>> From the heart of Silicon Valley, it's the CUBE, covering COMCAST Innovation Day, brought to you by COMCAST. >> Hey welcome back here ready Jeff Frick here with the CUBE. We're at the COMCAST Silicon Valley Innovation Center. You know there's innovation centers all over Silicon Valley we hadn't been to the COMCAST one until we came to this event, it's very very cool, I think it's like five storeys in this building, where they're developing a lot of new technologies, partnering with technologies, but today the focus is on customer experience, brought together a panel of people to talk about some of the issues, and we're excited to have a representative from a company that's really out on the edge of defining customer experience, and measuring customer experience, joined by Ganesh Subramanian. He is the senior director of product marketing for Gainsight. Ganesh, great to see you. >> Great, happy to be here. >> Yeah, so I'm a huge Nick Mehta fan, I've interviewed him before I've been following Gainsight for a long time and you know, it really struck me the first time that, that Nick said you know CRM is you know, basically order management. It's not customer relationship management, you know customer relationships are complicated, and they're multi-faceted and there's lots of touch points and you guys really try to build a solution to help customers manage, actually do manage that relationship so they have a great experience with their customer. >> Yeah that's totally right, and not to say that CRM isn't an important ingredient when you make that cake, but there's a lot of other touchpoints right? How are people interacting with your digital products? What is that customer journey across sales, services, support? How does all of that come together? So what Gainsight does is really provide the customer cloud to bring all of those solutions together so that businesses can really operate in a more customer-centric way. >> So you, you said an interesting thing earlier in the conversation about customer success being measured just by revenue, again kind of the CRMy kind of approach to worth versus measuring success and measuring lifetime value and measuring so many other things that can define a great relationship. What are some of those things that people should be thinking about? What are some of those other metrics out there beyond you know, did I get a, you know, a net increase value of my contract? >> Yeah absolutely you know one way we think about it at Gainsight is the two-by-two of customer experience and customer outcomes. So, you can think about experience just as how happy are people on a day-to-day basis interacting with you, your products, your organization, your team? The flipside's also true though. You can have the happiest customer, that isn't getting what they want out of their product or service. In a B2B context we think about it as tangible ROI or outcomes. So at Gainsight we're ultimately trying to make sure our clients are delivering on both of those vectors. They want happy, successful clients, ultimately that's going to lead to the recurring revenue cycle: retention, growth, adoption and advocacy. >> So where does that kind of tie together? 'Cause I'm sure there's a lot of people that think those are in conflict right? That if I bend over backwards and I provide this great experience and these great services and all these things that this is going to negatively impact my profitability, it's going to negatively impact my transactional value. How should they be measuring those things? How should they be balancing, 'cause 'cause, you know, you can sell dollars for 90 cents, have a really happy customer, not going to be in business very long. >> Yeah I think that's kind of the secret sauce right? True innovation, what we talked about today at COMCAST, a lot about, how do you take that next step forward? How do you improve your products and services in ways that make customers, customers for life? Right, and if you make the right investments, you actually find out that maybe it's, it's minor change, maybe it's process change in your call center or call service, maybe it's implementing AI in an appropriate way, so that you're able to deliver more value with less time, or maybe it's transformative, maybe it's something that's a new service you're offering all together, that's making customers get outsized or unrealized returns on their investment. Well, it doesn't matter what that investment was, if it's going to long term drive your company to higher valuations and greater competitive differentiation. So we don't think about customer experience on kind of below the line, what's going to get me the incremental ROI, we really think about it as a fundamental differentiator for your business. >> Right. Now you're in charge of, of kickin' off new products. >> That's right. >> And you know one of the things I think is really interesting about the COMCAST voice, which has had a lot of conversation today, is I still get emails from COMCAST telling me how I should use it! Right 'cause it's a different behavior, it's a different experience that I'm not necessarily used to. As you look forward, you know introducing new products, what are some of the, the kind of trends that you're keepin an eye on, what do you think is going to kind of change and impact some of the things you guys are bringing to market? What are some of the new things we should be thinking about in customer experience? >> Yeah absolutely. So one thing at Gainsight, one thing we've learned leading the customer success movement is that to be customer-centric is more than a given function, or a given team, customer success managers kind of took the mantle in B2B and started leading the charge, leading the way towards being more customer-centric but that team on their own can't do everything. Nor do they want to, or can they, right? So, one big change and one big innovation that we're leading the front on is how do you bring all those different teams together? Which is why we launched the Gainsight customer cloud. So what we're doing is we're bringing disparate data together, that used to be silohed in functional specific software, bring that into a single source of truth, to truly provide an actionable customer 360, one that provides meaning to different teams with the right context, and then drive action off of that. So whether it's an automated email to get, improve product adoption in the COMCAST example, or maybe it's some kind of escalation effort, where you need a cross-functional team to get together on the same page, to improve a red customer, or maybe it's something that's in the product itself, by just making the product easier to use or a little bit more intuitive, the, all of your end users will end up benefiting from that. What Gainsight's tryna do is to try figure out, how can we break down these walls across these different teams, make it easier for people to collaborate to improve the customer experience. >> So Ganesh I got to tease you right, 'cause everyone's eyes just rolled out when you said 360 view of the customer right, we've been talking about this forever. >> Yeah. >> So what's different, you know, what's different today? Not specifically for what you're tryna do with your product and share that too, but more generally, that, that we're getting closer to that vision. >> Yeah. >> That we're actually getting closer to delivering on, on the promise of a 360 view, and information from that view that will enable us to take positive action? >> I love that question, and I think whenever you hear the word 360 view or digital transformation, you're going to get a couple eye-rolls in the crowd right? And, I actually totally believe that, that, you know, to date I think we've done things in too much of a waterfall methodology. Let's spend three years, get a unified idea across all our disparate data sources, and then we're going to be customer-centric. I think we've learned our lessons over the course of time that, hey you know, the end result doesn't really materialize in the time frame and ROI you expected, so why don't we start with the other end of the spectrum? What are the gaps that customers are perceiving? If it's just, let me, go back to that example of product ease of use. Are we identifying that as a major gap? Then how do we go solve that? How do we reverse engineer that process? And by the way that doesn't just fall on the product team to make the product easier, services need to onboard customers more effectively, you need documentation so that they can access and understand the key aspects of your product in a more concrete way. So all of that needs to come together. So I think the biggest difference between what we used to talk about, with 360s and digital transformation, to where we are today, is really the context and the outcome you're trying to deliver, and then reverse engineer the 360 that's most meaningful to you. So to make that a little bit more clear, what does that mean at the grassroots level? If you're a services team member you're working on projects. Does a 360 view about the next opportunity from a financial or commercial perspective really matter to you? How far down in that 360 view do you have to scroll before you start seeing information that's relevant? So at Gainsight what we're trying to do is use a many-to-many relationship mapping so that if you're a services team member, or a sales member, the view you're accessing is curated to what you need to actually do. >> Right. >> And that'll drive adoption of the digital transformation efforts within your organization. >> Right. Which then obviously opens up the opportunity for automation and AI and ML to, as you said, context is so important to make sure the right information is getting to the right person at the right time for the context of the job that I have and building that customer relationship. >> That's right. Yeah we think about AI all the time how's that going to improve the customer experience? It starts with that data foundation and understanding hey what should we own and what should we leverage? And being very conscious about what you're about to do, and then second, thinking about those point problems and, again, reverse engineering how can we staff augment, or make the experience better, maybe make the lives of our employees a little bit better, when they're engaging with customers. Ultimately it's got to be in service of people. >> Right. Well Ganesh thanks for sharing your story. Again I think what you guys are doing, and Nick and Gainsight is so important in terms of redefining this beyond order management, and to actually customer relationship management. >> So, >> That's right. >> Thanks for spending a few minutes with us. >> Awesome. >> My pleasure. >> All - >> All right. >> Thank you. >> He's Ganesh I'm Jeff you're watching the CUBE we're at the COMCAST Innovation Center in Silicon Valley. Thanks for watching we'll see you next time.

