal that somebody's got Kubernetes, and there's actually, it's not so much, everything has to go one direction. It actually is what makes the business, right? Even for migrating to the cloud, there has to be a compelling business reason to do so. And I think a lot of companies are realizing that for the application side as well. What runs where and how do we run it? Do we migrate a legacy monolith to a microservice? How fast do we do it? What's the business impact of doing it? These are all critical things that DevOps teams are engaged with on a daily basis as part of the core workflows, so that's my take on that. >> Guys. Great segment. Thanks for coming on and sharing that insight. Congratulates the OpsRamp, doing really extremely well, right in the right position on ramp for operations to be DevOps, whatever you want to call it, you guys are in the center of it with a platform. I think that's what people want, delivering on these availability, automation, AI. Congratulations and thanks for coming on theCUBE for the Showcase Summit. >> Thanks so much. >> Thank you so much, John. >> Okay, theCUBE's coverage of AWS showcase hottest startups in cloud. I'm John Furrier, your host. Thanks for watching. (relaxing music)

(upbeat electro music) >> Hi and welcome to another CUBE Conversation. This one from BMC Helix's Immersion Days, Santa Clara Marriott in Santa Clara, California. I'm Peter Burris. As we think about what organizations have to do over the next few years, imagine a world in which technology's being applied to generating revenue where customer experience is dependent upon technology, where your overall operational fabric and framework and likelihood of staying in business is tied to how well your technology plant works. That's where we're going and bringing an IT capability that's capable of supporting and sustaining those demands on business is an absolutely essential thing for businesses of all side. Fundamental, we have to think about how digital services that's delivering those new sources of revenue, new experiences and operations management which is ensuring that the predictability and certainty of how operations work is at the heart of many of the changes within IT today. Got a great guest to talk about that. Vidya Srinivasan is the Product Strategy and Marketing Executive at BMC Software. Vidya, welcome back to the CUBE. >> Pleasure to be here. >> So I said a lot upfront but lets start by getting the simple update. Where is BMC Helix today? >> Yeah, so Peter you were there for our first launch last year. I think about a year and a half ago. So since then obviously we've come a long way. We've onboarded a lot of customers, existing customers as well as new logos. So we are at a point where our customers are happy with Helix. They want to see more. They're working with us to roll out chat bots, really implementing a lot of our AI automation technologies. And as you heard today, eighteen months in, we now have Helix kind of expanded into the ITOM world. So we are actually bringing together the conversions of ITSM and ITOM with our Helix platform. So now officially, Helix is able to support a lot of the IT operations management functions that include monitoring , that include remediation, that include capacity and cost optimization. So it's really bringing together the two worlds of IT. That's really a foundation for a lot of our IT organizations. So we are very happy to announce it today at the Immersion Day Events and we are looking forward to a great update probably in the next six months. Back with you. >> Well one of the many challenges that an IT organization faces is that the nature of the assess that they're trying to generate returns on or changing away from hardware up into often software defining for structure and software and data as well. And that's one of the catalysts for why this ITOM/ITSM conversions is starting to happen. So we have had these people in silos. What kind of tensions is that generating as businesses try to deploy and utilize their IT in new and expressive and innovative ways? >> Yeah that's a great question. When we talk about the foundation of anything to do with IT, right, is knowing what you have. And as people heard in the keynote today, it's turning your unknowns to knowns, right? A big part of the challenge with IT is not knowing what you have. So discovery, as you said, is one of the foundational solutions we have within the Helix Suite that helps customers discover what they have whether it's as assets, it could be software, especially in a software world. So really understanding what you have and then being able to proactively and predictively monitor those assets, knowing what vulnerabilities you have, being able to automatically remediate those, and ultimately it's delivering the ultimate service experience to the end customer. So that's where Helix as a whole with Discover, Monitor, Service, Re Media and Optimize gives you the whole good handle on what you have and be able to ultimately provide the service of the future that we all as consumers in our day to day lives expect, we'll start expecting in our work lives. >> Well there has historically been some tension between the ITOM people and the ITSM people. They've been very strong siloed, each intent on optimizing their own capabilities. That has undermined business in many respects and certainly undermined the IT mission because a lot of people look at IT as being the problem in large measure because they have been throwing information back over the fence and sometimes at each other. So in your experience, now Helix has been out there for a year and a half. In your experience, how are ITOM and ITSM groups starting to work better together? Utilizing tooling that's not built for just one but is actually built for the idea, the promise of a greater more converged set of functions? >> Yeah so I think the tug of ITSM and ITOM organizations continue to exist and the convergence starts happening when the organization starts starting to mature in their life cycles. So let's take a simple example of a ticket. You as an end user open a service request, it goes to a service desk, somebody picks it up, and ultimately if that ticket is associated with an asset or a service that's running somewhere and the actual Cloud instance or something is broken, that's a perfect example of an end user, an agent in an ITSM scenario and an IT operations person having to all work together to make the customer happy. So that is a typical scenario in every organization and every organization has multiple service desks and multiple lines of business, not just IT issues. So making sure that through our solutions, making sure that we can minimize the existence of IT silos is a big part of what Helix brings to the table. And as we rule out the capabilities, whether you call them Discover, you know, the five capabilities that we outlined or whatever you might be referring to within the organization. It is important to make sure that the ultimate platform that brings them together is seamlessly integrated, whether it's all on one physical platform or through integration strategies across other tools in the industry, but that's kind of the intent of bringing together these two worlds. >> But at least the data is working together. >> Exactly. >> So I want to highlight one of the things you said and why it's so important we start thinking about this differently. You noted the idea of a user, an ITSM or a Service Management professional and then someone who's on the operations side doing configurations or provisioning of resources. When that person that started that off, who generated that ticket, is an employee we have certain degree of control over how fast we can service them. When we start talking about that user being a customer, now we're really talking about service experience. We're really talking about the brand. We're really talking about revenue. How is the emergence of a new class of users, being customers and increasingly using things like Robotic Process Automation, other forms of software, that are generating these kinds of requirements, altering the demand for some of these advanced tools? >> Yeah there's quite a bit of things you touched in that question so from an end user standpoint, automation comes in various forms and obviously from an end user standpoint it's this channel of preference and that's where leveraging technologies like chat bots from an end user experience standpoint, being able to use your phone, it could be your tablet, whatever it might be or your voice assistance through your phone, all of those are things that customers are expecting because you know, that's how I communicate on a day to day basis so it's nothing new. On the RPA and the automation side on the back end of things there's definitely this notion of augmented, I know a lot of our speakers spoke about this earlier, this notion of augmented intelligence that we all need to kind of embrace in order for us to deliver that end user experience and end user doesn't have to be B2B. It can be B2E, B2C, whatever it might be. At some point at least in this world we are kind of getting to a point where it doesn't matter whether it's a B2B, B2C, or B2E. It's everybody is an end user and there is no delineation in terms of the experience that anybody expects. So that's kind of what we expect to transcend into the back office whether it's IT service desk or if it's the IT operation's persona. Being able to discover or scan things from your chat bot, from your tablet, instead of having a honking machine that you normally think of when you think of a knock. So those are all things I think are sort of going to be erased in terms of what we think of IT ops. as we look into the next three to five years. So that's the experience that I think, it's not just limited to an end user but across the IT organization. What does that experience look like for all the various personas to coexist and collaborate within the construct of an enterprise. >> So, you again, have been out with customers. Either taking remedy customers and bringing them to Helix or brand new customers and bringing them to Helix. What are some of the patterns of success that you're starting to see? Where does it tend to start? What kinds of outcomes are they achieving? Where do you see your happiest customers being? >> I think it's spectrum of customers right, so it's a range. There are customers who are at an early stage in terms of just thinking about how to move to Cloud so those customers are simply thinking about okay I've been using your OnPrem Solution Remedy for a while and we are at a point where we need to move it to in to a SaaS model. So there are customers who are just looking to lift and shift and move to a SaaS model. There are other customers who, it's a no-brainer, they started with us in a SaaS model and then now they're looking to leverage more of the NextGen experience, so they are looking at chat bots, they're looking at RPA bots and working with us on that. And then there are customers who are just looking to integrate with us on different fronts. They might be using other tools and then they're looking at leveraging our integration capabilities or whatever it might be so there's a variety of different customers in different stages but obviously a big part of this shift we are seeing that's common across these is the move to SaaS and the fact that they don't want to worry about running their operations as much as they want to reinvent and innovative and grow. So that's the common theme that we're seeing across the variety of customers that we're helping today. >> Vidya Srinivasan, Product Strategy, Marketing Executive, BMC Software, once again thanks for being on the CUBE. >> Thank you very much for having me. >> And from the BMC Helix Immersion Days at Santa Clara Marriott in Santa Clara, California, I'm Peter Burris. Once again this has been a CUBE Conversation. Until next time. (upbeat electro music)

>> Live from San Francisco. It's theCUBE covering IBM Think 2019 brought to you by IBM. >> Welcome back everyone, this is the CUBE's live coverage in San Francisco at Moscone Center for IBM Think 2019. I'm John Furrier with Dave. Volante Dave it's been in AI, it's been cloud, it's been in data changing the game. We've got two great guests here Murali Nemani, CMO of ScienceLogic, your CEO has been on the CUBE before and Joe Damassa who is the VP of strategy and offerings for hybrid cloud service at IBM. Thanks for joining us. >> Welcome. >> Appreciate it. >> Thank you guys. >> Welcome to CUBE. So day four of four days coverage, yes, you can see the messaging settling the feedback settling, AI clearly front and center, role of data in that and then cloud scale across multiple capabilities. Obviously on premise multi cloud is existing already. Software's changing all this. >> Right. >> And so AI impacting operations is key. So how do you guys work together? What's the relationships in ScienceLogic and IBM? Could you just take a minute to explain that? >> I think I mean, clearly, as you talked about the hybrid nature of what we're dealing with, with the complexity of it, it's all going to be about the data. You know, software is great, but it's about software that collects the data, analyzes the data, and gives you the insights so you can actually automate and create value for our clients. So it's really this marriage, it's a technology but it's a technology that allows us to get access to the data so we can make change, it's all about the data. >> And so a lot of what IBM has been doing is building the analytics engines and Watson it's for them. Our partnership has been really building the data and the data lake and the real time aspects of collecting and preparing that data so that you can really get interesting outcomes out of it. So when you think about predictive models, when you think about the the way that data can be applied to doing things like anomaly detection that ultimately accelerate and automate operations. That's where the relationship really starts taking hold. >> So you guys are specialized in AIops and IT apparatus as that transforms with scale and data which you need machine running, you need a kind of gave it automation. >> Yes. >> And which is the devops use of operations is don't go down, right, up and running, high availability. >> Yeah. >> So on the cloud services side, talk about where the rubber is meeting the road from a customer standpoint, because the cultural shift from IT Service Management, IT operations has been this manual, some software here and there, but it's been a process. Older processes change a little bit, but this is a new game. Talk about how you guys are engaging the customers. >> Well, a part of it I mean, it's interesting when you step back and you stop breathing, you're on exhaust in terms of pushing what you're trying to sell and you listen to your customers what we're hearing is that they all understand the destination. They understand they're moving to the cloud, they understand the value that's going to bring, they're having a hard time getting started. It's how do I start the journey ? I've got all of this estate and traditional IT operations capabilities it's kind of move. How do I modernize it? How do I make it so it's portable across different environments. And so when you step back, you know, we basically said, hey, you need the portability of the platform. So what we're doing with Red Hat, what we're doing with IBM, cloud private, it creates that portable containerizing ability to take our existing workloads and start moving them, right. And then the other thing that the clients need are the services. Who's going to help me advise me on what workloads should move, which one shouldn't, most of the staff fails because you move the wrong things. How do you manage that? How do you build it? And then when you're done, and you've got this hybrid complex environment, how do we actually get insights to it and the data I need to operationalize it? How do I do IT apps, when I don't own everything within the four walls of my data set. >> Now, are you guys going to market together? You guys sell each other products, the relationship with ScienceLogic and IBM is it a partnership, is it a joint development? Can you explain a little bit more on how you guys work together? >> Well, we're one of the largest sort of services provider in the industry. So as we bring, our products, our technologies and our capabilities to market, we bring ScienceLogic into those deals, we use ScienceLogic in our services so that we can actually deliver the value to our clients. So it is sort of a co development, co joint partnership plus also our goal to market. >> So you use that as a tool to do discovery and identify the data that's in and the data that we're talking about is everything I need to know about my IT operations, my applications, the dependencies. Maybe you could describe a little bit more. >> Sure if you think about one of the things that Joe was mentioning is, today, the workloads are shifting, you're going from, let's say management performance monitoring and management platforms that you need to evolve from, to incorporate new technologies like containers and microservices and server-less architectures. That's one area of how did the tool sets fundamentally evolve to support the latest technologies that are being deployed? So think about that. Second is, how do you consolidate those set of tools now you're managing? Because you're adopting cloud based technologies or new capabilities, and so get consolidation there. And the third is, these workloads that are now migrating out of your private cloud or private data center into public clouds, right? And then that workload migration, I think it is Forrester level saying, about 20% of the total workloads are currently in some sort of a public cloud environments. So there's a lot of work to do in terms of getting to that tipping point of where workloads are now truly in a multi cloud hybrid cloud. So as IBM accelerates that transition and their core competencies in helping these large enterprises make that transition, you need a common manageable environment, that the common visibility across those workloads. So that's at the heart of what we're pulling, and then the data sets happened to be data sets that are coming either from the application layer, data coming from the log management systems, it could be data coming from a service desk in terms of the kind of CMDB based data sets, and we're building a data lake that ultimately allows you to see across these heterogeneous system. >> It could be service request to get that really touches the business process so you can now start to sort of map the value and how change is going to affect that value, right? >> Yeah, exactly. >> Yeah. >> I mean, what's interesting about ScienceLogic as a partner, it's the breadth of their platform in terms of the different things they can monitor, the depth, the ability to go into containers, and kind of understand what the applications are doing in them and the scale in terms of the types of devices. So when you think about, the types of devices, we're going to have to manage everything from, sensors in an Internet of Things, environment to routers, to sophisticated servers and applications that can be running anywhere, you need the flexibility of the platform that they have in order to be able to deliver that. >> And I think that's a key point when you talking about containers and Kubernetes, we heard your CEO Jeannie remitting mentioned Kubernetes, onstage like, that's great, good time(mumbles) I know no one like Kubernetes now it's mainstream. >> Yeah. >> So this is showing them what's going on the industry which is the on premise decomposition of on premise with cloud private, you guys have. >> Yes. >> Is giving them the ability to use containers to manage their existing stuff and do that work and then have the extension to cloud, public cloud or whatever public cloud. This gives them more mount modern capabilities. So the question is, this change the game we know that but how has it changed AIOps and what does it mean? So I guess the first question is, what is AIOps? And what is this new on premise with cloud private and full public cloud architecture look like in AIOps 2.0? >> So for me, it's a very simple definition. It's really using algorithmic mechanisms, right? Towards automating operations, right? It's a very simple way, simplistic way of looking at it. But ultimately, the end game is to automate operations because you need to move at the pace of business and machine speed. And if you want to go, move in machine speed, you can have, I mean, you can't throw enough humans at this problems, right? Because of the pace of change, the familiarity of the workloads spinning up and sitting down. We have a bank as a customer who turns up containers for every 90 seconds and then turn them down. Just can't keep that in that real time state of change and being able to understand the topological relationships between the application layer and the underlying infrastructure so that you can truly understand the service health because when an application degrades in performance, the biggest issue is a war room's scenario where everyone's saying, it's not me, it's not me and because everyone's green on their front, but it's now how do you get that connective tissue all the way running-- >> Well it's also not only the change, it's also the velocity of data coming off that exhaust or the changes and services is thrown off tons of data that you need machines now I mean, that's kind of the thing. >> Exactly, yeah. And I would add to that, I think part of the definition of AIOps is evolving. We know where we're coming from is more fit for purpose analytics, right? I have this problem, I'm the collect this data, I'm going to put these automations in place too address it. We need to kind of take it data Model approach that says, how do I ingest all of this data? You know, even at the start, when you're looking at which workloads and you're doing discovery and assessment of workloads, that data should go into a data lake that can be used later when you're actually doing the operations and management of those workloads. So what data do we collect at every stage of the migration and the transformation of it, and including the operational data? And then how do we put a form analytics on it, and then get the true insights? I think we're just scratching the surface of applying to AI, because it's all been very narrow cast, narrow focus, I have this problem, I collect this data, I can automate this server, it needs to move much beyond that to it... >> And services are turning up and on and off so fast as a non deterministic angle here, and you got state, non deterministic, I mean, those are hard technical computer science problems to solve >> Yeah. >> That's you don't just put a processor around say, oh, yeah. >> Well, let's back to the the scalability of the platform, the ability in real time to be monitoring and looking at that data and then doing something right. >> All right now, humans aren't completely removed from the equation, right? And so I'm interested in how the humans are digesting and visualizing all this data, especially at this speed there a visualization component? How does that all evolving? >> Yeah, I think that to me I mean, that's part of the biggest challenges. You humans are a, they have to be the ones that kind of analyze what's coming and say, what does this mean when you haven't already algorithmically built it into your automation technology, right? And then they also don't have to be the one to train, the system is doing to actually do it. So one of the things that were are that struggling with not struggling with, we're experimenting with is, how best to visualize this, right? We do some things now, we've got a hybrid cloud management platform, we're teaming with the product guys, and it's the ability to have four consoles. One from a consumption, how do I consume services from Amazon, IBM Cloud on premise, how do I deploy it? So in a Dev apps model, how do I fulfill that very quickly and operational councils, right, and then cost on asset management so you can actually have at glance say, oh, you know, I've got a big Hadoop cluster which been spun up, I'm paying $100,000 for it and it has zero utilization. So how do you visualize that so you can say oh, I'm need to put a rule in that if somebody's spinning something up on, you know, IBM Cloud and they're not using it, I either shut it down, or I sent messages out, right, for governance in top of it. So it's putting business rules and logic in terms, in addition to visualization to help automate. >> And Jeannie talked about this at our keynote efficiency versus innovation around how to manage and this is where the scale comes in. Because if you know that something's working, you want to to double down on it, you can then, kind of automate that away and then you just move someone, the humans to something else. This is where the AIOps I think it's going to be, I think, going to change the category. I mean, it's a Gartner Magic Quadrant for the IT operations. >> Right. >> AI potentially decimates that, I mean... >> Yeah, there's this argument that you know, you have these nice quadrants or let's say nicely defined market segments. You have the NPMD, the ITSM, the ITOM, you know, you have APM and so what's happening is in this world of AIOps, none of those D marks really fit anymore because you're seeing the convergence of that. And then the other transition that's happening is this movement from, you know, classic ops or Dev and a dev to Ops, Dev Ops and now dev sec Ops, you know, you're trying to get worlds to converge. And so when we talk about the data and being able to build data models, those data models need to converge across those domains. So a lot of the work we do is collect data sets from log management, from service desk and service management, from APM etc, and then build that data model in real time. So you can.... >> It kind of building an Uber or CMDB or I mean, right? (loud laughter) I mean, do most of your clients have a single CMDB? Probably not, right? >> Yeah. So this is sort of a new guidepost, isn't it? >> Yeah, a part of it is. There are these data puddles if you will, all right data exist in a lot of different places How do you bring them together so you can federate different data sources, different catalogs into a common platform because if a user is trying to decide, okay, should I spin this up on, you know, this environment or that one, you want the full catalog of capabilities that are on premise in your CMDB system with the legacy environment out of the catalogs that may exist on Amazon or Azure, etc and you want data across all that. >> It seems that everything's a data problem now. And datas are being embedded into the applications which are then the workflows are defining infrastructure, architecture, or are sole cloud, multi cloud, whatever the resource is, so we had JPMorgan Chase on top data geek on and she was talking about, we have models for the models and IBM has been talking about this concept of reasoning around the data. This is why I always like the cognition kind of angle of cognitive, because that's not just math, math is math, you do math on, you know, supervised machine learning and knowing processes to be efficient, but the cognition and the reasoning really helps get at that data set, right. So can you guys react to that? I mean, is everything a data problem? Is that how you should look at it and how does reasoning fit into all this? >> Well, I mean, that's back to your point about what is the humans role in this, right. So we're moving in a services business from primarily labor base with tools to make them more efficient to the technology doing the work. But the humans have to then say, when the technology get stumped, what does that mean? So should I build a new, how do I train it better? How do I, you know, take my domain expertise? How do I do the deep analytics to tell me all right, how do I solve those problems in the future? So the role changes I think Jenny talks about in terms of new collar workers. I mean, these are data scientists, these are people that understand the dynamics of the inner relationship of the different data, the data models that need to get built and they are guiding in effect the automation. >> I thought your CTO was on theCUBE talking about, Paul was talking about, you know, take the heavy and Rob Thomas was also on, the GM of the data plus AI team. I think he really nailed it. If you guys to take away the heavy lifting of the setup work then the data science who're actually there to do the reasoning or help assist in managing what's going on and putting guard rails around whatever business policy is. >> Today, I mean, we talked to in this about 79 percent I think it's a gardener stat of 79 percent of the data scientists. And these are these PhDs, they're highly valuable, spend their time collecting, preparing, cleansing those data models, right? So, you're now really applying that PhD level knowledge base towards solving a problem, you're just trying to make sense of the data. So one, do you have a holistic and a few? Two, is there a way to automate those things so you can then apply the human aspects towards the things that Joe was talking about. So that's a big part of what we're trying to come together in terms of the market for. >> Well guys thanks for the insight, thanks for coming on, great job. I think we talked for you know, an hour and on cultural shift because you mentioned the sets in here Ops and devs. It's a melting pot and it's a cultural shifts. I think that topic is worth following up on. But I'll let you guys just get a quick plug for you. I know you going to an event coming up and you got some work. You can talk about what you guys are doing. You got an event coming up, what your pitch, give a quick flag. >> Yeah, so we've got our symposium, which is our big user conference. It's in April. It's right in, it's on April 22 to 23rd to the 25th. It's in downtown Washington DC, Cherry Blossom festival season at the Ritz Carlton. And so a lot of that, we'll have theCUBE there as well. >> Yeah of course. >> So, we're looking forward to it. A lot of great energy to be carried over. >> We love going to the District. (laughs loudly) >> What don't we say, you guys are great, great to visit. So give the plugs with a service you're doing. Just give an update on what you guys are up to. >> Yeah, I think I mean, we're also we're investing the technology when we're full on board with the containerization, as we talked about, we're putting together a services portfolio. I think Jenny mentioned that we're taking a whole bunch of capability across IBM Global Technology Services, Global Business Services, and really coalescing into about, you know, 23 offerings to help customers advise on cloud, move to cloud build for cloud and manage on cloud and then you've seen the announcements here about what we're doing around the multi cloud management system. Those four console I talked about how do we help, you know, put a gearbox in place to manage the complexity of the hybrid nature that our customers are dealing with. >> It seems IBM got clear visibility on what's happening with cloud, cloud private, I think a really big announcement. I think it's not talked about in the show and I'll always kind of mentioned the key linchpin but you see cloud, multi cloud, hybrid cloud, you got AI and you got partnerships, ecosystem now its execution time, right? >> Yeah, exactly and, and frankly, that's the challenge, right? So we used to be able to manage it all on the four runs, right? Your SAP instances was in the data center, your servers were in the data center, your middleware is in the data center. Now I got my applications running in Salesforce.com often software as a service. I've got three or four different infrastructures of service providers. But I still have the legacy that I got to deal with. I mean the integration problems are just tremendous. >> Chairman VP of strategy at IBM hybrid cloud and Murali Nemani, CMO ScienceLogic, AI operations, bringing in hybrid clouds to theCUBE bringing all the coverage day four. I'm with Dave Volante, it's all about cloud AI developers all happening here in San Francisco this week. Stay with us from this short break. (upbeat music)

