>> Announcer: Live from San Jose, California, it's theCUBE, covering Big Data Silicon Valley 2017. (light techno music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Big Data SV, wrapping up with two days of wall-to-wall coverage of Big Data SV which is associated with Strata Comp, which is part of Big Data Week, which always becomes the epicenter of the big data world for a week here in San Jose. We're at the historic Pagoda Lounge, and we're excited to have our next two guests, talking a little bit different twist on big data that maybe you hadn't thought of. We've got Ravi Dharnikota, he is the Chief Enterprise Architect at SnapLogic, welcome. - Hello. >> Jeff: And he has brought along a customer, Katharine Matsumoto, she is a Data Scientist at eero, welcome. >> Thank you, thanks for having us. >> Jeff: Absolutely, so we had SnapLogic on a little earlier with Garavs, but tell us a little bit about eero. I've never heard of eero before, for folks that aren't familiar with the company. >> Yeah, so eero is a start-up based in San Francisco. We are sort of driven to increase home connectivity, both the performance and the ease of use, as wifi becomes totally a part of everyday life. We do that. We've created the world's first mesh wifi system. >> Okay. >> So that means you have, for an average home, three different individual units, and you plug one in to replace your router, and then the other three get plugged in throughout the home just to power, and they're able to spread coverage, reliability, speed, throughout your homes. No more buffering, dead zones, in that way back bedroom. >> Jeff: And it's a consumer product-- >> Yes. >> So you got all the fun and challenges of manufacturing, you've got the fun challenges of distribution, consumer marketing, so a lot of challenges for a start-up. But you guys are doing great. Why SnapLogic? >> Yeah, so in addition to the challenges with the hardware, we also are a really strong software. So, everything is either set up via the app. We are not just the backbone to your home's connectivity, but also part of it, so we're sending a lot of information back from our devices to be able to learn and improve the wifi that we're delivering based on the data we get back. So that's a lot of data, a lot of different teams working on different pieces. So when we were looking at launch, how do we integrate all of that information together to make it accessible to business users across different teams, and also how do we handle the scale. I made a checklist (laughs), and SnapLogic was really the only one that seemed to be able to deliver on both of those promises with a look to the future of like, I don't know what my next Sass product is, I don't know what our next API point we're going to need to hit is, sort of the flexibility of that as well as the fact that we have analysts were able to pick it up, engineers were able to pick it up, and I could still manage all the software written by, or the pipelines written by each of those different groups without having to read whatever version of code they're writing. >> Right, so Ravi, we heard you guys are like doubling your customer base every year, and lots of big names, Adobe we talked about earlier today. But I don't know that most people would think of SnapLogic really, as a solution to a start-up mesh network company. >> Yeah, absolutely, so that's a great point though, let me just start off with saying that in this new world, we don't discriminate-- (guest and host laugh) we integrate and we don't discriminate. In this new world that I speak about is social media, you know-- >> Jeff: Do you bus? (all laugh) >> So I will get to that. (all laugh) So, social, mobile, analytics, and cloud. And in this world, people have this thing which we fondly call integrators' dilemma. You want to integrate apps, you go to a different tool set. You integrate data, you start thinking about different tool sets. So we want to dispel that and really provide a unified platform for both apps and data. So remember, when we are seeing all the apps move into the cloud and being provided as services, but the data systems are also moving to the cloud. You got your data warehouses, databases, your BI systems, analytical tools, all are being provided to you as services. So, in this world data is data. If it's apps, it's probably schema mapping. If it's data systems, it's transformations moving from one end to the other. So, we're here to solve both those challenges in this new world with a unified platform. And it also helps that our lineage and the brain trust that brings us here, we did this a couple of decades ago and we're here to reinvent that space. >> Well, we expect you to bring Clayton Christensen on next time you come to visit, because he needs a new book, and I think that's a good one. (all laugh) But I think it was a really interesting part of the story though too, is you have such a dynamic product. Right, if you looked at your boxes, I've got the website pulled up, you wouldn't necessarily think of the dynamic nature that you're constantly tweaking and taking the data from the boxes to change the service that you're delivering. It's not just this thing that you made to a spec that you shipped out the door. >> Yeah, and that's really where the auto connected, we did 20 from our updates last year. We had problems with customers would have the same box for three years, and the technology change, the chips change, but their wifi service is the same, and we're constantly innovating and being able to push those out, but if you're going to do that many updates, you need a lot of feedback on the updates because things break when you update sometimes, and we've been able to build systems that catch that that