>>From around the globe. It's the cube with digital coverage of IBM. Think 2021 brought to you by IBM. >>Welcome to the cubes coverage of IBM. Think the digital experience I'm Lisa Martin. I've got two guests with me here today. Ryan Dennings joins us manager of ECM solutions at auto owners insurance company, Ryan, welcome to the program. Thank you. And Michelle Christianson is here as well. VP of enterprise report management practice at end choice, Michelle. It's good to have you on the program. Thank you. Thank you. So let's, let's go ahead and start with you. You guys are a customer of and choice and IBM, talk to us a little bit about auto owners company. I know this is a fortune 500. This was founded in 1916. You've got about nearly 3 million policy holders, but give us an overview of auto owners insurance. >>Sure. So I don't want to said insurance is an insurance company. That's headquartered in Lansing, Michigan. We write insurance in 26 States throughout the United States. Um, just by our name being auto owners insurance, which is how we started. Um, we write all personal lines, commercial lines and also have a life insurance company, >>So comprehensive and that across those nearly 3 million policy holders. Michelle, tell us a little bit about end choice. I know this, you guys are an IBM gold business partner, but this is end choices first time on the cubes. So give us a background. Sure, sure. Great. So in choice are an IBM gold business partner. Uh, we have had 28 years success with IBM as a business partner. Our headquarters are in areas, um, Austin, Texas, and, uh, Tempe, Arizona, as well as Shelton Connecticut. We cover all of North America and we are a hundred percent focused on the IBM digital business automation space. We have about 500 customers now that we've helped, uh, through the years. And we continue to be a leading support provider as well as an implementation partner with all the IBM solutions. And talk to me a little bit Michelle, about how it is that you work with with, um, auto owners. >>So we assisted auto owners recently in their digital transformation journey and they were, uh, dealing with an antiquated product and wanted to get for moving forward, you know, provided better customer satisfaction, um, experience, um, for their clients agents. And so we partnered with them and with IBM and bringing them a content manager on demand solution as well as navigator and several other products within the IBM digital business automation portfolio. Excellent client. Oh, sorry Michelle, go ahead. Nope. That's that's fine. All right, Ryan, tell us a little bit about auto owners, your relationship with IBM and choice and how is it helping you to address some, the challenges in the market today? >>Sure. So I don't know if this has a long-term relationship with IBM. Um, originally starting back as we go as a mainframe customer and then, you know, more recently, um, helping us with different modern technology initiatives. Uh, they were instrumental in the nineties when we created our initial web offerings. And then more recently they've been helping us with our digital business automation, which has helped us to, um, mature our content, offering it. >>So you have had a long standing relationship with IBM. Right. And you mentioned the nineties, ah, a time when we didn't have to wear a mask on our faces. So a couple of decades it goes back. Yeah. >>Yes. For sure. Yes. Even further than that back, you know, back into the seventies from the mainframe side of things, >>Uh, the seventies, another good time. All right. So Michelle had talked to me a little bit about what end choices doing with IBM solutions to help auto owners from a digital transformation perspective is as I said, this is a company that was founded in 1916. And I always love to hear how history companies like that are actually working with technology companies to facilitate that transformation a lot harder than it sounds well. That's correct. Just as I mentioned, we're focused on helping customers develop their strategies, their digital strategy and creating those transformative solutions. So we're helping organizations like auto owners, um, with their journey by first realizing, um, their existing, existing, digital state, what challenges they might have and what needs they might need. And then we break that down or we deconstruct those technical and process. And finally we re-invent, um, their strategic offering with modern capabilities. >>So we're focused on technologies like RPA machine learning, artificial intelligence, they're more efficient, scalable, and secure. So any way we can bring those technologies into the equation we go forward. So this offers us, our clients, um, smarter and more into intuitive interfaces, creating basically a better user experience and a better user experience then becomes disruptive to their competition. So they gain a better place in the market space. Ryan talked to us about that process as much as you were involved in it. I liked that Michelle said, you know, we kind of look at the environment, we deconstruct it and then we reinvent it. Talk to me about how IBM and enChoice have ha has helped auto owners to do that so that your digital infrastructure is much more modern. And I presume much more resilient when there are market dynamics like we're living in now. >>Yeah, for sure. So, you know, we've, we've gone through a couple of transformation journeys at auto owners with IBM. Um, when I started the team about seven years ago, we originally started using file NATS and data cap and case manager and content aggregator, um, as our first, um, movement from a traditional, um, platform that we had for content management into a more modern platform. And that helped us a lot to improve our business process, um, improve how we capture content and bring it into the system and make it actionable more recently, we've been working with Michelle and the team on our, um, migration to a content management on demand platform. And that's really going to be transformative in terms of how we're able to present content and documents and bills, um, to our agents and customers, um, to be able to transform that content and show it in ways that are, um, important, um, for our customers to be able to see it to, um, engage from, with auto owners in a, in a digital era. >>So Ryan, just a couple of questions on that is that, is that a facilitation of like the digitization of processes that had some paper involved cause you guys have about 48,000 agents. So a lot of folks, a lot of content, tell me a little bit more about how, um, that like content manager on demand, for example, and what you're doing with ETF, how has that really revolutionizing and driving part of that digital transformation? >>Sure. So, uh, you know, there's two parts to that in terms of that content management management on demand journey. Um, one is the technology portion of it, but IBM's provided and that suite of software gives us some functionality that we haven't had in the past. Um, specifically some functionality around searching and searchability of our content, um, that will make it easier for people to find the content that they're looking for, um, ability to implement, uh, records management policies and other things that help us manage that content more effectively, um, as well as, um, some different options to be able to present the content, uh, to our customers and agents in a, in a better and more modern way. Um, and I'm choices role rolling that has really been, sorry, guide us on that journey, um, to help us make the right choices along the way on the project and help us get to a successful implementation and production. >>Excellent. Michelle, talk to me about hybrid cloud AI data, a big theme of, uh, IBM think is your, how is enChoice using hybrid cloud and AI, you mentioned some of the ways, but kind of break into that a little bit more about how you're helping customers like auto owners and others really take advantage of those modern technologies. Well, sure, sure. So, um, of course with the Calpec offerings that IBM has come forward with and where we focus in the cloud Pak for automation, um, several of those offerings are, some of them are, um, uh, built specifically to, uh, survive or to, to, um, be hosted in a hybrid environment. And as we working with auto owners, um, transforming their platforms going forward, for example, they just invested in, in a, um, a, uh, I just lost the word here. I, they just invested in a new platform mainframe platform where they're going to be leveraging the red hats and from there they'll drive forward into containerization. >>So, um, Ryan mentioned, uh, some of the ways that we'll be presenting the content for his agents and his customers and a particular, um, that entire viewing platform itself can be moved to a containerization state. So, um, so it's going to be a lot easier for him to transition into that and to maintain it and to management manage it. And of course, um, just that whole, um, the ease of function around it will be a lot easier. So we are in our area as an IBM business partner. Um, we work with, uh, these solutions to try to stay ahead of the game, to try to be able to assist our customers to understand what makes sense, when is it time to move into those? Um, it's great to take advantage of the new stuff, but nobody wants to be, you know, the bleeding game. We want to be the leading game. >>And, um, so that's some of the areas we focus with our clients to really stay tight with the labs tight with IBM and understanding their strategies and convey those and educate our customers on those excellent leading edge. Ran, talk to me a little bit. I love this a bank, uh, sorry. Uh, an insurance company from the early 19 hundreds moving into the using container technology. I'll have stories like that. Talk to me a little bit about hybrid cloud AI and how those technologies are going to be facilitators of the continuation of the digital transformation and probably enabling more opportunities for your agents to meet more needs from, from your policy holders. >>Yeah, for sure. So, uh, first and foremost, um, we were a red hat open shift, uh, customer before IBM acquired them and we were doing microservices development and things like that on the platform. Um, and then we were super excited about IBM's digital business automation strategy to, uh, move to cloud pack, um, and have that available for software products to run on OpenShift. Um, at the end of last year, we updated our license thing so that we can move in that direction and we're starting to, um, deploy, um, digital business automation products on our OpenShift platform, which is super exciting for me. It's going to make for faster upgrades, more scalability. Um, just a lot of ease of use things, um, for my team, um, to make their jobs easier, but also easier for us to adapt new upgrades and software offerings from IBM. Um, there's also a number of products that are in the, um, containerized or OpenShift only offering as they're initially coming out, whether it's mobile capture or automated document processing, um, the same a couple, um, and those are both things that we're looking at auto owners to continue to mature in this space and be able to offer more functionality to our associates, our customers, and our agents, um, to continue to grow the business >>Very forward-thinking uh, awesome Ryan, thanks for sharing with us. What auto insurance or auto owners insurance is doing, how you're being successful and how, how you've done so much transformation already. I want to throw the last question to Michelle. Take us out Michelle with what's next from end choices perspective in terms of your digital transformation. Um, well we have been a hundred percent focus on helping all of our customers develop their digital strategy and, uh, and creating their own transformative solutions. So as we continue to work with our clients, take them through the journey. Um, as I mentioned before, we try to encourage them not to focus on the, the technology itself, but really to focus on creating their exceptional customer experience when driving their digital strategy. And we see ourselves as, you know, helping transform our clients experience such that, you know, customer experience becomes what enChoice does best. >>So we see not only our own organization going through the transformation, but making sure that we're taking our clients with us and with 500 clients, we're, we're really busy. So that's always good. That is good. It sounds like the last year has been, uh, very fruitful for you. And I love that you mentioned customer experience, Michelle. I think that is so important and as well as employee experience, but having a good customer experience, especially these days. Table-stakes I thank you both so much for sharing what you guys are doing with IBM solutions, the transformation that you're both of your companies are on, and we look forward to hearing what's to come. Thank you both for your time. Thank you. Thank you for Rand Dunnings and Michelle Christiansen. I'm Lisa Martin. You're watching the cubes coverage of IBM. Think that digital experience.

Jeff Rick here with the kubera in Mountain View California had a really cool startup phantom they're coming at this autonomous vehicle thing from a very different direction they're not a car company it's a pure software play but it really has a huge impact on the autonomous vehicle industry autonomous vehicles are met for no driver you guys have a driver but you're really assisted driving from a remote location a third party who can provide a safety solution for a number of AV operators right let's say if it's a you know one of the big OMS of ride-sharing companies they can connect to vehicle remotely and when they move the steering wheel or press the gas or brake it would actually happen in real time right we think we have the ultimate fallback mechanism at this point which is actually still a human right the machine is very very good but for these edge case scenarios you still need to bring a human back into the loop road construction areas severe weather conditions all this stuff happen all the time ok an autonomous vehicles may struggle with the situation so phantom Otto provides a solution whatever the situation is get you around an obstruction pull you over to the side of the road so you're not blocking traffic and in a much safer situation and a human's cognitive ability to process information on the fly we think that's the hidden key to making autonomous vehicles a reality it's in life-saving technologies you use a lot of off-the-shelf really simple hardware to execute this there's logitech little steering wheels over there at the big curve sam sunscreens basic cameras on the car so i get in it right we just work regardless of the kind of vehicle that a company might utilize we have to be able to control that vehicle smoothly and safely how do you guys deal with the ladies issue obviously that's our secret sauce but we've been able to get that very very low we connect multiple network at the same time a PMT horizon you know and t-mobile and a few networks right once they're bonded to get a much stronger connection these are life-saving vehicles everyone wants these deployed as rapidly as possible but we also want that deployment itself to be as safe as possible triple A's did a survey recently issued 75% of consumers are afraid of trusting the Machine and that's on this vehicle if you take a step back and look at the forest and not the trees you have 1.2 million people dying every year worldwide due to traffic accident fatalities 40,000 in the u.s. in 2016 and 94% is due to human error if we had that happen even just for two weeks in aviation in the u.s. aviation wouldn't exist right it doesn't know it so if you eliminate the human for the most part from that equation you can save a lot of lives we do view there's going to be you know a big consumer adoption kind of hurdle to overcome and a piece of that is having the passengers in the car comfortable and feeling that someone it has their back right I saw somewhat of an awakening in the government like we're really scared of this being deployed but in reality we should be scared of this not being deployed right we are working with a variety of cybersecurity firms for making sure that our solution is extremely secure from the hardware that we can offer in the car to the software to the actual control center the operation center where the drivers driving you making sure that we have ended in security the a I would say it's about 97 98 percent of the way then a reality of having autonomous vehicles interacting with other autonomous vehicles might create new edge case scenarios that don't exist yet I think the regulators are coming to the realization at this point that if we want to get these vehicles deployed right now we need to have some sort of bridge to that technological gap to get us from 98 percent to a hundred percent right now it's a relatively small number of cars a small number of players but we see a huge opportunity and huge growth in the sector of the next five years it's okay I'll go take a drive yeah sure okay we're gonna check out we're gonna take a drive we'll see you in the car [Music] we are driving a Lincoln MKZ 2017 and the reason this vehicle is so good for autonomous vehicle development is because a lot of the driving steering gas and brakes is enabled through some a system called drive-by-wire okay that means it's an electronic signal that goes through the canvas and initiates these features locomotions in the vehicle electronically we can create an artificial electronic signal and inject it where it processes that information and artificially move the steering wheel or the brakes or the gas light that way [Music] [Music] getting ready check ready three two one there we go besides operating is our safety driver we haven't started going yet so you you are on call we look both ways now this is kind of interesting as I can see what then can't see who you can see what I can see so it's kind of an infinite loop you can see almost 360 degrees around the park dan can hear everything that we can hear in the vehicle if someone is haunting that and making a right-hand turn and you think not a very good right seat driver if I complain about people getting too close to the curb but good job then stand nice and wide for every latitude longitude coordinate we would get data points such as bandwidth and latency and if there's ever some sort of dead zone he or she would know that in advance and know that they could not engage to be able you give a geofence that off and to write if there's a dead zone correct make the car go around it even if it's something looked at this is good crap how consistent is the coverage the mobile cover do you find say t-mobile is not good in a certain area but AT&T is good okay then we would use AT&T service it's the latency of shifting we're always going to make sure that you can steer that you can have breaks and other stuff that isn't as high of a priority Falls lower down the list we're now going to go into gas station gas stations obviously don't have lane markings you're doing with pedestrians different vehicles coming in and out but for us obviously since we're being driven by a human we'll be able to go through just as though it was a human in the driver seat it's really just about a human being able to read the motions of the car right take a few inches forward then you pause it's understanding that or they give a scenario so that you understand when you can move forward or one you might need to peel back but at the end of the day you hope that at some point the autonomous vehicles will be able to handle an increase moon remember of easy choices well gathering data critical data right edge case scenario data so that we can feed that back to our customers so that they can have the data that they need to further train these vehicles [Music] that was fun great job out there thank you what does it feel like a driving this thing driving remotely is actually very different from driving a car normally and I know might sound obvious but there's a lot of things we take for granted driving the car for example you don't actually understand the momentum shifts that are happening in the vehicle so you don't know how hard you're braking or you might have a dip different depth perception because the optics on the cameras all these things kind of add up into completely different driving experience as I'm developing the system I'm testing it and seeing exactly the information that I need in order to create that safe and smooth driving experience and so I'm looking at what's difficult for me as a remote operator or what information am i lacking and then I go back and develop those things so at the federal level there's a bill in the house and the bill in the Senate neither of which have been passed but we expect that one will go the distance this year so you might actually have the rare scenario where the regulation outpaces the technology which is a good problem to have right it's not a problem at all I mean a human who's going to intervene on your behalf will be really important right on the business standpoint we have several deals are already closed some pilots planned over the next few months so you'll be seeing a lot more I think of us very soon out in the market thanks for sharing the right and taking care of us appreciate it thank you we're at phantom Auto in Mountain View California thanks for watching we'll catch you next time [Music]

>> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in Mountain View, California, at a really cool start-up, Phantom Auto. They're coming at this autonomous vehicle thing from a very different direction. They're not a car company, it's not BMW and Audi and Nissan and all the other people you hear about. It's a pure software play, but it really has a huge impact on the autonomous vehicle industry. We're excited with the guy who's putting all these development, business development deals together. He's Jordan Sanders, director of business development and operations. Jordan, great to see you. >> Yeah, thanks for having me. >> So, again, when I first heard about you guys I thought, "Okay, do I order "this to drive my grandfather to the store," because he shouldn't be driving even though he has his driver's license, but no, that's not it at all. You guys have a very specific target market and it's really more a biz dev than a direct-to-consumer market. >> Yeah, exactly, so we are a B2B business and our target customers are those who are closest to getting their autonomous vehicles on the road. And so, that's frankly where we're seeing the most traction for now, at this point, from customers. As you get closer to true deployment of level four robo-taxis you realize a need for remote assistance, and we think we have the best solution on the market. >> Jeff: Right. >> To actually remotely drive the car and have a human in the loop to promote safety and service. >> So, as you look at your kind of tam, your ecosystem that you're going to market with, obviously we all know Waymo. We see the cars driving around all the time, the Nest is right up the street, but how's that landscape evolving? You know, we obviously hear about Uber, we hear about Lyft, you hear little bits and pieces about BMW and different car companies. As you sit back from where you're sitting, how do you kind of segment the market, how do you figure out where you're going to go next? >> Yeah, it's an interesting question. I mean, right now, you know, there's obviously a lot of excitement around this market and where it will be in five years. Right now the number of actual autonomous vehicles deployed is relatively low, and so that is frankly what our business is tied to. Again, it's enabling every vehicle on the road to actually operate safely, and so in terms of total addressable market, how we see it evolving, right now it's a relatively small number of cars and a relatively small number of players, but we see huge opportunity and huge growth in the sector over the next five years and 10 years. >> Right, and obviously a big integration challenge for you guys because each platform that you partner with is, you know, we hear all the time, some of them are using some shared infrastructure, some of them are trying to use their own, some are RADAR, some are LIDAR, some are camera, some are combination, so from a business development point of view you guys have to integrate with all those different platforms. >> That's correct, and so that's from the very beginning, we're building our end-to-end service to be very flexible and the software piece especially can integrate with any vehicle, with any vehicle manufacturer, because frankly we want to be open to the market and we don't want to just cover, you know, one customer's vehicles. We are sort of a third party who can provide a safety solution for a number of AV operators. >> Right, now the other interesting thing that people probably don't think about is, you know, we hear all about the technology in the cars and the machines, right, and IOT and it's all about machines, but in bringing a human operator into the equation it's not just to operate the vehicle, it's actually a person and all that that means. I wonder if you can kind of explain how that impacts people's autonomous car vehicle when there's actually a person involved. >> Yeah, definitely, so I think, you know, I think about this from a personal standpoint, so part of me is very excited for autonomous vehicles and I've ridden in several autonomous vehicles, feel very comfortable in them very quickly, but I also live in Silicon Valley and not everyone does just get to zip around in autonomous vehicles and is working in this industry, and so we do view there's going to be a, you know, a big consumer adoption kind of hurdle to overcome, and a piece of that is having the passengers in the car comfortable and feeling that, you know, someone has their back, right? >> Jeff: Right. >> So that's a key part of what we believe that we deliver is a human touch to self-driving cars, which we think is very important just at a psychological level, knowing that you have somebody who is monitoring your ride and is ready to intervene and protect you, you know, in the event that something goes wrong with the ride. And the other thing is by having a human in the loop it also enables all sorts of interesting ways of providing better service, and that's going to be a very, a key piece of whenever everyone inside the car is a passenger, there are no longer drivers, we're passengers. There are going to be lots of opportunities for enhancing passenger experience, and we think part of that can be, you know, providing a human service, an actual human on the other end making you feel comfortable and also connecting you with almost like a concierge. >> Right, and like OnStar has been around forever, right, that's probably the first kind of two way- >> You said that, not me, yeah. >> Two way communication, right, into the vehicle, which at first was I think mainly a safety feature. You crash and it sends out a 911 and then I think they kind of evolved it into a little bit of a concierge service. >> Exactly, so again, there's certainly that piece that we think is going to be really important for consumer adoption. I mean, I think AAA did a survey recently that showed 75% of consumers are afraid of trusting a machine, an autonomous vehicle. Now, we're very confident that the AV tech, once you get inside an autonomous vehicle that you very quickly realize, "Wow, this is a great driver," and we're very bullish on, you know, autonomous vehicle technology and believe that it's very reliable. But again, in those edge case scenarios, having a human who's going to intervene on your behalf and be able to actually operate the vehicle will be really important. >> Right, so somebody's watching this and going, "Ha-ha-ha," you know, "I'm a hacker, I'm going to hack into the stream," and it's not going to be Ben, the nice, smooth driver taking over the car but some person that maybe we don't want taking over the car. So, in terms of security and network infrastructure, how much are you leveraging your partners' infrastructure, how much are you leveraging your own, where does kind of security fit in this whole puzzle? >> Yeah, it's a great question and certainly one that, you know, we're hearing from a lot of customers. So, we are working with a variety of cybersecurity firms for making sure that our solution is extremely secure across multiple vectors, so whether it's just on the software piece or really our end-to-end solution, from the hardware that we can offer in the car, to the software, to the actual control center, the operation center where the driver's driving you, making sure that we have end-to-end security to avoid any situation like that. >> Right, so Jordan, for the people that aren't in Silicon Valley, what should they know about autonomous vehicles, how close are we, how much is it just, you know, stuff in the newspaper and you know, kind of nirvana still or just, you know, specialize Waymo vehicles that we see all the time in this neighborhood. How close is this to Main Street, how close is this to being that vehicle that picks me up when I get off the Caltrain to San Francisco and I need to go to a meeting over the Embarcadero? >> Yeah, so I think what people should know about this technology is that it is incredible technology that will be life-saving and that needs to get on the road, but that needs to happen in a safe manner and at a time where you can have full confidence in the operation and all settings, right. The technology is incredible, and so what Phantom Auto is here to do is to get these life-saving vehicles on the road quicker, and so what I would say to the average person who's a little uncertain of this technology is that it is incredible and you're going to enjoy the experience and it will be life-saving, and again, I think Phantom Auto is working to actually bring that experience to consumers by getting these robo-taxi services deployed. >> Jeff: Right. >> Pull out the safety driver and have a remote safety driver, a Phantom Auto remote operator ready to take over control of the vehicle in the event that you need assistance. >> And in terms of where you guys are as a company, right, you're a relatively small company, got this cool Lincoln here, where are you in terms of your company? Do you have POCs in place, do you have customers in place, kind of where is it in terms of the deployment of the technology within your ecosystem? >> Yeah, well we realize that we're bringing a very critical solution to these operators, so again, if you're an autonomous vehicle developer and operator and really thinking seriously about deployment you realize that you need a solution like ours, and so on the business standpoint we have several deals already closed, some pilots planned over the next few months, so you'll be seeing a lot more, I think, of us very soon out in the market. >> All right, now you're going to see more of us on the street. So, Jordan, let's stop talking and let's go take a ride in the car. >> Let's get in the car. >> All right, he's Jordan, I'm Jeff. We're getting in the car, thanks for watching. (techy music playing)

(click) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. It's 2018. We just got out of the CES show and all the rage is autonomous vehicles. You can't get away from it. It's what everybody's talking about. Tesla just announced their autonomous truck, their autonomous Roadster. We're here in Palo Alto, right on San Antonio Road. Googleplex and Waymo's are right up the street. So everyone is all about autonomous vehicles, but we're excited to be here at Phantom Auto and they're taking a slightly different approach for a slightly different problem. We're excited to have Shai Magzimof. He's the co-founder and CEO of Phantom Auto. Shai, great to see you. >> Nice talking to you, yeah. Thanks for having me. >> So Phantom Auto, you guys just got back from CES. You were giving demos, but you weren't stuck in, like, the little lane that was protected. You were actually driving people all over the streets. >> We were driving on the Strip, yeah, yeah. We actually were picking people from the hotel lobby, so the valet guys would let us in with an empty vehicle. These videos are actually also online, and we drove them off the Strip and back to the hotel, or to another destination. >> So you're doing a whole different thing. You do not have an autonomous vehicle. >> It's not an autonomous vehicle. >> You were the ultimate chauffeur driven vehicle. >> Right. Right. So again, for the show, we did our job to show that the vehicle can drive without a driver in the driver's seat, but what we do is actually a safety solution for autonomous vehicles. And that safety is basically what happens if an autonomous vehicle artificial intelligence doesn't work. Let's say there's something that it cannot see, or something that, you know, an unidentified object, road construction areas, severe weather conditions, all this stuff happens all the time. And autonomous vehicles may struggle with the situation so Phantom Auto provides a solution that we work with these companies. We provide them that solution that allows remote operations, so someone will connect remotely. >> So let's back up a couple steps. Autonomous vehicles are meant for no driver. You guys have a driver but you're really assisted driving with a person from a remote location. So how do you describe that in a short category? I'm sure the analysts will want you to have a category. >> The category would be the same way you think about air traffic control, right, or any type of control center, like call control centers. Any type of support for customers, you would have a bunch of people sitting in front of computers, in our case they're sitting at computers with steering wheels, we'll see that later, and they can connect to a vehicle remotely, and when they move the steering wheel or press the gas or brake, it would actually happen in realtime. So we have this software that allows this realtime, critical communication for autonomous vehicles. >> Now what's weird is when we first heard about you guys, I'm thinking, okay what is the use case? Am I going to send the Phantom Auto to go pick up my hundred-year old grandfather who probably shouldn't be driving anymore, where you're escorting it. But really it's a very different application, and I don't think most people understand that, in autonomous vehicles, there's a whole lot of use cases still that they haven't quite figured out. My favorite one is when two of them pull up to a four-way stop, and neither of them wants to go first. They get stuck in a friendly lock, right, they get paper-logger, some poor kid has his foot in the intersection and is trying to wave the car through and it won't go through. So it's corner cases that you guys are all about, to really enable that next-stage of machinery. >> When I started a company, right, I'm a big believer in autonomous vehicle, I wanted to make them happen faster and sooner because it's life-saving technology. This is going to change the world. We all want it faster. Now, the reason why we're still not there yet is because there are many corner cases, edge cases, these situations where the machine didn't train enough for, and in this situation they provide a cover. So we have a person that would sit in an office, he doesn't have to be so close nearby. When we were in Vegas a couple weeks ago, the driver was in Mountain View, so Mountain View, California, Silicon Valley to Vegas, and he moves the steering wheel and he moves it real time. >> But he's driving the car. >> Yeah. >> So one of the great knocks on cloud, right, is latency, and clearly the use case that's always brought up is if you're in a self-driving car, you don't have time for the data to get it to the cloud and back to make a decision if a little ball rolls out into the street. So latency is a big issue. How do you guys deal with the latency issue? >> That's our secret sauce, obviously, but I'm happy to share as much as I can. The high level description would be, we connect multiple networks at the same time. We would usually have only AT&T in your cellphone, right, or in your car, and then we have AT&T, Verizon, T-Mobile, and a few networks, all of these together are bonded, and once they're bonded they get a much stronger connection. It sounds maybe easy, okay so let's plug a few phones and then get a really good connection, but it's much more complicated than that. We share and split the data across multiple networks at the same time, we prioritize the data. So, like a brake, it's very important, right, so if the remote operator is pressing the brake, you want it to be first in the vehicle, where the right side of the camera is not as critical, so lower latency for the brake, and then a little bit higher latency for something less important. >> So you've got dynamic, kind of, latent distribution. >> It's all dynamic, realtime, you know, so that's what we do, our real core. We provide this communication, real time, critical layer of communication for the video streaming and back of the data from the remote operator, back and forth all the time. >> So that's one big piece of it. Another big piece of it is the communications between the occupants in the vehicle and the driver. Another really important piece that obviously most people aren't thinking about for autonomous vehicles because they don't have that use case. But that's a pretty important piece of your solution. >> Yeah, that's a big one. I'd say that for this, you don't need to do a lot of innovation. It could be a simple call with the driver remotely. But, we're all about safety, right, and we're all about giving passengers this psychological trust, and it is true, you want to sit in a car that drives 100% of the time. If I tell you that your car today would go in and drives only 95% of the time, you would not buy this car. Same thing with autonomous vehicles. So we provide a safety and service layer. On the safety side, it's about assisting the vehicle when there's an emergency. It could be post-emergency or before it happens. Let's say you're just stuck in the middle of the lane and you don't know what happens. Even if the driver remotely wouldn't actually drive the car, you still want to be able to talk to somebody, right. So, I'd start with first the person, the driver, the human being would greet you when you enter the vehicle. It's an autonomous vehicle, he would say hello, how are you, nice to meet you, my name is let's say Ben- >> Ben is going to be your driver. >> Your driver soon, and Ben is going to tell you that whenever you have a problem, if you need any assistance, he would be there for you. That already gives you like a whole different type of experience, and when you leave the vehicle too, he's not going to be there all the time engaged with the car. The car is going to drive on an autonomous AV system, but at least he's there in case you need him. >> And again, the attention thing, which is an issue, you see with some of the test autonomous cars out there we were talking before we turned the cameras on, where the engineer's got his hands ready to grab the wheel if there's an emergency. That's not really Ben's role here. The car is going to take evasive action in terms of emergency. It's more to get out of like these weird corner cases as you said. >> Correct, it's not a test driver. Today, most autonomous vehicle companies still require and mandate it, it's actually illegal. By the regs, you have to have a person in the car. We also have a person in the car, and we do that same thing, although when Ben is driving, he's not replacing that person. He's just assisting when the autonomous vehicle system would have an issue. >> Right. So the next thing I think that's pretty interesting about your company, as you said, you're a software company. There is hardware components, you can see the back of the car, we'll take some film of the driving station, but you use a lot of off the shelf, really simple hardware to execute this. There's Logitech, little steering wheels are over there, it feels like a big video game, you've got the big, curved Samsung screens, basic cameras on the car, so talk about the opportunity to build a software company and you're leveraging somebody else's autonomous vehicle technology to really get in the middle of this with just software, a pretty cool opportunity. >> I'll tell you what. The best time of my life was earlier this year, when I was just putting this whole thing together because it was plugging in the hardware and the software, I did it together with a team that's also here in the office. Obviously, it was more challenging because from a software person to try and build this hardware, you know, is more challenging, but I'd say today, you can get anything on Amazon, you buy on eBay a part you need, you plug it in and it would just work. So, again, we did a lot of iteration, I'd say we spent a bit more money than we were supposed to. But, that works. >> Right. And then the last piece of the puzzle that I think is fascinating is the way you're going to integrate in with other people's autonomous vehicle, so again, we talked about Waymo up the street, the Google one, Uber is working on theirs, Volvo, every day you read about BMW, et cetera et cetera, so you really get to take advantage of those hardware systems, the sensor systems, the control systems, not only from those autonomous vehicles, but you're seeing now all this stuff that's coming in factory, right, avoidance collision and radar and all types of sensors, so you will have to be able to take advantage of those different platforms and integrate your system into those various platforms. >> Right. So we would work with a company, let's say if it's one of the big OEMs or ride-sharing companies, we would know how their vehicle is set up, all we need for our solution to work is a bunch of cameras and a few modems, right, so cameras everybody have, it's one of the most essential things in an autonomous vehicle- >> Right, right. >> We would just tag into these cameras, use the modems that we need for the software to run, and that's about it. So it's a pretty straightforward solution to allow remote control assistant for autonomous vehicles. >> I'm just curious, when you're talking to customers or potential partners, what is the piece that really resonates with them when you kind of explain your solution and how it fits with what they're trying to accomplish? >> Right, so our solution is really trying to help them reach market faster, so we're not replacing anybody's work. We're adding another layer of support and safety so when yous computer crashed, when your software crashed in the car, we're going to be there with another redundancy system to support with a driver remotely. So, that's what we do at the service level. >> Okay, so can I go take a drive? >> Yeah, sure. Let's do it. >> All right, we're going to check it out, we're going to take a drive. We'll see you in the car. Thanks for watching. (upbeat music)