>> Narrator: Live from Las Vegas, it's the CUBE covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back everyone, we are wrapping up three big days of the CUBE's live coverage of ServiceNow Knowledge 18. I'm your host Rebecca Knight along with my cohost Dave Vellante and Jeffrick. It has been such fun co-hosting with you both. It's always a ghast to be with you so three days, what have we learned? We've learned we're making the world of work work better for people. Beyond that what do you think? >> New branding you know there which I think underscores ServiceNow's desire to get into the C-Suite. Become a strategic partner. Some of the things we heard this week, platform of platforms. The next great enterprise software company is what they aspire to, just from a financial standpoint. This company literally wants to be a hundred billion dollar valuation company. I think they got a reasonable shot at doing that. They're well on their way to four billion dollars in revenue. It's hard to be a software company and hit a billion. You know the number of companies who get there ar very limited and they are the latest. We're also seeing many products, one platform and platforms in this day and age beat products. Cloud has been a huge tailwind for ServiceNow. We've seen the SaaSification of industries and now we're seeing significant execution on the original vision at penetration into deeply into these accounts. And I got to say when you come to events like this and talk to customers. There's amazing enthusiasm as much of if not more than any show that we do. I mean I really got, what's your take? >> We go to so many shows and it's not hard to figure out the health of a show. Right you walk around the floor, what's the energy, how many people are there? What's the ecosystem I mean, even now as I look around we're at the very end of the third day and there is action at most of the booths still. So it's a super healthy ecosystem. I think it grew another 4,000 people from this year of the year of year growth. So it's clearly on the rise. SaaS is a big thing, I think it's really interesting play and the kind of simple workflow. Not as much conversation really about the no code and the low code that we've heard in the past. Maybe they're past that but certainly a lot of conversation about the vertical stack applications that they're building and I think at the end of the day. We talked about this before, it's competition for your screen. You know what is it that you work in everyday. Right if you use, I don't care what application. SalesForce or any SaaS application which we all have a lot of on our desktop today. If you use it as a reporting tool it's a pain. It's double entry, it's not good. But what is the tool that you execute your business on everyday? And that's really a smart strategy for them to go after that. The other thing that I just think is ripe and we talked about a little bit. I don't know if they're down playing it because they're not where they want to be at or they're just downplaying it but the opportunity for machine learning and artificial intelligence to more efficiently impact workflows with the data from the workflow is a huge opportunity. So what was a bunch of workflows and approvals and this and that should all get, most of it should just get knocked out via AI over a short period of time. So I think they're in a good spot and then the other thing which we hear over and over. You know Frank Slootman IT our homies I still love that line. But as has been repeated IT is everywhere so what a great way to get into HR. To get into legal, to get into facilities management, to get into these other things. Where like hey this is a really cool efficient little tool can I build a nice app for my business? So seemed to be executing on that strategy. >> Yeah CJ just said IT will always be at our core. Rebecca the keynote was interesting. It got mixed reviews and I think part of that is they're struggling we heard tat from some of our guests. There's a hybrid audience now. You got the IT homies, you got the DevOps crowd and then you got the business leaders and so the keynote on day one was really reaching an audience. Largely outside of the core audience. You know I think day two and day three were much more geared toward that direct hit. Now I guess that's not a bad thing. >> No and I think that I mean as you noted it's a hybrid audience so you're trying to reach and touch and inspire and motivate a lot of different partners, customers, analysts. People who are looking at your business in a critical way. The first day John Donahoe it struck me as very sort of aspirational. Really talking about what is our purpose, what do we do as an organization. What are our values, what problems are we trying to solve here and I think that that laying out there in the way that he did was effective because it really did bring it back to, here's what we're about. >> Yeah the other thing I learned is succession has been very successful. Frank Slootman stepped down last year as CEO. He's maintained his chairman title, he's now stepped down as chairman. Fred kind of you know went away for a little while. Fred's back now as chairman. John Donahoe came in. People don't really put much emphasis on this but Fred Luddy was the chief product officer. Dan McGee was the COO, CJ Desai took over for both of them. He said on the CUBE. You know you texted me, you got big shoes to fill. He said I kept that just to remind me and he seems to have just picked up right where those guys left off. You know Pat Casey I think is understated and vital to the culture of this company. You know Jeff you see that, he's like a mini Fred you know and I think that's critical to maintain that cultural foundation. >> But as we said you know going the way that Pat talked about kind of just bifurcation in the keynote and the audiences in the building and out of the building. Which I've never heard before kind of an interesting way to cut it. The people that are here are their very passionate community and they're all here and they're adding 4,000 every single year. The people that are outside of the building maybe don't know as much about it and really maybe that aspirational kind of messaging touched them a little bit more cause they're not into the nitty gritty. It's really interesting too just cause this week is such a busy week in technology. The competition for attention, eyeballs and time. I was struck this morning going through some of our older stuff where Fred would always say. You know I'm so thankful that people will take the time to spend it with us this week. And when people had choices to go to Google IO, Microsoft build, of course we're at Nutanix next, Red Hat Summit I'm sure I'm missing a bunch of other ones. >> Busy week. >> The fact that people are here for three days of conference again they're still here is a pretty good statement in terms of the commitment of their community. >> Now the other thing I want to mention is four years ago Jeff was I think might have been five years ago. We said on the CUBE this company's on a collision course with SalesForce and you can really start to see it take shape. Of the customer service management piece. We know that SalesForce really isn't designed for CSM. Customer Service Management. But he talked about it so they are on a collision course there. They've hired a bunch of people from SalesForce. SalesForce is not going to rollover you know they're going to fight hard for that hard, Oracle's going to fight hard for that. So software companies believe that they should get their fair share of the spend. As long as that spend is a 100%. That's the mentality of a software company. Especially those run by Marc Benioff and Larry Ellis and so it's going to be really interesting to see how these guys evolve. They're going to start bumping into people. This guy's got pretty sharp elbows though. >> Yeah and I think the customer relation is very different. We were at PagerDuty Summit last right talked to Nick Meta who just got nominated for entrepreneur of the year I think for Ink from GainSight and he really talked about what does a customer management verses opportunity management. Once you have the customer and you've managed that sale and you've made that sale. That's really were SalesForce has strived in and that's we use it for in our own company but once you're in the customer. Like say you're in IBM or you're in Boeing. How do you actually manage your relationship in Boeing cause it's not Boeing and your sales person. There's many many many relationships, there's many many many activities, there's somewhere you're winning, somewhere you're losing. Somewhere you're new, somewhere you're old and so the opportunity there is way beyond simply managing you know a lead to an opportunity to a closed sale. That' just the very beginning of a process and actually having a relationship with the customer. >> The other thing is so you can, one of the measurements of progress in 2013 this company 95% of its business was in IT. Their core ITSM, change management, help desk etc. Today that number's down to about two thirds so a third of the business is outside of IT. We're talking about multi-hundreds of millions of dollars. So ITOM, HR, the security practice. They're taking these applications and they're becoming multi-hundred million dollar businesses. You know some of them aren't there yet but they're you know north of 50, 75 we're taking about hundreds of customers. Higher average price, average contract values. You know they don't broadcast that here but you know you look at peel back the numbers and you can see just tremendous financial story. The renewal rates are really really high. You know in the mid 90s, high 90s which is unheard of and so I think this company is going to be the next great enterprise software company and their focus on the user experience I think is important because if you think about the great enterprise software companies. SalesForce, Oracle, SAP, maybe put IBM in there because they sort of acquired their way to it. But those three, they're not the greatest user experiences in the world. They're working on the UI but they're, you know Oracle, we use Oracle. It's clunky, it's powerful. >> They're solving such different problems. Right when those companies came up they were solving a very different problem. Oracle on their relational database side. Very different problem. You know ARP was so revolutionary when SAP came out and I still just think it's so funny that we get these massive gains of efficiency. We had it in the ARP days and now we're getting it again. So they're coming at it from a very different angle. That they're fortunate that there are more modern architecture, there are more modern UI. You know unfortunately if you're legacy you're kind of stuck in your historical. >> In your old ways right? >> Paradigm. >> So the go to market gets more complicated as they start selling to all these other divisions. You're seeing overlay, sales forces you know it's going to be interesting. IBM just consolidated it's big six shows into one. You wonder what's going to happen with this. Are they going to have to create you know mini Knowledges for all these different lines of business. We'll see how that evolves. You think with the one platform maybe they keep it all together. I hope they don't lose that core. You think of VM world, rigt there's still a core technical audience and I think that brings a lot of the energy and credibility to a show like this. >> They still do have some little regional shows and there's a couple different kind of series that they're getting out because as we know. Once you get, well just different right. AWS reinvents over $40,000 last year. Oracle runs it I don't even know what Oracle runs. A 65,000, 75,000. SalesForce hundred thousand but they kind of cheat. They give away lot of tickets but it is hard to keep that community together. You know we've had a number of people come up to us while we're off air to say hi, that we've had on before. The company's growing, things are changing, new leadership so to maintain that culture I think that's why Pat is so important and the key is that connection to the past and that connection to Fred. That kind of carried forward. >> The other thing we have to mention is the ecosystem when we first started covering ServiceNow Knowledge it was you know fruition partners, cloud Sherpas I mean it. Who are these guys and now you see the acquisitions, it's EY is here, Deloitte is here, Accenture is here. >> Got Fruition. >> PWC you see Unisys is here. I mean big name companies, Capgemini, KPMG with big install bases. Strong relationships it's why you see the sales guys at ServiceNow bellying up to these companies because they know it's going to drive more business for them. So pretty impressive story I mean it's hard to be critical of these guys, your price is too high. Okay I mean alright. But the value's there so people are lining up so. >> Yeah I mean it's a smoking hot company as you said. What do they needed to do next? What do you need to see from them next? >> Well I mean the thing is they laid out the roadmap. You know they announced twice a year at different cities wit each a letter of the alphabet. They got to execute on that. I mean this is one of those companies that's theirs to lose. It really is, they got the energy. They got to retain the talent, attract new talent, the street's certainly buying their story. Their free cash flow is growing faster than their revenue which is really impressive. They're extremely well run company. Their CFO is a rockstar stud behind the scenes. I mean they got studs in development, they got a great CEO they got a great CFO. Really strong chief product officer, really strong general managers who've got incredible depth in expertise. I mean it's theirs to lose, I mean they really just have to keep executing on that roadmap keeping their customer focus and you know hoping that there's not some external factor that blows everything up. >> Yeah good point, good point. What about the messaging? We've heard as you said, it's new branding so it's making the world of work work better, there's this focus on the user experience. The idea that the CIO is no longer just so myopic in his or her portfolio. Really has to think much more broadly about the business. A real business leader, I mean is this. Are you hearing this at other conferences too? Is it jiving with the other? >> You know everyone talks about the new way to work, the new to work, the new way to work and the consumers they sort of IT and you know all the millennials that want to operate everything on their phone. That's all fine and dandy. Again at the end of the day, where do people work? Because again you're competing everyone has, excuse me many many applications unfortunately that we have to run to get our day job done and so if you can be the one that people use as the primary way that they get work done. That's the goal... >> Rebecca: That's where the money is. >> That's the end game right. >> Well I owe that so the messaging to me is interesting because IT practitioners as a community are some of the most under appreciated. You know overworked and they're only here from the business when things go bad. For decades we've seen this the thing that struck me at ServiceNow Knowledge 13 when we first came here was wow. These IT people ar pumped. You know you walk around a show the IT like this, they're kind of dragging their feet, heads down and the ServiceNow customers are excited. They're leading innovation in their companies. They're developing new applications on these platforms. It's a persona that I think is being reborn and it sound exciting to see. >> It's funny you bring up the old chest because before it was a lot about just letting IT excuse me, do their work with a little bit more creativity. Better tools, build their own store, build an IT services Amazon likened store. We're not hearing any of that anymore. >> Do more with less, squeeze, squeeze. >> If we're part of delivering value as we've talked about with the banking application and link from MoonsStar you know now these people are intimately involved with the forward facing edge of the company. So it's not talking about we'll have a cool service store. I remember like 2014 that was like a big theme. We're not hearing that anymore, we've moved way beyond that in terms of being a strategic partner in the business. Which we here over and over but these are you know people that header now the strategic partner for the business. >> Okay customers have to make bets and they're making bets on ServiceNow. They've obviously made a bunch of bets on Oracle. Increasingly they're making bets on Amazon. You know we're seeing that a lot. They've made big bets on VM ware, obviously big bets on SAP so CIOs they go to shows like this to make sure that they made the right bet and they're not missing some blind spots. To talk to their peers but you can see that their laying the chips on the table. I guess pun intended, I mean they're paying off. >> That's great, that's a great note to end on I think. So again a pleasure co-hosting with both of you. It's been a lot of fun, it's been a lot of hard work but a lot of fun too. >> Thank you Rebecca and so the CUBE season Jeff. I got to shout out to you and the team. I mean you guys, it's like so busy right now. >> I thought you were going to ask if we were going next. I was going to say oh my god. >> Next week I know I'm in Chicago at VMON. >> Right we have VMON, DON, we've got a couple of on the grounds. SAP Sapphire is coming up. >> Dave: Pure Accelerate. >> Pure Accelerate, OpenStack, we're going back to Vancouver. Haven't been there for a while. Informatica World, back down here in Las Vegas Pure Storage, San Francisco... >> We got the MIT's CTO conference coming up. We got Google Next. >> Women Transforming Technology. Just keep an eye on the website upcoming. We can't give it all straight but... >> The CUBE.net, SiliconAngle.com, WikiBon.com, bunch of free content.- you heard it here first. >> There you go. >> For Rebecca Knight and Jeffrick and Dave Vellante this has been the CUBE's coverage of ServiceNow Knowledge 18. We will see you next time. >> Thanks everybody, bye bye.

>> Narrator: Live from Las Vegas, it's theCUBE. Covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back everyone to theCUBE's live coverage of ServiceNow Knowledge18. We're coming at you from Las Vegas, I'm your host, Rebecca Knight, along with my cohost Dave Vellante, we are theCUBE, we are the leader in live tech coverage. We are joined by Sebastiaan Laurijsse, he is the global senior director, IT, cyber security, digital transformations at NXP, thanks so much for coming on theCUBE Sebastiaan. >> Thank you for having me. >> Good to see you. >> Thank you. >> So I want to start out by asking you a little bit about NXP, what you do and then what your company does and then also what you do there. >> NXP is the leading semiconductors in providing products for automotive and our company vision is providing a sure connections and infrastructures for a smart world. And that's what we are trying to achieve by implementing new ways of working with making the world more autonomous, like autonomous driving et cetera, so that's really what we're trying to do. >> Dave: Cool company. >> We are really building the future of tomorrow. >> Yeah. >> Big, large company too right? >> Yeah. Roughly about 36 thousand employees currently. >> Wow, okay, yeah. >> So you said you're really building the future of tomorrow, unpack that a bit, tell our viewers exactly what you're doing there. >> So today what you have experienced also on this event is a lot about artificial intelligence and machine learning. NXP has been elected as the number three in the world as the provider of solutions for artificial intelligence. So if you really think what we are developing today, it's already started and will become available in five or three years from now. So it's, you only can imagine what the future brings us and what we will shape. >> When do you think owning your own car and driving your own car will become and exception? >> Driving your own car, you won't own a car anymore. It will be some kind of help that comes to your home on demand when you need it and it even predicts when you like to travel and then it comes by automatically. >> How far away is that, you think it's two decades? >> Nah I think here it's not about technology, I think we have the technology to even enable it today. >> Dave: It's policy. >> It's policy, regulation, compliancy that doesn't allow to lets go harvest all data to make the right decisions there. >> We had the insurance company on the other day and they were like, no we're going to figure this out. >> Out of necessity. >> We always figure this stuff out. >> Yeah it's really not about technology anymore, it's really about legal, what prevents us access the data to make the right decisions, right. >> It's amazing though just to watch the progression of automotive, I mean they're basically software defined vehicles now I mean how many semiconductors are in a car now? >> Yeah but also you can clearly see within that experience, we are transforming our business to more software because developing a product as hardware that needs to stay in for 15 years or longer if you look to a car. Then you would like to have the ability to be dynamic more on top of the product by using software so also our products are becoming software defined. >> So you're a very R and D centric culture. >> Sebastiaan: Yes. >> Maybe talk about that ethos and the cultural aspects, and maybe what the process looks like, share with our viewers. >> I think it's the most awesome part of the company. Of course we also manufacture our products but mainly R and D is so dynamic, we have so, tech savvy people and we have so much issues as IT and you think what are they consuming so much bandwidth on Netflix and then they tell me hey we are developing a product for 4K entertainment into the car. So I have an issue on my wider network, you're providing all kinds of services but you're building for entertainment into the car for the future. >> That car better be autonomous. >> Exactly. >> Yes. >> That's for the kids in the back seat I think. >> Yes. >> You once described ServiceNow as the platform of platforms can you talk a little bit about that from your R and D process? >> So what you clearly see and also I think that all companies will eventually become an IT company, yeah? Also the banking companies tell us now today they are an IT company with a banking license. What I truly believe in is that we need to close the gap between IT and the business so I think the future model is that IT will dissolve for a certain part into the business. But you don't want to have, of course you still have you shared services, you still have a hybrid model where you have the countries where you're providing support from, so you're not always as close to the business. You have 24 seven economy and you need to provide those services and what you don't want to build is human interfaces. So what you try to achieve by building the platform of platforms, the fabric is that you try to connect the business acumen, the business dynamics, the project management tools that requires management into the IT systems and since you can detect the phase where they are in if they are facing issues with their products the projects are slipping or delaying, you would like to increase automatically the severity of the incidents. So that they can automatically solve and you have a better understanding of the business priorities. >> NXP is really interesting because you're at the intersection of a lot of big trends. I'm mean you're a hardware-- >> Sebastiaan: IOT. >> You're hardware manufacturers, you're a software developer, security, AI, IOT and underlying all this is data. >> Yeah, the new money. >> Yeah, right so I'm just envisioning this pretty complicated matrix, I'm wondering if you could describe that in your terms. >> If you look from an IT infrastructure perspective the growth on data is enormous. To cope with that growth because the data allows us to make better products. Data could be a requirement but could be also the affect of the results. What we tried to prevent, the project in bringing to the real life that you feel your requirement of quality is increasing. We had consumer great, automotive great, and we had for the flying industry, also the same great. But however your norm is increasing, so what you clearly see by increasing the norm, we call that the total quality culture, you also would like to have a total quality product, you don't want to replace your phone one year from now and I think if you look four years back, a phone, one and a half years, two years and then you had a new one. But as products become more expensive, they become more part of your daily life, part of your personal brand even and it generates that data, we need, if you try to work on proper quality that will generate an enormous amount of data. But a data can use, you optimize your processes upfront in the future as such it becomes more cost efficient to develop new products. So it's really about the conditioning for more data is also conditionally need to optimize your processes. >> Where does ServiceNow fit in to all this? How do you use ServiceNow? >> So for me what you really see in ServiceNow today is the best work flow engine you can imagine. It really orchestrates all IT and connecting business processes. And I think the potential and I think if you look into the portfolio where they have HR, it's going beyond IT and now they often, as already said by John Donahue, they come in via the IT angle, ITSM but as the process become more and more part of your culture rather than inhabit a forced way of working then the platform starts supporting the culture of your organization because by machine learning a proper UI, visualization capabilities it becomes really part about metering, showing what you're doing and really helps you to orchestrate your daily work and that's also I think of the new company, it's a little too difficult to pronounce, have you ever, it's about orchestrating the future way of working. >> So we're hearing so much about this, making the world of work work better for people, you describe it as a work flow engine, really helping employees organize their work days, orchestrate their work days, improve them, can you describe the culture at NXP and sort of how ServiceNow is improving employees everyday lives. >> What we really try to do and it's also what we see it's easy to show the cost efficiency savings you have from a platform as ServiceNow. If you improve your onboarding by optimizing the process by three days, because that's your first point of engagement when you bring some people on board and if it goes fluently, work integration with ServiceNow providing the services, everything is ready at day one. Day one you're there, your laptop is ready, your provisions, your desk is ready, and you have orchestrated a process that's a flawless end user experience. And that's what we would like to provide with ServiceNow, orchestrate with ServiceNow, because that's what the uses is. If it's a need of any of the help of services, we would like them to go, shift left to ServiceNow and with help of knowledge help themselves. We are all doctor Google and we would like to have access to that information ourselves and not be dependent by the expert, we all become that expert. >> Are employees happier? I mean I think that's a question too. Because we know that from research that happier employees make more productive >> Are more productive. Workplaces. They're more likely to stay, recommend it to their friends and the network gets bigger, I mean what's your-- >> If you have a company that shapes the future, we have very happy employees. (laughing) >> Self fulfilling prophecy there. >> Yes. >> When did you go live with? >> So we are one of the first adopters in 2007 in Europe. So we really started then, I don't know the name because they talk about days, months and now they talk about locations. (laughing) But I think we did a big overhaul during some of our big integrations that we have done so we are really one of the first customers in Europe providing the product. >> And how far, where, what version you in now? >> We are ready to upgrade, we will skip one release if we go to-- >> It's coming to London. >> Yep, London. >> Oh okay. And you started with ITSM like most? >> ITSM, ITOM, so IT operation management and now we have the IT business management app like demand management, IT financial management, really orchestrating from demand to fulfilling. >> A lot of our guys have written that they feel like machine intelligence and ITOM go together very well. >> Yes. >> You agree with that and how do you see that affecting your business? >> So what we clearly see is that the mean time to detect, the mean time to repair, we would like to detect algae before they hit the end user. So you really would like to make sure that before they notice it's already been solved. Or when it goes wrong, they already say we're on top of it, we know, we know the impact, we know that the whole chain of events, a single network port or power outage somewhere in a room could cause a big effect on the whole IT service and therefore research now helps us to make sure that we are on top of the things. >> Sebastiaan you mentioned off camera that you are very intimately involved with ServiceNow and helping them with their roadmap, providing feedback so can you share with us some of the things that you talk about with them and what would you like to see, where's their white space, what's on their to do list from your perspective? >> So what, but of course, if you look to our portfolio, what we are doing as NXP. So a member of the product advisory council for IT operation management and I'm closely working also on the Lighthouse program with ServiceNow and all kind of new releases, what I really think if you see what you are investing, of course they are now coming forward with the chatbots, awesome but if I see how my children consume information, using YouTube and I think also John touched upon it, what we are building as NXP is in the flawless end user experience and everything as being you don't have a UI. If you look to your car, today you have a speedometer, an RPM meter, why do you have RPM on your dashboard, why? What's the value of you know? In the past you needed it to shift gears and why is it still there? Does it really add value? >> Cause it's cool. (laughing) We love dials, come on. >> So it's about the end user experience, it's about your lifestyle, your brand identity it's not as more about requirements so, of course UI is important, I believe it, what's more important I think to invest in that engine behind it machine learning, artificial intelligence and how to ingest data. So because what is really required to make smart decisions is a lot of data and still I think the platform has potential, but there's some room for improvement to get proper integration by onboarding more data making the right decisions and orchestrate the actions out of it and I think the learn think act, we have the same strategy as sense, think, act at NXP I think that's how robotics and AI will work in the future. >> Data is the fuel for your innovation. >> Yes. >> So it's a great point you're making. >> I wonder if you could talk a little bit about the feelings in Europe, you're based in the Netherlands, about automation and the future of jobs because in the United States there is a significant anxiety about the machines coming for our jobs and at least the media portray it that way and I'm curious from your perspective, what is the feeling in Europe? >> Of course I think I see the opportunity but automation will change of course, automation, machine learning, it will essentially change the whole way of working. Because what we say it's about helping the business by decision automation, making decisions so we try to reduce the human effort, we have a total equality culture but we still need more and more people to help them that ask the right questions. Because the innovation of course come from a lot of data But still have people who connect the dots of never existing connections before. If you have a lot of data and you don't know which questions to ask, would you build a new solution? So it's still about smart people and creativity and of course we know patterns, we know what people are doing. But still the real breakthroughs is being done by people and therefore we need those people still in the future. So the anxiety is there yes, automation is there but I think it's about building a joint incentive between your outsource provider, your source provider between your workforce is what's the incentive for them on automation because otherwise you get a culture of fear and anxiety and a lot of doubt and that will be counterproductive for your company value. >> What do you think as a journalist. I mean you're right, the mainstream media talks about this a lot and they're actually accurate, the data is there to suggest that machines are replacing humans and cognitive functions and that's a concern but there's not a lot written in the media about the opportunity, there is some about the opportunities but more importantly what to do about it, in other words, public policy, education, I mean maybe I'm just missing it but. >> No, I agree with you, I completely agree and also this idea that Sebastiaan is bringing up is showing, proving that this can work for you, I mean this is actually going to improve your work life by taking Carol out of the drudge work or show opportunities for humans and robots to work alongside of each other. >> Yes. >> Rebecca: So there you go. >> Well in tech you better be an optimist you know. >> It's true. >> Although it seems like Musk and Stephen Hawking weren't optimists but maybe they're thinking you know hundreds of years-- >> Light years ahead. >> Right, right, right, right. You report directly to the CIO, at this conference, we're hearing so much about the changing role of the CIO and how the CIO has to be thinking so much more broadly about the business than ever before I mean how do you see it? >> So that's an interesting question because that's exactly where we are in today so we have had the classic way of the CIO, financial risk control et cetera then we have the transforminal CIO, then we have the CDO, or we have the future COO who takes care of operations because today IT is often being seen in the enterprise companies as a shared service center, something you do with the lights off but clearly bank accounts, what I already told you before was we are now IT companies with a banking license as IT becomes more dominant, it becomes part of operations and yes, we need a transformational CIO, CDO or a new type of COO that sees IT as part of the operations and the way of working. And of course you can give the new title, but at the end it's just a smart guy who helps the company succeed and brings IT as one together to make success. It's not about the role or responsibility, I think there's still the name of a chief information, chief data officer it's still the right title because he makes sure he gets the right data towards the business to make the right decisions faster. >> Right, great. >> It's not about running only the lights on. When the lights doesn't go on, it's IT's fault, right? >> Rebecca: Always, always. >> Always. >> Yeah that need doesn't go away but it's table stakes now. >> Exactly, Sebastiaan, thanks so much for coming on theCUBE, it was a pleasure having you here. >> Thank you. >> I'm Rebecca Knight, for Dave Vallante we will have more from theCUBE's live coverage of ServiceNow Knowledge18 coming up just after this. (upbeat music)