>>Live from Las Vegas. That's the Q covering splunk.com 19 brought to you by Splunk. >>You know, kind of leaning on that heavily. Automation, certainly very important. But what does enterprise and what does enterprise security 6.0 bring to the table. So can you take us through the evolution of where you guys are at with, with Splunk, if you want to handle that enterprise security? So yeah, generally enterprise security has traditionally had really, really good use cases for like the external threats that we're talking about. But like you said, it's very difficult to crack the insider threat part. And so we leveraging machine learning toolkit has started to build that into Splunk to make sure that you know, you can protect your data. And, uh, you know, Tyler and I specifically did this because we saw that there was immaturity in the cybersecurity market for insider threat. And so one of the things that we're actually doing in this top, in addition to talking about what we've done, we're actually giving examples of actionable use cases that people can take home and do themselves. >>Like we're giving them an exact sample code of how to find some outliers. They give me an example of what, so the use case that we go over in the talk is a user logs in at a weird time of day outside of their baseline and they exfiltrate a large amount of data in a low and slow fashion. Um, but they're doing this obviously outside of the scope of their normal behavior. So we give some good searches that you can take home and look at how could I make a baseline, how could I establish that there's deviations from that baseline from a statistical standpoint, and identify this in the future and find the needle in the haystack using the machine learning toolkit. And then if I have a sock that I want to send notables to or some sort of some notification to how do we make that happen, how do we make the transition from machine learning toolkit over to enterprise security or however your SOC operates? >>How do you do that? Do you guys write your own code for that? Or you guys use Splunk? So Splunk has a lot of internal tools and there's a couple of things that need to be pointed out of how to make this happen because we're aggregating large amounts of data. We go through a lot of those finer points in the talk, but sending those through to make sure that they're high confidence is the, is the channel you guys are codifying the cross connect from the machine, learning to the other systems. All right, so I've got to ask, this is basically pattern recognition. You want to look at baselining, how do people, can people hide in that baseline data? So like I'll give you, if I'm saying I'm an evil genius, I say, Hey, I knew these guys looking for Romans anomalies in my baseline, so I'm going to go low and slow in my baseline. >>Can you look for that too? Yeah, there are. There absolutely are ways of, fortunately, uh, there's a lot of different people who are doing research in that space on the defensive side. And so there's a ton of use cases to look at and if you aggregate over a long enough period of time, it becomes incredibly hard to hide. And so the baselines that we recommend building generally look at your 90 day or 120 day out. Um, I guess viewpoint. So you really want to be able to measure that. And most insider threat that happen occur within that 30 to 90 day window. And so the research seems to indicate that those timelines will actually work. Now if you were in there and you read all the code and you did all of the work to see how all of the things come through and you really understood the machine learning minded, I'm sure there's absolutely a way to get in if you're that sophisticated. >>But most of the times they just trying to steal stuff and get out or compromise a system. Um, so is there other patterns that you guys have seen in terms of the that are kind of low hanging fruit priorities that people aren't paying attention to and what's the levels of importance to I guess get ahold of or have some sort of mechanism for managing insider threats? I passwords I've seen one but I mean like there's been a lot of recent papers that have come out in lateral movement and privilege escalation. I think it's an area where a lot of people haven't spent enough time doing research. We've looked into models around PowerShell, um, so that we can identify when a user's maliciously executing PowerShell scripts. I think there's stuff that's getting attention now that when it really needs to, but it is a little bit too late. >>Uh, the community is a bit behind the curve on it and see sharks becoming more of a pattern to seeing a lot more C sharp power shells kind of in hunted down kind of crippled or like identified. You can't operate that way, what we're seeing but, but is that an insider and do that. And do insiders come in with the knowledge of doing C sharp? Those are gonna come from the outside. So I mean, what's the sophistic I guess my question is what's the sophistication levels of an insider threat? Depends on the level a, so the cert inside of dread Institute has aggregated about 15,000 different events. And it could be something as simple as a user who goes in with the intent to do something bad. It could be a person who converted from the inside at any level of the enterprise for some reason. >>Or it could be someone who gets, you know, really upset after a bad review. That might be the one person who has access and he's being socially engineered as well as all kinds of different vectors coming in there. And so, you know, in addition to somebody malicious like that, that you know, there's the accidental, you're phishing campaigns here, somebody's important clicks on an email that they think is from somebody else important or something like that. And you know, we're looking fair for that as well. And that's definitely spear fishing's been very successful. That's a hard one to crack. It is. They have that malware and they're looking at, you can say HR data's out of this guy, just got a bad review, good tennis cinema, a resume or a job opening for, and that's got the hidden code built in. We've seen that move many times. >>Yeah, and natural language processing and more importantly, natural language understanding can be used to get a lot of those cases out. If you're ingesting the text of the email data, well you guys are at a very professional high end from Sai C I mean the history of storied history goes way back and a lot of government contracts do. They do a lot of heavy lifting from anywhere from development to running full big time OSS networks. So there's a lot of history there. What does sustain of the yard? What do you guys look at as state of the art right now in security? Given the fact that you have some visibility into some of the bigger contracts relative to endpoint protection or general cyber, what's the current state of the art? What's, what should people be thinking about or what are you guys excited about? What are some of the areas that is state of the art relative to cyber, cyber security around data usage. >>So, I mean, one of the things, and I saw that there were some talks about it, but not natural language