>>Hey, welcome back everyone. Day three of eight of us Reinvent 2022. I'm John Farmer with Dave Volante, co-host the q Dave. 10 years for us, the leader in high tech coverage is our slogan. Now 10 years of reinvent day. We've been to every single one except with the original, which we would've come to if Amazon actually marketed the event, but they didn't. It's more of a customer event. This is day three. Is the machine learning ai keynote sws up there. A lot of announcements. We're gonna break this down. We got, we got Andy Thra here, vice President, prince Constellation Research. Andy, great to see you've been on the cube before one of our analysts bringing the, bringing the, the analysis, commentary to the keynote. This is your wheelhouse. Ai. What do you think about Swami up there? I mean, he's awesome. We love him. Big fan Oh yeah. Of of the Cuban we're fans of him, but he got 13 announcements. >>A lot. A lot, >>A lot. >>So, well some of them are, first of all, thanks for having me here and I'm glad to have both of you on the same show attacking me. I'm just kidding. But some of the announcement really sort of like a game changer announcements and some of them are like, meh, you know, just to plug in the holes what they have and a lot of golf claps. Yeah. Meeting today. And you could have also noticed that by, when he was making the announcements, you know, the, the, the clapping volume difference, you could say, which is better, right? But some of the announcements are, are really, really good. You know, particularly we talked about, one of that was Microsoft took that out of, you know, having the open AI in there, doing the large language models. And then they were going after that, you know, having the transformer available to them. And Amazon was a little bit weak in the area, so they couldn't, they don't have a large language model. So, you know, they, they are taking a different route saying that, you know what, I'll help you train the large language model by yourself, customized models. So I can provide the necessary instance. I can provide the instant volume, memory, the whole thing. Yeah. So you can train the model by yourself without depending on them kind >>Of thing. So Dave and Andy, I wanna get your thoughts cuz first of all, we've been following Amazon's deep bench on the, on the infrastructure pass. They've been doing a lot of machine learning and ai, a lot of data. It just seems that the sentiment is that there's other competitors doing a good job too. Like Google, Dave. And I've heard folks in the hallway, even here, ex Amazonians saying, Hey, they're train their models on Google than they bring up the SageMaker cuz it's better interface. So you got, Google's making a play for being that data cloud. Microsoft's obviously putting in a, a great kind of package to kind of make it turnkey. How do they really stand versus the competition guys? >>Good question. So they, you know, each have their own uniqueness and the we variation that take it to the field, right? So for example, if you were to look at it, Microsoft is known for as industry or later things that they are been going after, you know, industry verticals and whatnot. So that's one of the things I looked here, you know, they, they had this omic announcement, particularly towards that healthcare genomics space. That's a huge space for hpz related AIML applications. And they have put a lot of things in together in here in the SageMaker and in the, in their models saying that, you know, how do you, how do you use this transmit to do things like that? Like for example, drug discovery, for genomics analysis, for cancer treatment, the whole, right? That's a few volumes of data do. So they're going in that healthcare area. Google has taken a different route. I mean they want to make everything simple. All I have to do is I gotta call an api, give what I need and then get it done. But Amazon wants to go at a much deeper level saying that, you know what? I wanna provide everything you need. You can customize the whole thing for what you need. >>So to me, the big picture here is, and and Swami references, Hey, we are a data company. We started, he talked about books and how that informed them as to, you know, what books to place front and center. Here's the, here's the big picture. In my view, companies need to put data at the core of their business and they haven't, they've generally put humans at the core of their business and data. And now machine learning are at the, at the outside and the periphery. Amazon, Google, Microsoft, Facebook have put data at their core. So the question is how do incumbent companies, and you mentioned some Toyota Capital One, Bristol Myers Squibb, I don't know, are those data companies, you know, we'll see, but the challenge is most companies don't have the resources as you well know, Andy, to actually implement what Google and Facebook and others have. >>So how are they gonna do that? Well, they're gonna buy it, right? So are they gonna build it with tools that's kind of like you said the Amazon approach or are they gonna buy it from Microsoft and Google, I pulled some ETR data to say, okay, who are the top companies that are showing up in terms of spending? Who's spending with whom? AWS number one, Microsoft number two, Google number three, data bricks. Number four, just in terms of, you know, presence. And then it falls down DataRobot, Anaconda data icu, Oracle popped up actually cuz they're embedding a lot of AI into their products and, and of course IBM and then a lot of smaller companies. But do companies generally customers have the resources to do what it takes to implement AI into applications and into workflows? >>So a couple of things on that. One is when it comes to, I mean it's, it's no surprise that the, the top three or the hyperscalers, because they all want to bring their business to them to run the specific workloads on the next biggest workload. As you was saying, his keynote are two things. One is the A AIML workloads and the other one is the, the heavy unstructured workloads that he was talking about. 80%, 90% of the data that's coming off is unstructured. So how do you analyze that? Such as the geospatial data. He was talking about the volumes of data you need to analyze the, the neural deep neural net drug you ought to use, only hyperscale can do it, right? So that's no wonder all of them on top for the data, one of the things they announced, which not many people paid attention, there was a zero eight L that that they talked about. >>What that does is a little bit of a game changing moment in a sense that you don't have to, for example, if you were to train the data, data, if the data is distributed everywhere, if you have to bring them all together to integrate it, to do that, it's a lot of work to doing the dl. So by taking Amazon, Aurora, and then Rich combine them as zero or no ETL and then have Apaches Apaches Spark applications run on top of analytical applications, ML workloads. That's huge. So you don't have to move around the data, use the data where it is, >>I, I think you said it, they're basically filling holes, right? Yeah. They created this, you know, suite of tools, let's call it. You might say it's a mess. It's not a mess because it's, they're really powerful but they're not well integrated and now they're starting to take the seams as I say. >>Well yeah, it's a great point. And I would double down and say, look it, I think that boring is good. You know, we had that phase in Kubernetes hype cycle where it got boring and that was kind of like, boring is good. Boring means we're getting better, we're invisible. That's infrastructure that's in the weeds, that's in between the toes details. It's the stuff that, you know, people we have to get done. So, you know, you look at their 40 new data sources with data Wrangler 50, new app flow connectors, Redshift Auto Cog, this is boring. Good important shit Dave. The governance, you gotta get it and the governance is gonna be key. So, so to me, this may not jump off the page. Adam's keynote also felt a little bit of, we gotta get these gaps done in a good way. So I think that's a very positive sign. >>Now going back to the bigger picture, I think the real question is can there be another independent cloud data cloud? And that's the, to me, what I try to get at my story and you're breaking analysis kind of hit a home run on this, is there's interesting opportunity for an independent data cloud. Meaning something that isn't aws, that isn't, Google isn't one of the big three that could sit in. And so let me give you an example. I had a conversation last night with a bunch of ex Amazonian engineering teams that left the conversation was interesting, Dave. They were like talking, well data bricks and Snowflake are basically batch, okay, not transactional. And you look at Aerospike, I can see their booth here. Transactional data bases are hot right now. Streaming data is different. Confluence different than data bricks. Is data bricks good at hosting? >>No, Amazon's better. So you start to see these kinds of questions come up where, you know, data bricks is great, but maybe not good for this, that and the other thing. So you start to see the formation of swim lanes or visibility into where people might sit in the ecosystem, but what came out was transactional. Yep. And batch the relationship there and streaming real time and versus you know, the transactional data. So you're starting to see these new things emerge. Andy, what do you, what's your take on this? You're following this closely. This seems to be the alpha nerd conversation and it all points to who's gonna have the best data cloud, say data, super clouds, I call it. What's your take? >>Yes, data cloud is important as well. But also the computational that goes on top of it too, right? Because when, when the data is like unstructured data, it's that much of a huge data, it's going to be hard to do that with a low model, you know, compute power. But going back to your data point, the training of the AIML models required the batch data, right? That's when you need all the, the historical data to train your models. And then after that, when you do inference of it, that's where you need the streaming real time data that's available to you too. You can make an inference. One of the things, what, what they also announced, which is somewhat interesting, is you saw that they have like 700 different instances geared towards every single workload. And there are some of them very specifically run on the Amazon's new chip. The, the inference in two and theran tr one chips that basically not only has a specific instances but also is run on a high powered chip. And then if you have that data to support that, both the training as well as towards the inference, the efficiency, again, those numbers have to be proven. They claim that it could be anywhere between 40 to 60% faster. >>Well, so a couple things. You're definitely right. I mean Snowflake started out as a data warehouse that was simpler and it's not architected, you know, in and it's first wave to do real time inference, which is not now how, how could they, the other second point is snowflake's two or three years ahead when it comes to governance, data sharing. I mean, Amazon's doing what always does. It's copying, you know, it's customer driven. Cuz they probably walk into an account and they say, Hey look, what's Snowflake's doing for us? This stuff's kicking ass. And they go, oh, that's a good idea, let's do that too. You saw that with separating compute from storage, which is their tiering. You saw it today with extending data, sharing Redshift, data sharing. So how does Snowflake and data bricks approach this? They deal with ecosystem. They bring in ecosystem partners, they bring in open source tooling and that's how they compete. I think there's unquestionably an opportunity for a data cloud. >>Yeah, I think, I think the super cloud conversation and then, you know, sky Cloud with Berkeley Paper and other folks talking about this kind of pre, multi-cloud era. I mean that's what I would call us right now. We are, we're kind of in the pre era of multi-cloud, which by the way is not even yet defined. I think people use that term, Dave, to say, you know, some sort of magical thing that's happening. Yeah. People have multiple clouds. They got, they, they end up by default, not by design as Dell likes to say. Right? And they gotta deal with it. So it's more of they're inheriting multiple cloud environments. It's not necessarily what they want in the situation. So to me that is a big, big issue. >>Yeah, I mean, again, going back to your snowflake and data breaks announcements, they're a data company. So they, that's how they made their mark in the market saying that, you know, I do all those things, therefore you have, I had to have your data because it's a seamless data. And, and Amazon is catching up with that with a lot of that announcements they made, how far it's gonna get traction, you know, to change when I to say, >>Yeah, I mean to me, to me there's no doubt about Dave. I think, I think what Swamee is doing, if Amazon can get corner the market on out of the box ML and AI capabilities so that people can make it easier, that's gonna be the end of the day tell sign can they fill in the gaps. Again, boring is good competition. I don't know mean, mean I'm not following the competition. Andy, this is a real question mark for me. I don't know where they stand. Are they more comprehensive? Are they more deeper? Are they have deeper services? I mean, obviously shows to all the, the different, you know, capabilities. Where, where, where does Amazon stand? What's the process? >>So what, particularly when it comes to the models. So they're going at, at a different angle that, you know, I will help you create the models we talked about the zero and the whole data. We'll get the data sources in, we'll create the model. We'll move the, the whole model. We are talking about the ML ops teams here, right? And they have the whole functionality that, that they built ind over the year. So essentially they want to become the platform that I, when you come in, I'm the only platform you would use from the model training to deployment to inference, to model versioning to management, the old s and that's angle they're trying to take. So it's, it's a one source platform. >>What about this idea of technical debt? Adrian Carro was on yesterday. John, I know you talked to him as well. He said, look, Amazon's Legos, you wanna buy a toy for Christmas, you can go out and buy a toy or do you wanna build a, to, if you buy a toy in a couple years, you could break and what are you gonna do? You're gonna throw it out. But if you, if you, if part of your Lego needs to be extended, you extend it. So, you know, George Gilbert was saying, well, there's a lot of technical debt. Adrian was countering that. Does Amazon have technical debt or is that Lego blocks analogy the right one? >>Well, I talked to him about the debt and one of the things we talked about was what do you optimize for E two APIs or Kubernetes APIs? It depends on what team you're on. If you're on the runtime gene, you're gonna optimize for Kubernetes, but E two is the resources you want to use. So I think the idea of the 15 years of technical debt, I, I don't believe that. I think the APIs are still hardened. The issue that he brings up that I think is relevant is it's an end situation, not an or. You can have the bag of Legos, which is the primitives and build a durable application platform, monitor it, customize it, work with it, build it. It's harder, but the outcome is durability and sustainability. Building a toy, having a toy with those Legos glued together for you, you can get the play with, but it'll break over time. Then you gotta replace it. So there's gonna be a toy business and there's gonna be a Legos business. Make your own. >>So who, who are the toys in ai? >>Well, out of >>The box and who's outta Legos? >>The, so you asking about what what toys Amazon building >>Or, yeah, I mean Amazon clearly is Lego blocks. >>If people gonna have out the box, >>What about Google? What about Microsoft? Are they basically more, more building toys, more solutions? >>So Google is more of, you know, building solutions angle like, you know, I give you an API kind of thing. But, but if it comes to vertical industry solutions, Microsoft is, is is ahead, right? Because they have, they have had years of indu industry experience. I mean there are other smaller cloud are trying to do that too. IBM being an example, but you know, the, now they are starting to go after the specific industry use cases. They think that through, for example, you know the medical one we talked about, right? So they want to build the, the health lake, security health lake that they're trying to build, which will HIPPA and it'll provide all the, the European regulations, the whole line yard, and it'll help you, you know, personalize things as you need as well. For example, you know, if you go for a certain treatment, it could analyze you based on your genome profile saying that, you know, the treatment for this particular person has to be individualized this way, but doing that requires a anomalous power, right? So if you do applications like that, you could bring in a lot of the, whether healthcare, finance or what have you, and then easy for them to use. >>What's the biggest mistake customers make when it comes to machine intelligence, ai, machine learning, >>So many things, right? I could start out with even the, the model. Basically when you build a model, you, you should be able to figure out how long that model is effective. Because as good as creating a model and, and going to the business and doing things the right way, there are people that they leave the model much longer than it's needed. It's hurting your business more than it is, you know, it could be things like that. Or you are, you are not building a responsibly or later things. You are, you are having a bias and you model and are so many issues. I, I don't know if I can pinpoint one, but there are many, many issues. Responsible ai, ethical ai. All >>Right, well, we'll leave it there. You're watching the cube, the leader in high tech coverage here at J three at reinvent. I'm Jeff, Dave Ante. Andy joining us here for the critical analysis and breaking down the commentary. We'll be right back with more coverage after this short break.