>> Hi, I'm Peter Burris and welcome to another Wikibon Action Item. (upbeat digital music) >> Cameraman: Three, two, one. >> Hi. Once again, we're broadcasting from our beautiful Palo Alto studios, theCUBE studios, and this week we've got another great group. David Floyer in the studio with me along with George Gilbert. And on the phone we've got Jim Kobielus and Ralph Finos. Hey, guys. >> Hi there. >> So we're going to talk about something that's going to become a big issue. It's only now starting to emerge. And that is, what will be the roadmap to automation? Automation is going to be absolutely crucial for the success of IT in the future and the success of any digital business. At its core, many people have presumed that automation was about reducing labor. So introducing software and other technologies, we would effectively be able to substitute for administrative, operator, and related labor. And while that is absolutely a feature of what we're talking about, the bigger issue is ultimately is that we cannot conceive of more complex workloads that are capable of providing better customer experience, superior operations, all the other things a digital business ultimately wants to achieve. If we don't have a capability for simplifying how those underlying resources get put together, configured, or organized, orchestrated, and ultimately sustained delivery of. So the other part of automation is to allow for much more work that can be performed on the same resources much faster. It's a basis for how we think about plasticity and the ability to reconfigure resources very quickly. Now, the challenge is this industry, the IT industry has always used standards as a weapon. We use standards as a basis of creating eco systems or scale, or mass for even something as, like mainframes. Where there weren't hundreds of millions of potential users. But IBM was successful at using that as a basis for driving their costs down and approving a superior product. That's clearly what Microsoft and Intel did many years ago, was achieve that kind of scale through the driving more, and more, and more, ultimately, volume of the technology, and they won. But along the way though, each time, each generation has featured a significant amount of competition at how those interfaces came together and how they worked. And this is going to be the mother of all standard-oriented competition. How does one automation framework and another automation framework fit together? One being able to create value in a way that serves another automation framework, but ultimately as a, for many companies, a way of creating more scale onto their platform. More volume onto that platform. So this notion of how automation is going to evolve is going to be crucially important. David Floyer, are APIs going to be enough to solve this problem? >> No. That's a short answer to that. This is a very complex problem, and I think it's worthwhile spending a minute just on what are the component parts that need to be brought together. We're going to have a multi-cloud environment. Multiple private clouds, multiple public clouds, and they've got to work together in some way. And the automation is about, and you've got the Edge as well. So you've got a huge amount of data all across all of these different areas. And automation and orchestration across that, are as you said, not just about efficiency, they're about making it work. Making it able to be, to work and to be available. So all of the issues of availability, of security, of compliance, all of these difficult issues are a subject to getting this whole environment to be able to work together through a set of APIs, yes, but a lot lot more than that. And in particular, when you think about it, to me, volume of data is critical. Is who has access to that data. >> Peter: Now, why is that? >> Because if you're dealing with AI and you're dealing with any form of automation like this, the more data you have, the better your models are. And if you can increase that amount of data, as Google show every day, you will maintain that handle on all that control over that area. >> So you said something really important, because the implied assumption, and obviously, it's a major feature of what's going on, is that we've been talking about doing more automation for a long time. But what's different this time is the availability of AI and machine learning, for example, >> Right. as a basis for recognizing patterns, taking remedial action or taking predictive action to avoid the need for remedial action. And it's the availability of that data that's going to improve the quality of those models. >> Yes. Now, George, you've done a lot of work around this a whole notion of ML for ITOM. What are the kind of different approaches? If there's two ways that we're looking at it right now, what are the two ways? >> So there are two ends of the extreme. One is I want to see end to end what's going on across my private cloud or clouds. As well as if I have different applications in different public clouds. But that's very difficult. You get end-to-end visibility but you have to relax a lot of assumptions about what's where. >> And that's called the-- >> Breadth first. So the pro is end-to-end visibility. Con is you don't know how all the pieces fit together quite as well, so you get less fidelity in terms of diagnosing root causes. >> So you're trying to optimize at a macro level while recognizing that you can't optimize at a micro level. >> Right. Now the other approach, the other end of the spectrum, is depth first. Where you constrain the set of workloads and services that you're building and that you know about, and how they fit together. And then the models, based on the data you collect there, can become so rich that you have very very high fidelity root cause determination which allows you to do very precise recommendations or even automated remediation. What we haven't figured out hot to do yet is marry the depth first with the breadth first. So that you have multiple focus depth first. That's very tricky. >> Now, if you think about how the industry has evolved, we wrote some stuff about what we call, what I call the iron triangle. Which is basically a very tight relationship between specialists in technology. So the people who were responsible for a particular asset, be it storage, or the system, or the network. The vendors, who provided a lot of the knowledge about how that worked, and therefore made that specialist more or less successful and competent. And then the automation technology that that vendor ultimately provided. Now, that was not automation technology that was associated with AI or anything along those lines. It was kind of out of the box, buy our tool, and this is how you're going to automate various workflows or scripts, or whatever else it might be. And every effort to try to break that has been met with screaming because, well, you're now breaking my automation routines. So the depth-first approach, even without ML, has been the way that we've done it historically. But, David, you're talking about something different. It's the availability of the data that starts to change that. >> Yeah. >> So are we going to start seeing new compacts put in place between users and vendors and OEMs and a lot of these other folks? And it sounds like it's going to be about access to the data. >> Absolutely. So you're going to start. let's start at the bottom. You've got people who have a particular component, whatever that component is. It might be storage. It might be networking. Whatever that component is. They have products in that area which will be collecting data. And they will need for their particular area to provide a degree of automation. A degree of capability. And they need to do two things. They need to do that optimization and also provide data to other people. So they have to have an OEM agreement not just for the equipment that they provide, but for the data that they're going to give and the data they're going to give back. The automatization of the data, for example, going up and the availability of data to help themselves. >> So contracts effectively mean that you're going to have to negotiate value capture on the data side as well as the revenue side. >> Absolutely. >> The ability to do contracting historically has been around individual products. And so we're pretty good at that. So we can say, you will buy this product. I'm delivering you the value. And then the utility of that product is up to you. When we start going to service contracts, we get a little bit different kind of an arrangement. Now, it's an ongoing continuous delivery. But for the most part, a lot of those service contracts have been predicated to known in advance classes of functions, like Salesforce, for example. Or the SASS business where you're able to write a contract that says over time you will have access to this service. When we start talking about some of this automation though, now we're talking about ongoing, but highly bespoke, and potentially highly divergent, over a relatively short period of time, that you have a hard time writing contracts that will prescribe the range of behaviors and the promise about how those behaviors are actually going to perform. I don't think we're there yet. What do you guys think? >> Well, >> No, no way. I mean, >> Especially when you think about realtime. (laughing) >> Yeah. It has to be realtime to get to the end point of automating the actual reply than the actual action that you take. That's where you have to get to. You can't, It won't be sufficient in realtime. I think it's a very interesting area, this contracts area. If you think about solutions for it, I would be going straight towards blockchain type architectures and dynamic blockchain contracts that would have to be put in place. >> Peter: But they're not realtime. >> The contracts aren't realtime. The contracts will never be realtime, but the >> Accessed? access to the data and the understanding of what data is required. Those will be realtime. >> Well, we'll see. I mean, the theorem's what? Every 12 seconds? >> Well. That's >> Everything gets updated? >> That's To me, that's good enough. >> Okay. >> That's realtime enough. It's not going to solve the problem of somebody >> Peter: It's not going to solve the problem at the edge. >> At the very edge, but it's certainly sufficient to solve the problem of contracts. >> Okay. >> But, and I would add to that and say, in addition to having all this data available. Let's go back like 10, 20 years and look at Cisco. A lot of their differentiation and what entrenched them was sort of universal familiarity with their admin interfaces and they might not expose APIs in a way that would make it common across their competitors. But if you had data from them and a constrained number of other providers for around which you would build let's say, these modern big data applications. It's if you constrain the problem, you can get to the depth first. >> Yeah, but Cisco is a great example of it's an archetype for what I said earlier, that notion of an iron triangle. You had Cisco admins >> Yeah. that were certified to run Cisco gear and therefore had a strong incentive to ensure that more Cisco gear was purchased utilizing a Cisco command line interface that did incorporate a fair amount of automation for that Cisco gear and it was almost impossible for a lot of companies to penetrate that tight arrangement between the Cisco admin that was certified, the Cisco gear, and the COI. >> And the exact same thing happened with Oracle. The Oracle admin skillset was pervasive within large >> Peter: Happened with everybody. >> Yes, absolutely >> But, >> Peter: The only reason it didn't happen in the IBM mainframe, David, was because of a >> It did happen, yeah, >> Well, but it did happen, but governments stepped in and said, this violates antitrust. And IBM was forced by law, by court decree, to open up those interfaces. >> Yes. That's true. >> But are we going to see the same type of thing >> I think it's very interesting to see the shape of this market. When we look a little bit ahead. People like Amazon are going to have IAS, they're going to be running applications. They are going to go for the depth way of doing things across, or what which way around is it? >> Peter: The breadth. They're going to be end to end. >> But they will go depth in individual-- >> Components. Or show of, but they will put together their own type of things for their services. >> Right. >> Equally, other players like Dell, for example, have a lot of different products. A lot of different components in a lot of different areas. They have to go piece by piece and put together a consortium of suppliers to them. Storage suppliers, chip suppliers, and put together that outside and it's going to have to be a different type of solution that they put together. HP will have the same issue there. And as of people like CA, for example, who we'll see an opportunity for them to be come in again with great products and overlooking the whole of all of this data coming in. >> Peter: Oh, sure. Absolutely. >> So there's a lot of players who could be in this area. Microsoft, I missed out, of course they will have the two ends that they can combine together. >> Well, they may have an advantage that nobody else has-- >> Exactly. Yeah. because they're strong in both places. But I have Jim Kobielus. Let me check, are you there now? Do we got Jim back? >> Can you hear me? >> Peter: I can barely hear you, Jim. Could we bring Jim's volume up a little bit? So, Jim, I asked the question earlier, about we have the tooling for AI. We know how to get data. How to build models and how to apply the models in a broad brush way. And we're certainly starting to see that happen within the IT operations management world. The ITOM world, but we don't yet know how we're going to write these contracts that are capable of better anticipating, putting in place a regime that really describes how the, what are the limits of data sharing? What are the limits of derivative use? Et cetera. I argued, and here in the studio we generally agreed, that's we still haven't figured that out and that this is going to be one of the places where the tension between, at least in the B2B world, data availability and derivative use and where you capture value and where those profitables go, is going to be significant. But I want to get your take. Has the AI community >> Yeah. started figuring out how we're going to contractually handle obligations around data, data use, data sharing, data derivative use. >> The short answer is, no they have not. The longer answer is, that can you hear me, first of all? >> Peter: Barely. >> Okay. Should I keep talking? >> Yeah. Go ahead. >> Okay. The short answer is, no that the AI community has not addressed those, those IP protection issues. But there is a growing push in the AI community to leverage blockchain for such requirements in terms of block chains to store smart contracts where related to downstream utilization of data and derivative models. But that's extraordinarily early on in its development in terms of insight in the AI community and in the blockchain community as well. In other words, in fact, in one of the posts that I'm working on right now, is looking at a company called 8base that's actually using blockchain to store all of those assets, those artifacts for the development and lifecycle along with the smart contracts to drive those downstream uses. So what I'm saying is that there's lots of smart people like yourselves are thinking about these problems, but there's no consensus, definitely, in the AI community for how to manage all those rights downstream. >> All right. So very quickly, Ralph Finos, if you're there. I want to get your perspective >> Yeah. on what this means from markets, market leadership. What do you think? How's this going to impact who are the leaders, who's likely to continue to grow and gain even more strength? What're your thoughts on this? >> Yeah. I think, my perspective on this thing in the near term is to focus on simplification. And to focus on depth, because you can get return, you can get payback for that kind of work and it simplifies the overall picture so when you're going broad, you've got less of a problem to deal with. To link all these things together. So I'm going to go with the Shaker kind of perspective on the world is to make things simple. And to focus there. And I think the complexity of what we're talking about for breadth is too difficult to handle at this point in time. I don't see it happening any time in the near future. >> Although there are some companies, like Splunk, for example, that are doing a decent job of presenting a more of a breadth approach, but they're not going deep into the various elements. So, George, really quick. Let's talk to you. >> I beg to disagree on that one. >> Peter: Oh! >> They're actually, they built a platform, originally that was breadth first. They built all these, essentially, forwarders which could understand the formats of the output of all sorts of different devices and services. But then they started building what they called curated experiences which is the equivalent of what we call depth first. They're doing it for IT service management. They're doing it for what's called user behavior. Analytics, which is it's a way of tracking bad actors or bad devices on a network. And they're going to be pumping out more of those. What's not clear yet, is how they're going to integrate those so that IT service management understands security and vice versa. >> And I think that's one of the key things, George, is that ultimately, the real question will be or not the real question, but when we think about the roadmap, it's probably that security is going to be early on one of the things that gets addressed here. And again, it's not just security from a perimeter standpoint. Some people are calling it a software-based perimeter. Our perspective is the data's going to go everywhere and ultimately how do you sustain a zero trust world where you know your data is going to be out in the clear so what are you going to do about it? All right. So look. Let's wrap this one up. Jim Kobielus, let's give you the first Action Item. Jim, Action Item. >> Action Item. Wow. Action Item Automation is just to follow the stack of assets that drive automation and figure out your overall sharing architecture for sharing out these assets. I think the core asset will remain orchestration models. I don't think predictive models in AI are a huge piece of the overall automation pie in terms of the logic. So just focus on building out and protecting and sharing and reusing your orchestration models. Those are critically important. In any domain. End to end or in specific automation domains. >> Peter: David Floyer, Action Item. >> So my Action Item is to acknowledge that the world of building your own automation yourself around a whole lot of piece parts that you put together are over. You won't have access to a sufficient data. So enterprises must take a broad view of getting data, of getting components that have data be giving them data. Make contracts with people to give them data, masking or whatever it is and become part of a broader scheme that will allow them to meet the automation requirements of the 21st century. >> Ralph Finos, Action Item. >> Yeah. Again, I would reiterate the importance of keeping it simple. Taking care of the depth questions and moving forward from there. The complexity is enormous, and-- >> Peter: George Gilbert, Action Item. >> I say, start with what customers always start with with a new technology, which is a constrained environment like a pilot and there's two areas that are potentially high return. One is big data, where it's been a multi vendor or multi-vendor component mix, and a mess. And so you take that and you constrain that and make that a depth-first approach in the cloud where there is data to manage that. And the second one is security, where we have now a more and more trained applications just for that. I say, don't start with a platform. Start with those solutions and then start adding more solutions around that. >> All right. Great. So here's our overall Action Item. The question of automation or roadmap to automation is crucial for multiple reasons. But one of the most important ones is it's inconceivable to us to envision how a business can institute even more complex applications if we don't have a way of improving the degree of automation on the underlying infrastructure. How this is going to play out, we're not exactly sure. But we do think that there are a few principals that are going to be important that users have to focus on. Number one is data. Be very clear that there is value in your data, both to you as well as to your suppliers and as you think about writing contracts, don't write contracts that are focused on a product now. Focus on even that product as a service over time where you are sharing data back and forth in addition to getting some return out of whatever assets you've put in place. And make sure that the negotiations specifically acknowledge the value of that data to your suppliers as well. Number two, that there is certainly going to be a scale here. There's certainly going to be a volume question here. And as we think about where a lot of the new approaches to doing these or this notion of automation, is going to come out of the cloud vendors. Once again, the cloud vendors are articulating what the overall model is going to look like. What that cloud experience is going to look like. And it's going to be a challenge to other suppliers who are providing an on-premises true private cloud and Edge orientation where the data must live sometimes it is not something that they just want to do because they want to do it. Because that data requires it to be able to reflect that cloud operating model. And expect, ultimately, that your suppliers also are going to have to have very clear contractual relationships with the cloud players and each other for how that data gets shared. Ultimately, however, we think it's crucially important that any CIO recognized that the existing environment that they have right now is not converged. The existing environment today remains operators, suppliers of technology, and suppliers of automation capabilities and breaking that up is going to be crucial. Not only to achieving automation objectives, but to achieve a converged infrastructure, hyper converged infrastructure, multi-cloud arrangements, including private cloud, true private cloud, and the cloud itself. And this is going to be a management challenge, goes way beyond just products and technology, to actually incorporating how you think about your shopping, organized, how you institutionalize the work that the business requires, and therefore what you identify as a tasks that will be first to be automated. Our expectation, security's going to be early on. Why? Because your CEO and your board of directors are going to demand it. So think about how automation can be improved and enhanced through a security lens, but do so in a way that ensures that over time you can bring new capabilities on with a depth-first approach at least, to the breadth that you need within your shop and within your business, your digital business, to achieve the success and the results that you want. Okay. Once again, I want to thank David Floyer and George Gilbert here in the studio with us. On the phone, Ralph Finos and Jim Kobielus. Couldn't get Neil Raiden in today, sorry Neil. And I am Peter Burris, and this has been an Action Item. Talk to you again soon. (upbeat digital music)

(upbeat music) >> Hi, I'm Peter Burris. Welcome another to theCUBE Conversation from beautiful Palo Alto. Here today, we are with Muddu Sudhakar, who's a CEO investor, and a long-time friend of theCUBE. Muddu, welcome to theCUBE. >> Thank you, Peter. Thanks for having me. >> So, one of the things we're going to talk about, there's a lot of things we could talk about, I mean, you've been around you've invested in a number of companies. You've got a great pedigree, a great track record. ServiceNow, and some other companies, I'll let you talk a bit more about that. But, one of the things we want to talk about is some of the big changes that are happening in the way that IT gets delivered within enterprises. The whole notion of IT operations management is on the forefront of everyone's mind. We've been talking about dev ops for a long time. It hasn't been universally adopted, it clearly needs some help; it's working really well in some places, not so well in other places. We're trying to bring that cloud-operating model into the enterprise. What are some of the things, based on your experience, talk a little bit about yourself, and then use that as a level into, what are some of the things that the IT organization, business overall, has to think about as they think about modernizing IT operations management, or ITOM. >> Great topic, it's very lengthy. We can go on for hours on this, right? As we are talking earlier, Peter, so I think operations IT management has been around for what, 20, 30, years? It started with, I guess, at the time of mainframes, to client server. But, as you rightfully said, we are in the age of cloud. How does cloud, AI machine learning, and the SaaS services going to impact ITOM, our IT operation management? I think that's, it's going to evolve, the question is how it's going to evolve. And, the one area that you are always passionate about talking about is cloud infrastructure itself, and the word that you use is called, Plastic infrastructure. The underlying infrastructure is changing so much. We are moving from virtual machines to server-less architectures, to containers. So this whole server-less architecture presents such a new concept, that the ITOM as itself should evolve to something new. I actually, I mean the industry word for this is, called AI operations. AI is just one piece. But how do you take hybrid cloud, how do you take the actual cloud substrate, and evolve IT operation management is such a big topic, on multiple areas, and how it is going to change industry. >> So, let's break it down a little bit. So, you mentioned the term plastic infrastructure. We've written a bunch about that here at Wikibon, and the basic notion of plastic infrastructure is that we can look at three generations of infrastructure, what we call static infrastructure, which might be brick, you add load to it, it might fall apart, but it was bound into the application. And in the world, or the era of elastic infrastructure is really where the cloud started, and the idea that you no longer had to purchase to your peak. That the elastic infrastructure would allow you to peak up, and peak down, but it would snap back into place, it was almost like a rubber brick. But this notion of plastic infrastructure, how do we add new workloads faster, is how do we but do so in a way that we don't have to manually go in and adust the infrastructure. That the infrastructure just responds to the new workloads in a plastic way. And snaps into a new form. Now to, we are going to need to be able to do that. If we're going to add AI, and we're going to add, you know, ML, machine learning, and all these other new application-oriented technologies to this. Can't imagine how we're going to add all that complexity to the application level, if we don't dramatically automate and simplify the operating load. And that's the basis of plastic infrastructure. What do you think? >> No, I completely, I think you kind of touched all the good points, but the areas that I can add on top of what you mentioned, is if you look at the plastic infrastructure, the one area is, so far IT operations management is built around a human being, around a dev ops, and around a IT admin. In the new world, it will be 90 to 80% to be done automated manner. Your trading is algorithmic, your in a self-driving car age, but at the IT operations management is around an IT admin and a dev ops. That got to change. I think cloud guys, the Amazon, Azure, Google, they're going to disrupt this because they have to do this in an automated manner, right? So that means, the plastic infrastructure will be able to run workloads, it should be malleable. It's like the, it should be changes shape and form. And it should be that's where the server-less really comes in. I don't want to pick a computer, and rent it for so many hours, that's still a silly concept, I think this whole virtualization, and virtual machines, is gone to the point of server-less. So, all these things. How do you manage the workloads? How do you manage your apps? To your point, apps have to be mapped downstream. I call it, as service maps. How do you build these dynamic service maps for your application? How do I know which component is failing at what point in time? Alright, asking what I call the root cause analysis. Do you expect a human being to identify that MongoDB, or a SQL server is down, because of this hardware issue? That has to be detected automatic manner, right? At least, a root cause and triage it to the point where a human being can come and say, I agree, or don't agree, able to take. Then, the final thing is, the infrastructure has to be, should be, take actions. Allow it to be at the point where the under, once you detect a problem, the infrastructure should be able to say algorithmically, progammatically to an API. I should be able to impact the change. The problem in chain infrastructure today is very much it's very much driven through scrapes and through admins. Can I do that in a programmatic manner? It hasn't happened yet. >> Then it should be, I mean when you stop and think about it AI for example, using AI as a general umbrella for a lot of different technologies that are based on you know, pattern-recognition, and anomaly detection. And all the other stuff that is associated with AI. But we have pretty good data sources in the infrastructure. We know how these tools operate, they are programmable, so they get, you know, a range of particular behaviors. But there are discernible patterns associated with those behaviors, so you'd think that infrastructure itself would be a great source, to start to building out some of these AI platforms, some of these new modeled, what we call data-first, type of applications. What do you think? >> Absolutely, you nailed it. I think, if you remember my previous company Caspida, which was acquired by Splunk. We did that for security. We created this whole area called user behavior analytics. Right? For security, understand the behavior of the users, understand the behavior of the attackers, actual inside it. Same thing needs to happen. >> But all represented through a device. >> Through a device. >> That had known characteristics. So we weren't saying, we're making big claims necessarily about people. Which have, you know, unbelievably complex, but when you start with, What is a person doing with a device? That set of behaviors is now constrained, which makes it a great source. >> Absolutely. So I think it, like given the sources in the IT operations area, if you were think about, for example, looking at the patterns and the behavior of the application, the storage, I call it like, think of like the four layers. You have apps, you have compute, your network, and storage. There are different patterns and behavior you can do it. You can do anomalies, and you can understand the various workflow of the patterns. But I call it the three P's problem with AI machine learning. The P's are, you actually said it five P's. The three P's, that I usually talk about is, the proactive, the predictive, and prescriptive nature. If I can take this data sources, whether they come from logs, events, alerts, and able to do this for those, I can do planning. I can be able to implement what changes I can do as a workflow and full actions. 