processing and sentiment analysis has gotten, has come a long way. It is much easier to understand, you know, or to have machines understand what, what people are trying to say or what they're doing. And especially, for example, if somebody's like web searching history, you know, and you might think of somebody might do a search for how do I hide downloading a file or something like that. And, and that's something that, well, we know immediately as people, but you know, we have, our customer for example, has 1000000001.2 billion events a day. So you know, if the billion, a billion seconds, that's 30 years. Yeah. So like that's, it's, it's a big number. You know, we, we, we hear those numbers thrown around a lot, but it's a big number to put it in perspective. >>So we're getting that a day and so how do we pick out, it's hard to step of that problem. The eight staff, you can't put stamp on that. Most cutting edge papers that have come out recently have been trying to understand the logs. They're having them machine learning to understand the actual logs that are coming in to identify those anomalies. But that's a massive computation problem. It's a huge undertaking to kind of set that up. Uh, so I really have seen a lot of stuff actually at concierge, some of the innovations that they're doing to optimize that because finding the needle in the haystack is obviously difficult. That's the whole challenge. But there's a lot of work that's being done in Splunk to make that happen a lot faster. And there's some work that's being done at the edge. It's not a lot, but the cutting edge is actually logging and looking at every single log that comes in and understanding it and having a robot say, boom, check that one out. >>Yeah. And also the sentiment, it gets better with the data because we all crushed those billions of events. And you can get a, you know, smiley face or that'd be face depending upon what's happening. It could be, Oh this is bad. But this, this comes back down to the data points you mentioned logs is now beyond logs. I've got tracing other, other signals coming in across the networks. So that's not, that's a massive problem. You need automation, you've got to feed the beast by the machines and you got to do it within whatever computation capabilities you have. And I always say it's a moving train hard. The Target's moving all the time. You guys are standing on top of it. Um, what do you guys think of the event? What's the, what's the most important thing happening here@splunk.com this year? I'd love to have both of you guys take away in on that. >>There's a ton of innovation in the machine learning space. All of the pipelines really that I've, I've been working on in the last year are being augmented and improved by the staff. That's developing content in the machine learning and deep learning space that's belongs. So to me that's by far the most important thing. Your, your take on this, um, between the automation. I know in the last year or so, Splunk has just bought a lot of different companies that do a lot of things that now we can, instead of having to build it ourselves or having to go to three or four different people on top to build a complete solution for the federal government or for whoever your customer is, you can, you know, Splunk is becoming more of a one stop shop. And I think just upgrading all of these things to have all the capabilities working together so that, for example, Phantom, Phantom, you know, giving you that orchestration and automation after. >>For example, if we have an EMS notable events saying, Hey, possible insider threat, maybe they automate the first thing of checking, you know, pull immediately pulling those logs and emailing them or putting them in front of the SOC analyst immediately. So that in, in addition to, Hey, you need to check this person out, it's, you need to check this person out here is the first five pages of what you need to look at. Oh, talking about the impact of that because without that soar feature. Okay. The automation orchestration piece of it, security, orchestration and automation piece of it without where are you know, speed. What's the impact? What's the alternative? Yes. So when we're, right now, when we're giving information to our EES or analysts through yes, they look at it and then they have to click five, six, seven times to get up the tabs that they need to make it done. >>And if we can have those tabs pre populated or just have them, you know, either one click or just come up on their screen for once they open it up. I mean their time is important. Especially when we're talking about an insider threat whom might turn to, yeah, the alternative is five X increase in timespan by the SOC analyst and no one wants that. They want to be called vented with the data ready to go. Ready, alert on it. All right, so final few guys are awesome insights. Walking data upsets right here. Love the inside. Love the love the insights. So final question for the folks watching that are Splunk customers who are not as on the cutting edge, as you guys pioneering this field, what advice would you give them? Like if you had to, you know, shake your friend egg, you know, get off your button, do this, do that. What is the, what do people need to pay attention to that's super urgent that you would implore on them? What would you, what would your advice be once you start that one? >>One of the things that I would actually say is, you know, we can code really cool things. We can do really cool things, but one of the most important things that he and I do as part of our processes before we go to the machine and code, the really cool things. We sometimes just step back and talk for a half an hour talk for an hour of, Hey, what are you thinking about? Hey, what is a thing that you know or what are we reading? What and what are we? And you know, formulating a plan because instead of just jumping into it, if you formulate a plan, then you can come up with you know, better things and augmented and implemented versus a smash and grab on the other side of just, all right, here's the thing, let's let's dump it in there. So you're saying is just for you jump in the data pool and start swimming around, take a step back, collaborate with your peers or get some kind of a game thinking plan. >>We spent a lot of hours, white boarding, but I would to to add to that, it's augment that we spent a lot of time reading the scientific research that's being done by a lot of the teams that are out solving these types of problems. And sometimes they come back and say, Hey, we tried this solution and it didn't work. But you can learn from those failures just like you can learn from the successes. So I recommend getting out and reading. There's a ton of literature in that space around cyber. So always be moving. Always be learning. Always be collaborating. Yeah, it's moving training guys, thanks for the insights Epic session here. Thanks for coming on and sharing your knowledge on the cube, the cube. We're already one big data source here for you. All the knowledge here at.com our seventh year, their 10th year is the cubes coverage. I'm John furry with back after this short break.