(upbeat music) >> Welcome back friends. It's theCUBE LIVE in Las Vegas at the Venetian Expo, covering the first full day of AWS re:Invent 2022. I'm Lisa Martin, and I have the privilege of working much of this week with Dave Vellante. >> Hey. Yeah, it's good to be with you Lisa. >> It's always good to be with you. Dave, this show is, I can't say enough about the energy. It just keeps multiplying as I've been out on the show floor for a few minutes here and there. We've been having great conversations about cloud migration, digital transformation, business transformation. You name it, we're talking about it. >> Yeah, and I got to say the soccer Christians are really happy. (Lisa laughing) >> Right? Because the USA made it through. So that's a lot of additional excitement. >> That's true. >> People were crowded around the TVs at lunchtime. >> They were, they were. >> So yeah, but back to data. >> Back to data. We have a couple of guests here. We're going to be talking a lot with customer challenges, how they're helping to overcome them. Please welcome Kevin Zawodzinski, VP of Sales Engineering at COMMVAULT. >> Thank you. >> And Paul Meighan, Director of Product Management at AWS. Guys, it's great to have you on the program. Thank you for joining us. >> Thanks for having us. >> Thanks for having us. >> Isn't it great to be back in person? >> Paul: It really is. >> Kevin: Hell, yeah. >> You cannot replicate this on virtual, you just can't. It's nice to see how excited people are to be back. There's been a ton of buzz on our program today about Adam's keynote this morning. Amazing. A lot of synergies with the direction, Paul, that AWS is going in and where we're seeing its ecosystem as well. Paul, first question for you. Talk about, you know, in the customer environment, we know AWS is very customer obsessed. Some of the main challenges customers are facing today is they really continue this business transformation, this digital transformation, and they move to cloud native apps. What are some of those challenges and how do you help them eradicate those? >> Well, I can tell you that the biggest contribution that we make is really by focusing on the fundamentals when it comes to running storage at scale, right? So Amazon S3 is unique, distributed architecture, you know, it really does deliver on those fundamentals of durability, availability, performance, security and it does it at virtually unlimited scale, right? I mean, you guys have talked to a lot of storage folks in the industry and anyone who's run an estate at scale knows that doing that and executing on those fundamentals day after day is just super hard, right? And so we come to work every day, we focus on the fundamentals, and that focus allows customers to spend their time thinking about innovation instead of on how to keep their data durably stored. >> Well, and you guys both came out of the storage world. >> Right. >> Yeah, yeah. >> It was a box world, (Kevin laughs) and it ain't no more. >> Kevin: That's right, absolutely. >> It's a service and a service of scale. >> Kevin: Yeah. So architecture matters, right? >> Yeah. >> Yeah. >> Paul, talk a little bit about, speaking of innovation, talk about the evolution of S3. It's been around for a while now. Everyone knows it, loves it, but how has AWS architected it to really help meet customers where they are? >> Paul: Right. >> Because we know, again, there's that customer first focus. You write the press release down the road, you then follow that. How is it evolving? >> Well, I can tell you that architecture matters a lot and the architecture of Amazon S3 is pretty unique, right? I think, you know, the most important thing to understand about the architecture of S3 is that it is truly a regional service. So we're laid out across a minimum of 3 Availability Zones, or AZs, which are physically separated and isolated and have a distance of miles between them to protect against local events like floods and fires and power interruption, stuff like that. And so when you give us an object, we distribute that data across that minimum of 3 Availability Zones and then within multiple devices within each AZ, right? And so what that means is that when you store data with us, your data is on storage that's able to tolerate the failure of multiple devices with no impact to the integrity of your data, which is super powerful. And then again, super hard to do when you're trying to roll your own. So that's sort of a, like an overview of the architecture. In terms of how we think about our roadmap, you know, 90% of our roadmap comes directly from what customers tell us matters, and that's a tenant of how we think about customer obsession at AWS and it really is how we drive a roadmap. >> Right, so speaking of customers Kevin, what are customers asking you guys- >> Yeah. >> for, how does it relate to what you're doing with S3? >> Yeah, it's a wonderful question and one that is actually really appropriate for us being at re:Invent, right? So we got, last three years we've had customers here with us on stage talking about it. First of all, 3 years ago we did a virtual session, unfortunately, but glad to be back as you mentioned, with Coca-Cola and theirs was about scale and scope and really about how can we protect hundreds of thousands of objects, petabyte to data, in a simple and secure way, right. Then last year we actually met with a ACT, Inc. as well and co-presented with them and really talked about how we could protect modern workloads and their modern workloads around whether it was Aurora or as well as EKS and how they continue to evolve as well. And, last but not least it's going to be, this year we're talking with Illinois State University as well about how they're going to continue to grow, adapt and really leverage AWS and ourselves to further their support of their teachers and their staff. So that is really helping us quite a bit to continue to move forward. And the things we're doing, again, with our customer base it's really around, focused on what's important to them, right? Customer obsession, how are we working with that? How are we making sure that we're listening to them? Again, working with AWS to understand how can we evolve together and really ultimately their journeys. As you heard, even with those 3 examples they're all very different, right? And that's the point, is that everybody's at a different point in the journey. They're at a different place from a modernization perspective. So we're helping them evolve, as they're helping us evolve as well, and transform with AWS. >> So very mature COMMVAULT stack, the S3 bucket and all the other capabilities. Paul, you just talked about coming together- >> Right. >> Dave: for your customers. >> Yeah, yeah, absolutely. And just, you know, we were talking the other day, Paul and I were talking the other day, it's been, you know, we've worked with AWS, with integration since 2009, right? So a long time, right? I mean, for some that may not seem like a long time ago, but it is, right? It's, you know, over a decade of time and we've really advanced that integration considerably as well. >> What are some of the things that, I don't know if you had a chance to see the keynote this morning? >> Yeah, a little bit. >> What are some of the things that there was, and in fact this is funny, funny data point for you on data. One of my previous guests told me that Adam Selipsky spent exactly 52 minutes talking about data this morning. 52 minutes. >> Okay. >> That there's a data point. But talk about some of the things that he talked about, the direction AWS is going in, obviously new era in the last year. Talk about what you heard and how you think that will evolve the COMMVAULT-AWS relationship. >> Yeah, I think part of that is about flexibility, as Paul mentioned too, architecture matters, right? So as we evolve and some of the things that we pride ourselves on is that we developed our systems and our software and everything else to not worry about what do I have to build to today but how do I continue to evolve with my customer base? And that's what AWS does, right? And continues to do. So that's really how we would see the data environment. It's really about that integration. As they grow, as they add more features we're going to add more features as well. And we're right there with them, right? So there's a lot of things that we also talk about, Paul and I talk about, around, you know, how do we, like Graviton3 was brought up today around some of the innovations around that. We're supporting that with Auto Scale right now, right? So we're right there releasing, right when AWS releasing, co-developing things when necessary as well. >> So let's talk about security a little bit. First of all, what is COMMVAULT, right? You're not a security company but you're an adjacency to security. It's sort of, we're rethinking security. >> Kevin: Yep. >> including data protection, not a bolt-on anymore. You guys both have a background in that world and I'm sure that resonates. >> Yeah. >> So what is the security play here? What role does COMMVAULT play? I think we know pretty well what role AWS plays, but love to hear, Paul, your thoughts as well on security. >> Yeah, I'll start I guess. >> Go on Paul. >> Okay. Yeah, so on the security side of things, there's a quite a few things. So again, on the development side of things, we do things like file anomaly detection, so seeing patterns in data. We talked a lot about analytics as well in the keynote this morning. We look at what is happening in the customer environment, if there's something odd or out of place that's happening, we can detect that and we'll notify people. And we've seen that, we have case studies about that. Other things we do are simple, simple but elegant. Is with our security dashboard. So we'll use our security dashboard to show best practices. Are they using Multi-Factor Authentication? Are you viewing password complexity? You know, things like that. And allows people to understand from a security landscape perspective, how do we layer in protection with their other systems around security. We don't profess to be the security company, or a security company, but we help, you know, obviously add in those additional layers. >> And obviously you're securing, you know, the S3 piece of it. >> Mmmhmm. >> You know, from your standpoint because building it in. >> That's right. And we can tell you that for us, security is job zero. And anyone at AWS will tell you that, and not only that but it will always be our top priority. Right from the infrastructure on down. We're very focused on our shared responsibility model where we handle security from the hypervisor, or host operating system level, down to the physical security of the facilities in which our services run and then it's our customer's responsibility to build secure applications, right. >> Yeah. And you talk about Graviton earlier, Nitro comes into play and how you're, sort of, fencing off, you know, the various components of the system from the operating system, the VMs, and then that is designed in and that's a new evolution that it comes as part of the package. >> Yeah, absolutely. >> Absolutely. >> Paul, talk a little bit about, you know, security, talking about that we had so many conversations this year alone about the threat landscape and how it's dramatically changing, it's top of mind for everybody. Huge rise in ransomware attacks. Ransomware is now, when are we going to get hit? How often? What's the damage going to be? Rather than, are we going to get hit? It's, unfortunately it's progressed in that direction. How does ensuring data security impact how you're planning the roadmap at AWS and how are partners involved in shaping that? >> Right, so like I said, you know, 90% of our roadmap comes from what customers tell us matters, right? And clearly this is an issue that matters very much to customers right now, right? And so, you know, we're certainly hearing that from customers, and COMMVAULT, and partners like COMMVAULT have a big role to play in helping customers to secure and protect their applications, right? And that's why it's so critical that we come together here at re:Invent and we have a bunch of time here at the show with the COMMVAULT technical folks to talk through what they're hearing from customers and what we're hearing. And we have a number of regular touch points throughout the year as well, right? And so what COMMVAULT gets from the relationship is, sort of, early access and feedback into our features and roadmap. And what we get out of it really is that feedback from that large number of customers who interface with Amazon S3 through COMMVAULT. Who are using S3 as a backup target behind COMMVAULT, right? And so, you know, that partnership really allows us to get close to those customers and understand what really matters to them. >> Are you doing joint engineering, or is it more just, hey here you go COMMVAULT, here's the tools available, go, go build. Can you address that? >> Yeah, no, absolutely. There's definitely joint engineering like even things around, you know, data migration and movement of data, we integrate really well and we talk a lot about, hey, what are you, like as Paul mentioned, what are you seeing out there? We actually, I just left a conversation about an hour ago where we're talking about, you know, where are we seeing placement of data and how does that matter to, do you put it on, you know, instant access, or do you put it on Glacier, you know, what should be the best practices? And we tell them, again, some of the telemetry data that we have around what do we see customers doing, what's the patterns of data? And then we feed that back in and we use that to create joint solutions as well. >> You know, I wonder if we could talk about cloud, you know, optimization of cloud costs for a minute. That's obviously a big discussion point in the hallways with customers. And on your earnings call you guys talked about specifically some customers and they specifically mentioned, for example, pushing storage to lower cost tiers. So you brought up Glacier just then. What are you seeing in the field in that regard? How are customers taking advantage of that? And where does COMMVAULT play in, sort of, helping make that decision? >> You want to take part one or you want me to take it? >> I can take part one. I can tell you that, you know, we're very focused on helping customers optimize costs, however necessary, right? And, you know, we introduced intelligent hearing here at the show in 2019 and since launch it's helped customers to reduce costs by over $750 million, right? So that's a real commitment to optimizing costs on behalf of customers. We also launched, you know, later in 2020, Glacier Deep Archive, which is the lowest cost storage in the cloud. So it's an important piece of the puzzle, is to provide those storage options that can allow customers to match the workloads that are, that need to be on folder storage to the appropriate store. >> Yeah, and so, you know, S3 is not this, you know, backup and recovery system, not an archiving system and, you know, in terms of, but you have that intelligence in your platform. 'Cause when I heard that from the earnings call I was like, okay, how do customers then go about deciding what they can, you know, when it's all good times, like yeah, who cares? You know, just go, go, go. But when you got to tighten the belt, how do you guys? >> Yeah, and that goes back to understanding the data pattern. So some of that is we have intelligence and artificial intelligence and everything else and machine learning within our, so we can detect those patterns, right? We understand the patterns, we learn from that and we help customers right size, right. So ultimately we do see a blend, right? As Paul mentioned, we see, you know, hey I'm not going to put everything on Glacier necessarily upfront. Maybe they are, it all depends on their workloads and patterns. So we use the data that we collect from the different customers that we have to share those best practices out and create, you know, the right templates, so to speak, in ways for people to apply it. >> Guys, great joint, you talked about the joint engineering, joint go to market, obviously a very strong synergistic partnership between the two. A lot of excitement. This is only day one, I can only imagine what's going to be coming the next couple of days. But I have one final question for you, but I have same question for both of you. You had the chance to create your own bumper sticker, so you get a shiny new car and for some reason you want to put a bumper sticker on it. About COMMVAULT, what would it say? >> Yeah, so for me I would say comprehensive, yet simple, right? So ultimately about giving you all the bells and whistles but if you want to be very simple we can help you in every shape and form. >> Paul, what's your bumper sticker say about AWS? >> I would say that AWS starts with the customer and works backwards from there. >> Great one. >> Excellent. Guys- >> Kevin: Well done. >> it's been a pleasure to have you on the program. Thank you- >> Kevin: Thank you. >> for sharing what's going on, the updates on the AWS-COMMVAULT partnership and what's in it for customers. We appreciate it. >> Dave: Thanks you guys. >> Thanks a lot. >> Thank you. >> All right. For our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