'Cause detecting is no good, if I can't take an action. That's where the prescriptiveness comes in. And I think that whole area of IT operations management, what needs to happen is mundane with a human being, will be automated. And then the question comes in is, Do you do this in batch mode, or real-time? >> You want to do it in real-time. But let me get those straight. So the three P's that you mentioned where, proactive, prescriptive? >> And predictive. >> And predictive. So, that's proactive, predictive, and prescriptive. And just, you know, to level it out, I noted that all this is based on patterns. >> Yes. >> That come out of some of these infrastructure technologies. So, as we think about where ITOM is going, you mentioned earlier AI, systems management, AI services management. When we think about kind of some of the next steps, who do you anticipate are going to be kind of at the leading, or leading the charge, as we move forward here. >> I think there'll be a new sheriff in town. May not, or to your point earlier, that when many sheriffs in town in this area. The great opportunity here is when all there is a fundamental change like this happens, there will be new players will win this market. Definitely the cloud guys have the right substrate. The Amazon's, the Azure's, and the Google's of the world. They have the right infrastructure, they are all moving towards the plastic infrastructure. They just have to do more on workload management. They need to do more on the AI operations. >> They have a, absolutely a sense of urgency, and pressing need. >> They have. >> Their business falls down if they don't do this. >> So I think those guys will definitely there. Then all the start-ups, right? I think there are a whole bunch of start-ups, each of them will be doing, from a small niche player, all over to platform players. It's a great opportunity, greenfield opportunity. It's going to open up a whole wonderful, new players will come in. Who will be the next generations' AIOps operations vendors. >> So, I'm going to ask you two questions, then. One, do you think the big boys, the HPE's, the Oracle's, the Cisco's, the IBM's, are going to be able to change their stripes enough, so that they can do both? We're tryin' to keep our stall base and upgrade, enhance it, and try to introduce this new cloud operating model? And we'll talk about the start-ups in a second. What do you think? Are the big boys going to be able to make this transition? >> I think they have to, their hand is already dealt. I call it, the cloud is a runaway train, the cloud today is 30, 40 billion dollars. If you are those mega-vendors, you don't, if you're not making on this, something is wrong with you. Right? I mean, in this day and age, if you're not making money on the cloud, with this, with what we're talking about. So what they do is, how can they, either they, have to offer a cloud services, public or a PERT. If you are not doing that, might as well get into this game of AIOps, so that you are actually making money on the apps, and on the infrastructure. So, all those big, large vendors that you mentioned, about the Cisco's of the world, the Oracle's of the, they have a genuine interest to make this happen. >> Got it. So in many respects, to kind of summarize that point, it's like, look, the cloud experience is being defined elsewhere. It's being defined by Azure. AWS, Google, GCP, and these vendors are going to have to articulate very, very clearly for their customer base, the role that they're going to play. And that could include bringing the cloud experience on premise, when and if, data is required on premise. >> Absolutely, and I actually call this cloud should be the aircraft carriers, right? As a world when it settles, eventually it won't have hundred aircraft carriers. You'll have this three or four large cloud vendors. On top of them, the people who manage the apps and services will be few. You don't need 20 vendors managing your infrastructure. So there'll be a huge consolidation game. The questions is, when that happens, the winners doesn't have to be the like c-Vendors. >> Right. >> The history always show the legacy always loses out. So that's where the start-ups have an opportunity. >> Alright. So let's talk about the start-ups. Are there any particular class of start-ups out there. Is this going to, or are some of the security guys who manage services going to be able to do a better job, because they can make claims about your data? Or some of the guys, some of the companies coming from middleware? Where do you think the start-up kind of epicenter is going to be as we see new companies introduced in this space? >> That's a good question, I don't have any one particular vendor in mind. But I think that definitely the vendors that will come into play will be people who can do log management better. We already know the IS Splunk's of the world. People who can do events and alerts management. People who can do incident problem change management, right? All those things, if you look at the whole area. And people who can do the whole application management, as earlier you were talking about the workload management. So I think each of these functions, there'll be winners coming in. Eventually all of them will be offered by one single person, as a full-stack solution for the cloud, on the cloud. The key problem that I keep noticing is, most vendors are keep still tied to the old infrastructure, which is mainframe, or physical servers. Nobody is building this thing for the cloud, in the cloud again. So somebody who has the right substrate to build this, as a playbook, will end up winning this game. >> Yeah, it's going to be an interesting period of time. Now, when we stop and think about, I made an assertion earlier, that for us to build more complex applications, which is where everybody talking about, it's essential, in our opinion, that we find ways to simplify and bring more automation to the infrastructure. If we think about servers, storage, network, those type of things, is there a particular part of the infrastructure that you think is going to receive treatment earlier, and therefore is going to kind of lead the way for how, the rest of this stuff. Is storage going to show CPU, and network? Or is network going to step up, because some of the changes that are happening? What do you think? >> That's a very good question. I think, look, the key think the key pain points for most people today, if you look at the way the complex questions are, if there's a problem in the network infrastructure, it's very hard to triage that, so that area has to be automated. I mean, you can't expect a human being to understand why my switch or network is not performing. >> It's just happening too fast >> Why like, why WIFI is not working on sixth floor and seventh floor. It's a very, so network will be one area, it's highly visible. The second will be in the database and storage area. Just because my storage disk is full, I don't want my database to be down. It's such an old pattern behavior, People will catch those things in an automated manner. Right? So storage, network, because. Where you see the higher level items is when an application is not performing well. Is it a performance problem? Or, why this component is tied to what component, right? Is this applicant is built on a load balancer, and a load balancer is talking to, and the database. Building that map of who's-connected-to-who, that's a new graph, algorithms graph to the unit. That doesn't exist today. So I think what'll happen is how do you manage an application, given a problem, and mapping that. That is I think the number one, that will start happening first. Everything else, people will happen over a period of time. But the apps that are visible, where a user and a customer can see the impact, will happen first. >> Yeah, actually we have a prediction here at Wikibon, what we call networks of data. Where the idea that we're going to the next round of network formation is going to be data assets explicitly connecting with each other. And then using that as a way of zoning data assets. And saying, this application requires data from these places, and then all the technology that allows you to either move it in. >> Right. >> Or keep pointers, or whatever else it might be. So this notion, you would agree then. You know, a graph of data is going to drive a lot of the change forward. >> And to actually take you to that, I actually talk about saying it doesn't require a single class of algorithm. I call it an ensemble of machine learning algorithms you need. You need some statistical, some probablistic, some Markovian algortithm, some Bayesian, and mainly graph algorithms. This data has to capture the behaviors and patterns that you want to put in a larger graph, that you should be able to mine on. That doesn't exist today. So everybody is most often, when they talk about like their dynamic thresholding, statistical, that actually is there in idea operation management. The next level of how do you build a graph, like too big to fail, in my opinion fails. What is it relying on, like if I come to Peter's house. How is your house looks like, the area, one-bedroom, you have two kitchens, You know what I'm saying. >> It looks like a network of data right now. >> Exactly, right. (laughing) >> Okay, so, I got one more question for you, Muddu. And that is, you work with us a lot, and some of the crowd chats you do. You're a great research partner for us. As you think about kind of the story that needs to be told to the CxO about some of these changes, how's it different from the story that needs to be told to the DI team leader? I can imagine what some of the differences are, but you're talking to both sides. What would you, what would you're advice and counsel be to companies that are trying to talk to the CEO about this, or the board, what do you think? What would you say to 'em? >> I think you kind of got it yesterday in the crowd chat. I think the key thing that the CIO or CxO or CEO needs to have this is, we used to call it Chief Data Officer, where the data is the key, that delimit was applied for the overall business. That same role needs to happen within the CIO now. How do I use my data to make my IT better? So that, maybe call it a CIO, a CDO for the CIO, is a big role that needs to happen, but the goal of that person and that entity should be is, How can you do, can I run my operation in a light sort manner? I call it IT as a service. People talk about IT and service. But IT as a service to me, is a bigger concept. >> Let me make sure I got this, 'cause this is crucially important point. So in many respects, we should be saying to the CEO, your data is an asset, you have to take steps to appreciate, dramatically and rapidly, appreciate the value of data as an asset, and that requires looking at the CIO with the CDO, data officer, and saying, your job, independent of any technology or any particular set of ITOM processes, your job is to dramatically accelerate how fast we're able to generate data. >> During decisions. >> Value out of our data being able to utilize these technology investments. >> Absolutely. Because that person, once you have the data as addition, what will happen is, you'll still use the existing process, but it gets you the new insight, What can I automate? What can I do more with less people, right? That has to happen, like if I'm a CEO, he should wake up and say, 90% of my things should be able to automate today, right? >> Okay, so let's talk about last question. You've been, you've led a lot of organizations through a lot of change. We're talking about a lot of change within the IT organization, when we talk about these things. What's one bit of advice that you have for that CIO or leader of IT, and help them take their people through the types of changes that we're talking about? >> Make bets. Don't be afraid of making bets, unless you make a bet you're never going to win. So every year, every quarter, make a new bet. Some bets, you are going to fail, some you're going to succeed. Unless you make a bet, you will not innovate. >> Peter: And understand the portfolio, and sustain those bets. And then, when you've lost, don't keep putting money out. >> Exactly, yeah, keep moving on. >> Great. Alright, so, Muddu, thank you very much for being here. >> Peter, always a pleasure. >> Alright, Muddu Sadhakar, investor, CEO, once again, this has been a CUBEConversation, thank you very much for being here. >> Thank you, Peter. >> And we'll talk to you soon. >> Muddu: Thank you always, and John too. (upbeat music)

>> Announcer: Live from Madrid, Spain, it's The Cube covering HPE Discover Madrid 2017. Brought to you by: Hewlett Packard Enterprise. >> Welcome back to HPE Discover, 2017 in Madrid. This is The Cube, the leader in live tech coverage, my name is Dave Vellante, I'm here to rap with my co-host, Peter Burris. >> Hey, Dave. >> Dave: Good couple a days. >> Oh, you know what I just discovered. I discovered The Cube is the antidote to jet lag. (laughs) >> That's right, when you get interesting people on. >> Oh, man. >> It pumps you up. >> Totally. Just unbelievable, exciting and it's all framed by... Well let's start where we talked about yesterday, we proposed that increasing what we're seeing in the industry is the new model of computing being established by Amazon and then the other poll, where it was known, we know that it's not all gonna be one cloud, it's not all gonna be a central cloud model, or essentialize cloud model. There's gonna be other places where data's gonna need to be processed. >> Dave: Well, that's what we believe. >> That's what we believe, and... There's physics behind that statement. There's legal regulations about data residency, behind that statement. But, we didn't know who was gonna step up and lead that other side and it's nice to see this conference indicate that HPE is in a position to help demonstrate, or help show the industry how cloud truly can go from centralized down to the edge. >> Yeah, and I think as I said a number of times, the strategy's coming into focus, you could debate it. You could say, "well, splitting it up was the wrong thing to do. "They lost their supply chain." But, Meg's argument, and then Antonio's argument always was, "look, we're gonna be more focused, "it's gonna allow us to do "a better job for our customers. "Yes, we're gonna be service's lead." They didn't say this. "Our margines are gonna be lower, "you don't have software anymore, "but that's okay, we can learn how to make money at that." And you know, the old HPE went through a similar transition. Kinda, got out of the HPEX business and got out of building it's own OS, and relying more on Microsoft and Intel and it made a lot of money. In those days. >> Peter: It did well. >> Did very well. It didn't invest under the herd regime the way it could have or should have and that hurt and then it spun out and made a lot of missteps but... Meg, to her credit, didn't make a lot of missteps. There was the initial entrance into the public cloud, they pulled back fast, they failed fast on that, good. Yeah, maybe there was some organizational issues early on but in general, the acquisitions have been solid, the strategy... >> And well integrated. >> And well integrated, absolutely. >> Peter: They've gotten value out of 'em. >> The strategies has been... I think clear internally, it wasn't always clear externally but they stayed calm about that, they didn't freak out about that. Helped that the stock price was going up a little bit, 'cause it was pretty depressed for a while. >> And shareholders weren't incontestable like they were for many years. >> That's right, and so, that gave them a little bit of time to bring it all together... It's finally here and I think Meg is stepping down at absolutely the right time. >> Or at a... She's stepping down at a good time, she's leaving a company that is much stronger than it was when she took it over. >> And that's what you want, one of the things I'm personally proud of when I left IDC it was in really good shape when I left, it wasn't a mess that I handed to somebody else. Had a lot of messes and IDC that I turned around as you well know. So, I think, I feel as though the company's in good shape and good hands. And, again, I think the... I don't know if you're a stock analyst or if you're pounding the table saying "buy this stock." 'Cause it is a relatively low margin business and there's a lot of competition, there's knife fights out there, it's not a high growth business, but on the flip side, it's clean, it throws off a lot of cash, they got a decent balance sheet and the customers love 'em. >> And that's the most important thing, it's the customers. Look, I... Disclosure, I actually did a significant consulting stint, here at HPE, right around the time of the compact acquisition and I saw what happened and for many years, the senior manager and team of HPE behaved as though they presumed that scale was it's own reward. If we get bigger, we'll find efficiencies, we'll find opportunities. Just being big, is the objective and I think that they have wandered in the desert trying to find those opportunities, that were the consequence of just being big and they never materialized. >> They weren't there. >> They never... It was like mirages on the horizon, they never materialized and I think if there's anything to your point that Meg has successfully done, is she's gotten the company to say, "don't chase the mirages, chase the customer. "Let's come back to what made HPE great for so long." And the idea that, if we stay focused on the customer and focus on technology, we can put them together in unique and interesting ways that will bind us to what customers are doing. And if you take a look at this event and the new messaging, and the things that they're focusing on it feels like, to me, that HPE is no longer wandering in the desert. You and I are smart guys, we are... Typically we can look at a company and we can see whether or not they know what they're doing and when you said, "well, you know what. "Maybe they had it all figured out inside, "and the rest of us couldn't see it." No, that's not the case. It was not figured out inside and that's what we saw but under Meg, it has become increasingly more figured out and the consequence of that... And it's been very, very plan full. She first was figured out and then she told Wall Street and Wall Street was happy with the numbers, and then she figured out and she started talking to customers when customers were there and now she's figuring it out, she's telling a broader market place. >> Well, and when she stopped by- >> And Antonio's got a great big story to tell. >> And both of those guys stopped by to see us. Meg spent 10 minutes with us, we were chatting here on the open mics and she was very good. Meg, one on one situation, in a small crowd is phenomenal. I've always said that about Meg. Not the greatest presence on stage, not a super dynamic speaker, she's not a Steve Jobs, obviously nobody is, but... But, man, is she credible in a one on one situation. One of the things she said to us was, "Y'know, we kinda got lucky..." My words, "with Aruba, we bought him "because we thought we could compete with "Cisco better, we bought him obviously "because it was a great business, a growth business," and boom all of a sudden, this intelligent edge thing hit. You sprinkle in a little Dr. Tom Bradicich and boom, off you go and you've got not only a great business, you got something that is becoming increasingly strategic for organizations. Great example, I mean the nimble acquisition. We heard, yesterday, Bill Philbin talking about, "well, when we got nimble-" was it Bill Philbin, no it was somebody else today it was... Alain Andreoli. He said, "we picked up nimble 'cause it was a great "flash company, but then we saw this inside thing, "we said, wow, we can spread this thing "across our entire portfolio." That's where- >> And the example he gave was: in six months, it's not running on... >> On three par and then it's gonna run... His goal, he says, "I'm not committing to this, "but my goal is by the end of the next year "it's gonna be running across the entire "server and storage and networking line." That would be a major accomplishment. If in fact, we'll see how much of this stuff is actually impactful to the business, how much it can actually save money you know, anticipate failures, I don't know. We'll see, it's AI, it's a perfect application. You guys have written a lot on the Wikibon team about AI for ITOM. >> Oh yeah, look... >> Dave: And this is a good example. >> I'm not the kinda guy, as you know, that gets all excited about technology for technology's sake. I like thinking about technology and how it's gonna be applied, more problems are gonna be solved and so as we, in Wikibon, started running around and getting all excited about AI, my challenge to the guys was: Well, show me the two concrete cases, where it's gonna have a material business impact and one of the most important cases is, it's got a material business impact and how IT runs itself because you cannot... IT cannot reduce the number of people it's got and take on these increasingly complex application, problems, and portfolios unless they get a lot of help and the best, most likely source of that help is by bringing a lot of these new AI technologies that are capable of taking concrete, real time action in response to what's happening within the infrastructure and the applications at any given time. >> Yeah, now... Couple other things, just observations. Ana Pinczuk came on, great leader, woman in tech, big proponent of advancing women's causes, especially in tech. She had mixed feelings about Meg stepping down, obviously you have a woman leader, I thought her comments there were... Were quite interesting, but she said, "But I am up for the challenge "to continue the mission." Which leads me to Antonio. Antonio is outwardly a humble guy, he may have a big ego I don't know, he's been on The Cube a number of times, but he certainly doesn't come across as a guy who's looking to get credit. He's a quiet but very competent leader, he knows the business very well. Really interesting to see what his relationship- >> Peter: Homegrown. >> Homegrown, which is 22 years at HPE, technology background, not a U.S... Born individual, now living in the U.S. obviously. But, somebody with international experience which is always been an attribute that's valued at HPE. Gonna be interesting to see what his relationship is with Wall Street. Will he be sort of a quiet leader that lets the CFO take front and center, which would be fine. Or will he slowly sort of advance, he's not been sitting on the earnings calls. I'm interested to see how he handles it, or he may just say, "you know what, "I'm gonna go execute in the business "and let the results speak for themselves." So, I'm kind of curious as to how that all... All plays out. It's a big job, it's a big role as you pointed out with me the other day. Big role for him, big job for him. Serious opportunities to make a mark in the industry. >> Again, and you raise a really great point. Meg had a very good reputation on Wall Street, the knock on her when she came on, was she didn't know customers. Antonio's got a great reputation with customers, you're asking the question: is he gonna get to know Wall Street? A great CEO has to be able to take care of customers and owners He seems very... Look, this is a, this whole simplification of how they're gonna bring cloud technologies to where their data's gonna require is apparently, based on what we heard, in large part Antonio's brain child. He conceived it, he invested in it, he nurtured it, he took risks for it, he put some skin in the game and now it's coming to fruition, that's great, and he's got customers lining up behind it. We'll see, this is another place where we'll see, but I don't think that there's... There's no reason to suspect, just looking at Antonio's track record, why Wall Street would abandon him. On the contrary, there's reasons to suspect that he will also be able to develop that set of skills that Wall Street needs to do their job. But, clearly this is a guy that's gonna turn on a lot of customers. >> Yeah, and as I say, it's gonna be interesting to see what his relationship, like look at a guy like Frank Slootman, who had a great relationship with Wall Street, everybody loved him 'cause he just performed but he's a hard-driving, in your face kinda guy, who developed close relationships with the street. It's gonna be, as they say, I gotta watch that, to see how Antonio interacts with them. I think it's important to have a relationship with... >> Peter: With your ownership, yeah it usually is. >> And I think that's the one big question mark here is, where has his presence been there but so we'll watch and I'm confident he'll step up to that. Okay. Let's see, The Cube... Next week? Cube-con? >> Peter: Yeah. >> Next week in Austin. Right, so development. You'll see The Cube expanding way beyond it's original infrastructure route, so obviously HPE Discover, big infrastructure show. But we're at Amazon Reinvent this week, it's our big cloud show. We obviously... All the IBM shows are being consolidated into one show called Think. This year The Cube will be there. But CES is gonna be January, we were there last year, likely be there again. Cisco live is on the radar, we're gonna be at Cisco live I think both in Barcelona and most likely in the states this year, so that's another big thing. A lot of developer shows, Docckercon, Kubecon, working with the Linux Foundation, developers are really the lynch pin, developers in cloud. Really big areas of growth. IOT, some IOT conferences that we're gonna be doin' this year. Obviously, our big data heritage we still do a lot of work there, so. It's been an unbelievable year, I think a 125 shows for The Cube. TheCube.net, new website, our new clipper tool, you see the clips that come out, so. A lot of innovation comin' out of Siliconangle Media, check out Siliconangle.com. Peter, the work that your team is doing on the Wikibon side, Wikibon.com. Unbelievable amounts of research that you guys are crackin' out. Digital business, AI, AI for ITOM stuff that we talked about, we still do some stuff in infrastructure, true private cloud. >> New computing architectures, memory based computer architectures. >> So, fantastic work there and... Yeah, so we're looking forward to another great year. Thanks everybody for these last two days, thanks to the crew, great job. Everybody at home. We're out. Dave Vellante for Peter Buriss from Madrid. Thanks for watching. (upbeat music)

>> Hi, I'm Peter Burris, and welcome once again to Action Item. (funky electronic music) Every week, Wikibon gathers together the research team to discuss seminal issues that are facing the IT industry. And this week is no different. In the next couple of weeks, somewhere near 100,000 people are gonna be heading to Las Vegas for the Amazon, or AWS re:Invent show from all over the world. And this week, what we wanna do is we wanna provide a preview of what we think folks are gonna be talking about. And I'm joined here in our lovely Palo Alto studio, theCUBE studio, by Rob Hof, who is the editor-in-chief of SiliconANGLE. David Floyer, who's in analyst at Wikibon. George Gilbert, who's an analyst Wikibon. And John Furrier, who's a CUBE host and co-CEO. On the phone we have Neil Raden, an analyst at Wikibon, and also Dave Vellante, who's co-CEO with John Furrier, an analyst at Wikibon as well. So guys, let's jump right into it. David Floyer, I wanna hit you first. AWS has done a masterful job of making the whole concept of infrastructure as a service real. Nobody should downplay how hard that was and how amazing their success has been. But they're moving beyond infrastructure as a service. What do we expect for how far up Amazon is likely to go up the stack this year at re:Invent? >> Well, I can say what I'm hoping for. I agree with your premise that they have to go beyond IAS. The overall market for cloud is much bigger than just IAS, with SaaS and other clouds as well, both on-premise and off-premise. So I would start with what enterprise CIOs are wanting, and they are wanting to see a multi-cloud strategy, both on-premise and multiple clouds. SaaS clouds, other clouds. So I'm looking for AWS to provide additional services to make that easier. in particular, services, I thought of private clouds for enterprises. I'm looking for distributed capabilities, particularly in the storage area so they can link different clouds together. I want to see edge data management capabilities. I'd love to see that because the edge itself, especially the low-latency stuff, the real-time stuff, that needs specialist services, and I'd like to see them integrate that much better than just Snowball. I want to see more details about AI I'd love to see what they're doing in that. There's tremendous potential for AI in operational and to improve security, to improve availability, recovery. That is an area where I think they could be a leader of the IT industry. >> So let me stop you there, and George I wanna turn to you. So AWS in AI how do we anticipate that's gonna play out at re:Invent this year? >> I can see three things in decreasing order of likelihood. The first one is, they have to do a better job of tooling, both for, sort of, developers who want to dabble in, well get their arms around AI, but who aren't real data scientists. And then also hardcore tools for data scientists that have been well served by, recently, Microsoft and IBM, among others. So this is this Iron Man Initiative that we've heard about. For the hardcore tools, something from Domino Data Labs that looks like they're gonna partner with them. It's like a data-science workbench, so for the collaborative data preparation, modeling, deployment. That whole life cycle. And then for the developer-ready tooling, I expect to see they'll be working with a company called DataRobot, which has a really nifty tool where you put in a whole bunch of training data, and it trains, could be a couple dozen models that it thinks that might fit, and it'll show you the best fits. It'll show you the features in the models that are most impactful. In other words, it provides a lot of transparency. >> So it's kind of like models for models. >> Yes, and it provides transparency. Now that's the highest likelihood. And we have names on who we think the likely suspects are. The next step down, I would put applying machine learning to application performance management and IT operations. >> So that's the whole AI for ITOM that David Floyer just mentioned. >> Yeah. >> Now, presumably, this is gonna have to extend beyond just AI for Amazon or AWS-related ITOM. Our expectation's that we're gonna see a greater distribution of, or Amazon take more of a leadership in establishing a framework that cuts across multi-cloud. Have I got that right, David Floyer? >> Absolutely. A massive opportunity for them to provide the basics on their own platform. That's obviously the starting point. They'll have the best instrumentation for all of the components they have there. But they will need to integrate that in with their own databases, with other people's databases. The more that they can link all the units together and get real instrumentation from an application point of view of the whole of the infrastructure, the more value AI can contribute. >> John Foyer, the whole concept of the last few years of AWS is that all roads eventually end up at AWS. However, there's been a real challenge associated with getting this migration momentum to really start to mature. Now we saw some interesting moves that they made with VMware over the last couple of years, and it's been quite successful. And some would argue it might even have given another round of life to VMware. Are there some things we expect to see AWS do this time that are gonna reenergize the ecosystem to start bringing more customers higher up the stack to AWS? >> Yeah, but I think I look at it, quickly, as VMware was a groundbreaking even for both companies, VMware and AWS. We talked about that at that research event we had with them. The issue that is happening is that AWS has had a run in the marketplace. They've been the leader in cloud. Every year, it's been a slew of announcements. This year's no different. They're gonna have more and more announcements. In fact, they had to release some announcements early, before the show, because they have, again, more and more announcements. So they have the under-the-hood stuff going on that David Floyer and George were pointing out. So the classic build strategy is to continue to be competitive by having more services layered on top of each other, upgrading those services. That's a competitive strategy frame that's under the hood. On the business side, you're seeing more competition this year than ever before. Amazon now is highly contested, certainly in the marketplace with competitors. Okay, you're seeing FUD, the uncertainty and doubt from other people, how they're bundling. But it's clear. The cloud visibility is clear to customers. The numbers are coming in, multiple years of financial performance. But now the ecosystem plays, really, the interesting one. I think the VMware move is gonna be a tell sign for other companies that haven't won that top-three position. >> Example? >> I will say SAP. >> Oh really? You think SAP is gonna have a major play this year where we might see some more stuff about AWS and SAP? >> I'm hearing rumblings that SAP is gonna be expanding their relationship. I don't have the facts yet on the ground, but from what I'm sensing, this is consistent with what they've been doing. We've seen them at Google cloud platform. We talked to them specifically about how they're dealing with cloud. And their strategy is clear. They wanna be on Azure, Google, and Amazon. They wanna provide that database functionality and their client base in from HANA, and roll that in. So it's clear that SAP wants to be multi-cloud. >> Well we've seen Oracle over the past couple of years, or our research has suggested, I would say, that there's been kind of two broad strategies. The application-oriented strategy that goes down to IAAS aggressively. That'd be Oracle and Microsoft. And then the IAAS strategy that's trying to move up through an ecosystem play, which is more AWS. David Floyer and I have been writing a lot of that research. So it sounds like AWS is really gonna start doubling down in an ecosystem and making strategic bets on software providers who can bring those large enterprise install bases with them. >> Yeah, and the thing that you pointed out is migration. That's a huge issue. Now you can get technical, and say, what does that mean? But Andy Jassy has been clear, and the whole Amazon Web Services Team has been clear from day one. They're customer centric. They listen to the customers. So if they're doing more migration this year, and we'll see, I think they will be, I think that's a good tell sign and good prediction. That means the customers want to use Amazon more. And VMware was the same way. Their customers were saying, hey, we're ops guys, we want to have a cloud strategy. And it was such a great move for VMware. I think that's gonna lift the fog, if you will, pun intended, between what cloud computing is and other alternatives. And I think companies are gonna be clear that I can party with Amazon Web Services and still run my business in a way that's gonna help customers. I think that's the number one thing that I'm looking for is, what is the customers looking for in multi-cloud? Or if it's server-less or other things. >> Well, or yeah I agree. Lemme run this by you guys. It sounds as though multi-cloud increasingly is going to be associated with an application set. So, for example, it's very difficult to migrate a database manager from one place to another, as a snowflake. The cost to the customer is extremely high. The cost to the migration team is extremely high, lotta risk. But if you can get an application provider to step up and start migrating elements of the database interface, then you dramatically reduce the overall cost of what that migration might look like. Have I got that right, David Floyer? >> Yeah, absolutely. And I think that's what AWS, what I'm expecting them to focus on is more integration with more SaaS vendors, making it a better place-- >> Paul: Or just software vendors. >> Or software vendors. Well, SaaS vendors in particular, but software vendors in