>> Live from Orlando, Florida It's the que Covering Enterprise Connect twenty nineteen. Brought to you by five nine. >> Welcome to the Cube. Lisa Martin from Orlando. Lots going on on the keeps. That obviously is. You could just tell him with Student a man we're at Enterprise Connect twenty nineteen For Day two. You can hear all the buzz in the Expo Hall behind the hundred forty. Vendors exhibiting new products and services were joined by service. Now. Thank you, Subramanian had a product management and customer service funky. Welcome to the Cube. >> Thank you. >> So service. Now give us a little bit of info about your role and some of the announcements that have come out from this week. >> We'LL Absolutely, yeah, so So it's now. I think all of your family, I'd say one of the leading cloud software vendors are sore purposes. Digitizing work flows in the cloud, and we do that for various different parts of an enterprise like the workflow employees, experience and customers. My role in service now is I need the product management for one off our product of another business units, which is customer service management that is focused on providing companies with the tools and technologies required for them to provide a great customer experience for their end customers. So in that role, my responsible for defying the product vision, the roadmap on working with the engineering teams to release the product and capabilities that our customers love to use. >> So, Frankie, we we've heard in the keynote this morning we heard service. Now come up. Tell us a little bit about at the show Some of the partnerships you're working with on you know, it's pretty diverse spectrum of activities going on. So where service now has important place? >> Absolutely. So So it's not like you mentioned place in multiple different areas on DH. Helps enterprise deliver great employees in customer experience is so in that sense, this is a very properly it show for us to be at where we're connecting different parts of the organization to collaborate and to tell you a great experiences and deliver outcomes for employees and customers. Way have several of our partners, including five nine right here and you know, we partner on various different areas like collaboration is a key area. Focus for us and you heard us mention Microsoft keynote earlier today we partner with them on integrating their teams and other products with our product. Portfolio. Five nine actually serves a different part, different purpose for us, where they enable contact centers to operate optimally. And they connect that with our customer service management, which actually covered combines both the customer engagement aspects and the customer service on the customer workflow aspects that beeper white. >> Let's dig into that a little bit more, thank you, because the last day or so students have been talking a lot about the customer experience and table stakes for any business because, as consumers were, we're so empowered. Weaken churn easily. There's always another provider that's going to be able to deliver something, and if we're unhappy, we have that opportunity. So see, access table stakes. Talk to us about why companies should make customer service part of those table stakes. >> So absolutely, Yeah, so if you look at the evolution off, you know how customer. So this is evolved over several years, it started off as a key component of customer relationship management software and customer relationship started with managing the customer records, a customer data so that companies can make sense of who their customers are and how to >> sell them, served >> them optimally. The second stage of evolution added several engagement capabilities and the customer experience layer on top. So how do we make sense of all of this data and intelligence that we collected about customers to provide contextual personalize experience to those and customers by customer service is not just about engagement and experience, right? Ultimately, customers are looking for outcomes. They want their services to be delicate, uninterrupted. For them, things like that. And that is where way of looking at the third stage of evolution, if you will wear connecting that customer, engaged one of the customer experience layer with different parts of the organization that needs to work together on a single platform to be able to deliver effortless customer experiences and delivered to the results and the outcomes that your customers come to expect. >> Thank you. Wonder if you could drill down a little bit. Do you have a customer example you future of that? Or, you know, just some specifics, Understand? Is how we're cutting across silos, helping have the business actors the whole toe improve that customer experience? >> Absolutely, absolutely. I can mention a couple of names. I mean, be drink our own champagne. So we are our customer as well. So it's now uses our own software, our solutions to actually deliver customer service and customer experiences. One of the other customers, a reference customer for us, is nice. I believe they're probably at the show as well. And if you look at what they have done, they have been able to connect their cloud data center operations, the product organization, the product engineering and are in the organization on customer service on a single platform so that when customers report issues, they're able to reduce the effort for customers But great self service experience that contextualized personalized. They're able to identify issues and drive all the way to root, cause the resolution on then provide that information back to customers so that it's not just about answering questions faster. It's about reducing call volumes. It's about eliminating the root cause of the issue so that the next customer does not face that and then have to call you again. >> So in terms of that integration, it's critical right for all of the key constituents interacting with a customer tohave the data the right time to be able to make the right be empowered to make the right decision. But that integration is challenging example of maybe, uh, an old guard company that has to transform to stay relevant and to be competitive. How do they undergo that Those process And maybe it's more of a cultural change to facilitate that integration. So ultimately they can deliver that personalized customer experience. You're saying that one more we demand as consumers. >> Yeah. I mean, there are many examples, but I mean most off it actually starts with the realization that we need to transform right on with more and more services products getting enable through technology and technology forward services. That is not an option for companies anymore, so it really starts with the realization it starts with driving the change top down. A lot of it is really driving the change management throughout the organization. It involves identifying your customer journeys, mapping them out and identifying in a water they touch points. It also is a huge challenge for many customer service executives in a lot of those companies where they still are in that traditional mode of operation where they find it difficult to hold the other parts of the organization responsible. Right customer service is not an island. Customer service is not just a responsibility off a single department within the company. It is a thinking that needs to. It's a mindset that needs to actually get partly down to every part of the organization. So, really, for me, that is where it starts. And that is where I think organization started transplant. And then it's about, you know, deploying the right tools and technologies to really make it happen. >> So I think a couple of themes that we've been digging into out this show is how cloud and A I are transforming a lot of this space is I don't think we even need to talk about the cloud peace when it comes to service now, because because that is a given. But from an aye aye standpoint, where does Aye, aye and ml fit into the solutions that you're building. >> That's a great question. And you know, we cannot have a conversation about customer service, our enterprise collaboration without mentioning the eye there. So if you go back to what I said a little earlier about, you know the third phase of evolution where we are now able to connect the different parts of the company. Different parts of the process is on a single platform. A lot of that actually ends up providing a lot of insights. Right lot of data you need to convert those daytime too inside, and that is really where it comes in. And then you need to people the surface. Those insights at the right points of consumption to be able to eliminate reparative mundane tasks on to provide value added capabilities for agents and for customers because nobody wants to waste their time doing the same thing over and over again, right? If you talked about customer service agent, what they really feel excited about is the ability to serve the customers, not being able to write down tons of notes and capturing all the interaction details. So that's something that they have to do. So if we can help them with those aspects with with automation with intelligence, that is what makes them more productive. And ultimately that results in a direct impact on customer experience. Positive >> when you're out in the