are able to identify changes that say, not one person could be able to do by looking at their own things or just with support. We have leading indicators across all sorts of different stability and performance and different devices, so if Xbox changes their protocols, we can identify that really quickly. And that's sort of the goal of having all the data in one place across customer support and manufacturing. We can easily pinpoint where in the many different complicated factors you can find the problem. >> Have issues. - Yeah. >> So, I've actually got questions for both of you. Ravi, starting with you, it sounds like you're trying to tackle a challenge that in today's tools would have included Kafka at the data integration level, and there it's very much a hub and spoke approach. And I guess it's also, you would think of the application level integration more like the TIBCO and other EAI vendors in a previous generation-- - [Ravi] Yeah. >> Which I don't think was hub and spoke, it was more point to point, and I'm curious how you resolve that, in other words, how you'd tackle both together in a unified architecture? >> Yeah, that's an excellent question. In fact, one of the integrators' dilemma that I spoke about you've got the problem set where you've got the high-latency, high-volume, where you go to ETL tools. And then the low-latency, low-volume, you immediately go to the TIBCOs of the world and that's ESB, EAI sort of tool sets that you look to solve. So what we've done is we've thought about it hard. At one level we've just said, why can integration not be offered as a service? So that's step number one where the design experience is through the cloud, and then execution can just happen anywhere, behind your firewall or in the cloud, or in a big data system, so it caters to all of that. But then also, the data set itself is changing. You're seeing a lot of the document data model that are being offered by the Sass services. So the old ETL companies that were built before all of this social, mobile sort of stuff came around, it was all row and column oriented. So how do you deal with the more document oriented JSON sort of stuff? And we built that for, the platform to be able to handle that kind of data. Streaming is an interesting and important question. Pretty much everyone I spoke to last year were, streaming was a big-- let's do streaming, I want everything in real-time. But batch also has it's place. So you've got to have a system that does batch as well as real-time, or as near real-time as needed. So we solve for all of those problems. >> Okay, so Katharine, coming to you, each customer has a different, well, every consumer has a different, essentially, a stall base. To bring all the telemetry back to make sense out of what's working and what's not working, or how their environment is changing. How do you make sense out of all that, considering that it's not B to B, it's B to C so, I don't know how many customers you have, but it must be in the tens or hundreds. >> I'm sure I'm not allowed to say (laughs). >> No. But it's the distinctness of each customer that I gather makes the support challenge for you. >> Yeah, and part of that's exposing as much information to the different sources, and starting to automate the ways in which we do it. There's certainly a lot, we are very early on as a company. We've hit our year mark for public availability the end of last month so-- >> Jeff: Congratulations. >> Thank you, it's been a long year. But with that we learn more, constantly, and different people come to different views as different new questions come up. The special-snowflake aspect of each customer, there's a balance between how much actually is special and how much you can find patterns. And that's really where you get into much more interesting things on the statistics and machine learning side is how do you identify those patterns that you may not even know you're looking for. We are still beginning to understand our customers from a qualitative standpoint. It actually came up this week where I was doing an analysis and I was like, this population looks kind of weird, and with two clicks was able to send out a list over to our CX team. They had access to all the same systems because all of our data is connected and they could pull up the tickets based on, because through SnapLogic, we're joining all the data together. We use Looker as our BI tool, they were just able to start going into all the tickets and doing a deep dive, and that's being presented later this week as to like, hey, what is this population doing? >> So, for you to do this, that must mean you have at least some data that's common to every customer. For you to be able to use something like Looker, I imagine. If every customer was a distinct snowflake, it would be very hard to find patterns across them. >> Well I mean, look at how many people have iPhones, have MacBooks, you know, we are looking at a lot of aggregate-level data in terms of how things are behaving, and always the challenge of any data science project is creating those feature extractions, and so that's where the process we're going through as the analytics team is to start extracting those things and adding them to our central data source. That's one of the areas also where having very integrated analytics and ETL has been helpful as we're just feeding that information back in to everyone. So once we figure out, oh hey, this is how you differentiate small businesses from homes, because we do see a couple of small businesses using our product, that goes back into the data and now everyone's consuming it. Each of those common features, it's a slow process to create them, but it's also increases the value every time you add one to