particular-- >> Well SAP's not a SaaS player, right? Well, they are a little bit, but most of their installations are still SAP on Oracle and moving them over, then my ass is gonna require a significant amount of SAP help. >> And one of the things I would love to see them have is a proper tier-one database as a service. That's something that's hugely missing at the moment, and using HANA, for example, on SAP, it's a tier-one database in a particular area, but that would be a good move and help a lot of enterprises to move stuff into AWS. >> Is that gonna be sufficient, though, given how dominant Oracle is in that-- >> No, they need something general purpose which can compete with Oracle or come to some agreement with Oracle. Who knows what's gonna happen in the future? >> Yeah, I don't know. >> Yeah we're all kinda ignoring here. It will be interesting to see. But at the end of the day, look, Oracle has an incentive also to render more of what it has, as a service at some level. And it's gonna be very difficult to say, we're gonna render this as a service to a customer, but Amazon can't play. Or AWS can't play. That's gonna be a real challenge for them. >> The Oracle thing is interesting and I bring this up because Oracle has been struggling as a company with cloud native messaging. In other words, they're putting out, they have a lot of open source, we know what they have for tooling. But they own IT. I mean if you dug up Oracle, they got the database as David pointed out, tier one. But they know the IT guys, they've been doing business in IT for years as a legacy vendor. Now they're transforming, and they are trying hard to be the cloud native path, and they're not making it. They're not getting the credit, and I don't know if that's a cultural issue with Oracle. But Amazon has that positioning from a developer cloud DNA. Now winning real enterprise deals. So the question that I'm looking for is, can Amazon continue to knock down these enterprise deals in lieu of these incumbent or legacy players in IT. So if IT continues to transform more towards cloud native, docker containers, or containers in Kubernetes, these kinds of micro services, I would give the advantage to Amazon over Oracle even though that Oracle has the database because ultimately the developers are driving the behavior. >> Oh again I don't think any of us would disagree with that. >> Yeah so the trouble though is the cost of migrating the applications and the data. That is huge. The systems of record are there for a reason. So there are two fundamental strategies for Oracle. If they can get their developers to add the AI, add the systems of intelligence. Make them systems of intelligence, then they can win in that strategy. Or the alternative is that they move it to AWS and do that movement in AWS. That's a much more risky strategy. >> Right but I think our kind of concluding point here is that ultimately if AWS can get big application players to participate and assist and invest in and move customers along with some of these big application migrations, it's good for AWS. And to your point John, it's probably good for the customers too. >> Absolutely. >> Yeah I don't think it's mutually exclusive as David makes a point about migrating for Oracle. I don't see a lot of migration coming off of Oracle. I look at overall database growth is the issue. Right so Oracle will have that position, but it's kind of like when we argued about the internet growth back in 1997. Just internet users growing was so great that rising tide flows. So I believe that the database growth is going to happen so fast that Amazon is not necessarily targeting Oracle's market share, they're going after the overall database market, which might be a smaller tier two kind of configuration or new architectures that are developing. So I think it's interesting dynamic and Oracle certainly could play there and lock in the database, but-- >> Here's what I would say, I would say that they're going after the new workload world, and a lot of that new workload is gonna involve database as it always has. Not like there's anything that the notion that we have solved or that database is 90% penetrated for the applications that are gonna be dominant matter in 2025 is ridiculous. There's a lot of new database that's gonna be sold. I think you're absolutely right. Rob Hof what's the general scuttlebutt that you're hearing. You know you as editor of SiliconANGLE, editor-in-chief of SiliconANGLE. What is the journalist world buzzing about for re:Invent this year? >> Well I guess you know my questions is because of the challenges that we're facing like we just talked about with migrating, the difficulty in migrating some of these applications. We also see very fast growing rivals like Google. Still small, but growing fast. And then there's China. That's a big one where is there a natural limit there that they're gonna have? So you put these things together, and I guess we see Amazon Web Services still growing at 42% a year or whatever it's great. But is it gonna start to go down because of all these challenges? >> 'Cause some of the constraints may start to assert themselves. >> Rob: Exactly, exactly. >> So-- >> Rob: That's what I'm looking at. >> Kind of the journalism world is kinda saying, are there some speed bumps up ahead for AWS? >> Exactly, and we saw one just a couple, well just this week with China for example. They sold off $300 million worth of data centers, equipment and such to their partner in China Beijing Sinnet. And they say this is a way to comply with Chinese law. Now we're going to start expanding, but expanding while you're selling off $300 million worth of equipment, you know, it begs a question. So I'm curious how they're going to get past that. >> That does raise an interesting question, and I think I might go back to some of the AI on ITOM, AI on IT operations management. Is that do you need control of the physical assets in China to nonetheless sell great service. >> Rob: And that's a big question. >> For accessing assets in China. >> Rob: Right. >> And my guess is that if they're successful with AI for ITOM and some of these other initiatives we're talking about. It in fact may be very possible for them to offer a great service in China, but not actually own the physical assets. And that's, it's an interesting question for some of the Chinese law issues. Dave Vellante, anything you want to jump in on, and add to the conversation? For example, if we look at some of the ecosystem and some of the new technologies, and some of the new investments being made around new technologies. What are some of your thoughts about some of the new stuff that we might hear about at AWS this year? >> Dave: Well so, a couple things. Just a comment on some of the things you guys were saying about Oracle and migration. To me it comes down to three things, growth, which is clearly there, you've talked about 40% plus growth. Momentum, you know the flywheel effect that Amazon has been talking about for years. And something that really hasn't been discussed as much which is economics, and this is something that we've talked about a lot and Amazon is bringing a software like marginal economics model to infrastructure services. And as it potentially slows down its growth, it needs to find new areas, and it will expand its tan by gobbling up parts of the ecosystem. So, you know there's so much white space, but partners got to be careful about where they're adding value because ultimately Amazon is gonna target those much in the same way, in my view anyway that Microsoft and Intel have in the past. And so I think you've got to tread very carefully there, and watch where Amazon is going. And they're going into the big areas of AI, trying to do more stuff with the Edge. And anywhere there's automation they are going to grab that piece of value in the value chain. >> So one of the things that we've been, we've talked about two main things. We've talked about a lot of investments, lot of expectations about AI and how AI is gonna show up in a variety of different ways at re:Invent. And we've talked about how they're likely to make some of these migration initiatives even that much more tangible than they have been. So by putting some real operational clarity as to how they intend to bring enterprises into AWS. We haven't talked about IoT. Dave just mentioned it. What's happening with the Edge, how is the Edge going to work? Now historically what we've seen is we've seen a lot of promises that the Edge was all going to end up in the cloud from a data standpoint, and that's where everything was gonna be processed. We started seeing the first indications that that's not necessarily how AWS is gonna move last year with Snowball and server-less computing, and some of those initiatives. We have anticipated a real honest to goodness true private cloud, AWS stack with a partnership. Hasn't happened yet. David Floyer what are we looking for this year? Are we gonna see that this year or are we gonna see more kind of circumnavigating the issue and doing the best that they can? >> Yeah, well my prediction last year was that they would come out with some sort of data service that you could install on your on-premise machine as a starting point for this communication across a multi cloud environment. I'm still expecting that, whether it happens this year or early next year. I think they have to. The pressure from enterprises, and they are a customer driven organization. The pressure from enterprises is going to mandate that they have some sort of solution on-premise. It's a requirement in many countries, especially in Europe. They're gonna have to do that I think without doubt. So they can do it in multiple ways, they can do it as they've done with the US government by putting in particular data centers, whole data centers within the US government. Or they can do it with small services, or they can have a, take the Microsoft approach of having an AWS service on site as well. I think with pressure from Microsoft, the pressure from Europe in particular is going to make this an essential requirement of their whole strategy. >> I remember a number of years going back a couple decades when Dell made big moves because to win the business of a very large manufacturer that had 50,000 work stations. Mainly engineers were turning over every year. To get that business Dell literally put a distribution point right next to that manufacturer. And we expect to see something similar here I would presume when we start talking about this. >> Yeah I mean I would make a comment on the IoT. First of all I agree with what David said, and I like his prediction, but I'm kind of taking a contrarian view on this, and I'm watching a few things at Amazon. Amazon always takes an approach of getting into new markets either with a big idea, and small teams to figure it out or building blocks, and they listen to the customer. So IoT is interesting because IoT's hard, it's important, it's really a fundamental important infrastructure, architecture that's not going away. I mean it has to be nailed down, it's obvious. Just like blockchain kinda is obvious when you talk about decentralization. So it'll be interesting to see what Amazon does on those two fronts. But what's interesting to note is Amazon always becomes their first customer. In their retail business, AWS was powering retail. With Whole Foods, and the stuff they're doing on the physical side, it'll be very interesting to see what their IoT strategy is from a technology standpoint with what they're doing internally. We get food delivered to our house from Amazon Fresh, and they got Whole Foods and all the retail. So it'll be interesting to see that. >> They're buying a lot of real estate. And I thought about this as well John. They're buying a lot of real estate, and how much processing can they put in there. And the only limit is that I don't think Whole Foods would qualify as particularly secure locations (laughing) when we start talking about this. But I think you're absolutely right. >> That only brings the question, how will they roll out IoT. Because he's like okay roll out an appliance that's more of an infrastructure thing. Is that their first move. So the question that I'm looking for is just kind of read the tea leaves and saying, what is really their doing. So they have the tech, and it's gonna be interesting to see, I mean it's more of a high level kind of business conversation, but IoT is a really big challenging area. I mean we're hearing that all over the place from CIOs like what's the architecture, what's the playbook? And it's different per company. So it's challenging. >> Although one of the reasons why it looks different per company is because it is so uncertain as to how it's gonna play out. There's not a lot of knowledge to fuse. My guess is that in 10 years we're gonna look back and see that there was a lot more commonality and patterns of work that were in IoT that many people expected. So I'll tell you one of the things that I saw last year that particularly impressed me at AWS re:Invent. Was the scale at which the network was being built out. And it raised for me an interesting question. If in fact one of the chief challenges of IoT. There are multiple challenges that every company faces with IoT. One is latency, one is intellectual property control, one is legal ramification like GDPR. Which is one of the reasons why the whole Europe play is gonna be so interesting 'cause GDPR is gonna have a major impact on a global basis, it's not just Europe. Bandwidth however is an area that is not necessarily given, it's partly a function of cost. So what happens if AWS blankets the world with network, and customers to get access to at least some degree of Edge no longer have to worry about a telco. What happens to the telco business at least from a data communication standpoint? Anybody wanna jump in on that one? >> Well yeah I mean I've actually talked to a couple folks like Ericson, and I think AT&T. And they're actually talking about taking their central offices and even the base stations, and sort of outfitting them as mini data centers. >> As pops. >> Yeah. But I think we've been hearing now for about 12 months that, oh maybe Edge is going to take over before we actually even finish getting to the cloud. And I think that's about as sort of ill-considered as the notion that PCs were gonna put mainframes out of business. And the reason I use that as an analogy, at one point IBM was going to put all their mainframe based databases and communication protocol on the PC. That was called OS2 extended edition. And it failed spectacularly because-- >> Peter: For a lot of reasons. >> But the idea is you have a separation of concerns. Presentation on one side in that case, and data management communications on the other. Here in this, in what we're doing here, we're definitely gonna have the low latency inferencing on the Edge and then the question is what data goes back up into the cloud for training and retraining and even simulation. And we've already got, having talked to Microsoft's Azure CTO this week, you know they see it the same way. They see the compute intensive modeling work, and even simulation work done in the cloud, and the sort of automated decisioning on the Edge. >> Alright so I'm gonna make one point and then I want to hit the Action Item around here. The one point I wanna make is I have a feeling that over, and I don't know if it's gonna happen at re:Invent this year but I have a feeling that over the course of the next six to nine months, there's going to be a major initiative on the part of Amazon to start bringing down the cost of data communications, and use their power to start hitting the telcos on a global basis. And what's going to be very very interesting is whether Amazon starts selling services to its network independent of its other cloud services. Because that could have global implications for who wins and who loses. >> Well that's a good point, I just wanna add color on that. Just anecdotally from my perspective you asked a question and I went, haven't talked to anyone. But knowing the telco business, I think they're gonna have that VMware moment. Because they've been struggling with over the top for so long. The rapid pace of innovation going on, that I don't think Amazon is gonna go after the telcos, I think it's just an evolutionary steamroller effect. >> It's an inevitability. >> It's an inevitability that the steamroller's coming. >> So users, don't sign longterm data communications deals right now. >> Why wouldn't you do a deal with Amazon if you're a telco, you get relevance, you have stability, lock in your cash flows, cut your deal, and stay alive. >> You know it's an interesting thought. Alright so let's hit the Action Item around here. So really quickly, as a preface for this, the way we wanna do this is guys, is that John Furrier is gonna have a couple hour one on one with Andy Jassy sometime in the next few days. And so if you were to, well tell us a little about that first John. >> Well every re:Invent we've been doing re:Invent for multiple years, I think it's our sixth year, we do all the events, and we cover it as the media partner as you know. And I'm gonna have a one on one sit down every year prior to re:Invent to get his view, exclusive interview, for two hours. Talk about the future. We broke the first Amazon story years ago on the building blocks, and how they overcame, and now they're winning. So it's a time for me to sit down and get his insight and continue to tell the story, and document the growth of this amazing success story. And so I'm gonna ask him specific questions and I wanted, love to know what he's thinking. >> Alright guys so I want each of you to pretend that you are, so representing your community, what would your community, what's the one question your community would like answered by Andy Jassy. George let's start with you. >> So my question would be, are you gonna take IT operations management, machine learn enable it, and then as part of offering a hybrid cloud solution, do you extend that capability on-prem, and maybe to even other vendor clouds. >> Peter: That's a good one, David Floyer. >> I've got two if I may. >> The more the merrier. >> I'll say them very quickly. The first one, John, is you've, the you being AWS, developed a great international network, with fantastic performance. How is AWS going to avoid conflicts with the EU, China, Japan, and particularly about their resistance about using any US based nodes. And from in-country telecommunication vendors. So that's my first, and the second is, again on AI, what's going to be the focus of AWS in applying the value of AI. Where are you gonna focus first and to give value to your customers? >> Rob Hof do you wanna ask a question? >> Yeah I'd like to, one thing I didn't raise in terms of the challenges is, Amazon overall is expanding so fast into all kinds of areas. Whole Foods we saw this. I'd ask Jassy, how do you contend with reality that a lot of these companies that you're now bumping up against as an overall company. Now don't necessarily want to depend on AWS for their critical infrastructure because they're competitors. How do you deal with that? >> Great question, David Vellante. >> David: Yeah my question is would be, as an ecosystem partner, what advice would you give? 'Cause I'm really nervous that as you grow and you use the mantra of, well we do what customers want, that you are gonna eat into my innovation. So what advice would you give to your ecosystem partners about places that they can play, and a framework that they should think about where they should invest and add value without the fear of you consuming their value proposition. >> So it's kind of the ecosystem analog to the customer question that Rob asked. So the one that I would have for you John is, the promise is all about scale, and they've talked a lot about how software at scale has to turn into hardware. What will Amazon be in five years? Are they gonna be a hardware player on a global basis? Following his China question, are they gonna be a software management player on a global basis and are not gonna worry as much about who owns the underlying hardware? Because that opens up a lot of questions about maybe there is going to be a true private cloud option an AWS will just try to run on everything, and really be the multi cloud administrator across the board. The Cisco as opposed to the IBM in the internet transformation. Alright so let me summarize very quickly. Thank you very much all of you guys once again for joining us in our Action Item. So this week we talked about AWS re:Invent. We've done this for a couple of years now. theCUBE has gone up and done 30, 35, 40 interviews. We're really expanding our presence at AWS re:Invent this year. So our expectation is that Amazon has been a major player in the industry for quite some time. They have spearheaded the whole concept of infrastructure as a service in a way that, in many respects nobody ever expected. And they've done it so well and so successfully that they are having an enormous impact way beyond just infrastructure in the market place today. Our expectation is that this year at AWS re:Invent, we're gonna hear a lot about three things. Here's what we're looking for. First, is AWS as a provider of advanced artificial intelligence technologies that then get rendered in services for application developers, but also for infrastructure managers. AI for ITOM being for example a very practical way of envisioning how AI gets instantiated within the enterprise. The second one is AWS has had a significant migration as a service initiative underway for quite some time. But as we've argued in Wikibon research, that's very nice, but the reality is nobody wants to bond the database manager. They don't want to promise that the database manager's gonna come over. It's interesting to conceive of AWS starting to work with application players as a way of facilitating the process of bringing database interfaces over to AWS more successfully as an onboarding roadmap for enterprises that want to move some of their enterprise applications into the AWS domain. And we mentioned one in particular, SAP, that has an interesting potential here. The final one is we don't expect to see the kind of comprehensive Edge answers at this year's re:Invent. Instead our expectation is that we're gonna continue to see AWS provide services and capabilities through server-less, through other partnerships that allow AWS to be, or the cloud to be able to extend out to the Edge without necessarily putting out that comprehensive software stack as an appliance being moved through some technology suppliers. But certainly green grass, certainly server-less, lambda, and other technologies are gonna continue to be important. If we finalize overall what we think, one of the biggest plays is, we are especially intrigued by Amazon's continuing build out of what appears to be one of the world's fastest, most comprehensive networks, and their commitment to continue to do that. We think this is gonna have implications far beyond just how AWS addresses the Edge to overall how the industry ends up getting organized. So with that, once again thank you very much for enjoying Action Item, and participating, and we'll talk next week as we review some of the things that we heard at AWS. And we look forward to those further conversations with you. So from Peter Burris, the Wikibon team, SiliconANGLE, thank you very much and this has been Action Item. (funky electronic music)

>> Hi, welcome once again to Action Item. (funky electronic music) I'm Peter Burris and this is Wikibon's weekly research meeting where we bring together some of the best minds in Silicon Valley to talk about some of the trends that are most important. We're broadcasting from here in the Cube studios in beautiful Palo Alto, California. And in the studio, I'm being joined by George Gilbert and David Floyer and on the phone we have Neil Raden, Jim Kobielus, Dave Vellante. Team, thanks very much for being part of this conversation today. What we're going to do today is we're going to bring forward some of Wikibon's predictions for 2018. In a previous show, we discussed what we learned in 2017, so some of the trends and some of the expectations that didn't play out as expected. This year we're going to dig a little bit deep into what we think is going to happen in 2018 and it all starts with a proposition that even as we go through significant industry change, we're not necessarily going to see the economics of the industry change as fast, which leads to prediction number one. David Floyer, what is it? >> So, my prediction is that volume is going to take a key role in the evolution of disruptive technologies. So for example, in AI and IOT and in true private cloud, volume is going to be the key determination of when it starts to take off, when it starts to hockey stick. >> So this has been something that's been featured in the industry for a while, Dave, but give us an example. What's the relationship between volume and AI? >> So if we take the relationship between AI and volume, AI is going sideways, and I would predict that it's going to go sideways in 2018 because every implementation is a snowflake until there are solutions out there which can be delivered in volume by vendors. Then that will the point at which things will take off. So an example, for example, automated cars. They are AI, when they start to come out in volume, there'll be volume manufacturers, volume of the census, volume of the processes, the on-processes, volume of everything that will drive down costs and make those implementations so quickly. >> And it's still software, so we're still worried about support and service on a very, very broad scale. >> David: Yeah. >> So that leads to our second quick prediction. Dave Vellante, build on this notion of volume. What's going to be the impact on a lot of the innovative smaller companies in 2018? >> Dave: So Peter, my prediction is got to go scale or go home, AKA go out of business. So we expect massive industry consolidation is going to take place in the next two years, certainly through 2019 as the business models of VC-backed tech startups are getting smashed by cloud and, to a great extent, open source. In a turnabout from the historical norms, innovations and cost reductions from the largest cloud players are moving at a pace that's faster than many, if not most startups are able to deliver. So finding white space is much, much harder. We see private equity as playing a key role here, providing capital for M and A and doing roll-ups that are going to create scale and large portfolios that can compete. >> So Neil Raden, as we think about what Dave just said, one of the key things that's happening is a lot of money's being put into some of the new technologies that are intended to provide more intelligence in a lot of different places. One of the large company leaders indicating or describing how this was going to play out was IBM with its Watson story. What's been going on with Watson? What's our prediction for how that's playing out and likely, what's a likely 2018 scenario for IBM and Watson? >> Neil: Well, not to sugarcoat it but Watson's been a dismal failure, and I think that IBM is going to reassess their whole approach to cognitive computing in 2018. Numbers don't lie, let me give you some numbers from 2016. They obviously don't have '17 yet. But these are reliable numbers from some institutional clients of mine. Their goal for 2016 was over 8,000 clients. They achieved 500. Their goals for business partners was over 4,000 and they achieved 329. So, you know, the numbers speak for themselves, but Watson hasn't caught on. It's a solution in search of a problem. It was a marketing stunt, really, that someone thought to be turned into a 20 billion dollar per year business. It's not even a product, really. It's dozens of subsystems that are linked with APIs. Some of them are interesting, but most already are available in the open source world. >> Well one of the things we talked about last week, Neil, was the idea that we're going to see more buy, as opposed to build, and we talked about the volume play there, and then we asked the question, is there going to be more software or is there going to be more services? It sounds like IBM's play to be a dominate player in AI-related services has not gone as well as expected. Is that kind of where we are right now? >> Neil: Well, yeah. If you look at one of the more public failures of Watson, which was MD Anderson Cancer Center, they pulled the plug on the project after 62 million dollars, but IBM only got about 20 million dollars of that, the rest of it went to PWC. So how they intend to split that business between global services and their partners, I really don't know. And the failure of Watson at MD Anderson wasn't entirely IBM's fault. A lot of it had to do with PWC's project management, and a lot of it had to do with the people at Anderson who basically started the project by looking at a very well-understood type of leukemia that had a well-understood etiology and treatment options. So when the auditors looked at it, they said we haven't learned anything for 62 million dollars, and that's been repeated at other projects. >> So it sounds like this is, again, tied back to the idea of scale, volume, and related issues. But it also sounds like there's a lot of question, ultimately, about what is AI? What isn't AI? What role is Watson going to play? Is it going to be private data? Is it going to be public data? A lot of questions are going to emerge over the course of next year. But there are domains where AI, ML, DL are likely to have some important success. And George, we've got a prediction about where they're likely to be successful in 2018. What are we thinking, what's one domain where we think at least machine learning is going to have a significant impact in 2018? >> Well, keying off David's point about volume, volume economics, we think that IT operations management is going to be one of the first horizontal applications that embeds machine learning. It's not about presenting, modeling, and tools to developers, it's just part of the application. The reason it's important, there's really two key reasons. We're building out shared ephemeral infrastructure, which is very different from the dedicated silos that we had for mission-critical applications. And this infrastructure, and the application landscape on top of it, is extremely hard to manage, and machine learning can help greatly. And I think investment in that will be driven also by a realization that this is training wheels for IOT in the sense that you're monitoring machines through data telemetry that they throw off, and you're using models to figure out how they should be operating versus how they are operating. >> So this has significant applications across IOT, ML, and how we get to volume because it's a controlled and pretty well-defined space. By that I mean, but nonetheless, it's related to the problem space, and by that I mean that bespoke applications, whether they're from AI or whatnot, are going to create new needs for new types of monitoring. But the classification of the tools and the classifications of the devices that will be monitored are pretty well-understood and they're controlled by the IT industry, so they ought to have pretty good definitions. Is that what we're thinking here, George? >> Yes, precisely, and the bespoke pieces can be modeled because they fall within a well-known domain. But I just want to add on the go to market side that keys off of what Dave Vellante said, which is that these IT operations management applications, they can come from cloud vendors, they can come from enterprise software vendors, but especially the ones that are going to be hybrid cloud are going to need enterprise sales forces to get them to market. You hear millions of, virtually millions of startups say our go to market strategy is land and expand. That doesn't get you enterprise wide, and for that you need an enterprise sales force, most expensive migratory workforce in the world, and startups don't have them. And that's why, one of the reasons, we will see roll-ups for scale. >> So we've talked about the need for scale, the impact on start-ups, the impact on big companies like IBM. One of the domains we think this is going to play out most successfully is in ITOM, IT operations management for some of these new technologies. But underneath all of this is a lot of new complexity because of distribution of function, distribution of data, distribution of application, and there needs to be a new technology concept that allows for that distribution to take place under control. And we talked about this a few weeks ago, but Jim Kobielus, what's our prediction for the world of blockchain or blockchain-like technologies are going to take in facilitating this new distribution of capability around digital business? >> Jim: Yeah, blockchain, we're predicting, it will be as fundamental to the growth of the worldwide digital infrastructure and digital markets as 40 plus, 30 to 40 years ago TCPIP was to the growth of what became the web and the internet. And why is that? Well, you know, when you look at the basic principles for development of any infrastructure where there's an innovation on the infrastructure side that is shared or standardized, robust, meaning secure, and distributed, it quickly becomes a common bond enabling growth of sharing and teaming and markets and so forth. So really, it's a layering process where we have TCPIP and you know, DNS and URL providing this shared address space. Layered on top of that was public key infrastructure, which is the foundation of the security that makes blockchain so strong. You know, PKI and SSL and all that is an enabler, that's another robust, shared common infrastructure. And then on top of that, what we see on top of that is they distributed robust shared record of transactions. That's blockchain, and really blockchain as an enabler for the new generation of digital crypto currencies such as bitcoin, enabling a shared robust and distributed currency or means of payment