field talking with customers as I imagined as the head of product management. Ur where do you find service now? Coming in and kind of educating the customers on the opportunities and the enabler. Is that a I can deliver to them? Are they still sort of on the fence about this, or where are you from? Maybe a consul Tate of perspective, >> right? Right. No, I think we're past that face where people are kind of questioning relevance off area where I think they passed that stage. Everybody understands the value that it delivers in different points are different points of consumption for different people. I think we're at the stage where people are now trying to understand how fast they can move with this, how they can apply this, how they can adopt these technologies within. And this is where service now is trying to really be a an enabler in that process. Right? So we don't want a adoption on air initiative within a company to be a science project. We don't want it to be in somewhere somewhere in the back office with, you know, a number of you know, geek scientists and all that we really want to bring it to the forefront and the way we are doing that is by embedding AI capabilities directly into the experience on also by product izing a lot of those solutions so that our customers don't have to start from the very basics. So we're not asking our customers to go and define their own data sets and, you know, bring a number of data scientists to identify features and things like that. What we're saying is we have already done the heavy lifting for our customers way have identified key scenarios that we can enable that can be covered with the eye, your product izing that we're building that into our product directly on bring those innovations into the market. So if you just one more point Just earlier this month, March sixth actually be announced, our latest version ofthe our product that we released two in market. It's called the Madrid release on my release. If you go and look at it, it's packed with a lot of those innovations. For example, customer service were able to identify when a customer service agent is working on a case way. We're able to identify similar issues that other people might have already reported something that might be already resolved on. The agents completely used that information and resolve this particular case that they're working on, or being able to identify an issue that might be impacting made in multiple customers. >> Yeah, I wonder if you could give us a little bit insight as just a changing role of the agents and some of the stresses and strains on them. They're some concern is like Okay, wait, do your customers look at automation is something that will displace agents, make their lives better. And you know, how much do they worry about that agent age retention and how happy their agents are >> right? I think that's a huge priority for most customer service organizations. I would say it should be a priority for all customer service organizations. Reason is very simple, right? A lot of these simple, easy capabilities are offered through self service. As a customer, I'm sure you don't want to. Our first option will not be to pick up a phone and call and talk to an agent that be probably a few steps down. The line on that experience should definitely be enabled and should be easy. But when issues show about agents desk. They're much more complex than what it used to be. And the expectation is that, you know, I don't want to be handed over to somebody else. The last thing I want to hear is Oh, wait, let me hand, You know, an expert. So that's where these agents need to be up skills. They need to be empowered with tools and technology that I think the term that we hear Houston the industrious they need to be super agents, right? They're not the people who sit and answer calling and pass it on to an expert for the other people who can actually take a column is all the issue all at the same point at the first time engagement? >> And if I understand it, it's some of the solutions and products that you're helping to build that take that agent and give them their superpowers. >> Exactly. Exactly. Yeah, that's our goal. So we have interfaces that we actually design and build specifically for that persona, Andi augment several of those experiences with applications off area and technology on. We also never hit a lot of partnerships in that process. For example, the ability for an agent to seamlessly look at the call coming in to be able to identify who the customer is. What is the issue? They might be calling about previous interactions. I've seen all of that stuff in a single pane of glass and that are, you know, optimizing accidents. That's a priority for us. That is something that we take into our products. >> And how is it that that agents want to be trained these days? And one of the gentlemen in the customer panel this morning was talking about, I think, from Continental Eiji that they identified about twenty different ways that internal users, whether their agents don't want to be trained if it's send me an E mail should be a video sent you a YouTube link. What are you guys finding as you're looking at these different personas, any sort of no top five training mechanisms boiling up to the top that you are going to be consistently delivering? >> I mean training and ups. Killing is a huge priority, because if I just look at you now, how do we make an agent a super agent? They need to be provided with the right kind of trainings and upscaling opportunities. There are various different ways. I mean, I'm not probably an expert on the training methodologies itself. One thing that we can all realizes it has to be relevant. It has to be provided at the point of consumption. And it also should be something that is captured back today, that learning other than the knowledge that gets created in the process of researching and resolving an issue, it gets institutionalized, get actually put back into a system that is leveraged by everyone else in the organization. So those capabilities that I think should be important for everyone. >> Last question for you is we're here, it Enterprise connect. What are some of the exciting things that people can see and feel in touch with service now, at this event >> at this event. So first, I would say we have a boot way are showing our product demonstrations and you can talk to several SAR experts who are here at the end. Even I have a small speaking assignment later, later today. So I have a session that I will be talking at what you will actually see some of our latest innovations that were bringing to the market with the new release. So you will see how we can expand or extend the customer self service too. Not just there, but also the mobile. They're releasing that mobile capabilities for agents, which can also be explained about your customers. You will see a brand new agent interface that I just talked about. How we are packaging some of the intelligence machine learning capabilities into that. And you will also see a lot of our powerful workflow platform. You know how you can apply that for orchestrating? Optimizing process is >> a lot to learn. A lot of knowledge to be gleaned. Thank you. Thank you so much for joining me on the cute this afternoon. We appreciate your time. >> Thank you for talking to you >> or student a man. I am Lisa Martin. You're watching the Cube.

(techy music playing) >> Hey, welcome back, everybody. Jeff Frick here at the CUBE, we're in our Palo Alto Studios for a CUBE Conversation. You know, it's kind of when we get a break, we're not at a show. It's a little bit quieter, a little calmer situation so we can have a little bit different kinds of conversations and we're excited to have our next guest and talk about a really important piece of this whole cloud thing, which is not only do you need to turn things on, but you need to also turn them off and that's what gets people in trouble, I think, on the cost comparison. We're joined by Krishna Subramanian, she is the co-founder and COO of Komprise, welcome. >> Thank you, thanks for having me on the show. >> Absolutely, so just real briefly for people that aren't familiar, just give them kind of the overview of Komprise. >> Komprise is the only solution that provides analytics and data management in a single package and the reason we started the company is because customers told us that they're literally drowning in data these days. As data for print continues to grow, a lot of it is in unstructured data and data, you know, what's unique about it is that you never just keep one copy of data because if your data is lost, like if your child's first year birthday picture is lost you wouldn't like that, right? >> Jeff: Do not bring that kind of stuff up in an interview. (laughs) We don't want to talk about lost photographs or broken RAID boxes, that's another conversation, but yes, you do not want to lose those pictures. >> So, you keep multiple copies. >> Right, right. >> And that's what businesses do. They usually keep a DR copy, a few backup copies of their data, so if you have 100 terabytes of data you probably have three to four copies of it, that's 400 terabytes and if 70% of that data hasn't been touched in over six months 280 of your 400 terabytes is being actively managed for no reason. >> Jeff: Right, right. >> And