>>Good evening, brilliant humans. My name is Savannah Peterson and very delighted to be streaming to you. Live from the Cube Studios here in Motor City, Michigan. I've got John Furrier on my left. John, this is our last interview of the day. Energy just seems to keep oozing. How >>You doing? Take two, Three days of coverage, the queue love segments. This one's great cuz we have a practitioner who's implementing all the hard core talks to be awesome. Can't wait to get into it. >>Yeah, I'm very excited for this one. If it's not very clear, we are a community focused community is a huge theme here at the show at Cape Con. And our next guests are actually a provider and a customer. Turning it over to you. Lisa and Jake, welcome to the show. >>Thank you so much for having us. >>It's great to be here. It is our pleasure. Lisa, you're with Cockroach. Just in case the audience isn't familiar, give us a quick little sound bite. >>We're a distributed sequel database. Highly scalable, reliable. The database you can't kill, right? We will survive the apocalypse. So very resilient. Our customers, mostly retail, FinTech game meet online gambling. They, they, they need that resiliency, they need that scalability. So the indestructible database is the elevator pitch >>And the success has been very well documented. Valuation obviously is a scorp guard, but huge customers. We were at the Escape 19. Just for the record, the first ever multi-cloud conference hasn't come back baby. Love it. It'll come back soon. >>Yeah, well we did a similar version of it just a month ago and I was, that was before Cockroach. I was a different company there talking a lot about multi-cloud. So, but I'm, I've been a car a couple of years now and I run community, I run developer relations. I'm still also a CNCF ambassador, so I lead community as well. I still run a really large user group in the San Francisco Bay area. So we've just >>Been in >>Community, take through the use case. Jake's story set us up. >>Well I would like Jake to take him through the use case and Cockroach is a part of it, but what they've built is amazing. And also Jake's history is amazing. So you can start Jake, >>Wherever you take >>Your Yeah, sure. I'm Jake, I'm CEO and co-founder of Offset. Oted is the commercial entity behind Spice Dvy and Spice Dvy is a permission service. Cool. So a permission service is something that lets developers and let's platform teams really unlock the full potential of their applications. So a lot of people get stuck on My R back isn't flexible enough. How do I do these fine grain things? How do I do these complex sharing workflows that my product manager thinks is so important? And so our service enables those platform teams and developers to do those kinds of things. >>What's your, what's your infrastructure? What's your setup look like? What, how are you guys looking like on the back end? >>Sure. Yeah. So we're obviously built on top of Kubernetes as well. One of the reasons that we're here. So we use Kubernetes, we use Kubernetes operators to orchestrate everything. And then we use, use Cockroach TV as our production data store, our production backend data store. >>So I'm curious, cause I love when these little matchmakers come together. You said you've now been presenting on a little bit of a road show, which is very exciting. Lisa, how are you and the team surfacing stories like Jakes, >>Well, I mean any, any place we can obviously all the social medias, all the blogs, How >>Are you finding it though? >>How, how did you Oh, like from our customers? Yeah, we have an open source version so people start to use us a long time before we even sometimes know about them. And then they'll come to us and they'll be like, I love Cockroach, and like, tell me about it. Like, tell me what you build and if it's interesting, you know, we'll we'll try to give it some light. And it's always interesting to me what people do with it because it's an interesting technology. I like what they've done with it. I mean the, the fact that it's globally distributed, right? That was like a really important thing to you. Totally. >>Yeah. We're also long term fans of Cockroach, so we actually all work together out of Workbench, which was a co-working space and investor in New York City. So yeah, we go way back. We knew the founders. I, I'm constantly saying like if I could have invested early in cockroach, that would've been the easiest check I could have ever signed. >>Yeah, that's awesome. And then we've been following that too and you guys are now using them, but folks that are out there looking to have the, the same challenges, what are the big challenges on selecting the database? I mean, as you know, the history of Cockroach and you're originating the story, folks out there might not know and they're also gonna choose a database. What's the, what's the big challenge that they can solve that that kind of comes together? What, what would you describe that? >>Sure. So we're, as I said, we're a permission service and per the data that you store in a permission service is incredibly sensitive. You need it to be around, right? You need it to be available. If the permission service goes down, almost everything else goes down because it's all calling into the permission service. Is this user allowed to do this? Are they allowed to do that? And if we can't answer those questions, then our customer is down, right? So when we're looking at a database, we're looking for reliability, we're looking for durability, disaster recovery, and then permission services are one of the only services that you usually don't shard geographically. So if you look at like AWS's iam, that's a global service, even though the individual things that they run are actually sharded by region. So we also needed a globally distributed database with all of those other properties. So that's what led us >>To, this is a huge topic. So man, we've been talking about all week the cloud is essentially distributed database at this point and it's distributed system. So distributed database is a hot topic, totally not really well reported. A lot of people talking about it, but how would you describe this distributed trend that's going on? What are the key reasons that they're driving it? What's making this more important than ever in your mind, in your opinion? >>I mean, for our use case, it was just a hard requirement, right? We had to be able to have this global service. But I think just for general use cases, a distributed database, distributed database has that like shared nothing architecture that allows you to kind of keep it running and horizontally scale it. And as your requirements and as your applications needs change, you can just keep adding on capacity and keep adding on reliability and availability. >>I'd love to get both of your opinion. You've been talking about the, the, the, the phases of customers, the advanced got Kubernetes going crazy distributed, super alpha geek. Then you got the, the people who are building now, then you got the lagers who are coming online. Where do you guys see the market now in terms of, I know the Alphas are all building all the great stuff and you guys had great success with all the top logos and they're all doing hardcore stuff. As the mainstream enterprise comes in, where's their psychology, what's on their mind? What's, you share any insight into your perspective on that? Because we're seeing a lot more of it folks becoming like real cloud players. >>Yeah, I feel like in mainstream enterprise hasn't been lagging as much as people think. You know, certainly there's been pockets in big enterprises that have been looking at this and as distributed sequel, it gives you that scalability that it's absolutely essential for big enterprises. But also it gives you the, the multi-region, you know, the, you have to be globally distributed. And for us, for enterprises, you know, you need your data near where the users are. I know this is hugely important to you as well. So you have to be able to have a multi-region functionality and that's one thing that distributed SQL lets you build and that what we built into our product. And I know that's one of the things you like too. >>Yeah, well we're a brand new product. I mean we only founded the company two years ago, but we're actually getting inbound interest from big enterprises because we solve the kinds of challenges that they have and whether, I mean, most of them already do have a cockroach footprint, but whether they did or didn't, once they need to bring in our product, they're going to be adopting cockroach transitively anyway. >>So, So you're built on top of Cockroach, right? And Spice dv, is that open source or? >>It >>Is, yep. Okay. And explain the role of open source and your business model. Can you take a minute to talk about the relevance of that? >>Yeah, open source is key. My background is, before this I was at Red Hat. Before that we were at CoreOS, so CoreOS acquisition and before that, >>One of the best acquisitions that ever happened for the value. That was a great, great team. Yeah, >>We, we, we had fun and before that we built Qua. So my co-founders and I, we built Quay, which is a, a first private docker registry. So CoreOS and, and all of those things are all open source or deeply open source. So it's just in our dna. We also see it as part of our go-to market motion. So if you are a database, a lot of people won't even consider what you're doing without being open source. Cuz they say, I don't want to take a, I don't want to, I don't want to end up in an Oracle situation >>Again. Yeah, Oracle meaning they go, you get you locked in, get you in a headlock, Increase prices. >>Yeah. Oh yeah, >>Can, can >>I got triggered. >>You need to talk about your PTSD there >>Or what. >>I mean we have 20,000 stars on GitHub because we've been open and transparent from the beginning. >>Yeah. And it >>Well, and both of your projects were started based on Google Papers, >>Right? >>That is true. Yep. And that's actually, so we're based off of the Google Zans of our paper. And as you know, Cockroach is based off of the Google Span paper and in the the Zanzibar paper, they have this globally distributed database that they're built on top of. And so when I said we're gonna go and we're gonna make a company around the Zabar paper, people would go, Well, what are you gonna do for Span? And I was like, Easy cockroach, they've got us covered. >>Yeah, I know the guys and my friends. Yeah. So the question is why didn't you get into the first round of Cockroach? She said don't answer that. >>The question he did answer though was one of those age old arguments in our community about pronunciation. We used to argue about Quay, I always called it Key of course. And the co-founder obviously knows how it's pronounced, you know, it's the et cd argument, it's the co cuddl versus the control versus coo, CTL Quay from the co-founder. That is end of argument. You heard it here first >>And we're keeping it going with Osted. So awesome. A lot of people will say Zeed or, you know, so we, we just like to have a little ambiguity >>In the, you gotta have some semantic arguments, arm wrestling here. I mean, it keeps, it keeps everyone entertained, especially on the over the weekend. What's, what's next? You got obviously Kubernetes in there. Can you explain the relationship between Kubernetes, how you're handling Spice dv? What, what does the Kubernetes piece fit in and where, where is that going to be going? >>Yeah, great question. Our flagship product right now is a dedicated, and in a dedicated, what we're doing is we're spinning up a single tenant Kubernetes cluster. We're installing all of our operator suite, and then we're installing the application and running it in a single tenant fashion for our customers in the same region, in the same data center where they're running their applications to minimize latency. Because of this, as an authorization service, latency gets passed on directly to the end user. So everybody's trying to squeeze the latency down as far as they can. And our strategy is to just run these single tenant stacks for people with the minimal latency that we can and give them a VPC dedicated link very similar to what Cockroach does in their dedicated >>Product. And the distributed architecture makes that possible because it's lighter way, it's not as heavy. Is that one of the reasons? >>Yep. And Kubernetes really gives us sort of like a, a level playing field where we can say, we're going going to take the provider, the cloud providers Kubernetes offering, normalize it, lay down our operators, and then use that as the base for delivering >>Our application. You know, Jake, you made me think of something I wanted to bring up with other guests, but now since you're here, you're an expert, I wanna bring that up, but talk about Super Cloud. We, we coined that term, but it's kind of multi-cloud, is that having workloads on multiple clouds is hard. I mean there are, they are, there are workloads on, on clouds, but the complexity of one clouds, let's take aws, they got availability zones, they got regions, you got now data issues in each one being global, not that easy on one cloud, nevermind all clouds. Can you share your thoughts on how you see that progression? Because when you start getting, as its distributed database, a lot of good things might come up that could fit into solving the complexity of global workloads. Could you share your thoughts on or scoping that problem space of, of geography? Yeah, because you mentioned latency, like that's huge. What are some of the other challenges that other people have with mobile? >>Yeah, absolutely. When you have a service like ours where the data is small, but very critical, you can get a vendor like Cockroach to step in and to fill that gap and to give you that globally distributed database that you can call into and retrieve the data. I think the trickier issues come up when you have larger data, you have huge binary blobs. So back when we were doing Quay, we wanted to be a global service as well, but we had, you know, terabytes, petabytes of data that we were like, how do we get this replicated everywhere and not go broke? Yeah. So I think those are kind of the interesting issues moving forward is what do you do with like those huge data lakes, the huge amount of data, but for the, the smaller bits, like the things that we can keep in a relational database. Yeah, we're, we're happy that that's quickly becoming a solved >>Problem. And by the way, that that data problem also is compounded when the architecture goes to the edge. >>Totally. >>I mean this is a big issue. >>Exactly. Yeah. Edge is something that we're thinking a lot about too. Yeah, we're lucky that right now the applications that are consuming us are in a data center already. But as they start to move to the edge, we're going to have to move to the edge with them. And it's a story that we're gonna have to figure out. >>All right, so you're a customer cockroach, what's the testimonial if I put you on the spot, say, hey, what's it like working with these guys? You know, what, what's the, what's the, you know, the founders, so you know, you give a good description, little biased, but we'll, we'll we'll hold you on it. >>Yeah. Working with Cockroach has been great. We've had a couple things that we've run into along the way and we've gotten great support from our account managers. They've brought in the right technical expertise when we need it. Cuz what we're doing with Cockroach is not you, you couldn't do it on Postgres, right? So it's not just a simple rip and replace for us, we're using all of the features of Cockroach, right? We're doing as of system time queries, we're doing global replication. We're, you know, we're, we're consuming it all. And so we do need help from them sometimes and they've been great. Yeah. >>And that's natural as they grow their service. I mean the world's changing. >>Well I think one of the important points that you mentioned with multi-cloud, we want you to have the choice. You know, you can run it in in clouds, you can run it hybrid, you can run it OnPrem, you can do whatever you want and it's just, it's one application that you can run in these different data centers. And so really it's up to you how do you want to build your infrastructure? >>And one of the things we've been talking about, the super cloud concept that we've been issue getting a lot of contrary, but, but people are leaning into it is that it's the refactoring and taking advantage of the services. Like what you mentioned about cockroach. People are doing that now on cloud going the lift and shift market kind of had it time now it's like hey, I can start taking advantage of these higher level services or capability of someone else's stack and refactoring it. So I think that's a dynamic that I'm seeing a lot more of. And it sounds like it's working out great in this situation. >>I just came from a talk and I asked them, you know, what don't you wanna put in the cloud and what don't you wanna run in Kubernetes or on containers and good Yeah. And the customers that I was on stage with, one of the guys made a joke and he said I would put my dog in a container room. I could, he was like in the category, which is his right, which he is in the category of like, I'll put everything in containers and these are, you know, including like mis critical apps, heritage apps, since they don't wanna see legacy anymore. Heritage