across the worldwide economy. So, in many ways, blockchain is an enabler for this new generation of truly robust and shared currency and transactions with a mutable, secured, shared record. It's just going to be a growth accelerator for the world economy in the 21st century going forward. >> So in many respects, technology takes off when network formation occurs. TCPIP was a foundation for network formation for distributed computing. What we're basically saying is a blockchain becomes a crucial feature of how application networks get constructed over the course of the next 10 years. Have I got that right, David Floyer? >> Absolutely, that's the key. The guy who sold the first telephone was a genius, the second was easier, and it gets easier and easier as that work grows, and blockchain is a key contributor to the development of those networks, and a one-to-one relationship, many, many one-to-one relationships that can occur from that, away from centralization and to a much more distributed environment. >> So I think we've got time for one more prediction really quickly, and I'll bring it up, and then I want to open it up for conversation because this is an interesting one. We come back to this notion of global network formation, blockchain being what we think, or blockchain-like technologies being a crucial element of that. But let's talk about how the relationship between technology, the cloud, and global economies are likely to evolve. For the most part, when people think about the cloud today, we think about US-based companies: Amazon, Microsoft, Google, Facebook, IBM also in there. But there's some other companies are going to have a say on how the cloud industry evolves over the course of the next five years: Alibaba, Tencent, Baidu. So our prediction is that in 2018, we're going to see a lot more conversation about the role that China plays in establishing some of the new rules for how cloud, application networks, and security plays on a global basis, and that's going to facilitate the emergence of Alibaba, Tencent, and Baidu, also on the global stage as cloud-computing companies. What are you guys' thoughts? Dave Vellante, let me start with you. >> Dave: Well I think we're going to see the emergence of, we've seen the emergence of the China cloud and we're going to see that seep through other parts of Asia Pacific. As we discussed earlier as a team in our private meeting, Europe is going to be a very interesting pivot point because if China can control at least portions of Europe and use that as a lure for China, that's going to give them a leg up on global cloud. >> So that leads ultimately to a series of questions about what will be the relationship between formation of cloud industries, the evolution of the cloud industries, and geopolitical concerns. And I think what we need to do, guys, is dedicate an entire research meeting to that question because it's going to be one of the most important dictators of how the industry evolves over the next few years, and ultimately how businesses and enterprises need to start establishing crucial partnerships with their key and strategic suppliers. So look in the last couple minutes we want to do our Action Item round. Now, what we do here at the Action Item show is we start off having a conversation and then we go into the Action Item, what are you going to do differently Monday as a consequence of the information we're talking about? So let's do that now, hit some Action Items, what you heard from the five, six predictions that we talked about. David Floyer, what's your Action Item? >> So my Action Item is for CIOs and CTOs, is to take a pause on IOT and look for vendors that have solutions which can be put in easily and quickly and span OT and IT in the IOT space. >> Neil Raden, what's your Action Item? >> Neil: Well, I think there's a lot of activity around AI and there's going to be an explosion of it in 2018 but most of it's not really going to be AI, it's going to be machine learning, and machine learning is really just math and floating points. AI is different. AI is neuroscience, it's neurology, it's biology and physics and sociology, it's more science. I think that some machine learning is there on the event horizon of AI, but it's not. So we need to make sure we're clear about what announcements and what technology is machine-learning versus artificial intelligence. >> Jim Kobielus, what's your Action Item? >> Jim: I think my Action Item is to revisit IBM's prospects in the AI market in deep learning going forward. And revisit on a positive note actually because IBM officially turned around their cognitive strategy in the last year. Do they focus on the power AI flight form which is really framework agnostic and so forth. And really the AI space that's actually shaping up is different from the one that IBM and others envisioned at the start of this decade, and so it really is 2018, we're going to see IBM come out strong, I believe, as a provider of, one of the providers of the core framework agnostic data deep learning development platforms in the industry, that's my prediction. >> David Vellante, what's your Action Item? >> Dave: I think if you're a startup, you really have to take a hard look at your business and the value that you're bringing to market and be honest, if you're not delivering something that the cloud guys can't deliver or don't want to deliver, then I think you've really got to think about pivoting or exiting the business that you're in. And as part of that, I think you've got to find, to George's point, distribution channels and distribution partners that can help you with go to market at scale or you're in big trouble. >> George Gilbert, Action Item. >> We've been talking about sort of the cloud wars and my recommendation to CIOs and senior IT leaders would be that if you want to hedge your bets, you don't want to be all in on one cloud, it's not dividing a workload across different clouds. Pick a cloud for a workload or for an application because its portability is, it's sort of more of a dream than a reality. It's not about moving containers around, you're in an API ecosystem, you're subject to data gravity, so it's almost like if you're going to do the equivalent of distributed computing, you're going to put some part of the application on one cloud and some part in another cloud. >> So the Action Item is be smart about the relationship between new style of applications and architecture and cloud choices. Okay, let me summarize the meeting very quickly. This has been a great conversation about predictions in 2018, you expect to see more from us over the course of the next month, this is going to be a major theme of ours in November and into December. So, quickly the findings are these. The technology industry made a major mistake with the dot com boom, and the mistake was a presumption that technology change necessarily meant economic change. That is a false assumption. The economics of technology have been pretty well understood for quite some time and they're going to assert themselves even as we go through this significant transformative period in the technology industry. And the economics of volume are going to continue to be important. And we expect that those economics, coupled with the three factors of what's driving cloud architecture decisions, the realities of physics, geopolitical concerns, and literature property concerns, are going to lead to some significant changes in 2018 that we've only just conceived of. One, we expect that we're going to see an emergence of true private cloud that will continue to be crucial to how businesses think about their information technology overall infrastructure and plant, and that's going to have an impact ultimately on where AI gets developed, more from software vendors based on volume. Two, we expect to see a significant impact on, ultimately, what happens in the VC fronted world as startups, which have historically just presumed that there was no need for go to market, that everything was going to be try and buy and then we'd scale from there, start to hit the business realities of the consistency of the economics of volume. Three, IBM we think is repositioning, and somewhat paradoxically is likely to become more successful as a consequence, as a provider of the technologies that make possible some of these new comprehensive, complex AI and related oriented technologies, and not just as a service provider. Very importantly, ITOM is going to become increasingly important and we'll see AI, machine learning be an essential feature of that, in fact, one of the places where we learn how to do it right. And the final one is lots going on with blockchain, but we expect greater distribution of applications, greater distribution of data, and the security technologies and the technologies for bringing that together and supporting the network formation of data and applications must be in place, and that's going to be a major area of technology and innovation in 2018. Alright, so this closes out our Action Item for this week. Once again, I'm Peter Burris. I'd like to, as always, thank the Wikibon team for participating with me today and we look forward to once again visiting with you from the Cube studios here in Palo Alto, California on the next Action Item. Thank you very much. (funky electronic music)

>> Narrator: Live from Washington DC, it's The Cube. Covering .conf2017, brought to you by Splunk. >> And welcome back to .conf2017 here on The Cube. We continue our coverage from the Walter Washington Convention Center. Dave Vellante, John Walls, if you're wondering where we are, I mean physically, the White House is about a mile that way, and the U.S. Capitol is about a mile that way. So we're kind of sandwiched between where it's all happening, Dave. >> Yeah, I mean this exhibit hall is about a mile that way and a mile that way. (laughing) >> Yeah, if you're hungry, leave now for lunch. It's going to be a bit of a hike. We're going to talk about analytics, obviously, at this show, but with Cisco's Altaf Karim, Senior Manager of service line and product lead, so a practice lead. So Altaf, thank you for being with us here. >> You're very welcome. >> Thanks for the time. Let's talk about the Cisco network optimization service, and obviously how that comes into play with analytics, what that's all about. I know that's certainly near and dear to your mission. >> Sure. So as you mentioned, Cisco's network optimization service, it's a consulting-based service offer that we provide to hundreds of customers globally, where we're actually providing some experts in the field of Cisco products. These consultants know Cisco products in and out. Our span reaches globally in many different industries, and what we do is we really work with our customers first, our consultants work with our customers first to identify what sort of business outcomes that they're trying to achieve. These could be related to things like high availability, performance, and then really work from there to understand what types of things need to happen from an assessment standpoint, or architecture, or deployment standpoint, that they can optimize to make the most use of their network. Some of the key benefits of Cisco optimization service are increased productivity for our customers, better user experience, as well as customers who have made an investment in IT. Our consultants are able to work with them and devise a strategy on faster time to value of that investment. So those are some of the key tenets of-- >> Mr. Vellante: So this is a for-pay service, correct? >> Yes. >> Okay, and it starts presumably with an assessment, where you got to get the right people in the room, and maybe you have some automated tooling to help me do discovery, and things like that, and you're maybe looking at machine data and so forth. Take us through the life-cycle of an engagement. Where does it start? How do we engage? How does one engage with you? Where does it start and where does it go? >> Yeah, sure. So, it all starts with our consultants working with our customers first, as I said, to understand what types of business objectives are they trying to accomplish. We then essentially backtrack from there, and understand what things in the network can we control. For example, high availability, process of change management, improved performance on their network, and essentially devise KPIs and metrics that essentially back into the business outcome that they're trying to accomplish. And of course, we have a whole slew of capabilities around analytics, that our consultants bring to the table to essentially become proactive, and help the customer achieve those business outcomes. >> So it might be a customer comes to you and says hey, I'm having problems with my network. It's down too much, it's not performing the way I want. I think it's change management related, you know it probably is, but I don't know where to start. So you bring a tiger team in, and then what? You use all kinds of tooling and other expertise to surface the problem? >> Yeah, sure. So, your question actually delves into what types of KPI can our consultants provide to our customers, to show them how their network is doing, right? And so there's a couple of different ways to do this. One is, you can take a look at what data is available to you, and start to sift through that. And that can be a very cumbersome process that is lengthy. You're really looking for that needle in the haystack to try to figure out what types of insights you can find to make an impact to the business outcome. Another way to approach it is the way we do it from a process standpoint, is inwards from the customer's business outcome. What exactly are we trying to impact? Is it network performance? Is it high availability? And then, our consultants will actually come up with metrics and KPIs based on intellectual capital that our service offer has, and essentially create custom applications based on Splunk, to essentially provide those insights and views and visibility into the network, back to the customer. >> So is it fair to say that Splunk would be the primary ITOM tool, if I can use that term? Splunk doesn't really talk about ITOM, I guess, directly, but to me it's ITOM, IT operations management, but that is the primary platform that you guys would use and deploy? >> I would say that's one of the primary components. Splunk plays a very, very strategic role in how our consultants interact with our customers. So if you think about the premise behind and the value proposition behind network optimization service, is our leading-edge and world-class expertise in networking. And that's what we're known for. And so now when you think about analytics, especially proactive and predictive, you really need the right mixture in ingredients of things to come together, to provide meaningful analytics back to customers. And really, if you think about a trifecta of domain expertise, data science, as well as an understanding of potentially open-source technologies and platforms. But in this case, we're actually strategically using Splunk to play the piece of that last bit. And so what that means is we have consultants who are world-class, leading experts in networking, but we're also training them and asking them to walk a little bit in the shoes of data analysts. And, if you think about an audience or a constituent that is highly technical, quantitative-minded, Splunk is a pretty easy platform for them to learn and start to make an impact by creating custom applications, KPIs, and metrics, for their own customers, that they can use to be proactive and be preemptive, and provide those insights back to the customer. So that's the role that Splunk plays in our service. How much of your business is sort of Aspirin versus vitamin? In other words, how much is it, I got a pain point, I need a tactical solution to that pain point, versus you know what? I'm thinking about re-architecting my network, east west problem, right? Help me think that through, how I sort of transition from my legacy network to a more modernized network. How much is each of those? >> I would say they both play a pretty significant fare. Depending on where the customer is in the life cycle and what they're trying to accomplish, we certainly have a healthy dosage of customers who we work with transactionally, to architect new networks, to deploy new technology, to help them realize their IT spend in a quicker way. But then, a very significant part of our business also is, what do you do on the day two? You can build all this great stuff, right? But if you don't optimize it for peak performance, if you don't optimize it for high availability, or if it's not keeping up with your evolving needs and standards, then you might get in trouble. You're not using the most out of your network. So that's a healthy business as well. >> You mentioned KPIs. What are you tracking? And, what data matters? How do you determine what's relevant, what's not? You know, big problems, or big challenges at least. >> Yeah. That's a very important question, right? And to me, coming from a services background, it's very much rooted in knowing what your domain is about, because as I mentioned before, if you start with all the plethora of data that's available to you, and start to sift through it, you may or may not find something, right? But, our consultants work with the customer and identify what are specific things that we care to monitor, and what are specific KPI that we want to essentially do trending on, or to identify patterns around, so that we can accomplish some sort of business outcome. So for example, if you care about network performance, you're looking at metrics about capacity or bandwidth, or QOS. If you care about customer experience, you're probably, from a wifi standpoint, looking at signal strengths, looking at disassociations, how often and how quickly customers can connect to wifi networks. So really, it depends on what the customer is looking for. And our approach is that we have solid expertise in a number of networking disciplines ranging from routing, switching, wireless, data center, and others. So we have analytic service offers that go deep into each of those technology areas, and we can figure out what KPI to monitor to best achieve that business outcome, but then we also can bring all of that back together and provide that holistic network perspective, and one of the key things that we want to look at, to make sure network is operating optimally. >> Does your practice bleed into the security vector at all? Is that an adjacent area, or is that sort of a main area? >> Yeah. I would say security is paramount for our customers. For the network optimization service, it's actually an adjacent area, but it's definitely something that we work to include into all of our consultative guidance and recommendations to our customers. >> To whom do you sell, I mean, typically? When you initiate an engagement, is it a head of network? Is it a CIO level? And who do you get involved in the sort of initial meeting, and throughout the lifecycle of the project? >> Yeah. That's a really good question, and I would say that it varies depending on what types of analytics that they're also looking for. So let me give you a couple of different examples. So one example is the IT director or IT manager, who is really looking for a tool or analytics, visibility, insights, into how pieces of their network are performing so that they can achieve high availability, increase in network performance, or can better process their change management. So that's one type of buyer. But the other type of buyer is also at the CIO level, which is increasingly also more interested in using analytics to figure out where they are, and benchmark themselves against how others in their industry, or their peers, may be doing. So we've actually started to begun a lot of interesting conversations there, where some of the analytics that we can provide to our customers who opt in, is really rooted around benchmarking how they're doing in different areas such as performance, their software feature, their software or hardware or feature diversity compared to others in their own industry, and really can identify along with our consultative guidance which areas are really important for them to pay attention to, because they're doing something potentially different than everyone else in their industry. >> How about this challenge of IT networks, they're organic, they're constantly changing. So are you coming in, fixing a problem, and then I got to call you back? Or are you teaching me how to fish? >> I would say we're doing a little bit of both. So there's definitely reactive and remediation portions of our service offer. Unfortunately, that happens more than you would like, because you don't think about what to fix until something actually goes wrong. But, one of our flagship service offers, the network optimization services, is all about proactive and optimizing an existing network, so you make sure you're never getting to a place where you end up having to remediate something. And it's not just about remediation or fixing something that's broken, it's really about fine-tuning a well-oiled machine, to make sure that you're getting the most out of your IT investment. >> Yeah, but what kind of a, you talk about machine learning here, capabilities, what do you have in that vein? >> Yeah, so that's a really good question. When we start talking about proactive, and the predictive aspects of our consulting as well as our analytics, machine learning plays a pretty significant role, and I can only expect the contribution that will make to increase exponentially over time. A perfect example, one example of how we use machine learning is actually the machine learning tool kit inside of Splunk. So, if you think about our main premise behind network optimization, is to provide consulting, and provide recommendations on how to optimize the network. But when you think about what a network is, and it's a living and a breathing thing, each network is different, right? No network is the same. So, what machine learning, and especially the machine learning toolkit from Splunk, allows us to do is for a specific customer, it actually allows us to create a baseline of normalcy. What is normal for hundreds and thousands of KPIs and variables, for that specific customer? I think if we asked a human to do that, they'd probably still be going on-- (imitates gunshot) exactly, right? And so, that's an example of how we use machine learning toolkit from Splunk, and not only identifying what is normal for that customer, but then we can use supervised learning to start to identify anomalies and trends and patterns, and really begin to enable our consultants with the data and foresight around what types of things are happening on that network, so that they can in turn be proactive, and be predictive and preemptive in their exchanges with the customer. >> And these services are done on a T&M basis, or a fixed fee, or both? >> They're done both ways. We're pretty flexible, and there's a whole slew of offers outside of what I just talked about, that are available as well. >> What's typical of people? It just depends, right? >> I would say for pinpoint specific things that need to get done, they're more transactional in nature. And then when you're looking for entire lifecycle in a suite of services to help you optimize and be proactive and predictive and preemptive, that's where we have a subscription-based offer that is our optimization offer. >> Okay, and then you guys will actually, well you'll do this mostly remotely, I presume, but you go on site periodically to just impress the flesh and feel-out the culture? >> Absolutely. When we actually start an engagement with a customer, it's quite common for us to go on site, work to get to know the customer, the players, the network, understand what the business outcomes are, make sure that we're devising our deliverables in a way that actually impacts some sort of outcome, and they're not just rooted in some networking measures that don't necessarily make any impact there, right? So that's really important to us. So we definitely go on site. But of course, one of the value propositions of our offer is our intellectual capital. And when we talk about some of the analytics applications that engineers are building for a specific customer, now talk about that happening across hundreds of customers and engineers, devising new ways to create insights and visibilities in their own customer, and the sharing that happens between the engineers, so that they can bring those learning back to their own customer. >> Well, the door's open for business at Cisco, and Altaf Karim, we appreciate your time sharing with us why and how, and what you're doing, and wish you all the best of luck down the road too. Thanks for being with us here, first time on The Cube, right? >> First time on The Cube. >> Alright. >> Thank you for having me. >> You are now an alum. Welcome to the club. >> Great. >> Alright, Altaf Karim, joining us here on The Cube. We'll continue live from Washington D.C., right after this. (electronic theme music)

>> Announcer: Live from Washington, DC, it's the Cube, covering .conf2017, brought to you by Splunk. (busy electronic music) >> Welcome back here on the Cube, as we wrap up our coverage of Splunk's .conf2017, we're live in our nation's capital, Washington, DC, just kind of sandwiched between the US Capitol, which is right up there, and they have a little healthcare discussion going on, the White House about a mile and a half in the other direction, they're probably talking healthcare tonight, too, I would imagine, a little bit. We're talking Splunk. Dave Vellante, John Walls. Dave, a good couple, actually, a great couple of days here. Without getting into all the specifics, but just the range of guests that we have talking about the application of Splunk shows you about the breadth of this technology and how it's reaching to so many parts of the American enterprise today. >> Well, John, we've been talking about all week that this is our seventh Splunk .conf. The Cube started following this company pre-IPO, we've seen their rocket ship ascendancy. The kind of last several years, the stock has kind of gone sideways. The street hasn't been as sanguine as before. But it looks like new management, under the guidance of Doug Merritt, a new sales organization, has really started to put this company back on track, not that it was ever off the rails, but you can see a path to go, I mean, it's a $1.2 billion company with a $10 billion valuation, so that's nothing to sneeze at. You can see this company has the potential to really be one of the next big software players. You've seen a number of companies emerge. Salesforce was the cloud company, right? But you've seen a number of companies like Splunk emerge from sort of the mid 2000 time frame into a real powerhouse. I mean, getting to a billion dollar software company, that's a real milestone, not many make it. I'm impressed with that, they're growing at 30% plus per year. The things that we confirm this week that the traditional CIM, security, log file, digging through these log files, that's giving way and has given way to a better way, where you're reading machine data, you're able to search that and begin to automate and remediate in a proactive fashion. To a practitioner, when you talk to people around here, the Splunk way is the better way, no doubt. Now, what you've seen, and I tell you, early on in Splunk I heard from a lot of vendors, "We've got the Splunk killer." Well, Splunk seems alive and well. >> Right, it hasn't happened yet. >> Yeah, and it's because, in my opinion, they're this customer focused company that talks to customers, gets that feedback into their system, and as Doug Merritt would say, they're innovating faster than the competition. Now, there's some startups going after them, probably trying to attack their cloud model, their pricing model. But I feel like Splunk is in a really good position here. The other piece of this is the IT management component of it. You're starting to see a lot of companies really glom onto what they're doing, and what they call, I call it ITOM, they have an acronym they use, ITSI. Really bringing analytics to IT management, understanding what's going wrong, where it's going wrong, and how to remediate it. Those are really the two big use cases. The other concern that we heard from Wall Street was pricing. I don't hear that, certainly from the loyal customers. >> You've asked a lot of folks today, just what do you think, what do you like? The response has been, I'd say, fairly positive. >> Yeah, I've been pushing on the Cube, and also, at lunch, when you're not on camera, I've only really found one area of concern. Somebody in the government said, well, at the volume we're doing, it gets kind of expensive for us. But generally speaking, most users that we've talked to have said, I like that pay by the data drink model, and it's machine data, kind of log data, so it's not like massive amounts of data, although it's going to grow. One of these days it's going to be more metadata than there is data. >> John: Right. >> But I think in general, Splunk has a good handle on that, subscription models moving to a cloud model. But still, plenty of their base is perpetual model. Fundamentally, this company, I think has some significant upside. I think there's still some skeptics out there on the street, but the customer base is not skeptical, and ultimately, that, to me, is the end arbiter. If customers are happy, they're willing to spend, they see value, they're committed, the base is growing, we see 7,000 people here versus 4,000 last year, that's a 40% growth. When we first found this company, we said, this is going to be one of the next big things, along with some others, like ServiceNow, Pin Tableau early on, even though they've had some bumps in the road, guys like Nutanix, Red Hat. You talk to their customers, they're passionate, you definitely see that here. >> Let's talk about the customer focus a little bit, because that's the hallmark, right? It kind of reminds you of AWS a little bit, but, anyway, we're going to focus on the customer. We hear that from everybody who's sat down here and people we've talked to on the show floor, they have, it's a very direct relationship, it's a warm relationship. It's not customer, client, it's right here, they're sympatico. >> Yeah, I mean, I think there are different models. Andy Jassy talks about this, Doug Merritt talked about it. There's a lot of ways to skin the cat. You could be a customer focused, customer first company, where you make all your decisions based on what the customer is saying, and maybe that's an overstatement. But there's another model which is competitor focused. There's a competitor, I'm going to go kill them. There's some very successful examples of that. I mean, I would put EMC in that category, even though they're very customer focused, you cross them as a competitor, they're going to put you in their sites and shoot you. I think Microsoft has some sort of similar characteristics there, you saw Microsoft decimate its competitors in the past. I would say Oracle, in that sense. Not that these companies don't care about their customers, of course they do. But they're fanatical about the competition. >> John: The competition, right, right, right. >> I think companies like Splunk, I think they are concerned about the competition. They don't ignore it, same with AWS. But if you put the customer first, do right by the customer, good things happen, and it's a good philosophy. >> Right. Going forward now, I mean, Splunk is a company that's based on change, right, it's all about transformation, it's all about speed and providing these services. I mean, what do they have to do, in your mind, the next 12, 18 months to really separate themselves and take that quantum leap off the 1.2 where they are now, to get to that maybe $4 billion or $5 billion level? >> Number one is, don't screw it up. I mean, OK, that's obvious. >> Good rule. >> But I think the second thing is, the TAM expansion. One of, I think, Doug Merritt's big challenges is, how do they expand their TAM beyond those two core areas, security, obviously, huge area, and just sort of IT operations management, or again, what I call ITOM, they don't use that term. How do they grow beyond that? Where do they grow? I think there's a couple of ways to think about that. One is, I mean, Splunk is, it can be, it can start delivering apps that are very deep, that's what it's doing around security. You saw ransomware applications, for example, going depth. As a platform, Splunk has breadth. But they don't sell the platform per se. They really, what they do is they sell the solutions around that platform. The platform is there, though. To me, Splunk could become a big data development platform. What I want to see is I want to see this ecosystem grow dramatically. I think that's, for them to get to 5 billion, this ecosystem has to explode. I think they have to start becoming a developer outreach, developer friendly company, so that the ecosystem can innovate on behalf of that platform. They have a very powerful platform. It's like George and I were talking about this morning, it's Hadoop like in it's a big data pipeline, but it's integrated and it's a lot simpler. To us, Splunk can start expanding its TAM by building out applications with its ecosystem on top of that platform. I think that's an interesting challenge. We've tested that a little bit here. Splunk's shy about going there, they haven't gone there yet. I think they have to be careful, because you don't want to scare away the ecosystem either. I mean, remember Microsoft, their timing was good. >> You know what your sweet spot is, too. You can't leave your core. >> Yeah, you don't want to lose that. Like I said, they don't want to screw it up. >> You've got to take care of your core. >> Security's a big market, no question about it, as is the IT ops market as well. But there's a lot more runway. If they're going to get to be a $5 billion or a $10 billion company, it's unquestionable that they're going to bump into the other big platform players. >> Right, right. What's the horizon for something like that? I mean, it's not a 12 month play, right? I mean, you're talking about-- >> No, I think it's a five year vision. But it has to start to unravel over the next 12 to 18 months, in my view. A few things I would look for is, again, expansion of the ecosystem, ie, number of partners, the substance of those partnerships and then purposeful, deliberate developer outreach. I'd love to see these guys do a little dev con within .conf to see who shows up. Again, they don't play up their developer tools in a big way, they're not really, little hackathons going on here, there may be, but they're not front and center, no hackathon award winners that we're interviewing on the Cube. It'd be interesting to see what would happen if they released some low code SDKs. Have a little, I'd like to see them test the water there to see who comes out. I bet you they'd be oversold. >> John: Right. A lot of cool T-shirts, though. >> A lot of cool T-shirts. It's a fun company, too. >> It is, no, it is. >> That's the other thing. I mean, this is geek fest, right? There's a lot of great, fun, senses of humor, there are self deprecating, funny T-shirts. I think we're the only two guys that I've seen in here all week with ties on, as a matter of fact. >> Usually the only one. >> Well, just, I know I was being with you and I had to dress up for the occasion. Really enjoyed it. Great working with you. >> John, thank you, it's been a pleasure. >> Dave Vellante, here on the Cube. He gave you the playbook, Splunk, now just follow it and let's see where you are five years from now, right? He was there for you. We're done, .conf2017 wrapping up our live coverage here on the Cube. It's been great having you along for the ride, so, so long from our nation's capitol. (busy electronic music)