the central group. >> One last question-- >> It's an interesting way to think of the wifi service and the connected devices an integration challenge, as opposed to just an appliance that kind of works like an old POTS line, which it isn't, clearly at all. (all laugh) With 20 firmware updates a year (laughs). >> Yeah, there's another interesting point, that we were just having the discussion offline, it's that it's a start-up. They obviously don't have the resources or the app, but have a large IT department to set up these systems. So, as Katharine mentioned, one person team initially when they started, and to be able to integrate, who knows which system is going to be next. Maybe they experiment with one cloud service, it perhaps scales to their liking or not, and then they quickly change and go to another one. You cannot change the integration underneath that. You got to be able to adjust to that. So that flexibility, and the other thing is, what they've done with having their business become self-sufficient is another very fascinating thing. It's like, give them the power. Why should IT or that small team become the bottom line? Don't come to me, I'll just empower you with the right tool set and the patterns and then from there, you change and put in your business logic and be productive immediately. >> Let me drill down on that, 'cause my understanding, at least in the old world was that DTL was kind of brittle, and if you're constantly ... Part of actually, the genesis of Hadoop, certainly at Yahoo was, we're going to bring all the data we might ever possibly need into the repository so we don't have to keep re-writing the pipeline. And it sounds like you have the capability to evolve the pipeline rather quickly as you want to bring more data into this sort of central resource. Am I getting that about right? >> Yeah, it's a little bit of both. We do have a central, I think, down data's the fancy term for that, so we're bringing everything into S3, jumping it into those raw JSONs, you know, whatever nested format it comes into, so whatever makes it so that extraction is easy. Then there's also, as part of ETL, there's that last mile which is a lot of business logic, and that's where you run into teams starting to diverge very quickly if you don't have a way for them to give feedback into the process. We've really focused on empowering business users to be self-service, in terms of answering their own questions, and that's freed up our in list to add more value back into the greater group as well as answer harder questions, that both beget more questions, but also feeds back insights into that data source because they have access to their piece of that last business logic. By changing the way that one JSON field maps or combining two, they've suddenly created an entirely new variable that's accessible to everyone. So it's sort of last-leg business logic versus the full transport layer. We have a whole platform that's designed to transport everything and be much more robust to changes. >> Alright, so let me make sure I understand this, it sounds like the less-trained or more self-sufficient, they go after the central repository and then the more highly-trained and scarcer resource, they are responsible for owning one or more of the feeds and that they enrich that or make that more flexible and general-purpose so that those who are more self-sufficient can get at it in the center. >> Yeah, and also you're able to make use of the business. So we have sort of a hybrid model with our analysts that are really closely embedded into the teams, and so they have all that context that you need that if you're relying on, say, a central IT team, that you have to go back and forth of like, why are you doing this, what does this mean? They're able to do all that in logic. And then the goal of our platform team is really to focus on building technologies that complement what we have with SnapLogic or others that are accustomed to our data systems that enable that same sort of level of self-service for creating specific definitions, or are able to do it intelligently based on agreed upon patterns of extraction. >> George: Okay. >> Heavy science. Alright, well unfortunately we are out of time. I really appreciate the story, I love the site, I'll have to check out the boxes, because I know I have a bunch of dead spots in my house. (all laugh) But Ravi, I want to give you the last word, really about how is it working with a small start-up doing some cool, innovative stuff, but it's not your Adobes, it's not a lot of the huge enterprise clients that you have. What have you taken, why does that add value to SnapLogic to work with kind of a cool, fun, small start-up? >> Yeah, so the enterprise is always a retrofit job. You have to sort of go back to the SAPs and the Oracle databases and make sure that we are able to connect the legacy with a new cloud application. Whereas with a start-up, it's all new stuff. But their volumes are constantly changing, they probably have spikes, they have burst volumes, they're thinking about this differently, enabling everyone else, quickly changing and adopting newer technologies. So we have to be able to adjust to that agility along with them. So we're very excited as sort of partnering with them and going along with them on this journey. And as they start looking at other things, the machine learning and the AI and the IRT space, we're very excited to have that partnership and learn from them and evolve our platform as well. >> Clearly. You're smiling ear-to-ear, Katharine's excited, you're solving problems. So thanks again for taking a few minutes and good luck with your talk tomorrow. Alright, I'm Jeff Frick, he's George Gilbert, you're watching theCUBE from Big Data SV. We'll be back after this short break. Thanks for watching. (light techno music)