apps, these are huge enterprises and they wanna put everything in the cloud. Everything >>You so want your dog that gets stuck on the airplane when it's on the tarmac. >>Oh >>God, that's, she was the, don't take that analogy. Literally don't think about that. Well that's, >>That's let's not containerize. >>There's always supply chain concern. >>It. So I mean going macro and especially given where we are cncf, it's all about open source. Do y'all think that open source builds a better future? >>Yeah and a better past. I mean this is, so much of this software is founded on open source. I, we wouldn't be here really. I've been in open source community for many, many years so I wouldn't say I'm biased. I would say this is how we build software. I came from like in a high school we're all like, oh let's build a really cool application. Oh you know what? I built this cuz I needed it, but maybe somebody else needs it too. And you put it out there and that is the ethos of Silicon Valley, right? That's where we grew up. So I've always had that mindset, you know, and social coding and why I have three people, right? Working on the same thing when one person you could share it's so inefficient. All of that. Yeah. So I think it's great that people work on what they're really good at. You know, we all, now you need some standardization, you need some kind of control around this whole thing. Sometimes some foundations to, you know, herd the cats. Yeah. But it's, it's great. Which is why I'm a c CF ambassador and I spend a lot of time, you know, in my free time talking about open source. Yeah, yeah. >>It's clear how passionate you are about it. Jake, >>This is my second company that we founded now and I don't think either of them could have existed without the base of open source, right? Like when you look at I have this cool idea for an app or a company and I want to go try it out, the last thing I want to do is go and negotiate with a vendor to get like the core data component. Yeah. To even be able to get to the >>Prototypes. NK too, by the way. Yeah. >>Hey >>Nk >>Or hire, you know, a bunch of PhDs to go and build that core component for me. So yeah, I mean nobody can argue that >>It truly is, I gotta say a best time if you're a developer right now, it's awesome to be a developer right now. It's only gonna get better. As we were riff from the last session about productivity, we believe that if you follow the digital transformation to its conclusion, developers and it aren't a department serving the business, they are the business. And that means they're running the show, which means that now their entire workflow is gonna change. It's gonna be have to be leveraging services partnering. So yeah, open source just fills that. So the more code coming up, it's just no doubt in our mind that that's go, that's happening and will accelerate. So yeah, >>You know, no one company is gonna be able to compete with a community. 50,000 users contributing versus you riding it yourself in your garage with >>Your dogs. Well it's people driven too. It's humans not container. It's humans working together. And here you'll see, I won't say horse training, that's a bad term, but like as projects start to get traction, hey, why don't we come together as, as the world starts to settle and the projects have traction, you start to see visibility into use cases, functionality. Some projects might not be, they have to kind of see more kind >>Of, not every feature is gonna be development. Oh. So I mean, you know, this is why you connect with truly brilliant people who can architect and distribute sequel database. Like who thought of that? It's amazing. It's as, as our friend >>You say, Well let me ask you a question before we wrap up, both by time, what is the secret of Kubernetes success? What made Kubernetes specifically successful? Was it timing? Was it the, the unambitious nature of it, the unification of it? Was it, what was the reason why is Kubernetes successful, right? And why nothing else? >>Well, you know what I'm gonna say? So I'm gonna let Dave >>First don't Jake, you go first. >>Oh boy. If we look at what was happening when Kubernetes first came out, it was, Mesosphere was kind of like the, the big player in the space. I think Kubernetes really, it had the backing from the right companies. It had the, you know, it had the credibility, it was sort of loosely based on Borg, but with the story of like, we've fixed everything that was broken in Borg. Yeah. And it's better now. Yeah. So I think it was just kind and, and obviously people were looking for a solution to this problem as they were going through their containerization journey. And I, yeah, I think it was just right >>Place, the timing consensus of hey, if we just let this happen, something good might come together for everybody. That's the way I felt. I >>Think it was right place, right time, right solution. And then it just kind of exploded when we were at Cores. Alex Povi, our ceo, he heard about Kubernetes and he was like, you know, we, we had a thing called Fleet D or we had a tool called Fleet. And he's like, Nope, we're all in on Kubernetes now. And that was an amazing Yeah, >>I remember that interview. >>I, amazing decision. >>Yeah, >>It's clear we can feel the shift. It's something that's come up a lot this week is is the commitment. Everybody's all in. People are ready for their transformation and Kubernetes is definitely gonna be the orchestrator that we're >>Leveraging. Yeah. And it's an amazing community. But it was, we got lucky that the, the foundational technology, I mean, you know, coming out of Google based on Go conferences, based on Go, it's no to coincidence that this sort of nature of, you know, pods horizontally, scalable, it's all fits together. I does make sense. Yeah. I mean, no offense to Python and some of the other technologies that were built in other languages, but Go is an awesome language. It's so, so innovative. Innovative things you could do with it. >>Awesome. Oh definitely. Jake, I'm very curious since we learned on the way and you are a Detroit native? >>I am. Yep. I grew up in the in Warren, which is just a suburb right outside of Detroit. >>So what does it mean to you as a Michigan born bloke to be here, see your entire community invade? >>It is, I grew up coming to the Detroit Auto Show in this very room >>That brought me to Detroit the first time. Love n a I a s. Been there with our friends at Ford just behind us. >>And it's just so interesting to me to see the accumulation, the accumulation of tech coming to Detroit cuz it's really not something that historically has been a huge presence. And I just love it. I love to see the activity out on the streets. I love to see all the restaurants and coffee shops full of people. Just, I might tear up. >>Well, I was wondering if it would give you a little bit of that hometown pride and also the joy of bringing your community together. I mean, this is merging your two probably most core communities. Yeah, >>Yeah. Your >>Youth and your, and your career. It doesn't get more personal than that really. Right. >>It's just been, it's been really exciting to see the energy. >>Well thanks for going on the queue. Thanks for sharing. Appreciate it. Thanks >>For having us. Yeah, thank you both so much. Lisa, you were a joy of ball of energy right when you walked up. Jake, what a compelling story. Really appreciate you sharing it with us. John, thanks for the banter and the fabulous questions. I'm >>Glad I could help out. >>Yeah, you do. A lot more than help out sweetheart. And to all of you watching the Cube today, thank you so much for joining us live from Detroit, the Cube Studios. My name is Savannah Peterson and we'll see you for our event wrap up next.

(bright music) (crowd talking indistinctly in the background) >> Hey guys, welcome back to Detroit, Michigan. theCUBE is live at KubeCon + CloudNativeCon 2022. You might notice something really unique here. Lisa Martin with our newest co-host of theCUBE, Savannah Peterson! Savannah, it's great to see you. >> It's so good to be here with you (laughs). >> I know, I know. We have a great segment coming up. I always love talking couple things, cars, one, two, with companies that have been around for a hundred plus years and how they've actually transformed. >> Oh yeah. >> Ford is here. You have a great story about how you, about Ford. >> Ford brought me to Detroit the first time. I was here at the North American International Auto Show. Some of you may be familiar, and the fine folks from Ford brought me out to commentate just like this, as they were announcing the Ford Bronco. >> Satish: Oh wow. >> Which I am still lusting after. >> You don't have one yet? >> For the record. No, I don't. My next car's got to be an EV. Although, ironically, there's a Ford EV right behind us here on set today. >> I know, I know. >> Which we were both just contemplating before we went live. >> It's really shiny. >> We're going to have to go check it out. >> I have to check it out. Yep, we'll do that. Yeah. Well, please welcome our two guests from Ford, Satish Puranam, is here, The Technical Leader at Cloud and Rebecca Risk, Principal Architect, developer relations. We are so excited to have you guys on the program. >> Clearly. >> Thanks for joining us. (all laugh) >> Thank you for having us. >> I love you're Ford enthusiasts! Yeah, that's awesome. >> I drive a Ford. >> Oh, awesome! Thank you. >> I can only say that's one car company here. >> That's great. >> Yes, yes. >> Great! Thank you a lot. >> Thank you for your business! >> Absolutely. (all laugh) >> So, Satish, talk to us a little bit about- I mean I think of Cloud as a car company but it seems like it's a technology company that makes cars. >> Yes. Talk to us about Ford as a Cloud first, technology driven company, and then we're going to talk about what you're doing with Red Hat and Boston University. >> Yeah, I'm like everything that all these cars that you're seeing, beautiful right behind us it's all built on, around, and with technology, right? So there's so much code goes into these cars these days, it's probably, it's mind boggling to think that probably your iPhones might be having less code as opposed to these cars. Everything from control systems, everything is code. We don't do any more clay models. Everything is done digital, 3D, virtual reality and all that stuff. So all that takes code, all of that takes technology. And we have been in that journey for the last- since 2016 when we started our first mobile app and all that stuff. And of late we have been like, heavily invested in Google. Moving a lot of these experiences, data acquisition systems AI/ML modeling for like all the autonomous cars. It's all technology and like from the day it is conceived, to the day it is marketed, to the day when you show up for a servicing, and hopefully soon how you can buy and you know, provide feedback to us, is all technology that drives all of this stuff. So it's amazing for us to see everything that we go and immerse ourselves in the technology. There is a real life thing that we can see what we all do for it, right? So- >> Yes, we're only sorry that our audience can't actually see the car, >> Yep. >> but we'll get some B-roll for you later on. Rebecca, talk a little bit about your role. Here we are at KubeCon, Savannah and I and John were talking when we went live this morning, that this is huge. That the show floor is massive, a lot bigger than last year. The collaboration and the spirit of the community is not only alive and well, as we heard in the keynote this morning, it's thriving. >> Yeah. >> Talk about developer relations at Ford and what you are helping to drive in your role. >> Yeah, so my team is all about helping developers work faster with different platforms that my team curates and produces, so that our developers don't have to deal with all of the details of setting up their environments to actually code. And we have really great people, kind of the top software developers in the company, are part of my team to produce those products that other people can use, and accelerate their development. And we have a great relationship with the developers in the company and outside with the different vendor relationships that we have, to make sure that we're always producing the next platform with the next tech stack that our developers will want to continue to use to produce the really great products that we are all about making at Ford. >> Let's dig in there a little bit because I'm curious and I suspect you both had something to do with it. How did you approach your Cloud Native transformation and how do you evaluate new technologies for the team? >> It's sometimes- many a times I would say it's like dogfooding and like experimentation. >> Yeah. Isn't anything in innovation a lot of- >> Yeah, a lot of experimentation. We started our, as I said, the Cloud Native journey back in 2016 with Cloud Foundry and things, technologies around that. Soon realized, that there was like a lot of buzz around that time. Twelve-Factor was a thing, Stateless was a thing. And then all those Stateful needs to drive the Stateless. So where do we do that thing? And the next logical iteration was Kubernetes was bursting upon the scene at that time. So we started doing a lot of experimentation. >> Like the Kool-Aid man, burst on the Kubernetes scene- >> Exactly right. >> Through the wall. >> So, the question is like, why can't we do? I think we were like crazy enough to say that Kubernetes people are talking about our serverless or Twelve-Factor on Kubernetes. We are crazy enough to do Stateful on Kubernetes and we've been doing it successfully for five years. So it's a lot about experimentation. I think good chunk of experiments that we do do not yield the results that we get, but many a times, some of them are like Gangbusters. Like, other aspects that we've been doing of late is like partnering with Becky and rest of the organization, right? Because they are the people who are like closest to the developers. We are somewhat behind the scenes doing some things but it is Becky and the rest of the architecture teams who are actually front and center with the customers, right? So it is the collaborative effort that we've been working through past few years that has been really really been useful and coming around and helping us to make some of these products really beautiful. >> Yeah, well you make a lot of beautiful products. I think we've all, I think we've all seen them. Something that I think is really interesting and part of why I was so excited for this interview, and kind of nudged John out, was because you've been- Ford has been investing in technology in a committed way for decades and I don't think most people are aware of that. When I originally came out to Dearborn, I learned that you've had a head of VR who happens to be a female. For what it's worth, Elizabeth, who's been running VR for you for two and a half decades, for 25 years. >> Satish: Yep. >> That is an impressive commitment. What is that like from a culture perspective inside of Ford? What is the attitude around innovation and technology? >> So I've been a long time Ford employee. I just celebrated my 29th year. >> Oh, wow! >> Congratulations! >> Wow, congrats! That's a huge deal. >> Yeah, it's a huge deal. I'm so proud of my career and all that Ford has brought to me and it's just a testament. I have many colleagues like me who've been there for their whole career or have done other things and come to Ford and then spent another 20 years with us because we foster the culture that makes you want to stay. We have development programs to allow you to upscale and change your role and learn new things and play with the new technologies that people are interested in doing and really make an impact to our community of developers at Ford or the company itself and the results that we're delivering. So to have that, you know, culture for so many years that people really love to work. They love to work with the people that they're working with. They love to stay engaged and they love the fact that you can have many different careers within the same umbrella, which we call the "blue oval". And that's really why I've been there for so long. I think I probably had 13 very unique and different jobs along the way. It's as if I left, and you know shopped around my skills elsewhere. But I didn't ever have to leave the company. It's been fabulous. >> The cultural change and adoption of- embracing modern technology- Cloud Native automotive software is impressive because a lot of historied companies, you guys have been there a long time, have challenges with that because it's really hard to get an entire moving, you'll call it the blue oval, to change and adapt- >> Savannah: I love that. >> and be willing to experiment. So that that is impressive. Talk about, you go by Becky, so I'll call you Becky, >> Rebecca/Becky: Yeah. >> The developer culture in terms of the developers really being the center of the nucleus of influencing the direction in which the company's going. I imagine that they probably are fairly influential. >> Yeah, so I had a very- one of the unique positions I held was a culture change for our department, Information Technology in 2016. >> Satish: Yeah. >> As the teacher was involved with moving us to the cloud, I was responsible- >> You are the transformation team! This is beautiful. I love this. We've got the right people on the show. >> Yeah, we do. >> I was responsible for changing the culture to orient our employees to pay attention to what do we want to create for tomorrow? What are the kind of skills we need to trust each other to move quickly. And that was completely unique. >> Satish: Yeah. >> Like I had men in the trenches delivering software before that, and then plucked out because they wanted someone, you know who had authentic experience with our development team to be that voice. And it was such a great investment that Ford continues to do is invest in our culture transformation. Because with each step forward that we do, we have to refine what our priorities are. And you do that through culture transformation and culture management. And that's been, I think really, the key to our successful pivots that we've made over the last six years that we've been able to continue to refine and hone where we really want to go through that culture movement. >> Absolutely. I think if I could add another- >> Please. >> spotlight to it is like the biggest thing about Ford has been among various startup-like culture, right? So the idea is that we encourage people to think outside the box, right? >> Savannah: Or outside the oval? >> Right! (laughs) >> Lisa: Outside the oval, yes! >> Absolutely! Right. >> So the question is like, you can experiment with various things, new technologies and you will get all the leadership support to go along with it. I think that is very important too and like we can be in the trenches and talk about all of these nice little things but who the heck would've thought that, you know Kubernetes was announced in 2015, in late 2016, we have early dev Kubernetes clusters already running. 