>> Announcer: Live from Washington D. C., it's the CUBE. Covering .conf2017. Brought to you by Splunk. (electronic music) >> Welcome back to the nation's capitol everybody. This is the CUBE, the leader in live tech coverage. And we're here at day two covering Splunk's .conf user conference #splunkconf17, and my name is Dave Vellante, I'm here with with co-host, George Gilbert. As I say, this is day two. We just came off the keynotes. I'm over product orientation today. George, what I'd like to do is summarize the day and the quarter that we've had so far, and then bring you into the conversation and get your opinion on what you heard. You were at the analyst event yesterday. I've been sitting in keynotes. We've been interviewing folks all day long. So let me start, Splunk is all about machine data. They ingest machine data, they analyze machine data for a number of purposes. The two primary use cases that we've heard this week are really IT, what I would call operations management. Understanding the behavior of your systems. What's potentially going wrong, what needs to be remediated. to avoid an outage or remediate an outage. And of course the second major use case that we've heard here is security. Some of the Wall Street guys, I've read some of the work this morning. Particularly Barclays came out with a research note. They had concerns about that, and I really don't know what the concerns are. We're going to talk about it. I presume it's that they're looking for a TAM expansion strategy to support a ten billion dollar valuation, and potentially a much higher valuation. It's worth noting the conference this year is 7,000 attendees, up from 5,000 last year. That's a 40% increase, growing at, or above actually, the pace of revenue growth at Splunk. Pricing remains a concern for some of the users that I've talked to. And I want to talk to you about that. And then of course, there's a lot of product updates that I want to get into. Splunk Enterprise 7.0 which is really Splunk's core analytics platform ITSI which is what I would, their 3.0, which I would call their ITOM platform. UBA which is user behavior analytics 4.0. Updates to Splunk Cloud, which is a service for machine data in the cloud. We've heard about machine learning across the portfolio, really to address alert fatigue. And a new metrics engine called Mstats. And of course we heard today, enterprise content security updates and many several security-oriented solutions throughout the week on fraud detection, ransomware, they've got a deal with Booz Allen Hamilton on Cyber4Sight which is security as a service that involves human intelligence. And a lot of ecosystem partnerships. AWS, DellEMC was on yesterday, Atlassian, Gigamon, et cetera, growing out the ecosystem. That's a quick rundown, George. I want to start with the pricing. I was talking to some users last night before the party. You know, "What do you like about Splunk? "What don't you like about Splunk? "Are you a customer?" I talked to one prospective customer said, "Wow, I've been trying to do "this stuff on my own for years. "I can't wait to get my hands on this." Existing customers, though, only one complaint that I heard was your price is to high, essentially is what they were telling Splunk. Now my feeling on that, and Raymo from Barclays mentioned that in his research note this morning. Raymo Lencho, top securities analyst following software industry. And my feeling George is that historically, "Your price is too high," has never been a headwind for software companies. You look at Oracle, you look at ServiceNow, sometimes customers complain about pricing too high. Splunk, and those companies tend to do very well. What's your take on pricing as a headwind or tailwind indicator? >> Well the way, you always set up these questions in a way that makes answering them easy. Because it's a tailwind in the sense that the deal sizes feed an enterprise sales force. And you need an enterprise sales force ultimately to be pervasive in an organization. 'Cause you can't just throw up like an Amazon-style console and say, "Pick your poison and put it all together." There has to be an advisory, consultative approach to working with a customer to tell them how best to fit their portfolio. >> Right. >> And their architecture. So yes, the price helps you feed that what some people in the last era of enterprise software used to call the most expensive migratory workforce in the world., which is the sales, enterprise sales organization. >> Sure, right. >> But what's happened in the different, in the change from the last major enterprise applications, ERPCRM, and what we're getting into now, is that then the data was all generated and captured by humans. It was keyboard entry. And so there was no, the volumes of data just weren't that great. It was human, essentially business transactions. Now we're capturing data streaming off everything. And you could say Splunk was sort of like the first one out of the gate doing that. And so if you take the new types of data, customer interactions, there are about ten to a hundred customer interactions for every business transaction. Then the information coming out of the IT applications and infrastructure. It's about ten to a hundred times what the customer interactions were. >> Yeah. >> So you can't price the, Your pricing model, if it stays the same will choke you. >> So you're talking about multiple orders of magnitude >> Yes. >> Of more data. >> Yeah. >> And if you're pricing by the terabyte, >> Right. >> Then that's going to cross your customers. >> Right. But here's what I would argue though George. I mean, and you mentioned AWS. AWS is another one where complaints of high pricing. But if, to me, if the company is adding value, the clients will pay for it. And when you get to the point where it becomes a potential headwind, the company, Oracle is a classic at this, will always adjust its pricing to accommodate both its needs as a public organization and a company that has to make money and fund R & D, and the customers needs, and find that balance where the competition can't get in. And so it seems to me, and we heard this from Doug Merritt yesterday, that his challenge is staying ahead of the game. Staying, moving faster than the cloud guys. >> Yeah. >> In what they do well. And to the extent that they do that, I feel like their customers will reward them with their loyalty. And so I feel as though they can adjust their pricing mechanisms. Yeah, everybody's worried about 606, and of course the conversions to subscriptions. I feel as though a high growth, and adjustments to your pricing strategy, I think can address that. What do you think about that? >> It's... It sounds like one of those sayings where, the friends say, "Well it works in practice, "but does it work in theory?" >> No, no. But it has worked in practice in the industry hasn't it? So what's different now? >> Okay. So take Oracle, at list price for Oracle 12C, flagship database. The price per processor core, with all the features thrown in, is something like three hundred thousand, three hundred fifty thousand per core. So you take an average Intel high end server chip, that might have 24 cores, and then you have two sockets, so essentially one node server is 48 times 350. And then of course, Oracle will say, "But for a large customer, we'll knock 90% off that," or something like that. >> Yeah, well exactly. >> Which is exactly what the Splunk guys told me yesterday. But it's-- >> But that's what I'm saying. They'll do what they have to do to maintain the footprint in the customer, do right by the customer, and keep the competition out. >> But if it's multiple orders of magnitude different. If you take the open source guys where essentially the software's free and you're just paying for maintenance. >> (laughs) Yeah and humans. >> Yeah, yeah. >> Okay, that's the other advantage of Splunk, as you pointed out yesterday, they've got a much more integrated set of offerings and services that dramatically lower. I mean, we all know the biggest cost of IT is people. It's not the hardware and software but, all right, I don't want to rat hole on pricing, but that was a good discussion. What did you learn yesterday? You've sat through the analyst meeting. Give us the rundown on George Gilbert's analysis of .conf generally and Splunk as a company specifically. >> Okay, so for me it was a bit of an eye opener because I got to understand sort of, I've always had this feeling about where Splunk fits relative to the open source big data ecosystem. But now I got a sense for what their ambitions are, and what their tactical plan is. I've said for awhile, Splunk's the anti-Hadoop. You know, Hadoop is multiple, sort of dozens of animals with three zookeepers. And I mean literally. >> Yeah. >> And the upside of that is, those individual projects are advancing with a pace of innovation that's just unheard of. The problem is the customer bears the burden of putting it all together. Splunk takes a very different approach which is, they aspire apparently to be just like Hadoop in terms of platform for modern operational analytic applications, but they start much narrower. And it gets to what Ramie's point was in that Wall Street review, where if you take at face value what they're saying, or you've listened just to the keynote, it's like, "Geez, they're in this IT operations ghetto, "in security and that's a La Brea tar pit, "and how are they ever going to climb out of that, "to something really broad?" But what they're doing is, they're not claiming loudly that they're trying to topple the giants and take on the world. They're trying to grow in their corner where they have a defensible moat. And basically the-- >> Let me interrupt you. >> Yeah. >> But to get to five billion >> Yeah. >> Or beyond, they have to have an aggressive TAM expansion strategy, kind of beyond ITOM and security, don't they? >> Right. And so that's where they start generalizing their platform. The data store they had on the platform, the original one, is kind of like a data lake in the sense that it really was sort of the same searchable type index that you would put under a sort of a primitive search engine. They added a new data store this time that handles numbers really well and really fast. That's to support the metrics so they can have richer analytics on the dashboard. Then they'll have other data stores that they add over time. And for each one, you're able to now build with their integrated tool set, more and more advanced apps. >> So you can't use a general purpose data store. You've got to use the Splunk within data. It's kind of like Work Day. >> Yeah, well except that they're adding more over time, and then they're putting their development tools over these to shield them. Now how seamlessly they can shield them remains to be seen. >> Well, but so this is where it gets interesting. >> Yeah. >> Splunk as a platform, as an application development platform on which you can build big data apps, >> Yeah. >> It's certainly, conceptually, you can see how you could use Splunk to do that right? >> And so their approaches out of the box will help you with enterprise security, user, they call it user behavior analytics, because it's a term another research firm put on it, but it's really any abnormal behavior of an entity on the network. So they can go in and not sell this fuzzy concept of a big data platform. They said, they go in and sell, to security operations center, "We make your life much, much easier. "And we make your organization safer." And they call these curated experiences. And the reason this is important is, when Hadoop sells, typically they go in, and they say, "Well, we have this data lake. "which is so much cheaper and a better way "to collect all your data than a data warehouse." These guys go in and then they'll add what more and more of these curated experiences, which is what everyone else would call applications. And then the research Wikibon's done, depth first, or rather breadth first versus depth first. Breadth first gives you the end to end visibility across on prem, across multiple clouds, down to the edge. But then, when they put security apps on it, when they put dev ops or, some future big data analytics apps as their machine learning gets richer and richer, then all of a sudden, they're not selling the platform, because that's a much more time-intensive sale, and lots more of objectives, I'm sorry, objections. >> It's not only the solutions, those depth solutions. >> Yes, and then all of a sudden, the customer wakes up and he's got a dozen of these things, and all of a sudden this is a platform. >> Well, ServiceNow is similar in that it's a platform. And when Fred Luddy first came out with it, it's like, "Here." And everybody said, "Well, what do I do with it?" So he went back and wrote a IT service management app. And they said, "Oh okay, we get it." Splunk in a similar way has these depth apps, and as you say, they're not selling the platform, because they say, "Hey, you want to buy a platform?" people don't want to buy a platform, they want to buy a solution. >> Right. >> Having said that, that platform is intrinsic to their solutions when they deliver it. It's there for them to leverage. So the question is, do they have an application developer kit strategy, if you will. >> Yeah. >> Whether it's low code or even high code. >> Yeah. >> Where, and where they're cultivating a developer community. Is there anything like that going on here at .conf? >> Yeah, they're not making a big deal about the development tools, 'cause that makes it sound more like a platform. >> (laughs) But they could! >> But they could. And the tools, you know, so that you can build a user interface, you can build dashboards, you can build machine learning models. The reason those tools are simpler and more accessible to developers, is because they were designed to fit the pieces underneath, the foundation. Whereas if you look at some of the open source big data ecosystem, they've got these notebooks and other tools where you address one back end this way, another back end that way. It's sort of, you know, you can see how Frankenstein was stitched together, you know? >> Yeah so, I mean to your point, we saw fraud detection, we saw ransomware, we see this partnership with Booz Allen Hamilton on Cyber4Sight. We heard today about project Waytono, which is unified monitoring and troubleshooting. And so they have very specific solutions that they're delivering, that presumably many of them are for pay. And so, and bringing ML across the platform, which now open up a whole ton of opportunities. So the question is, are these incremental, defend the base and then grow the core solutions, or are they radical innovations in your view? >> I think they're trying to stay away from the notion of radical innovation, 'cause then that will create more pushback from organizations. So they started out with a google-search-like product for log analytics. And you can see that as their aspirations grow for a broader set of applications, they add in a richer foundation. There's more machine learning algorithms now. They added that new data store. And when we talked about this with the CEO, Doug Merritt yesterday at the analyst day, he's like, "Yes, you look out three to five years, "and the platform gets more and more broad. "and at some point customers wake up "and they realize they have a new strategic platform." >> Yeah, and platforms do beat products, and even though it's hard sell, if you have a platform like Splunk does, you're in a much better strategic position. All right, we got to wrap. George thanks for joining me for the intro. I know you're headed to New York City for Big Data NYC down there, which is the other coverage that we have this week. So thank you again for coming on. >> Okay. >> All right, keep it right there. We'll be back with our next guest, we're live. This is the CUBE from Splunk .conf2017 in the nation's capitol, be right back. (electronic music)

(upbeat music) >> Welcome to this week's edition of Wikibon Research Meeting on the Cube. This week we're going to talk about a rather important issue that raises a lot of questions about the future of the industry and that is, how are information technology organizations going to manage the wide array of new applications, new types of users, new types of business relationships that's going to engender significant complexity in the way applications are organized, architected and run. One of the possibilities is that we'll see an increased use of machine learning, ultimately inside information technology and operations management applications and while this has tremendous potential, it's not without risk and it's not going to be simple. These technologies sound great on paper but they typically engender an enormous amount of work and a lot of complexity themselves to run. Having said that, there are good reasons to suspect that this approach will in fact be crucial to ultimately helping IT achieve the productivity that it needs to support digital business needs. Now a big challenge here is that the technology, while it looks good, as I said, nonetheless is pretty immature and today's world, there's a breadth first and a depth first approach to thinking about this. Breadth first works on or worries about end to end visibility into how applications work across multiple clouds, on premise in the cloud, across applications, wherever they might be. You get an enormous amount of visibility and alerts but you also get a lot of false positives and that creates a challenge because these tools just don't have enormous visibility into how the individual components are working or how their relationships are set up, they just look at the broad spectrum of how work is being conducted. The second class is looking at depth first which is really based on the digital twin notion that's popular within the IOT world and that is vendors delivering out of the box models that are capable of doing a great job of creating a digital simulacrum of a particular resource so that it can be modeled and tracked and tested. Now again, a lot of potential, a lot of questions about how machine learning and iTom are going to come together. George, what is one of the key catalysts here? Somewhere in here there's a question about people. >> Okay there's a talent question, always with the introduction of new technology, it's people processed technology. The people end of the equation here is that we've been trying to upskill and create a new class of application developer as Jim has identified. This new class is a data scientist and they focus on data intensive applications and machine learning technology. The reason I bring up the technology is when we have this landscape that you described, that is getting so complex where we're building on business transaction applications, extending them with systems of engagement and then the operational infrastructure that supports both of them, we're getting many orders of magnitude more complexity in multiple dimensions and in data and so we need a major step function in the technology to simplify the management of that because just the way we choked on the deployment, mainstream deployment of big data technology in terms of lack of the specialized administrators, we are similarly choking on the deployment of very high value machine learning applications because it takes a while to train a new generation of data scientists. >> So George, we got a lot of challenges here in trying to train people but we're also expecting that we're going to be better trained technology with some of these new questions, so Jim let me throw it to you. When we think ultimately about this machine learning approach, what are some of the considerations that people have to worry about as they envision the challenges associated with training some of these new systems? >> Yeah I think one of the key challenges with training new systems for iTom is, do you have a reference data set? The predominant approach to machine learning is something called supervised learning where you're training it on rhythm against some data that represents what you're trying to detect or predict or classify. If for IT and operations management, you're looking for anomalies, for unprecedented events, black swan events and so forth. Clearly, if they're unprecedented, there's probably not going to be a reference data set that you can use to detect them or hopefully before they happen and neutralize them. That's an important consideration and supervised learning breaks down if you can't find a reference data example. Now there are approaches to machine learning, they're called cluster analysis or unsupervised learning, alert to something called cluster analysis algorithms which would be able to look for clusters in the data that might be indicative of correlations that might be useful to drill into, might be indicative of anomalous events and so forth. What I'm getting as it that when you're then considering ML, machine learning in the broader perspective of IT and operations management, do you go supervised learning, do you go with unsupervised learning for the anopolis, do you, if you want to remediate it, that you have a clear set of steps to follow from precedent, you might also want something called reinforcement learning. What I'm getting at is that all the aspects of training the models to acquire the knowledge necessary to manage the IT operations. >> Jim, let me interrupt, what we've got here is a lot of new complexity and we've got a need for more people and we've got a need for additional understanding of how we're going to train these systems but this is going to become an increasingly challenging problem. David Floyer, you've done some really interesting research on with the entire team that we call unigrid. Unigrid is looking at the likely future of systems as we're capable of putting more data proximate to other data and use that as a basis for dramatically improving our ability to, in a speedy, nearly real-time way, drive automation between many of these new application forms. It seems as though depth first, or what we're calling depth first, is going to be an essential element of how unigrid's going to deploy. Take us through that scenario and what do you think about how these are going to come together? >> Yes, I agree. The biggest, in our opinion, the biggest return on investment is going to come from being able to take the big data models, the complex models and make those simple enough that they can, in real time, help the acceleration, the automation of business processes. That seems to be the biggest return on this and unigrid is allowing a huge amount more data to be available in near real-time, 100 to 1000 times more data and that gives us an opportunity for business analytics which includes of course AI and machine learning and basic models, etc. to be used to take that data and apply it to the particular business problem, whether it be fraud control, whether it be any other business processing. The point I'm making here is that coding techniques are going to be very, very stretched. Coding techniques for an edge application in the enterprise itself and also of course coding techniques for pushing down stuff to the IOT and to the other agents. Those coding techniques are going to focus on performance first to begin with. At the same time, a lot of that coding will come from ISVs into existing applications and with it, the ISVs have the problem of ensuring that this type of system can be managed. >> So George, I'm going to throw it back to you at this point in time because based on what Dave has just said, that there's new technology on the horizon that has the potential to drive the business need for this type of technology, we'll get to that in a little bit more detail in a second, but is it possible that at least the depth first side of these ML and IT and iTom applications could become the first successful packaged apps that use machine learning in a featured way? >> That's my belief, and the reason is that even though there's going to be great business value in linking, say big data apps and systems of record and web mobile apps, say for fraud prevention or detection applications where you really want low latency integration, most of the big data applications today are more high latency integration where you're doing training and inferencing more in batch mode and connecting them with high latency with the systems of record or web and mobile apps. When you have that looser connection, high latency connection, it's possible to focus just on the domain, the depth first. Because it's depth first, the models have much more knowledge built in about the topology and operation of that single domain and that knowledge is what allows them to have very precise and very low latency remediation either recommendations or automated actions. >> But the challenge with just looking at it from a depth first standpoint is that as the infrastructure, as the relationships amongst technologies and toolings inside an infrastructure application portfolio is that information is not revealed and becomes more crucial overall to the operation of the system. Now we got to look a little bit at this notion of breadth first, the idea of tooling support end to end. That's a little bit more problematic, there's a lot of tools that are trying to do that today, a lot of services trying to do that today, but one of the things that's clearly missing is an overall good understanding of the dependency that these two tools have on machine learning. Jim, what can you tell us about how overall some of these breadth first products seem to be dependent or not on some of these technologies. >> Yeah, first of all breadth first products, what's neat is above, basically an overall layer is graph analysis, graph modeling to be able to follow a hundred interactions of transactions and business flows across your distributed IT infrastructure, to be able to build that entire narrative of what's causing a problem or might be causing a problem. That's critically important but as you're looking at depth first and you just go back and forth between depth first, like digital twin as a fundamental concept and a fundamentally important infrastructure for depth first, because the digital twin infrastructure maintains the data that can be used for training data for supervised machine learning looking into issues from individual entities. If you can combine overall graph modeling at the breadth first level for iTom with the supervised learning based on digital twin for depth first, that makes for a powerful combination. I'm talking in a speculative way, George has been doing the research, but I'm seeing a lot of uptake of graph modeling technology in the sphere, now maybe George could tell us otherwise, but I think that's what needs to happen. >> I think conceptually, the technology is capable of providing this George, I think that it's going to take some time however, to see it fully exploited. What do you got to say about that? >> I do want to address Jim, your comments about training which is the graph that you're referring to is precisely the word when I use topology figuring that more people will understand that and it's in the depth first product that the models have been pre-trained, supervised and trained by the vendor so they come baked in to know how to figure out the customer's topology and build what you call the graph. Technically, that's the more correct way of describing it and that those models, pre-trained and supervised have enough knowledge also to figure out the behavior which I call the operations of those applications, it's when you get into the breadth first that it's harder because you have no bounds to make assumptions about, it's harder to figure out that topology and operational behavior. >> But coming back to the question I asked, the fact that it's not available today, as depth first products accrete capabilities and demonstrate success, and let's presume that they are because there is evidence that they are, that will increase the likelihood that they are generating data that can then be used by breadth first products. But that raises an interesting question. It's a question that certainly I've thought about as well, is that is, Nick, ultimately where is the clearing house for ascertaining the claims these technologies will not and work together, have you seen examples in the past of standards, at this level of complexity coming together that can ensure that claims in fact, or that these technologies can in fact increasingly work together. Have we've seen other places where this has happened? >> Good question. My answer is that I don't know. >> Well but there have been standards bodies for example that did some extremely complex stuff in IO. Where we saw an explosion in the number of storage and printer and other devices and we saw separation of function between CPUs and channels where standards around SCUZI and what not, in fact were relatively successful, but I don't know that they're going to be as, but there is specific engineering tests at the electricity and physics level and it's going to be interesting to see whether those types of tests emerge here in the software world. All right, I want to segue from this directly into business impacts because ultimately there's a major question for every user that's listening to this and that is this is new technology, we know the business is going to demand it in a lot of ways. The machine learning in business activities, as David Floyer talked about, business processes, but the big question is how is this going to end up in the IT organization? In fact is it going to turn into a crucial research that makes IT more or less successful? Neil Raden, we've got examples of this happening again in the past, where significant technology discontinuities just hit both the business and IT at the same time. What happened? >> Well, in a lot of cases it was a disaster. In many more cases, it was a financial disaster. We had companies spending hundreds of billions of dollars implementing an ERP system and at the end, they still didn't have what they wanted. Look, people not just in IT, not just in business, not just in technology, consistently take complex problems and try to reduce them to something simple so they can understand them. Nowhere is that more common than in medical research where they point at a surrogate endpoint and they try to prove the surrogate endpoint but they end up proving nothing about the disease they're trying to cure. I think that this problem now, it's gone beyond an inventory of applications and organizations, far too complex for people to really grasp all at once. Rather than come up with a simplified solution, I think we can be looking to software vendors to be coming up with packages to do this. But it's not going to be a black box. It's going to require a great deal of configuration and tuning within each company because everyone's a little different. That's what I think is going to happen and the other thing is, I think we're going to have AI on AI. You're going to have a data scientist work bench where the work bench recommends which models to try, runs the replicates, crunches the numbers, generates the reports, keeps track of what's happening, goes back to see what's happened because five years ago, data scientists were basically doing everything in R and Java and Python and there's a mountain of terrible code out there that's unmaintainable because they're not professional programmers, so we have to fix that. >> George? >> Neil, I would agree with you for the breadth first products where the customer has to do a lot of the training on the job with their product. But in the depth first products, they actually build in such richly trained models that there really is, even in the case of some of the examples that we've researched, they don't even have facilities for customers to add say the complex event processing for analytics for new rules. In other words, they're trained to look at the configuration settings, the environment variables, the setup across services, the topology. In other words it's like Steve Jobs says, it just works on a predefined depth first domain like a big data stack. >> So we're likely to see this happen in the depth first and then ultimately