>> From Cambridge, Massachusetts, it's theCUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. (futuristic music) >> Welcome back to Cambridge, Massachusetts everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events and we extract the signal from the noise. This is day two, we're sort of wrapping up the Chief Data Officer event. It's MIT CDOIQ, it started as an information quality event and with the ascendancy of big data the CDO emerged and really took center stage here. And it's interesting to know that it's kind of come full circle back to information quality. People are realizing all this data we have, you know the old saying, garbage in, garbage out. So the information quality worlds and this chief data officer world have really come colliding together. Robert Abate is here, he's the Vice President and CDO of Global IDS and also the co-chair of next year's, the 14th annual MIT CDOIQ. Robert, thanks for coming on. >> Oh, well thank you. >> Now you're a CDO by background, give us a little history of your career. >> Sure, sure. Well I started out with an Electrical Engineering degree and went into applications development. By 2000, I was leading the Ralph Lauren's IT, and I realized when Ralph Lauren hired me, he was getting ready to go public. And his problem was he had hired eight different accounting firms to do eight different divisions. And each of those eight divisions were reporting a number, but the big number didn't add up, so he couldn't go public. So he searched the industry to find somebody who could figure out the problem. Now I was, at the time, working in applications and had built this system called Service Oriented Architectures, a way of integrating applications. And I said, "Well I don't know if I could solve the problem, "but I'll give it a shot." And what I did was, just by taking each silo as it's own problem, which was what EID Accounting Firm had done, I was able to figure out that one of Ralph Lauren's policies was if you buy a garment, you can return it anytime, anywhere, forever, however long you own it. And he didn't think about that, but what that meant is somebody could go to a Bloomingdale's, buy a garment and then go to his outlet store and return it. Well, the cross channels were different systems. So the outlet stores were his own business, retail was a different business, there was a completely different, each one had their own AS/400, their own data. So what I quickly learned was, the problem wasn't the systems, the problem was the data. And it took me about two months to figure it out and he offered me a job, he said well, I was a consultant at the time, he says, "I'm offering you a job, you're going to run my IT." >> Great user experience but hard to count. >> (laughs) Hard to count. So that's when I, probably 1999 was when that happened. I went into data and started researching-- >> Sorry, so how long did it take you to figure that out? You said a couple of months? >> A couple of months, I think it was about two months. >> 'Cause jeez, it took Oracle what, 10 years to build Fusion with SOA? That's pretty good. (laughs) >> This was a little bit of luck. When we started integrating the applications we learned that the messages that we were sending back and forth didn't match, and we said, "Well that's impossible, it can't not match." But what didn't match was it was coming from one channel and being returned in another channel, and the returns showed here didn't balance with the returns on this side. So it was a data problem. >> So a forensics showdown. So what did you do after? >> After that I went into ICICI Bank which was a large bank in India who was trying to integrate their systems, and again, this was a data problem. But they heard me giving a talk at a conference on how SOA had solved the data challenge, and they said, "We're a bank with a wholesale, a retail, "and other divisions, "and we can't integrate the systems, can you?" I said, "Well yeah, I'd build a website "and make them web services and now what'll happen is "each of those'll kind of communicate." And I was at ICICI Bank for about six months in Mumbai, and finished that which was a success, came back and started consulting because now a lot of companies were really interested in this concept of Service Oriented Architectures. Back then when we first published on it, myself, Peter Aiken, and a gentleman named Joseph Burke published on it in 1996. The publisher didn't accept the book, it was a really interesting thing. We wrote the book called, "Services Based Architectures: A Way to Integrate Systems." And the way Wiley & Sons, or most publishers work is, they'll have three industry experts read your book and if they don't think what you're saying has any value, they, forget about it. So one guy said this is brilliant, one guy says, "These guys don't know what they're talking about," and the third guy says, "I don't even think what they're talking about is feasible." So they decided not to publish. Four years later it came back and said, "We want to publish the book," and Peter said, "You know what, they lost their chance." We were ahead of them by four years, they didn't understand the technology. So that was kind of cool. So from there I went into consulting, eventually took a position as the Head of Enterprise and Director of Enterprise Information Architecture with Walmart. And Walmart, as you know, is a huge entity, almost the size of the federal government. So to build an architecture that integrates Walmart would've been a challenge, a behemoth challenge, and I took it on with a phenomenal team. >> And when was this, like what timeframe? >> This was 