>> Hi, I'm Peter Burris and welcome to another CUBE conversation. Today we're going to talk about a really interesting topic. At least it's interesting to me. And that is, if we go back, and the old adage that when you automate bad process or bad business, you just get more bad business at scale. And, when we think about customer service over the last number of years or customer engagement over the last number of years, in many respects we've done a great job of automating really bad practices. And all that has led to is an increased frustration amongst consumers who are trying to utilize an engagement form if they want, more digital engagement, but end up being even more frustrated because it still takes the same amount of time and it still has the same failure rates. And to discuss that today, we've got Mahesh Ram, who's the founding partner of Solvvy, to talk a bit about some of these transformations that are taking place in terms of how digital engagement's going to change the way that businesses interact with consumers. Mahesh, welcome to theCUBE. >> Oh, it's great to be here. I'm a fan of theCube and honored to be here. So, Mahesh, let's start. Tell us a little bit about yourself and tell us a little bit about Solvvy. Sure, my background is in technology and I've built two successful start-ups in the past. The last one was a company that was acquired by Pearson in 2012, focused on automation for non-native English speakers. But my entire career has been spent really thinking about ways in which we can use technology to make people's lives better and improve existing workflows and processes. And so, it's why Solvvy attracted me, why it's so exciting, and I think that this is the most interesting thing I've ever done in my career, so I'm excited about that. >> Now Solvvy has a pretty decent reputation as being a thought we are in this domain of not just cutting the cost of engagement, but actually improving the quality of engagement. How does it do that? >> I think it's a great question. It starts with the mission of the company, I think. That's the easiest way to say it. Our mission is to enable every interaction between consumers and business to be effortless. Anywhere, any time, and any channel. So if you start with that mission, you really start to focus in on what's most important. What's most important is to deliver that amazing experience for that end user or that consumer, and at the same time, drive down the operational cost for the business, i.e. improve their efficiency. And so our vision for the company, is to take our intelligent AI and machine learning automation technology which is world-class and is better than anything else on the market and apply it to deliver on a vision which is we want these interactions between the consumer and the business to be successfully completed in five minutes or less. >> Five minutes? >> Yes, and today it's measure in hours, eight hours, 12 hours, 24 hours. That's the vision and we're well on the way to accomplishing that. >> Alright, so as a thought leader, give us an example of how business is doing that, and then we'll get into some of the technology questions. But, first off, what is the competitive advantage of being able to complete a client engagement under five minutes versus eight hours? >> Well, first of all, I think, again, if we put the end-user, the consumer, at the focal point, we're talking about a fundamental change in what they expect from business. They expect immediacy, they expect accuracy, they want you to respect their time. In fact, I think some of the latest analyst reports says that valuing consumer's time is the single biggest driver to brand loyalty. So if you've got that situation, you've got an obligation to the consumer to deliver what they want. Well, now put yourself in the shoes of the consumer, which we all are. I'm a consumer, I come to a business, I'm asking you a question about a product, a service, a defect, anything, an order that's missing. I expect to get an answer very quickly because my time is precious and I know that someone like me has asked that question in the past. Why has it not been possible in the past for me as a consumer to get that answer right away, leveraging the expertise that has already happened in an enterprise? And when I do that, when Solvvy is able to enable that for the business, there's multiple benefits. The consumer is happier, their CSAT goes up, their customer satisfaction goes way up. Their time is respected, they get their answer in a minute or less, as opposed to hours. The business is happy because there's no ticket created, there's no need for a human agent to go back and forth with you, ask you a bunch of things, and maybe come back to you six hours later and now you're upset. Maybe you switched brands in the meanwhile because you're so angry of having to wait. So, the benefit is, I sometimes say to our customers, "It's the magic X", the CSAT goes up, and the cost goes down and that's never been possible before Solvvy. >> How does it work? >> How it works is very simple. The first thing we do is we engage with the business. So, the business is our customer, right? They buy the product or they buy our SATS platform. It's a SATS platform built on AI and machine learning technology that was developed by my two co-founders during their PHD work at Carnegie Mellon. So, at it's core, is the ability to understand natural language expressions of issues, by the end user, by the consumer. So typically people give us their life story, but they're asking for a refund. The ability to parcel that in that conversation and say I think you want a refund, let me help