Komprise analyzes and finds all that data for you and shows you how much you can save by managing it at lower cost, then it actually moves and archives and reduces the cost of managing that data so you can save 70% or more on your storage. >> Right, so there's a couple components to that that you talked about. So, break it down a little bit more. One is how actively is the data managed, how hot is the data, you know, what type of storage the data is based on, its importance, its relevance and how often you're accessing it. So, one of the big problems, if I heard you right, is you guys figure out what stuff is being managed that way, as active, high value, sitting on flash, paying lots of money, that doesn't need to be. >> That's exactly right, we find that all the cold data on your current storage... We show you how much more you're spending to manage that data than you need to. >> So, how do you do that in an environment where, you know, that data is obviously connected to applications, that data might be in my data center, it could be Amazon or could be at GCP, how do you do that without interfering with my active applications on that data, because even though some of it might be ready for cold storage there might be some of it, obviously, that isn't. So, how do you manage that without impacting my operations? >> That's a great question, because really, you know, data management is like a good housekeeper. You should never know that the housekeeper is there, they should never get in the way of what you're doing, but they keep your house clean, right? And that's kind of what Komprise does for your data, and how do we do that? Well, we do that by being adaptive. So, Komprise connects to your storage just through open protocols. So, we don't make any changes to your environment and our software automatically slows itself down and runs in the background to not interfere with anything active on your storage. So, we are like a good partner to your storage. You don't even know we're there, we're invisible to all the active work and yet we're giving all these important analytics and when we move the data, all the data looks like it's still there, so it's fully transparent. >> Okay, you touched on a couple things. So, one is how do you sit there without impacting it? I think you said you partner with all the big data, or excuse me, all the big storage providers. >> Krishna: Yes. >> You partner with all the three big cloud providers, just won an award at re:Invent, congratulations. >> Krishna: Thank you. >> So, how do you do that, where does your software sit, does it sit in the data center or does it sit at Amazon and how does it interact with other management tools that I might already have in place? >> That's a great question, so Komprise runs as a hybrid cloud service, and essentially there is a console that's running in the cloud, but the actual analysis and data movement is done by virtual machines that are running at the customer's site and you literally just point our virtual machine at any storage you have and we work through standard protocols, through NFS, SMB CIFS, and REST S3, so whether you have NetApp storage or EMC storage or Windows File Servers or Hitachi NAS or you're putting data on Amazon or Azure or Google or an object storage, it doesn't actually matter. Komprise works with all those environments because we are working through open standards, and because we're adaptive we're automatically running in the background, so it's working through open standards and it's non-intrusive. >> Okay, and then if you designate that some percentage of this storage does not need to be in the high, expensive environment, you actually go to the next step and you actually help manage it and move it, so how does that impact my other kind of data management procedures? >> Yes, so it's a great question. So, most of the time you would probably have some DR copy and some backups running on your hot storage, on your flash storage, say, and you don't want to change that and you don't want users to point anywhere else, so what Komprise does is it takes the cold data from all that storage and when it moves that data it's fully transparent. The moved data looks like it's still there on that storage, it's just that the footprint is reduced now, so for 100MB file you just have a one kilobyte link on that storage, and we don't use any stub files, we don't put any agents on the storage, so we don't make any changes to your active environment. It's fully transparent, users and applications think all the data is still there, but the data is now sitting in something lower cost and it's dynamically managed through open standards, just like you and I are talking now and I don't need a translator between us because we both understand English. >> Jeff: Right. >> But maybe if I were speaking Japanese you might need a translator, right? >> Jeff: I would, yeah. (laughs) Yes. >> Krishna: That was just a guess, I didn't know. So, that's kind of how we do it, we work through the open standards and in the past solutions were... We didn't do that, they would have a proprietary protocol and that's why they could only work with some storage and not all, and they would get in the way of all the access. >> But do I want it to look like it looked before if in fact it's ready to be retired into cold storage or Glacier or whatever, because I would imagine there's a reason and I don't know that I necessarily want the app to have access. I would imagine my access and availability of stuff that's in cold storage is very different kind of profile than the hot stuff. >> It depends, you know, sometimes some data you may want to truly archive and never be able to see it live. Like, maybe you're putting it in Glacier, and you can control how the data looks, but sometimes you don't want to interrupt what the applications are doing. You want to just go to a lower cost of storage, like an object storage on-premise. >> Right. >> But you still want the data accessible because you don't want a vague user and application behavior. >> Jeff: Right, right. >> Yeah. >> Okay, so give us a little bit more information on the company. So, you've been around for three years. We talked a little bit before we turned the cameras on, you know, kind of how many people do you have, how many customers, how many rounds of funding have you guys raised? >> Komprise is growing rapidly. We have about 60 people, we have a headquarters in Campbell, California, we also have offices in Bangalore, India. We just hired a new VP of worldwide sales and we're putting field sales teams in different regions, we have over 60 customers worldwide. Our customer base is growing rapidly. Just this last quarter we added about four times the number of customers, and we're seeing customers all the way from general mix and healthcare to big insurance and financial services companies, anywhere where there's data, you know. Universities, all the major research universities are our customers and government institutions, you know, state and local governments, et cetera. So, these are all good markets for us. >> Right, and you said it's a services, like a SAS model, so you charge based on how much data that's under management. >> Yeah, we charge for all the data that's under management and it's a fraction of what you pay to store the data, so our cost is like less than half a penny a gig a month. >> Right, it's pretty interesting, you know, we just got back from AWS re:Invent as well, over 40,000 people, it's bananas. But this whole kind of rent versus buy conversation is really interesting to me, and again, I always go back to Netflix. If anybody uses a massive amount of storage and a massive amount of network and computing where they own like, I don't know, 50% of the Friday night internet traffic, right, in the States is Netflix and they're still on Amazon. I think what's really interesting is that if you... The flexibility of the cloud to be able to turn things on really easily is important, but I think what people often forget is it's also you need to turn it off and so much activity around better managing your investment and the resources at Amazon to use what you need when you need it, but don't pay for what you don't need when you don't, and that seems to be, you know, something that you guys are right in line with and consistent with. >> Yeah, I think that's actually a good way to put it. Yeah, don't pay for data when you don't need to, right? You can still have it but you don't need to pay for it. >> Right, well Krishna, thanks for taking a few minutes out of your day to stop by and give us the story on Komprise. >> Yeah, thank you very much, thanks for having me. >> All right, pleasure, she's Krishna, I'm Jeff, you're watching the CUBE. We're at Palo Alto Studios, CUBE Conversation, we'll see you next time, thanks for watching. (techy music playing)