2010, and by the end of 2010 we had presented an architecture to the CIO and the rest of the organization, and they came back to me about a week later and said, "Look, everybody agrees what you did was brilliant, "but nobody knows how to implement it. "So we're taking you away, "you're no longer Director of Information Architecture, "you're now Director of Enterprise Information Management. "Build it. "Prove that what you say you could do, you could do." So we built something called the Data CAFE, and CAFE was an acronym, it stood for: Collaborative Analytics Facility for the Enterprise. What we did was we took data from one of the divisions, because you didn't want to take on the whole beast, boil the ocean. We picked Sam's Club and we worked with their CFO, and because we had information about customers we were able to build a room with seven 80 inch monitors that surrounded anyone in the room. And in the center was the Cisco telecommunications so you could be a part of a meeting. >> The TelePresence. >> TelePresence. And we built one room in one facility, and one room in another facility, and we labeled the monitors, one red, one blue, one green, and we said, "There's got to be a way where we can build "data science so it's interactive, so somebody, "an executive could walk into the room, "touch the screen, and drill into features. "And in another room "the features would be changing simultaneously." And that's what we built. The room was brought up on Black Friday of 2013, and we were able to see the trends of sales on the East Coast that we quickly, the executives in the room, and these are the CEO of Walmart and the heads of Sam's Club and the like, they were able to change the distribution in the Mountain Time Zone and west time zones because of the sales on the East Coast gave them the idea, well these things are going to sell, and these things aren't. And they saw a tremendous increase in productivity. We received the 2014, my team received the 2014 Walmart Innovation Project of the Year. >> And that's no slouch. Walmart has always been heavily data-oriented. I don't know if it's urban legend or not, but the famous story in the '80s of the beer and the diapers, right? Walmart would position beer next to diapers, why would they do that? Well the father goes in to buy the diapers for the baby, picks up a six pack while he's on the way, so they just move those proximate to each other. (laughs) >> In terms of data, Walmart really learned that there's an advantage to understanding how to place items in places that, a path that you might take in a store, and knowing that path, they actually have a term for it, I believe it's called, I'm sorry, I forgot the name but it's-- >> Selling more stuff. (laughs) >> Yeah, it's selling more stuff. It's the way you position items on a shelf. And Walmart had the brilliance, or at least I thought it was brilliant, that they would make their vendors the data champion. So the vendor, let's say Procter & Gamble's a vendor, and they sell this one product the most. They would then be the champion for that aisle. Oh, it's called planogramming. So the planogramming, the way the shelves were organized, would be set up by Procter & Gamble for that entire area, working with all their other vendors. And so Walmart would give the data to them and say, "You do it." And what I was purporting was, well, we shouldn't just be giving the data away, we should be using that data. And that was the advent of that. From there I moved to Kimberly-Clark, I became Global Director of Enterprise Data Management and Analytics. Their challenge was they had different teams, there were four different instances of SAP around the globe. One for Latin America, one for North America called the Enterprise Edition, one for EMEA, Europe, Middle East, and Africa, and one for Asia-Pacific. Well when you have four different instances of SAP, that means your master data doesn't exist because the same thing that happens in this facility is different here. And every company faces this challenge. If they implement more than one of a system the specialty fields get used by different companies in different ways. >> The gold standard, the gold version. >> The golden version. So I built a team by bringing together all the different international teams, and created one team that was able to integrate best practices and standards around data governance, data quality. Built BI teams for each of the regions, and then a data science and advanced analytics team. >> Wow, so okay, so that makes you uniquely qualified to coach here at the conference. >> Oh, I don't know about that. (laughs) There are some real, there are some geniuses here. >> No but, I say that because these are your peeps. >> Yes, they are, they are. >> And so, you're a practitioner, this conference is all about practitioners talking to practitioners, it's content-heavy, There's not a lot of fluff. Lunches aren't sponsored, there's no lanyard sponsor and it's not like, you know, there's very subtle sponsor desks, you have to have sponsors 'cause otherwise the conference's not enabled, and you've got costs associated with it. But it's a very intimate event and I think you guys want to keep it that way. >> And I really believe you're dead-on. When you go to most industry conferences, the industry conferences, the sponsors, you know, change the format or are heavily into the format. Here you have industry thought leaders from all over the globe. CDOs of major Fortune 500 companies who are working with their peers and exchanging ideas. I've had conversations with a number of CDOs and the thought leadership at this conference, I've never