2017, we are live with workloads on Kubernetes! >> Savannah: Early adopters over here. >> Yeah. >> Yeah. >> I'm like all of this thing doesn't happen without lot of foresight and support from the leadership, but it's also the grassroot efforts that is encouraged all along to be on the front end of all of these things and try different things. Some of them may not work >> Savannah: Right. >> But that's okay. But how do we know we are doing something, if you're not failing? We have to fail in order to do something, right? >> Lisa: I always say- >> So I think that's been a great thing that is encouraged very often and otherwise I would not be doing, I've done a whole bunch of stuff at Ford. Without that kind of ability to support and have an appetite for, some of those things would not have been here at all. >> I always say failure is not a bad F-word. >> Satish: Yep. >> Savannah: I love that. >> But what you're talking about there is kind of like driving this hot wheel of experimentation. You have to have the right culture and the mindset- >> Satish: Absolutely. >> to do that. Try fail, move on, learn, iterate, go. >> Satish: Correct. >> You guys have a great partnership with Red Hat and Boston University. You're speaking about that later today. >> Satish: Yes. >> Unpack that for us. What, from a technical perspective, what are you doing and what's it resulting in? >> Yeah, I think the biggest thing is Becky was talking about as during this transformation journey, is lot has changed in very small amount of time. So we traditionally been like, "Hey, here's a spreadsheet of things I need you to deliver for me" to "Here is a catalog of things, you can get it today and be successful with it". That is frightening to several of our developers. The goal, one of the things that we've been working with Q By Example, Red Hat and all the thing, is that how can we lower the bar for the developers, right? Kubernetes is great. It's also a wall of YAML. >> It's extremely complex, number one complaint. >> The question is how can I zero on? I'm like, if we go back think like when we talk about in cars with human-machine interfaces, which parts do I need to know? Here's the steering wheel, here's the gas pedal, or here's the brake. As long as you know these two, three different things you should be fairly be okay to drive those things, right? So the idea of some of the things with enablementing we are trying to do is like reduce that barrier, right? Reduce- lower the bar so that more people can participate in it. >> One of the ways that you did that was Q By Example, right, QBE? >> Satish: Yes, Yes. >> Can you tell us a little bit more about that as you finish this answer? >> Yeah, I think the biggest thing with Q By Example is like Q By Example gives you the small bite-sized things about Kubernetes, right? >> Savannah: Great place to start. >> But what we wanted to do is that we wanted to reinforce that learning by turning into a real world living example app. We took part info, we said, Hey, what does it look like? How do I make sure that it is highly available? How do I make sure that it is secure? Here is an example YAML of it that you can literally verbatim copy and paste into your editor and click run and then you will get an instant gratification feedback loop >> I was going to say, yeah, they feel like you're learning too! >> Yes. Right. So the idea would be is like, and then instead of giving you just a boring prose text to read, we actually drop links to relevant blog posts saying that, hey you can just go there. And that has been inspirational in terms of like and reinforcing the learning. So that has been where we started working with the Boston University, Red Hat and the community around all of that stuff. >> Talk a little bit about, Becky, about some of the business outcomes. You mentioned things like upskilling the workforce which is really nice to hear that there's such a big focus on it. But I imagine too, there's more participation in the community, but also from an end customer perspective. Obviously, everything Ford's doing is to serve the end customers >> Becky: Right. How does this help the end customer have that experience that they really, these days, demand with patience being something that, I think, is gone because of the pandemic? >> Right? Right. So one of the things that my team does is we create the platforms that help Accelerate developers be successful and it helps educate them more quickly on appropriate use of the platforms and helps them by adopting the platforms to be more secure which inherently lead to the better results for our end customers because their data is secure because the products that they have are well created and they're tested thoroughly. So we catch all those things earlier in the cycle by using these platforms that we help curate and produce. And that's really important because, like you had mentioned, this steep learning curve associated with Kubernetes, right? >> Savannah: Yeah. >> So my team is able to kind of help with that abstraction so that we solve kind of the higher complex problems for them so that developers can move faster and then we focus our education on what's important for them. We use things like Q By Example, as a source instead of creating that content ourselves, right? We are able to point them to that. So it's great that there's that community and we're definitely involved with that. But that's so important to help our developers be successful in moving as quickly as they want and not having 20,000 people solve the same problems. >> Satish: (chuckles) Yeah. >> Each individually- >> Savannah: you don't need to! >> and sometimes differently. >> Savannah: We're stronger together, you know? >> Exactly. >> The water level rises together and Ford is definitely a company that illustrates that by example. >> Yeah, I'm like, we can't make a better round wheel right? >> Yeah! So, we have to build upon what we have already been built ahead of us. And I think a lot of it is also about how can we give back and participate in the community, right? So I think that is paramount for us as like, here we are in Detroit so we're trying to recruit and show people that you know, everything that we do is not just old car and sheet metal >> Savannah: Combustion. >> and everything and right? There's a lot of tech goes and sometimes it is really, really cool to do that. And biggest thing for us is like how can we involve our community of developers sooner, earlier, faster without actually encumbering them and saying that, hey here is a book, go master it. We'll talk two months later. So I think that has been another journey. I think that has been a biggest uphill challenge for us is that how can we actually democratize all of these things for everybody. >> Yeah. Well no one better to try than you I would suspect. >> We can only try and hope everything turns out well, right? >> You know, as long as there's room for the bumpers on the lane for if you fail. >> Exactly. >> It sounds like you're driving the program in the right direction. Closing question for you, what's next? Is electric the future? Is Kubernetes the future? What's Ford all in on right now, looking forward? (crowd murmuring in the background) >> Data is the king, right? >> Savannah: Oh, okay, yes! >> Data is a new currency. We use that for several things to improve the cars improve the quality of autonomous driving Is Level 5 driving here? Maybe will be here soon, we'll see. But we are all working towards it, right? So machine learning, AI feedback. How do you actually post sale experience for example? So all of these are all areas that we are working to. We are, may not be getting like Kubernetes in a car but we are putting Kubernetes in plants. Like you order a Marquis or you order a Bronco, you see that here. Here's where in the assembly line your car is. It's taking pictures. It's actually taking pictures on Kubernetes platform. >> That's pretty cool. >> And it is tweeting for you on the Twitter and the social media platform. So there's a lot of that. So it is real and we are doing it. We need more help. A lot of the community efforts that we are seeing and a lot of the innovation that is happening on the floor here, it's phenomenal. The question is how we can incorporate those things into our workflows. >> Yeah, well you have the right audience for that here. You also have the right attitude, >> Exactly. >> the right appetite, and the right foundation. Becky, last question for you. Top three takeaways from your talk today. If you're talking to the developer community you want to inspire: Come work for us! What would you say? >> If you're ready to invest in yourself and upskill and be part of something that is pretty remarkable, come work for us! We have many, many different technical career paths that you can follow. We invest in our employees. When you master something, it's time for you to move on. We have career growth for you. It's been a wonderful gift to me and my family and I encourage everyone to check us out careers.ford.com or stop by our booth if you're happen to be here in person. >> Satish: Absolutely! >> We have our curated job openings that are specific for this community, available. >> Satish: Absolutely. >> Love it. Perfect close. Nailed pitch there. I'm sure you're all going to check out their job page. (all laugh) >> Exactly! And what you talked about, the developer experience, the customer experience are inextricably linked and you guys are really focused on that. Congratulations on all the work that you've done. We got to go get a selfie with that car girl. >> Yes, we do. >> Absolutely. >> We got to show them, we got to show the audience what it looks like on the inside too. We'll do a little IG video. (Lisa laughs) >> Absolutely. >> We will show you that for our guests and my cohost, Savannah Peterson. Lisa Martin here live in Detroit with theCUBE at KubeCon and CloudNativeCon 2022. The one and only John Furrier, who you know gets FOMO, is going to be back with me next. So stick around. (all laugh) (bright music)

(upbeat music) >> From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Welcome to theCUBE's coverage of IBM Think, The Digital Experience. I'm Lisa Martin. I've got two guests with me here today. Ryan Dennings joins us, Manager of ECM Solutions at Auto-Owners Insurance Company, Ryan, welcome to the program. >> Thank you. And Michelle Christensen is here as well, VP of Enterprise Report Management Practice at enChoice, Michelle, it's good to have you on the program. >> Thank you. Thank you. So let's, Ryan let's go ahead and start with you. You guys are a customer of enChoice and IBM, talk to us a little bit about Auto-Owners Company. I know this is a fortune 500. This was founded in 1916. You've got about nearly 3 million policy holders but give us an overview of Auto-Owners Insurance. >> Sure. So Auto-Owners Insurance is an insurance company that's headquartered in Lansing, Michigan. We write insurance in 26 States throughout the United States. Despite our name being Auto-Owners Insurance, which is how we started, we write all personal lines, commercial lines, and also have a life insurance company. >> So comprehensive and that across those nearly 3 million policy holders. Michelle, tell us a little bit about enChoice. I know this, you guys are an IBM Gold Business Partner but this is enChoice's first time on the Cube, so give us a background. >> Sure, sure, great. So enChoice are an IBM Gold Business Partner. We have had 28 years success with IBM as a business partner. Our headquarters are in areas of Austin, Texas, and Tempe, Arizona, as well as Shelton, Connecticut. We cover all of North America and we are a hundred percent focused on the IBM Digital Business Automation Space. We have about 500 customers now that we've helped through the years and we continue to be a leading support provider as well as an implementation partner with all the IBM Solutions. >> And talk to me a little bit Michelle about how it is that you work with with Auto-Owners. >> So we assisted Auto-Owners recently in their digital transformations journey and they were dealing with an antiquated product and wanted to get moving forward, you know provide a better customer satisfaction experience for their client's agents, and so we partnered with them and with IBM and bringing them a content manager on-demand solution as well as navigator and several other products within the IBM Digital Business Automation Portfolio. >> Excellent, Ryan Oh, sorry Michelle, go ahead. >> Nope. That's that's fine. All right, Ryan, tell us a little bit about Auto-Owners, your relationship with IBM and enChoice and how is it helping you to address some of the challenges in the market today? >> Sure. So Auto-Owners has a long-term relationship with IBM originally starting back years ago as a mainframe customer and then, you know more recently helping us with different modern technology initiatives. They were instrumental in the nineties when we redid our initial web offerings, and then more recently they've been helping us with our Digital Business Automation which has helped us to mature our content offering at Owners. >> So you have had a long standing relationship with IBM, Ryan, and then you mentioned the nineties at a time when we didn't have to wear masks on our faces. (laughing) So a couple of decades it goes back, yeah? >> Yes. For sure. Yes. Even further than that, that, you know back into the seventies from the mainframe side of things. >> The seventies, another good time. (laughing) All right. So Michelle, talk to me a little bit about what enChoice is doing with IBM Solutions to help Auto-Owners from a digital transformation perspective is as I said this is a company that was founded in 1916, and I always love to hear how history companies like that are actually working with technology companies to facilitate that transformation. It's a lot harder than it sounds. >> Well, that's correct. Yes. As I mentioned, we're focused on helping customers develop their strategy, their digital strategy and creating those transformative solutions. So we're helping organizations like Auto-Owners with their journey, by first realizing their existing digital state, what challenges they might have and what needs they might need, and then we break that down or we deconstruct those technical and processizations and finally we re-invent their strategic offering with modern capabilities. So we're focused on technologies like RPA, machine learning, artificial intelligence, they're more efficient, scalable, and secure, so any way we can bring those technologies into the equation we go for it. So this offers us, our clients smarter and more intuitive interfaces creating basically a better user experience, and a better user experience then becomes disruptive to their competition. So they gain a better place in the market space. >> Ryan talked to us about that process as much as you were involved in it. I liked that Michelle said, you know we kind of look at the environment, we deconstruct it and then we re-invent it. Talk to me about how IBM and enChoice has helped Auto-Owners to do that so that your digital infrastructure is much more modern, and I presume much more resilient when there are market dynamics like we're living in now. >> Yeah, for sure. So, you know, we've, we've gone through a couple of transformation journeys at Auto-Owners with IBM. When I started the team about seven years ago we originally started using file NATS and data cap, and case manager, and content aggregator as our first movement from a traditional platform that we had for content management into a more modern platform, and that helped us a lot to improve our business