>> Announcer: Live from San Francisco, it's theCube. Covering DevNet Create 2017, brought to you by Cisco. >> Hey welcome back everyone. Live here in San Francisco, exclusive coverage with theCube at Cisco's inaugural DevNet Create event. I'm John Furrier with my co-host Peter Burris. We're breaking down the new foray into the open source world with a big presence. Cisco expanding their DevNet core developer classic program and creating an open source model with collaboration, 90% of that activity is non-Cisco, really a good formula. And to help us this down is Krish Subramanian, Principal Analyst at Rishidot Research, formerly of Red Hat, formerly of a start up that was recently sold. Can't talk about it because it's not released yet. Friend of theCube, Cube alumni, part of the Clouderati, going way back. Krish, we've seen a lot of the waves of how cloud has evolved from the early days. I remember when EngineYard was a startup, Haruku was a couple guys, we were having our meetups. >> And the AWS was still like people who weren't able to make money. >> They were poo-pooing the hell out of it. It was EC2 and S3 with a couple different, I mean RightScale did everything back then, so think about the changes. And now Cisco here with the formula, they have the right formula, I got to give them props for that, doing it right. They're not trying to come in and do a land grab and sort of, "Ahh, we're Cisco", throwing their elbows around. Really doing it right, your thoughts? >> Yeah, definitely, come back to what some other legacy companies tried to do. Cisco didn't try to jump in and say, "Hey, we are going to run public cloud, compete with Amazon", and sort of take them down. They sort of waited for right moment, they initially started with the InterCloud, which will go much further, but when IoT came into picture, they were there right for that and they were there taking advantage of that. And with the increasing focus on developers, they are going right to capture the minds of developers. Especially for IoT, that is critical for Cisco to go-- >> Well, I'm really glad you're on with Peter. We have two analysts here who know the industry up and down, from every dimension. Of course, I'll add my color, but I want to ask both of you guys a couple questions. One, do you think Cisco's making the right moves by coming out and really focusing on their core competency, which is the network? They also bought AppDynamics, so that is a big purchase. So, you got apps meets infrastructure, programmable infrastructure, which means infrastructure as code. You really can't have infrastructure as code unless Cisco gets behind it, they're the leader. So, with IoT looming, this seems like a good move for Cisco. What do you think? >> Yeah, definitely, they are going in the right direction, so it's really like IoT's still in the early stages and we have to wait and see how it is going to evolve, but Cisco is very persistent. Especially I like the AppDynamics acquisition because they are clearly telling the world that we understand that applications are the future and developers need the right tools if they are to develop their apps on Cisco infrastructure. And with the emphasis on programmability, Cisco is taking right steps towards capturing developer attention and I hope with successful events like this, they will be able to get there. >> Peter, I want to go to you for a second because we just found out, in talking to Suzy, I did not know this, but in your previous life, when you ran research at META Group, folks may not know what that was, it was a big research firm at the time, you did some really similar work around the infrastructure developer. >> Yeah >> Okay, and our comment was, "What is old is now new". I got a degree in operating systems and computer science and that seems to be the model. What is this notion of an infrastructure developer? It was mentioned in the keynote today. Does that exist in this new scenario? Do you see it being viable? It seems like the messaging is tight. What is your reaction to this notion? You've done a lot of work on that. >> Well, as a way of answering the question John, and I'll play off of something you just said, when we talk about the degree with which this is relevant to Cisco, here's what I say. Everybody's always looking for what is it that's different from today, relative to yesterday? And there's a lot of things that are different. One of the most important ones is that yesterday's computing industry emphasized a priority set of models about how you do things. So, if you thought about the network, the network had a modeled structure. You sat there and you designed a network to be as relevant to as many things as possible. Same with the database. You sat there and you designed the database to be as relevant to whatever notion of applications. When we start talking about the new world, now what we're discovering is the data is going to force a reconfiguration. That's what big data is. In many respects, it's non-structured, non-modeled data, but we still want to do analytics. Same thing with the network. We want the network to evolve and emerge, have emerging characteristics that allow us to do things that we never really anticipated when we first put this stuff down. And so, the thing that an infrastructure developer, at least as we conceived it, and we were way ahead and probably wrong for that reason, but the way we conceive it is someone has to take some degree of responsibility for starting to characterize, fill that gap, characterize the services in the infrastructure that need to be made available to application developers in a way that makes coherent and consistent sense so that an application written to an infrastructure, in fact, may become a service to another application at some point in time in the future, because they make consistent assumptions about where they operate within that margin between the application and the infrastructure. >> John: Does that environment exist today, in your opinion? >> It does in certain places. It does in certain places. I think the whole notion of containers is making, in Kubernetes for example, is making some very powerful presumptions about how applications are going to interact with each other in the future. Now, we had SOA, but we also talked about Conway's law, it just never happened because the structure of the organizations that were using SOA just guaranteed you end up with monolithic, crap applications anyway. >> Explain Conway's law real quick for people who didn't-- >> Yeah, Conway's law is, it's been mentioned in theCube a couple times, basically, it's a suggestion that the structure of the application is a reflection of the structure of the organization that created it. And so, if you have a silo-based application development organization that's looking at the application for the finance group, or the marketing group, you are going to get a structured, siloed-oriented application, no matter what underlying technology you use. And that's been that way forever. >> And so, Krish, I want to get your thoughts because let's take that to the next level. So, one of the benefits of cloud was horizontally scalable model. That really kind of, to me, was the big ah-ha moment around software. And with DevOps, which is now called cloud native, which is the same thing, infrastructures code was, hey, I'm not not an infrastructure person. I just want it to be available for me and help me configure it out and programmable, as Suzy was saying. Okay, so if you take what Peter's saying about data, you've lived through the infrastructure as a service, platform as a service, SAS wars or evolution, however you want to look at it. And, now you see that kind of coalescing into SAS and infrastructure and PAS kind of folding away and kind of becoming less of a contentious conversation. But, now that same thing's happening with data, we believe. I mean I think, maybe he may disagree, but now data's now the new data layer. What's your thoughts on that? Because now, if you inject data into what was the old cloud stack, new things are really possible. >> Yeah, the thing is, data brings in a new dimensionality to what we are seeing right now. Everything from infrastructure to application, everything requires a mindset change in terms of seeing them as services. So, even if it is a physical hardware you are dealing with, you have to make it more service-like by putting an API in front of it. So, it's changing the way how we consume these services. But, data is the one that is bringing business value to customers. When you make data easily, sort of like, inter operate with the services, let's say call it, for lack of a better term, a services ocean kind of IT model you have in your enterprise. So, when you offer to bring data into it, it offers you a lot of opportunities which didn't exist in the past. It opens up new avenues in which you could manipulate data, make sense out of it and probably get more value than what you were getting in the past. >> What's interesting, if you bring micro services, if you think about Docker and Kubernetes, as you were saying, and you bring data now into the equation and the notion of microservices, you can apply all that microservices knowledge to data. That's what you were saying, from what I hear. Or concepts of-- >> Sort of like you will bring data close to take, earlier as Peter pointed out, data was in silos, representative of the organizational structure. So, by taking a more services approach and spreading the services across these siloed, PAS, siloed organization, you are bringing the entire organization into one single umbrella, sharing the data and thereby benefiting much more than what they were getting in the past. >> So John, in the opening, one of the things we talked about, and I'll repeat it here because he's