you get that is a very powerful piece of IP. So we go to companies and we say, "Just tell us where all of your knowledge assets are." You don't have to touch it, don't create anything new, don't build a new silo because they already have the silo, and we simply go out and index it all, learn from it, and start building a knowledge graph for that business. It's specific to how that business handles resolutions, but it also learns how customers have asked questions in the past, and how agents have answered it. So again, your best expertise is captured and used in that knowledge graph. We then say, "In less than an hour, "in one line of java scripts, here's a model "you can put in front of your consumer." You can put it anywhere you want, and it says when you need help, click on it, pops up, on mobile you can speak the question, and tells the consumer, "Just tell me what your issue is." It understands the intension of that question or the issue, and then goes in the knowledge graph, and says, "Hmmm, can I find an answer "in this knowledge graph that can help you help yourself?" And if so, it matches it. And it's actually giving you a specific resolution. It's not making you wade through pages of material. It's saying, "Here's the three steps you need to do "to reset your account." Now that is instant and immediate for the consumer. They don't have to hunt, they don't have to search. And it says, "Have I helped you?" And we're putting the power in the hands in the consumer. We're saying, "We don't want it to be false fiction." We're saying, you the consumer can say, "Nope, this didin't help me," and now the company can then guide you to the right flow. They can get you on a chat, if you're a V.I.P. user, maybe they get you on a phone call, whatever it might be. But, by putting the consumer at the center, by delivering real value to them, we've accomplished both sides, right? CSATS higher, the cost goes down, because we are actually self-serving anywhere from 15 to 40% of the tickets or issues that used to cost the business money, being self-served now, and so that's a pretty miraculous transformation for the business and for the consumer. >> Well, in today's world, attention is everything. Every, as you said, every experience, every engagement has to be a source of value to the customer. And so, not only do you get a better customer, but you presumably also get a richer set of interactions because the customer now believes that the system actually is helpful, is useful. Does that data then go back into the system, so that it becomes even knowledgeable about the nature of the problems, the nature of the resolutions, anticipatory about how to improve things, and maybe product people can get visibility in this stuff? Is that kind of where all this goes? >> It's a very organic system and it learns constantly. It think that's the really powerful thing about it. So, it learns many things. So it learns when you ask me questions. It learns if I have not given you a good answer. It actually learns from the negative. I still passed you to the agent because then it follows it all the way through and says, "How did the agent answer?" And, it learns from that interaction. And so because we know we can't self-serve every question that a consumer has, but we're getting better, better, and better. In fact, our self-service rates have doubled just in the last 12 months, because of the machine learning and the ability to learn. And we actually learn across all the businesses we do business with. We learn things for example that consumer review show more than a paragraph of text, they don't engage in self-service. We show bullets, they are much more likely to interact. Those are implicit learnings that system uses to more accurately to give you responses. But there's another flip side to this which is when we see 100% of the conversations between the consumer and your business, we're now able to go to business and give them categorical views of what's actually going on once their product or service is shipped to the consumer, which they've never had before. We're now able to say to them, "We think that payment "page that people are using to renew might be broken "because there seems to be a lot "more issues associated with that." Now that's something that the engineer who built that page may not know, or if the person said, "That's broken," they'd say, "How do you know? "Show me the data." And now you can actually go with a data driven model and say, "We can tell you. "This is 14% of the issues this week, "and two weeks ago, it was two percent. "Can you tell me what's changed?" Or you can put a dollar value on it. "This product seems to be defective "and it's costing us money "because we keep having to do returns. "Here's the number of situations where that's happened "in the last week, it's costing us "two million dollars a year, fix it." That's the kind of incite that the vps of customer experience or customer support have had to spend hundreds of hours to try to massage and get, and it doesn't give them a seat at the table with the strategy with product and marketing. >> But every company has been talking about the need to build their community, where basically a community is defined by folks who have something in common and are taking common actions. But one of the challenges has been, is how do I provide value so that I get that type of interaction? Let me ask you a question. Are we ultimately suggesting, we all seem to be getting to a point where the quality of engagement is such, and while it keeps costs low, that it might actually catalyze even greater engagement with the customer base so that you learn not just initially, you not only learn something about a product, or for example, you