seen this type of thought leadership in any conference. >> Yeah, I mean the percentage of presentations by practitioners, even when there's a vendor name, they have a practitioner, you know, internal practitioner presenting so it's 99.9% which is why people attend. We're moving venues next year, I understand. Just did a little tour of the new venue, so, going to be able to accommodate more attendees, so that's great. >> Yeah it is. >> So what are your objectives in thinking ahead a year from now? >> Well, you know, I'm taking over from my current peer, Dr. Arka Mukherjee, who just did a phenomenal job of finding speakers. People who are in the industry, who are presenting challenges, and allowing others to interact. So I hope could do a similar thing which is, find with my peers people who have real world challenges, bring them to the forum so they can be debated. On top of that, there are some amazing, you know, technology change is just so fast. One of the areas like big data I remember only five years ago the chart of big data vendors maybe had 50 people on it, now you would need the table to put all the vendors. >> Who's not a data vendor, you know? >> Who's not a data vendor? (laughs) So I would think the best thing we could do is, is find, just get all the CDOs and CDO-types into a room, and let us debate and talk about these points and issues. I've seen just some tremendous interactions, great questions, people giving advice to others. I've learned a lot here. >> And how about long term, where do you see this going? How many CDOs are there in the world, do you know? Is that a number that's known? >> That's a really interesting point because, you know, only five years ago there weren't that many CDOs to be called. And then Gartner four years ago or so put out an article saying, "Every company really should have a CDO." Not just for the purpose of advancing your data, and to Doug Laney's point that data is being monetized, there's a need to have someone responsible for information 'cause we're in the Information Age. And a CIO really is focused on infrastructure, making sure I've got my PCs, making sure I've got a LAN, I've got websites. The focus on data has really, because of the Information Age, has turned data into an asset. So organizations realize, if you utilize that asset, let me reverse this, if you don't use data as an asset, you will be out of business. I heard a quote, I don't know if it's true, "Only 10 years ago, 250 of the Fortune 10 no longer exists." >> Yeah, something like that, the turnover's amazing. >> Many of those companies were companies that decided not to make the change to be data-enabled, to make data decision processing. Companies still use data warehouses, they're always going to use them, and a warehouse is a rear-view mirror, it tells you what happened last week, last month, last year. But today's businesses work forward-looking. And just like driving a car, it'd be really hard to drive your car through a rear-view mirror. So what companies are doing today are saying, "Okay, let's start looking at this as forward-looking, "a prescriptive and predictive analytics, "rather than just what happened in the past." I'll give you an example. In a major company that is a supplier of consumer products, they were leading in the industry and their sales started to drop, and they didn't know why. Well, with a data science team, we were able to determine by pulling in data from the CDC, now these are sources that only 20 years ago nobody ever used to bring in data in the enterprise, now 60% of your data is external. So we brought in data from the CDC, we brought in data on maternal births from the national government, we brought in data from the Census Bureau, we brought in data from sources of advertising and targeted marketing towards mothers. Pulled all that data together and said, "Why are diaper sales down?" Well they were targeting the large regions of the country and putting ads in TV stations in New York and California, big population centers. Birth rates in population centers have declined. Birth rates in certain other regions, like the south, and the Bible Belt, if I can call it that, have increased. So by changing the marketing, their product sales went up. >> Advertising to Texas. >> Well, you know, and that brings to one of the points, I heard a lecture today about ethics. We made it a point at Walmart that if you ran a query that reduced a result to less than five people, we wouldn't allow you to see the result. Because, think about it, I could say, "What is my neighbor buying? "What are you buying?" So there's an ethical component to this as well. But that, you know, data is not political. Data is not chauvinistic. It doesn't discriminate, it just gives you facts. It's the interpretation of that that is hard CDOs, because we have to say to someone, "Look, this is the fact, and your 25 years "of experience in the business, "granted, is tremendous and it's needed, "but the facts are saying this, "and that would mean that the business "would have to change its direction." And it's hard for people to do, so it requires that. >> So whether it's called the chief data officer, whatever the data czar rubric is, the head of analytics, there's obviously the data quality component there whatever that is, this is the conference for, as I called them, your peeps, for that role in the organization. People often ask, "Will that role be around?" I think it's clear, it's solidifying. Yes, you see the chief digital officer emerging and there's a lot of tailwinds there, but the information quality component, the data architecture component, it's here to