probably going to see it and I'd love to hear your comments on it, is that we went to hardware-defined networking. And then we went to software-defined networking. And, Wikibon's working on a proposition and I'm sure we'll find reasons why it's not going to play out, but again, I'd like to hear your position, is what I'll call data-defined infrastructure. So, we were on theCube last week at Informatica and we heard a lot about the role that metadata's going to play in discovery of data resources and whatnot. I can imagine adding metadata when we start talking about dependencies and time and location and things that are relevant to how a network or how an infrastructure might configure itself to serve the data, becoming a feature of the programmability of the underlying infrastructure so that we end up, in five years, we do talk about data-defined infrastructure. Just as today, we're talking about software-defined infrastructure, where the infrastructure, literally, responds to the needs of the data because that, ultimately, is the most flexible way of think about this. What do you think? >> Yeah, I fully agree with you. In fact, data brings in a new dimensionality to the equation where applications, it's a morph based on what is there in the data. So, on-the-fly, the infrastructure needs to be modified. So, data sort of brings in a new way of doing infrastructure from what we have done in the past. I fully agree with the role of data in that and how, through the application, that influences how we deal with infrastructure. It does change completely. >> All right, so I got to ask you guys a question. Journeys, is journey to DevOps, journey to digital transformation, certainly has a lot of cloud, has a lot of open source involved with it. We're seeing the Ford CEO get fired, he hasn't been on the job for four years, right? So, you guys both work with end users and advise them, so what's your advise to CXOs where, hey the clock now is, I thought four years was short. It really should be seven to 10 on the transformation scale, but people are getting axed in their third year, so they got to show results. How does an executive make all this stuff happen in such a short time? Or should they just reset expectations? >> When the executive comes in, he, or she, not only should look at their core business, they should also think that they are a technology business and change the mindset completely. That mindset change needs a push from the top that's going to accelerate the change down the lane and I think the executive should think that they are becoming a CEO, or CXO, of a technology company, rather than a manufacturing company or a automobile company kind of thing. >> I think that's true, but look, we haven't studied what happened at Ford in detail because I'm sure there's some subtleties in there that we just don't fully understand, but on the surface, it sounds like he might have gotten a little bit of a raw deal, just from the pure standpoint of-- >> Well the stock was down 39%, so my guess is total Wall Street hatchet job, but -- >> Peter: Exactly. >> We don't know a lot of the politics, but Val Bercovici, who was on earlier, who has a lot of experience in organizations that net app since 97, or late 90s, brought an interesting point, you were saying earlier. Tesla creates a car that's a service. And so, to me, I hate to use the cliche, "Everything as a service", but essentially, that's what software's going to. So, where you make up a day, that's why I'm kind of poking at the data thing because I think you're on to som-- >> But it's the end of the day, Tesla still has to have a shop that bends metal, there's still some car manufacturing things that have to happen. And, in many respects, whether the old CEO is saying, well the value proposition is, someday this autonomous vehicle is going to happen, but right now, we still got to build cars that can compete in the world market. There's a lot of subtleties here. There are-- >> Yeah, but Tesla does upgrade with software over the network. >> For an 80 to $100,000 price point and there's about four billion people that are going to buy cars in the next five years that may, or may not, be able to buy a 80,000 to $100,000 car. So anyway, coming back to your core point, I think what it really means is that if you're in a situation where you don't have visibility in a how, some of these new, digital approaches are going to create value for your business, you're doomed. So, I think the first thing you got to do is you got to be very explicit. This is how digital technology's going to create value for my business, that's number one. And, be able to articulate that to, virtually, anybody that's capable of understanding it, including Wall Street. But, to do that, you have to step back and say, and what is it about that digital technology that's going to create value for my business. And the thing that's going to do it, or not, is the data. >> And the asset configuration around, the work around the assets. >> Especially the asset configuration, as it's defined by the data. And, increasingly, there's an economics terms, what we're going to see happen over the next 10 years is the asset specificities are going to go down dramatically. In other words, the ability to which, or the degree to which an asset can only be configured to a specific purpose. Software's going to change that dynamic dramatically. And that, in many respects, is one of the fundamental, underlying things that's going on here. But, at the end of the day, you have to say, what role is data going to play in my business? How am I going to articulate that role by saying that I'm going to incorporate digital in this way? And then, put in place a plan that demonstrates that you're competent about some of these things. And, if your shareholders don't like it, they're not going to like it from anybody, not just you. >> Krish, I want to get your thoughts on the Cloud Native Compute Foundation. Why it's so successful. Why, in your opinion, do you think, there just booming with vendors, a lot of cash infusion, a lot of activity, projects went from one, three, 10. We had Dan on earlier, a lot of growth in the cloud native. And then, also, Kubernetes as a, kind of as an emerging, really interesting dynamic, vis-a-vis multicloud. So why cloud native is so popular and the impact of Kubernetes. >> Cloud native is popular because of late, developers are understanding that the role we are building applications is not going to work in cloud. When containers came into picture, that really made it easy for developers to develop cloud native apps. It got them to take advantage of the more distributed nature of the underlying infrastructure. So, the containers are the main reason why cloud native has become the household term, even in the enterprises. That could be one of the reason why Cloud Native Foundation is popular. Because they came at the right time to host all these development projects and evangelize with the developers and take steps in that. As far as Kubernetes is concerned, it worked at Google's CE. If it can work at Google's CE and then solve Google's problem, it should be able to help-- >> If it's good for Google, it's good for me. That's their strategy. >> And also, people are slowly realizing that as more and more enterprises go to cloud, they are realizing that going with a single cloud provider may not solve all their problems because different cloud providers have different set of services. So, they want to take advantage of all that. But, they want a single pane of glass to manage everything. Kubernetes is this general to be that at the cloud-- >> Krish, thanks for coming on. Peter, thanks for the comments, I'll just wrap up the analyst segment by saying, in my opinion, I think Cisco's making a good move here because, to your point about Google and Kubernetes is, and that's one of many examples of great software being contributed to open source. And open source, for all the times I've been involved with it since I was in college, is this more great software coming to the table now than ever before and that's creating great innovation. So, combined with the cloud and cloud native and Kubernetes, a perfect storm of innovation is coming. And it's coming, not from vendors, it's coming from open source. And, so the smart vendors are putting their toe in the water and really figuring it out. And again, the-- >> Peter: It is coming from vendor support though. >> Well the vendors are smart by putting their people in open source as a proxy for contribution. That's the open source model. That, to me, is the new R&D. It's a new innovation strategy, coupled with some proprietary R&D. Not saying they should be going all open source. >> I agree with it completely. In fact, I would even go one step further and say open source is completely disrupting the traditional enterprise software in modern business. Think about someone like Capital One putting critical software as open source and disrupting all the vendors in the space, so it's-- >> Well, let's continue the conversation in studio or tomorrow. Again, open source is horizontally scaling as well. Great stuff, great projects. More exclusive coverage from the inaugural event for Cisco's DevNet Create after this short break. (up-tempo music) >> Hi, I'm April Mitchell and I'm the senior director of strategy--