might actually learn things about how to facilitate adoption, because customers are willing to engage more often and more deeply as a consequence of a good experience in using Solvvy related type technologies? >> It is the opportunity to use that customer engagement when they're contacting your business about an issue or problem, the opportunity is first I have to take care of your issue. You won't listen to me if I don't take care of your issue well, but if I do that, I have an enormous opportunity to educate you. How can you do better with the product or service I sold you" Perhaps you need something that's on top of that. Maybe you're a free user and by subscribing to the premium product, you'd get all the benefits that you're frustrated about. And maybe that's an time to give you an offer. So, I think that notion of personalized recommendation is something that is actually never been possible before with the old systems. The idea was that support was kind of a backwater in many ways, which it should never have been. And in fact, the leading brands like Zappos realized quickly that by winning on that basis, you could actually dominate the market. But, it was often the case that the people in support felt like goal keepers. Just keep the issues away, but in fact, now in an integrated world, it's very difficult with subscription based businesses for example, to know when you're buying and when you're asking for support. It's subscription service, I could cancel at any time. So now I'm engaging with your brand. I'm asking a question, "Hey how do I get "more of x,y,z shipped to my house?" It's an enormous opportunity to not only answer my question, but then suggest things, recommend things, play books, so if you think about that experience, how would I enhance the consumer experience, that interactive conversational flow is the perfectplace to do it. >> I would think it would also allow you to envision other types of engagement, because as long as the consumer finds it valuable, to have that conversation, then they'll be willing to enter into that conversation. Well, so let me step back, where does this all go? Because we've been talking about being able to do this for a number of years, and as I said in the preamble, in many respects, all we did was digitize bad process, but now we're talking about bringing technology to bear and dramatically improving the process. Five minute resolution, pretty good. As a consumer, I'd like that, so where does this go? What's the limit of utilizing these technologies to incorporate or enhance engagement? >> So, let me illustrate with an example that I think is very compelling of the power of how this is going to change our world. So, one of our customers is Eero, the smart wifi system. You're probably familiar with it. One of the most innovative products on the planet. Now, we've been working with Eero for well over a year, and they just published a case study of what we've accomplished with them. So, we have self-served 45% of the issues that would have come into them, that have conversations that come into them regarding issues, and that's a fantastic number. They had never seen anything close to it. And that's a great outcome for the business and the consumer. Under one minute is the average time for resolution for those 45%. Imagine again, how much time I've saved you, me, all of us as consumers of Eero. But the better story that I like is two weeks ago, we got a call from the CEO of one of the leading mid-western electronics distributors in the world, and he had said, "I'm going to have my support team, "customer experience team contact you guys, "because I was at home, I bought an Eero smart wifi system, "I went home and tried to install it, and I had trouble. "And I went on, and Solvvy gave me the exact steps "it took to solve the issue, "and I never had to contact them, "and I was able to get the wifi up and running in minutes, "and I was on my way. "And I'm delighted with my Eero system, "and it was because of this interface." And he said, "I think my company should be using it too." And, that was one of many, many catalytic events for us, that realized, wow, we're touching over 200 million consumers with our service. We're reaching all the way out, and we're extending these brand's promise into the consumers' homes, into their devices. _ Two hundred million? >> Two hundred million. >> So that's literally 10% of the population that's online. >> If you're talking about the world leading brands, so we're working with the leading brands that are reaching these people, so by extension Solvvy is as well. And so, you're talking about companies, leading gaming companies, on-demand companies, consumer electronics. These are all companies that self-service automation. And it's intelligent automation, right? It doesn't require a lot of work from the business. As I said, we implement in less than an hour. With one line of java script, we've developed very powerful, unsupervised machine learning models, that can just take all that transcript date from all the past conversations that consumers have had with your business, automatically learn the best stuff from it, and then be able to show the users the right issues. So the customer journey is where we're so focused, right? Because the customer journey is opportunity for the brand to really create a market-leading position and we're enhancing that conversation. >> Fantastic. >> Mahesh Ram, founding CEO of Solvvy, thank you very much for coming on theCube. It's been a great a great conversation about the evolution of customer service, and where it goes. >> It's my pleasure, an honor to be on theCUBE. >> So once again, this is Peter Burris, this has been a CUBE conversation, until next time.