stay. And this is the premiere conference, the premiere event, that I know of anyway. There are a couple of others, perhaps, but it's great to see all the success. When I first came here in 2013 there were probably about 130 folks here. Today, I think there were 500 people registered almost. Next year, I think 600 is kind of the target, and I think it's very reasonable with the new space. So congratulations on all the success, and thank you for stepping up to the co-chair role, I really appreciate it. >> Well, let me tell you I thank you guys. You provide a voice at these IT conferences that we really need, and that is the ability to get the message out. That people do think and care, the industry is not thoughtless and heartless. With all the data breaches and everything going on there's a lot of fear, fear, loathing, and anticipation. But having your voice, kind of like ESPN and a sports show, gives the technology community, which is getting larger and larger by the day, a voice and we need that so, thank you. >> Well thank you, Robert. We appreciate that, it was great to have you on. Appreciate the time. >> Great to be here, thank you. >> All right, and thank you for watching. We'll be right back with out next guest as we wrap up day two of MIT CDOIQ. You're watching theCUBE. (futuristic music)

>> Live from San Diego, California It's the queue covering Sisqo Live US 2019 Tio by Cisco and its ecosystem. Barker's >> back to San Diego. Everybody watching the Cube, the leader and live tech coverage. My name is Dave Volante, and I'm with my co host, Steuben. Amanda, this is Day two for Sisqo. Live 2019. We're in the definite. So still. I was walking around earlier in the last interview, and I think I saw Ron Burgundy out there. Stay classy Sleeping Gow is here. He's the founder and CEO of Met Net Brain Technology's just outside of Boston. Thanks very much for coming on the Q. Thank you there. So you're very welcome. So I want to ask you, I always ask Founders passion for starting companies. Why did you start? >> Well, maybe tired of doing things, Emmanuel. Well, that's alongside the other side of Yes, I used Teo took exam called a C C. I a lot of folks doing here. I failed on my first try. There was a big blow to my eagle, so I decided that we're gonna create a softer help them the past. This is actually the genesis of nettle. I met a friend help people three better doing their network management. >> That's a great story. So tell us more about that brain. What do you guys all about? >> Sure, we're the industry. First chasing time. Little confirmations after our mission is to Democrat ties. Merrick Automation. Every engineer, every task. They should've started with automation before human being touched. This task, >> you know, way go back. Let's say, 10 years ago people were afraid of automation. You know, they thought I was going to take away their jobs. They steal and they still are. We'll talk about that. You get this and I want to ask you about the blockers. They were fearful they wanted the touch thing. But the reality is people talk about digital transformation. And it's really all about how you use data, how your leverage data. And you can't be spending your time doing all this stuff that doesn't add value to your business. You have to automate that and move up to more valuable test. But so people are still afraid of automation. Why, what's the blocker there? >> They have the right reason to be afraid. Because so many automation was created a once used exactly wass right. And then you have the cost ofthe tradition automation. You have the complexity to create in their dark automation. You guys realize that middle confirmation You cannot have little gotta measure only work on a portion of your little way. You have to walk on maturity if not all of your narrow right. So that's became very complex. Just like a You wanna a self driving car? 10 You can't go buy a Tesla a new car. You can drive on a song. But if you want to your Yoder Puta striving always song Richard feared it. That's a very complex Well, let's today, Netto. Condemnation had to deal with you. Had a deal with Marty Venna Technology Marty, years of technology. So people spent a lot of money return are very small. There's so they have a right to a fair afraid of them. But the challenges there is what's alternative >> way before you're there. So there, if I understand it, just playing back there, solving a very narrow problem, they do it once, maybe twice. Maybe a rudimentary example would be a script. Yeah, right, right. And then it breaks or it doesn't afford something else in the network changes, and it really doesn't affect that, right? >> Yeah. I mean, you know, I think back to money network engineers. It's like, Well, I'm sitting there, I've got all my keep knobs and I get everything done and they say, No, don't breathe on it because it's just the way I want it less. It can't be that doesn't scale. It doesn't respond to the business. I need to be able to, you know, respond fast what is needed. And things are changing in every environment. So it's something that I couldn't, as you know, a person or a team keep up with myself, and therefore I need to have more standardized components, and I need to have intelligence that can help me. >> Let's sit and let's >> s so we've laid out the generalized way that we've laid out the problem. What's what's the better approach? >> Well, give you looking out of the challenge today is you have to have Dave ups, which a lot of here they have not engineer know howto script and the mid off the engineer who know how little cooperates walk together. So there's a date, a part of