see what happens in the breadth first but at the end of the day, it still has to continue to attract capital to make these technologies work, make them evolve and make the business cases possible. David, again you have spent a lot of time looking at this notion of business case and we can see that there's a key value to using machine learning in say fraud detection, but putting shoes on the cobbler's children of IT has been a problem for years. What do you think? Are we going to see IT get the resources it needs starting with depth first but so that it can build out a breadth oriented solution? >> My view is that for what it's worth, is we're going to focus or IT is going to focus on getting in applications which use these technologies and they will go into the places for that business where it makes most sense. If you're an insurance company, you can make hundreds of millions of dollars with fraud detection. If you are in other businesses, you want to focus on security or potential security. The applications that go in with huge amounts more data and more complexity within them, initially in my view will be managed as specific applications and the requirements of AI requirements to manage them will be focused on those particular applications, often by the ISVs themselves. Then from that, they'll be learning about how to do it and from that will come broader type of solutions. >> That's further evidence that we're going to see a fair amount of initial successes more in the depth first side, application specific management. But there's going to be a lot of efforts over the next few years for breadth first companies to grow because there's potentially significant increasing returns from being the first vendor out there that can build the ecosystem that ties all of these depth first products together. Neil, I want to leave you with a last thought here. You mentioned it earlier and you've done a lot of work on this over the years, you assert that at the end of the day, a lot of these new technologies, similar to what David just said, are going to come in through applications by application providers themselves. Just give us a quick sense of what that scenario's going to look like. >> I think that the technology sector runs on two different concepts. One is I have a great idea, maybe I could sell it. Did you hear that, I just got a message my connection was down there. Technology vendors will say that I have a, >> All right we're actually losing you, so Dave Alante, let me give you the last word. When you think about some of the organizational implications of doing this, what do we see as some of the biggest near term issues that IT's going to have to focus on to move from being purely reactive to actually getting out in front and perhaps even helping to lead the business to adopt these technologies. >> Well I think it's worth instructive to review the problem that's out there and the business impact that it'll have an what many of the vendors have proposed through software, but I think there are also some practical things that IT organizations can do before they start throwing technology at the problem. We all know that IT has been reactive generally to operations issues and it's affected a laundry list of things in the business, not only productivity, availability of critical systems, data quality, application performance and on and on. But the bottom line is it increases business risk and cost and so when the organizations that I talk to, they obviously want to be proactive. Vendors are promising that they have tools to allow them to be more proactive, but they really want to reduce the false positives. They don't want to chase down trivial events and of course cloud complicates all this. What the vendor community has done is it's promised end to end visibility on infrastructure platforms including clouds and the ability to discover and manage events and identify anomalies in a proactive manner. Maybe even automate remediation steps, all important things, I would suggest that these need to map to critical business processes and organizations need to have an understanding or they're not going to understand the business impact and it's got to extend to cloud. Now, is AI and ML the answer, maybe, but before going there, I would suggest that organizations look at three things that they can do. The first is, the fact is that most outages on infrastructure come from failed or poorly applied changes, so start with good change management and you'll attack probably 70% of the problem in our estimation. The second thing that we, I think would point to users, is that they should narrow down their promises and get their SLA's firmed up so they can meet them and exceed them and build up credibility with an organization before taking on wider responsibilities and increasing project skills and I think the third thing is start acting like a cloud provider. You got to be clear about the services that you offer, you want to communicate the SLA's, you know clearly they're associated with those services and charge for them appropriately so that you can fund your business. Do these three things before you start throwing technology at the problem. >> That's a great wrap. The one thing I'd add to that Dave, before we actually get to the wrap itself is that I find it intriguing that the processes of thinking through the skills we need and the training that we're going to have to do of people and increasing the training, whether it's supervised, unsupervised, reinforced, of some of these systems, will help us think through exactly the type of prescriptions that you just put forward. All right, let's wrap. This has been a great research meeting. This week, we talked about the emergence of machine learning technologies inside IT operations management solutions. The observation we make is that increasingly, businesses becoming dependent on multicloud including a lot of SAS technologies and application forms and using that as a basis for extending their regional markets and providing increasingly specialized services to customers. This is putting an enormous pressure on the relationship between brand, customer experience and technology management. As customers demand to be treated more uniquely, the technology has to respond, but as we increase the specificity of technology, it increases the complexity associated with actually managing that technology. We believe that there will be an opportunity for IT organizations to utilize machine learning and related AI type and big data technologies inside their iTom capabilities but that the journey to get there is not going to be simple. It's not going to be easy and it's going to require an enormous amount of change. The first thing we observe is that there is this idea of what we call breadth first technology or breadth first machine learning in iTom, which is really looking end to end. The problem is, without concrete deep models, we look at individual resources or resource pools, end up with a lot of false positives and you lose a lot of the opportunity to talk about how different component trees working together. Depth first, which is probably the first place that machine learning's going to show up in a lot of these iTom technologies, provides an out of the box digital twin from the vendor that typically involves or utilizes a lot of testing on whether or not that twin in fact is representative and is an accurate simulacrum of the resource that's under management. Our expectation is that we will see a greater utilization of depth first tooling inactivity, even as users continue to experiment with breadth first options. As we look on the technology horizon, there will be another forcing function here and that is the emergence of what we call unigrid. The idea that increasingly you can envision systems that bring storage, network and computing under a single management framework at enormous scale, putting data very close to other data so that we can run dramatically new forms of automation within a business, and that is absolutely going to require a combination of depth first as well as breadth first technology to evolve. A lot of need, lot of change on how the IT organization works, a lot of understanding of how this training's going to work. The last point we'll make here is that this is not something that's going to work if IT pursues this in isolation. This is not your old IT where we advocated for some new technology, bought it in, played for it, create a solution and look around for the problem to work with. In fact, the way that this is likely to happen and it further reinforces the depth first approach of being successful here is we'll likely see the business demand certain classes of applications that can in fact be made more functional, faster, more reliable, more integratable through some of these machine learning like technologies to provide a superior business outcome. That will require significant depth first capabilities in how we use machine learning to manage those applications. Speed them up, make them more complex, make them more integrated. We're going to need a lot of help to ensure that we're capable of improving the productivity of IT organizations and related partnerships that actually sustain a business's digital business capabilities. What's the bottom line? What's the action item? The action item here is user organizations need to start exploring these new technologies, but do so in a way that has proximate near term implications for how the organization works. For example, remember that most outages are in fact created not by technology but by human error. Button up how you think about utilizing some of these technologies to better capture and report and alert folks, alert the remainder of the organization to human error. The second thing to note very importantly, is that the promises of technology are not to be depended upon as we work with business to establish SLA's. Get your SLA's in place so the business can in fact have visibility to some of the changes that you're making through superior SLA's because that will help you with the overall business case. Now very importantly, cloud suppliers are succeeding as new business entities because they're doing a phenomenal job of introducing this and related technologies into their operations. The cloud business is not just a new procurement model. It's a new operating model and start to think about how your overall operating plans and practices and commitments are or are not ready to fully incorporate a lot of these new technologies. Be more of a cloud supplier yourselves. All right, that closes this week's Friday research meeting from Wikibon on the Cube. We're going to be here next week, talk to you soon. (upbeat music)

>> Narrator: Live from Orlando, Florida, it's theCUBE covering ServiceNOW Knowledge17, brought to you by ServiceNOW. >> Dave: We're back, this is theCUBE, the leader in live tech coverage. We go out to the events and we extract the signal from the noise. I'm Dave Vellante with Jeff Frick. Farrell Hough is here she's the general manager of the service management business unit at ServiceNOW, great to see you. >> Farrell: Yes, great to see you, thanks for having me. >> Dave: Awesome, you're welcome. Awesome keynote this morning, you have your baby, which is ITSM, we know, but at the financial analyst meeting and you know, you represent today's keynote, you represented, you know, more than just ITSM, which is, you know, good. But let's start there, so, awesome keynote, lot of energy, so much meat (chuckles). >> Farrell: Yes. >> Dave: In Jakarta. >> Farrell: Absolutely. We have been busy, for sure, in our IT portfolio. In ITSM we really spent a lot of time and energy in giving back to our customer base and making sure that critical capabilities and features in ITSM, have a lot of depth behind them as well. So making sure service level management's solid, service catalog, which is 99% adopted across our customer base, servicing over half a million end users, that making sure that that's solid. And then additionally, making it really easy for new customers to join onto ITSM as well by giving out of the box best practices and a guided set up format like a wizard format that they can within just a couple of hours stand up a brand new incident management process prescribed by ServiceNOW and feel confident in what they're getting. >> Dave: Yeah, so I didn't realize the number was that high in terms of adoption of service catalog. What do you see for CMDB, I mean, when you first started following ServiceNOW it was mixed, 'cause it kind of gets political, but now, today, when you talk to customers it's like, oh yeah that's a big initiative of ours, or we're already there, or what do you see? >> Farrell: Absolutely. I don't have the exact percentage in front of me but I believe that it's upwards of 70% adoption in our customer base. And that is a difference from where we were in the past, for sure. >> Dave: Which is like the mainspring of innovation, 'cause once you get there, with service catalog and CMDB-- >> Farrell: Yep, you get all your assets in there, you get all your services defined, it's go time. >> Dave: Then your operating leverage is huge in terms of when you bring out new function and the impact on the organization, the business impact, can be really enormous. >> Farrell: Absolutely. >> Jeff: And best practice out of the box is a huge, huge coo, everyone we've talked to, you know, they're smart enough now to now customization is bad. Keep it to a minimum, keep it to a minimum, do config but not customizations, so that all those upgrades are easier, easier, easier. So to come out of the box with an integrated best practices workflow, great, great solutions for the customers to get up and running quickly. >> Farrell: It is, and you know, they're asking for prescription, and we're going to give it to them. We've got our own services arm, we have a partner community, we know between all of us in this huge ecosystem what's working and what's not, and we're going to put it in the product and make sure our customers, existing and new, get best practice out of the box. >> Dave: So, kind of three areas you talked about today: service management, we just touched on, we didn't talk about the surveys, but that's cool, that's a nice little feature you guys have added. >> Farrell: Oh yes, that's right. >> Dave: So, you have new and improved surveys. Operations managements, so that's ITOM piece right? >> Farrell: Yep. >> Dave: And then business management. So give us the high level on office management. >> Farrell: I will, yeah, sure. So we announced this year that we're putting out the cloud management platform, and the adoption of cloud is long past it's tipping point. We're seeing cloud being adopted everywhere and cloud resources are extremely easy to procure, stand up, and use, and IT may or may not know about it. And that becomes just a huge problem in terms of cost and even in terms of security and compliance and when we're able to-- we made an acquisition roughly a year ago, the ITOM team, and this is basically the next generation cloud management platform, where now you're able to have a cloud portal where a end user can go and consume and, just like a service catalog, they're going to have a service catalog of cloud services that you've already provisioned very easily with the drag and drop interface, that accounts for all your policy already in those services. And so it makes it very very easy for the business to continue to operate at the pace and the skill that they need to, but for IT to make sure that we have the consistency and the compliance that we need to protect the business overall and manage cost, all with a really great user experience at the same time. So we're thrilled to be able to put out a cloud management platform. And then the second major thing that came out in the IT operations management space was around service mapping. When we went to market with service mapping it was for all on prem services and mapping out what that looked like. This time around we're just bookending it and kind of closing the gap and saying okay, let's look at what's off prem, and let's look what's in the cloud. So you get a holistic view and are able to discover resources in the cloud and on prem as well and you get that holistic view of your services mapped going forward. >> Dave: So I have to ask you, so we're always asking, when ServiceNOW gets into HR, it's like oh does ServiceNOW compete with Workday, no. And when ServiceNOW gets into security, it's like does ServiceNOW compete with FireEyes, et cetera, no no. Now when you talk about this multi-cloud, sort of mapping visibility, there's a lot of talk about, we call it sometimes inter-clouding and inter-cloud management, how far to do you go into that, I mean, can I actually orchestrate across clouds? Is it just giving you visibility, well not just, but, how should I think about the positioning of ServiceNOW in that space of cloud management? >> Farrell: We're out there to create flexibility for customers and we'll start to make it happen that you can orchestrate across different clouds regardless of what they look like. We're not totally there yet, but that's the direction it's going. >> Dave: Well nobody's there. >> Farrell: Yep. >> Dave: This is jump all for the industry. And it's got to be a huge market, I mean, everybody's doing multi-clouds. In fact somebody told me, today David Flora told me in Europe there was a mandate in the banking sector that you have to have a second source for cloud. >> Jeff: Oh really? >> Dave: Yeah, I don't know the context, but good news for the cloud vendors, right? Good news for somebody-- >> Farrell: Exactly. >> Dave: --who manages that. So, okay, and now what about, are we done with ops-- >> Farrell: That was operations management, yep done with that. >> Dave: And then how about business management? >> Farrell: Alright, on the business management side, the big news if the software asset management. We're able to deliver another new product this year, and that's really going to put a lot of power back in the hands of IT. You're no longer caught on your heels with a software audit, realizing you're out of compliance. We struggle with visibility and understanding where are all these software assets, who are they allocated to, are they actually using them, how much is it costing us, and when we're able to have visualization to that because it's on the ServiceNOW platform and we understand where all those items exist, we're able to go in and very easily reclaim licenses, or reallocate them, and to me that's found money. And I just love that. I think that's going to be great, and guess what? You want to find your sourcing for your next IT project it's right there. >> Jeff: Right, right, and you're being humble. I mean that was the thing where the biggest roar came up from the crowd, without a doubt. Super, super well received. >> Dave: We were talking to CJ this morning about how it works and you get the platform, the platform comes out with all these features, and then the business units take advantage of those features. Now of course he described it differently, he said you start with the customer, and then you figure out what to put in the platform knowing that the business units are going to take advantage of it. But when you think about intelligent automation you gave an example of predictive maintenance today, so that's a use case for that so called AI or deep learning, machine learning. So talk about that a little bit. And then I want to get into the DX continuum piece as well. >> Farrell: Yeah, absolutely. When we're sitting on this data set that our customers have and they want us to take advantage of it for them, on their behalf, we're able to go back and apply algorithms to those data sets to say what's the norm? And did it have a good outcome? And all that data is in there, we're able to model it now, you're not having to go do that in some--export that into some other system to try to figure out, with some advanced analytics, what's that looking like, you're able to be able to say very clearly, listen, here's what the normal pattern of behavior is, and establish that for everything else going forward. So it becomes really clear where outliers exist and what suspect events or suspect alerts look like in your environment and then you can fire off a process to say look, this looks like a problem, and with certain signposts associated to it, go ahead and automatically open up that incident. You apply it to change management where you're talking about predictive maintenance. Something has enough failures automatically schedule a change window or decommission it, fail it over, back it out, move it out of the way, so that it's not causing a problem anymore. We put so much on humans to do for so long because the technology wasn't there to allow us to do it, well it's time, it's here now. And so we can take some of the burden away. >> Dave: I just had a thought, we talk in this industry so much about consumerization of IT and trying to mimic consumers, Fred Luddy talks about all the time. What you just described, I thought about an experience of an iPhone user, and anytime you do a migration, my wife just migrated from an android to an iPhone, what question was asked, is it backed up? What you just described is proactive. You're way beyond is it backed up, you're at the point of, we're going to just eliminate any possibility of a disruption. So I guess my question there is, is enterprise IT finally, not only catching up, but in some regards surpassing, this consumerization trend? >> Farrell: Hey, I think there's an opportunity to leapfrog, all the way, and I'm behind a 100%. I do, I think exactly that. And why not get way out ahead and over our skis with that and over-deliver and show that yep, we can see what's coming, we're sitting on all this data. When you choose to go to the cloud, and all that data is accessible, and you're on a single platform, it's all intermingled. You're not having to stitch together, create a data lake that's got all these different integrations pulling data and trying to sort it out from there with some data scientists or some business analysts looking at it, you're now able to lean in way more with your operation and really start to take care of it and truly own it. >> Jeff: I was just going to say my favorite part of your keynote today was kind of teeing off what you said, which is using machine learning and artificial intelligence on relatively simple looking processes that are painful, cumbersome, and horrible, like categorization, prioritization, assignment, to take the first swag, let the machine take the first swag at that stuff, and take that burden off the person because it's tedious, it's cumbersome, and it's painful, so it's this really elegant use of machine learning and AI, which is talked about all the time, on a relatively, again, simple looking activity, that just delivers tremendous value. >> Farrell: Yeah, I'm really really excited about that part because there's a lot of mystic and-- ah, I don't know what the right word is, maybe misunderstanding potentially, which can lead to mistrust of AI and machine learning and what's really going to come of it. And when we're able to say using supervised machine learning, which is the model that we're going after with the auto-classification, you can work with customers to be able to to let them tune the level of accuracy that they are comfortable with. And so you're building trust right away with a really simple example of auto-classification or auto-categorization, that is so frustrating for both parties. The person who is filing the incident, and the for the person who's going to be supporting and fulfilling on that incident as well. And I just love that fact that we can start to dip our toe into this pool and wade in and create trust along the way so we don't leave anyone behind or create mistrust in our user-base that we're just trying to get rid of them in some capacity or pull the wool over their eyes, we're not and we're going to be really transparent about in the way we do it and I think that's phenomenal. >> Jeff: And it's dynamic right, so it continues to learn. You have Spotify, you have a playlist, I like this, I don't like this, the playlist hopefully gets better, so. >> Farrell: That's right, because it took your input. >> Jeff: Correct, right. >> Farrell: And so taking input from the end users is going to then help train that system over time, that's correct. >> Dave: I got so many questions for you. (Jeff laughs) >> Farrell: Okay! Give 'em to me. >> Dave: So the auto-classification piece, that comes from the DX continuum acquisition-- >> Farrell: It does, yes. >> Dave: So explain that, I know you guys re-platformed everything, but what did that give you and let's get into auto-classification a little bit. >> Farrell: Okay, well it gave us some incredibly talented smart engineers and some really great intellectual property in terms of algorithms that we are able to now apply. When we re-platform something we're making sure that it works in the ServiceNOW platform stack and that it is going to be available and pervasive for every application that gets built on top of the platform. >> Dave: Okay so, you had said before, we're not just building a data lake, which, I want to talk to you about that too, 'cause a date lake as we know turns into a data swamp and it's just a mess and then you got to really do a lot of heavy lifting. >> Farrell: Smelly, don't like that. >> Dave: Right? Not good. So-- >> Jeff: Scary critters. >> Dave: You're auto-classifying at the point of creation I presume, or use of that data set. So how does that all work? How is it being applied? Where do you see customers getting value out of this? Explain that a little. >> Farrell: Well really I see in the ITSM side and the IT Space and in the ITSM side specifically, anything that you've got to apply a drop down field to, whether you're an end customer doing it through a service portal, or you're an IT worker, too, like let's help those guys out, why not? Anytime you need to fill out a field through a drop down mechanism, it's one discreet set of values, that's a candidate there. Now you want to have a large data set, which is why incidents, incident category, or assignment, assignment group, or what skill set might be required to work that particular incident, works because there's tons and tons and tons of incidents out there so we have lots of examples around what it could possibly be. And then that's what the data model would be built on. This auto-classification is not meant for the obscure or the random or the infrequent. So when we're talking about high volumes that a service desk sees, this is the perfect setup to apply it. >> Dave: So how will it work? I'll have a corpus of data with a bunch of incidents and I'll just sort of tell the machine go classify this? >> Dave: And it'll do some kind of process? >> Farrell: You're going to have a set of data a portion of the records you're going to use for the training model, the other portion you're going to leave behind, almost as the control group. And you're going to go apply the algorithms to that training set of data and it's going to start to learn and you're going to tell it what fields you want it to learn from and pay attention to and spit a model out on the other side on and it's going to crunch through all that data and it's going to give you a model on the other side, and you'll look at it and see if you agree, and then you're going to take that model and you'll apply it to that control set and you're going to look at what level of accuracy came out on the other side and you'll decide with that data set what accuracy level you want to have. For me, 70% accuracy will work for me on password reset. 'Cause, in all likelihood, what's it going to be? But maybe for a VPN issue I want 90%. You'll be able to start applying accuracy by category to then tune in exactly how you want things to work to make sure you get that good user experience. >> Dave: And then you'll continue to train that model and iterate. >> Farrell: Yes, absolutely. And you'll be able to train it and often as you like. I mean on demand, like yep, I want to train it again. And when you have a service desk worker who goes back in and re-categorizes, because yeah, that wasn't quite right, that's just the same thing as clicking the like button, thumbs up, thumbs down, on Spotify. You're right that you've just given it feedback. When you train it again, it takes that feedback into account. >> Dave: And then the subsequent incidents get auto-classified. >> Farrell: They get the learning. They get the learning. There's not magical learning that happens in this particular case, the technology's not evolved to that state, there's no unicorn back there that's doing all the learning for you. It takes feedback and it'll take some tuning, but hopefully in being able to make the feedback mechanism very easy, the tuning happens naturally, therefore the model gets better over time. >> Dave: Well it's a great use case because it's relatively narrow, and you have tons of data, and it can be implemented right away. >> Jeff: And like you said, even if it just helps you partially down the road, it's better than zero down the road, especially these repeatable processes that have to happen over and over and over, it's like oh please shoot me, this is the work that machines are supposed to do because it's mundane and repeatable and-- >> Farrell: Mind-numbing. >> Jeff: Mind-numbing, thank you. Let me get to solving the customer problem. >> Farrell: That's right. >> Dave: Okay so when we first encountered ServiceNOW we did our first Knowledge, it was from 2013, and it was at the height of the big data sort of hype-cycle. And so we would ask, of course we asked, well what about data, what about big data? The response was always well we got a lot of data and we're looking at that. But now we're here. And you mentioned earlier, it's not some data lake that you're processing as offloading your data warehouse, so what are you doing in that space? So it's not a data lake, it's a corpus of data and you're basically applying these AI and intelligent automation models to, can you explain a little bit about how that works? >> Farrell: Sure, well first off we won't do anything, we have to have our customer's permission to be able to use their data, they showed interest in machine learning services then they will give us permission to leverage their data and all customer data is separated too, within their own instance, within their own database, there's no co-mingling of data, so there will be no data lake whatsoever. But what we are able to do, and it's on a personal level, which I just love, because that's who we are as a company, that we're offering personalized supervised machine learning, personalized auto-classification, we're not taking all the data of all of our customers, kind of aggregating it up and then building models against that, and then saying oh I think this model would pertain to you and then it's only 25% accurate or even relevant. We're building a model very specific to you. And working with your data set and we have access to it, with your permission, and we'll go build that model, using the training set as we described, and then go test it out, and then help you go re-deploy it. So we'll pull that data into a central instance, help retrain it, and then move it back into your instance so that model is always constantly tuned and then you get to decide when you retrain it. >> Dave: So who's we in that example? You have a team of data scientists that do this? >> Farrell: This will be in our platform team. It's a platform service. You don't need data scientists to, I would say on the customer side, maybe if they were wanting to interpret some of that data or do something with it maybe they'd have a data scientist. This is just tried and true engineering and having a good service model behind it, it's just a central instance. >> Jeff: Do--I'm sorry, I interrupted. >> Farrell: No, I was just going to say through our acquisition DX Continuum, those engineers are building those training models and will keep them up to date, but they're not literally turning a crank when that data comes in and it'll be-- >> Dave: So it's a model that they apply, it scales, it's part of the service. Now you iterate that over time-- >> Farrell: That's right. >> Dave: But it's the-- >> Farrell: And you can build out other training models. So we just talked about auto-classification for instant, but this can extend in other areas as well. >> Jeff: Well I was going to say, do you think it's an opportunity for the ecosystem that has specialty expertise around, pick your favorite topic area, we're talking to someone about oil and gas earlier today, that they know what the model is way beyond just simple correlation to take in this and it flow and predict that, I think the example was that the well cap's going to break, or whatever. So do you see that potentially as an ecosystem contribution as well around more specific use cases? >> Farrell: Well I think that would be super cool. If we had customers of similar ilk, whatever that looked like, wanting to collaborate and share and crowdsource something for a greater good that wasn't competitive, I think that that would be amazing to be able to do that. And we would be able to facilitate it. We don't have any current plans to do that right now but I could absolutely see it. >> Dave: Well we've talked about the ecosystem through for years, to see it just burgeoning and awesome story. Thank you for coming on theCUBE and doing a brain dump on us and educating us. >> Farrell: Yeah, thank you so much-- >> Jeff: You really had a great opening line, "exciting time to be in IT," that was your opening line, the key night, I know you've got the excitement >> Farrell: It is! This is the best time to be in IT. I mean oh my gosh, it's fabulous. >> Dave: You're exploding. Alright Farrell, thanks very much. >> Farrell: Alright, thank you. >> Dave: Alright, keep it right there buddy, we'll be back with our next guest, theCUBE, we're live from Orlando, be right back. (techno music)