it. There's a knowledge. A part of this too has to meet to create a narrow coordination and that Ned Ogata may have to be a scale. So the challenge traditional thoracotomy here, why is for short lie on if you're going down? Technical level is wise A terra, too many data and structure and the otherwise Our knowledge knowledge cannot be codified. So you have the knowledge sitting people's head, right, Eh Programa had to walk in with a narrow canyon near together. You make it a cost hire. You make it a very unskilled apple. So those are the challenge. So how fast Motor way have to do so neither brand for last 15 years You decide to look differently that we created some saying called operating system off total network and actually use this to manage over 1,000 of mental models technology. And he threw problem. You can't continually adding new savings into this problem. So the benefit of it is narrow. Canyon near anybody can create automation. They don't have to know how to writing a code. Right? And Deborah, who knows the code can also use this problem. All the people who are familiar with technology like and people they can integrate that never >> pray. Okay, so you have all this data I wish I could say is unstructured So he doesn't have any meaning. Data's plentiful insights aren't, uh And then you have this what I call tribal knowledge. Joe knows how to do it, but nobody else knows how to do it. So you're marrying those two. How are you doing that? Using machine intelligence and and iterating building models, can you get that's amore colors? Tow How you go about that? What's the secret sauce >> way? Took a hybrid approach. First call on you have to more than the entire network. With this we'll kind of operating system called on their own way have about 20 12,000 valuables modeling a device and that 12,000 valuable adults across your let's say 1,000 known there or there will be 12,000,000 valuables describing your medal. That's that's first. Zang on top of 12,000,000 valuables will be continually monitored. A slow aye aye, and the machine learning give something called a baseline data. But on top of it, the user, the human being will have the knowledge young what is considered normal what is considered abnormal. They can add their intelligence through something called excludable rumble on couple of this system, and their system now can be wrong at any time. Which talking about where somebody attacking you when that OK is un afford all you through a human being, all our task Now the automation can be wrong guessing time. So >> this the expert, the subject matter expert, the main expert that the person with the knowledge he or she can inject that neck knowledge into your system, and then it generates and improves overtime. That's right, >> and it always improve, and other people can open the hood. I can't continue improving. Tell it so the whole automation in the past, it was. Why is the writer wants only used once? Because it's a colossal? It's a script. You I you input and output just text. So it wasn't a designer with a company, has a motive behind it. So you do it, You beauty your model. You're writing a logical whizzing a same periods off, we decided. We think that's you. Cannot a scale that way. >> OK, so obviously you can stop Dave from inputting his lack of knowledge into the system with, you know, security control and access control. Yeah, but there must be a bell curve in terms of the quality of the knowledge that goes into the system. You know, Joe might be a you know, a superstar. And, you know, stew maybe doesn't know as much about it. No offense, too. Student. So good. So how do you sort of, you know, balance that out? Do you tryto reach an equilibrium or can you wait? Jos Knowledge more than Stu's knowledge. How does that work? >> So the idea that this automation platform has something called excludable Rambo like pseudo Rambo can sure and implacably improved by Sri source One is any near themselves, right? The otherwise by underlying engine. So way talk about a I and the machine learning we have is that we also have a loo engine way. Basically, adjusting that ourselves certainly is through Claverie Partner, for example, Sisko, who run many years of Qatar where they have a lot of no house. Let's attack that knowledge can be pushed to the user. We actually have a in our system that a partnership with Cisco attack South and those script can be wrong. slow. Never prayer without a using woman getting the benefit of without talking with attack. Getting the answer? >> Yes, I think you actually partially answered. The question I have is how do you make sure we don't automata bad process? Yeah. So And maybe talk a little bit about kind of the training process to your original. Why of the company is to make things easier. You know, What's the ramp up period for someone that gets in giving me a bit of a how many engineers you guys have >> worked with? The automatic Allied mission. Our mission statement of neda prayer is to Democrat ties. Network automation, you know, used to be network automation on ly the guru's guru to it. Right, Dave off. Send a satchel. And a young generation. My generation who used come, Ally, this is not us, right? This is the same, you know. But we believe nowadays, with the complicity of middle with a cloud, computing with a cybersecurity demand the alternative Genetic automation is just no longer viable. So way really put a lot of starting to it and say how we can put a network automation into everyone's hand. So the things we tell as three angle of it, while his other missions can be created by anyone, the second meaning they've ofthe net off. Anyone who know have knowledge on metal can create automation. Second piece of automation can lunched at any time. Somebody attacking you middle of the night. They don't tell you Automation can lunch to protect Theo, and they're always out. You don't have people the time of the charter. Automation can lunch the tax losses, so it's called a lunch. Any time certain want is can adapt to any work follow. You have trouble shooting. You have nettle changes. You have compliance, right? You have documentation workflow. The automation should be able to attack to any of this will clothe topping digression tomorrow. We have when service now. So there's a ticket. Human being shouldn't touches a ticket before automation has dies, she'll write. Is a human should come in and then use continually use automation. So >> So you talk about democratizing automation network automation. So it's so anybody who sees a manual process that's wasting time. I can sort of solve that problem is essentially what you're >> doing. That's what I did exactly what we >> know So is there, uh, is there a pattern emerging in terms of best practice in terms of how customers are adopting your technology? >> Yes. Now we see more animal customer creating This thing's almost like a club, the power user, and we haven't caught it. Normal user. They have knowledge in their heads. Pattern immunity is emergent. We saw. Is there now work proactively say, How can I put that knowledge into a set of excludable format so that I don't get escalate all the time, right? So that I can do the same and more meaningful to me that I be repeating the same scene 10 times a month? Right? And I should want it my way. Caught a shift to the left a little while doing level to the machine doing the Level one task level two. Level three are doing more meaningful sex. >> How different is what you're doing it net brain from what others are doing in the marketplace. What's the differentiation? How do you compete? >> Yeah, Little got 1,000,000 so far has being a piecemeal, I think, a fragment. It's things that has done typical in a sweeping cracker. Why is wholesale Hardaway approach you replace the hardware was esti N S P. Where's d? Let there's automation Capitol Building Fifth, I caught a Tesla approached by a Tesla, and you can drive and a self driving. The second approaches softer approach is as well. We are leading build a model of your partner or apply machine learning and statistics and was behind but also more importantly, open architecture. Allow a human being to put their intelligence into this. Let's second approach and insert approaches. Actually service little outsourcer take you, help you We're moving way or walk alone in the cloud because there's a paid automation there, right so way are focusing on the middle portion of it. And the landscaper is really where we have over 2,000 identifies customer and they're automating. This is not a just wall twice a week, but 1,000 times a day. We really excited that the automation in that escape scale is transforming how metal and is being managed and enable things like collaboration. But I used to be people from here. People from offshore couldn't walk together because knowledge, data and knowledge is hard to communicate with automation. We see collaboration is happening more collaboration happening. So we've >> been talking about automation in the network for my entire career. Feels like the promise has been there for decades. That site feels like over the last couple of years, we've really seen automation. Not just a networking, but we've been covering a lot like the robotic process automation. All the different pieces of it are seeing automation. Bring in, gives a little bit look forward. What? What do you predict is gonna happen with automation in I t over the next couple of years? A >> future that's great Way have a cloud computing. We have cyber security. We have the share of scale middle driving the network automation to the front and center as a solution. And my prediction in the next five years probably surrounded one izing automation gonna be ubiquitous. Gonna be everywhere. No human being should touch a ticket without automation through the first task. First right second way. Believe things called a collaborative nature of automation will be happy. The other was a local. Automation is following the packet from one narrow kennedy to the other entity. Example would be your manager service provider and the price they collaborated. Manager Nettle common little But when there's something wrong we don't know each part Which part? I have issues so automation define it by one entity Could it be wrong Across multiple So is provider like cloud provider also come Automation can be initiated by the Enterprise Client way also see the hado A vendor like Cisco and their customer has collaborated Automation happening So next five years will be very interesting The Manu away to manage and operate near Oca will be finally go away >> Last question Give us the business update You mentioned 2,000 customers You're hundreds of employees Any other business metrics you Khun, you can share with us Where do you want to take this company >> way really wanted behind every enterprise. Well, Misha is a Democrat. Eyes network automation way Looking at it in the next five years our business in a girl 10 times. >> Well, good luck. Thank you. Thanks very much for coming on the queue of a great story. Thank you. Thank you for the congratulations For all your success. Think Keep right! Everybody stew and I will be back. Lisa Martin as well as here with an X guest Live from Cisco Live 2019 in San Diego. You watching the cube right back