process, improve how we capture content and bring it into the system and make it actionable. More recently, we've been working with Michelle and the enChoice team on our migration to a content management on-demand platform, and that's really going to be transformative in terms of how we're able to present content and documents and bills to our agents and customers, to be able to transform that content and show it in ways that are important for our customers to be able to see it, to engage with Auto-Owners in a, in a digital era. >> So Ryan, just a couple of questions on that, is that is that a facilitation of like the digitization of processes that had some paper involved cause you guys have about 48,000 agents, so a lot of folks, a lot of content, tell me a little bit more about how that like content manager on-demand, for example and what you're doing with ECF, how has that really revolutionizing and driving part of that digital transformation? >> Sure. So, you know, there's two parts to that in terms of that content management on-demand journey. One is the technology portion of it, but IBM's provided, and that suite of software gives us some functionality that we haven't had in the past. Specifically, some functionality around searching and searchability of our content that will make it easier for people to find the content that they're looking for, ability to implement records management policies and other things that help us manage that content more effectively, as well as some different options to be able to present the content to our customers and agents in a in a better and more modern way and enChoice's role in that has really been to guide us on that journey to help us make the right choices along the way on the project and help us get to a successful implementation and production. >> Excellent. Michelle, talk to me about Hybrid Cloud AI Data a big theme of IBM Think this year. How is enChoice using Hybrid Cloud and AI? You mentioned some of the other ways but kind of break into that a little bit more about how you're helping customers like Auto-Owners and others really take advantage of those modern technologies. >> Well, sure, sure. So of course with the Cloud Pak offerings that IBM has come forward with and where we focus in the Cloud Pak for automation, several of those offerings are some of them are built specifically to survive or to to be hosted in a hybrid environment, and as we're working with Auto-Owners transforming their platforms going forward for example, they just invested in, in a, a I just lost the word here. They just invested in a, a new platform, mainframe platform where they're going to be leveraging the red hats, and from there they'll drive forward into containerization. So Ryan mentioned some of the ways that we'll be presenting the content for his agents and his customers in a particular that entire viewing platform itself can be moved to a containerization state. So, so it's going to be a lot easier for him to transition into that and to maintain it and to manage it. And of course, just that whole, the ease of function around it will be a lot easier. So we are in our area as an IBM business partner, we work with these solutions to try to stay ahead of the game, to try to be able to assist our customers to understand what makes sense, when is it time to move into those. It's great to take advantage of the new stuff but nobody wants to be, you know, the bleeding game. We want to be the leading game. And so that's some of the areas we focus with our clients to really stay tight with the labs, tight with IBM and understanding their strategies and convey those and educate our customers on those. >> Excellent leading edge. Ryan, talk to me a little bit. I love this a bank, sorry an insurance company from the early 1900's moving into the using container technology. I love stories like that. Talk to me a little bit about Hybrid Cloud AI and how those technologies are going to be facilitators of the continuation of the digital transformation, and probably enabling more opportunities for your agents to meet more needs from from your policy holders. >> Yeah, for sure. So first and foremost, we were a Red Hat OpenShift customer before IBM acquired them and we were doing microservices development and things like that on the platform, and then we were super excited about IBM's digital business automation strategy to move to a Cloud Pak and have that available for software products to run on OpenShift. At the end of last year, we updated our licensing so that we can move in that direction, and we're starting to deploy digital business automation products on our OpenShift platform which is super exciting for me. It's going to make for faster upgrades, more scalability, just a lot of ease of use things for my team to make their jobs easier but also easier for us to adapt new upgrades and software offerings from IBM. There's also a number of products that are in the containerized or OpenShift only offering as they're initially coming out, whether it's mobile capture or automated document processing to name a couple. And those are both things that we're looking at Auto-Owners to continue to mature in this space and be able to offer more functionality to our associates, our customers, and our agents to continue to grow the business. >> Very forward-thinking, awesome Ryan. Thanks for sharing with us what Auto-Owners Insurance is doing, how you're being successful and how you've done so much transformation already. I want to throw the last question to Michelle. Take us out Michelle with what's next from enChoice's perspective in terms of your digital transformation. >> Well, we have been a hundred percent focused on helping all of our customers develop their digital strategy and and creating their own transformative solutions. So as we continue to work with our clients, take them through the journey, as I mentioned before, we try to encourage them not to focus on the, the technology itself, but really to focus on creating their exceptional customer experience when driving their digital strategy. And we see ourselves as, you know helping transform our client's experience such that you know customer experience becomes what enChoice does best. So we see not only our own organization going through the transformation, but making sure that we're taking our clients with us and with 500 clients we're, we're really busy. So that's always good. >> That is good. It sounds like the last year has been very fruitful for you, and I love that you mentioned customer experience, Michelle. I think that is so important and as well as employee experience, but having a good customer experience, especially these days. Table-stakes. I thank you both so much for sharing what you guys are doing with IBM Solutions, the transformation that both of your companies are on and we look forward to hearing what's to come. Thank you both for your time. >> Thank you. >> Thank you for Ryan Dennings and Michelle Christiansen. I'm Lisa Martin. You're watching theCUBE's coverage of IBM Think The Digital Experience. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Hello, everyone, and welcome back to the cubes. Continuing coverage of AWS reinvent 2020 Virtual the Cube Virtual. We're here covering the partner ecosystem and some of the new innovations coming from the reinvent community. Let's talk about something that anyone who drives a vehicle can relate to. Roadside assistance with me or Beth Davidson, chief marketing officer at a zero, and Raj borrows the vice president and c t o at zero folks, welcome to the Cube. >>Hello, nice to see you. >>So let's start with you. Maybe talk a little bit about your your mission, how you work with automakers. You've got, you know, a lot of good pipeline, their insurers and other others in the in the ecosystem. Tell us about the company. >>Absolutely. So for 50 years, we've been helping consumers with their cars. Um, that's what it comes down Thio. We know that one in three people has a roadside event every year on the way you think about that is, you know, if in three years you haven't had a roadside event, tick tock. You know, statistically, it's coming for you. We work with everybody. We work with the auto manufacturers. We work with the insurers. What we're trying to do is get closer to consumers. On the reason you may have never heard of a Gero is that's by design. Were white label. We work for our clients typically on. Do you know they trust us with their consumers? They trust us with their brands. Um, and we're just in the business of getting consumers back on the road. >>Thank you for that. So talk a little bit about how you approach this problem. I mean, you looked out roadside assistance, and you know, we can again all relate. Oh, am I up to date or at least the car? So there's gotta be some kind of 800 number in my glove compartment somewhere, right? So what was the state of roadside assistance before you guys got involved? And maybe we could get into sort of how you solve the problem. >>Yeah, I think that's a great question, Dave, as we look at roadside assistance, everyone things about picking up the phone number 800 number from the glove box compartment And over the years we have invested heavily on bringing a fully digital experience to our customers from insurance companies to AM. And when this Alexa opportunity came up earlier this summer, he said, Hi. How about taking that digital experience, adding, all the Alexa do goods goods about voice interaction, making it very interactive for the users to request that experience in a very normal consumer friendly, friendly were and brought that we integrated all those services got that whole uber like experience with for roadside assistance? >>Yeah. Now. So, Beth, you know, I reminded when, like the smart TV first came out, you had a type in right, and we're really getting spoiled now. It should be easy as a blink. Okay, so you're unveiling blink, you know, what's this service all about? >>So this service is about, you know, trying to get to consumers as easy as we can and getting removing the friction. Right? So what Rogers just talking about is again we asked consumers. We say, you know, imagine that tomorrow you went out and there was a flat tire on your car in your driveway. What do you dio? And universally, they pause and They're like, I don't know. I haven't thought about it, right. And then they start making up stuff. Like maybe I'm gonna go through the glove box. Maybe I'm going to go through my files. But wouldn't it be great if they could just kind of talked to the air and say, Alexa, what? Doe ideo and have it work for them, you know, And that's one friction. The second friction is consumers actually don't know their addresses or don't know it. Well, we joke around the office about the difference between saying you're on route one and Route one A is is the difference between 20 minutes of that tow truck getting to you in time. You know, these air points of friction that technology can help us with, you know, and then with payments even better, Right? So the fact that you can pay for this thing with Amazon pay and you don't have to worry about having cash for a driver or have a credit card. I mean, there's just so many points of friction that are reduced by using Alexa. >>Okay, so let's talk about the the integrations here in the technical aspects of how you put everything together and made it work, and we'll get into some of the cloud aspect >>Attack launched. We're asking users to tell what they want, and they can tell the whole address. They can get the address from the Alexa device. Or if it is Alexa Auto. The GPS will provide us the Latin belong. And we take that address and we get what kind of experience they want. Whether it is a flat tire, we're going to send somebody else to put despair. If it is a jump start, we're gonna put send somebody Thio jumps out the vehicle. So depending on that, we put pull all that information together, get this consent for the user to charge their an Amazon parrot card on profile, and then go So it's literally to come to sentences. And then we're on. We're on to sending you experience with some of the text messages that will allow you to truck tractor truck coming down to your driver. >>Now I'll show my age. So yeah, we've all I don't have all but I've been locked out of the car many times Now, in the old days, used to be able to get a coat hanger and pop it open. But so? So that people still get locked out of their cars. >>Yes, cars. More often than not, it's, you know, the key. Fob stopped working, right? Lost the battery of my key fob these days. But it's the equivalent. >>Alright, so All right, so right. What else do you guys do in the cloud? Do you use a W s for your own business? Maybe share with us some of >>the over the years. For the past 78 years, we have, uh, integrated and got all of our technologies into the AWS cloud. And we have now revamped and re innovated on top of those and create a new product lines. We have accident scene management. We do, um, handle automatic clash notifications for some of our partner customers. We dio dealer service appointments, so we do a lot of these things. And all of these are not possible without the amazing teams. 20 or so teams that we have across three continents working on 50 plus, uh, approved services on aws, uh, innovating around the clock, bringing these new innovations to our market. >>So, Beth, you were saying earlier that you, you know, want to reach out to the consumer. I mean, how do you market? Uh, you obviously go through through partners. And I'm curious system, What's your go to market and maybe how you're different from from others in the marketplace, >>right? Eso again because we're white label with most of the client side business that we do, we help our clients message better on DSO. We talked to them about how often you have to remind people that this isn't a one and done, um, on the skill store for Alexa. You know how we're different is you know, you don't aske much as I love the branding that we came up with blank roadside. You know, you don't actually have to use it. You don't have to say, Alexa, open my blank roadside. You could just say, Alexa, help me with my flat tire, which really helps cut out the fact that I actually need to market the brand like a traditional market or would have had Thio. But our biggest problem is how do you market something to someone in that moment of need, right? How do I How do I prime you to get you to think about it way, way before you ever actually have the problem. >>And how do you charge for the service? >>Eso It's it's a flat fee on did. It's better than what consumers would be able to get on their own. Or at least we believe so. But it is a flat fee for any kind of road service, so it's flat tire. It's dead batteries. It's winching you out. You know, it's it's all of those things. Um, that can happen to you that are just kind of those minor everyday mishaps. >>Okay? And so and so do I. How do I get it? Do I do I have tow hope that my you know, if I'm leasing a car that the auto has it, can I go direct? How doe I >>all direct? It's all direct. So you don't have to worry about an I d number membership number. You're just paying for it out of your Amazon account on. Do you know you don't have to worry about knowing your how many digit vin number. You know, none of that stuff. It's just one and done. >>Awesome. So, Raja, I wonder if you could talk a little bit about your your scale. Um, maybe I don't know if you can share any metrics and what What factors? The cloud generally and a W s specifically has has played and enabling that scale. >>Yeah, we have amazing number of integrations with our Fortune 100 insurance companies. Um, over 35 insurance companies and we have 100 and 70 b two b clients today, Um, and we integrate with them were deeply, um, uh integrated into the building systems into their coverage systems. And all of that is to be able to provide that sub minute sub second experience to our customers when they're calling in, uh, when they need the service. Um, right now we do over a billion AP A calls. As a result of these transactions, all these integrations or for quarter and all of these, uh, our third parties, service providers who go around the on the roads and provide this location information today off the tow trucks to us, all of these 8 8000 or so trucks extreme that information to us almost on every hour. So we bring all that information together on the AWS platform, stream it back shaded back in a very secure private manner back to the customers, right at the moment of need. >>Yeah, So I mean, without the cloud, you'd be backing up. You know, the servers to the truck to the loading dock. And it would just take so much longer toe spin up new products. I would imagine that you guys have a lot of ideas about new data products or new services that you can you can provide. Um, you probably I'm sure you can tell us what they are, But but in terms of the time, it takes you to conceive toe to get to the market. That must be impressed with the cloud. >>Yeah, it's a fraction of what it used to take years ago when we were not in AWS, right? And it also allows us to not to spend all this time on worrying about the same thing that you used to worry about for every project. Now you can actually think about how, what how you let be able to leverage new innovations that are coming in and actually improve improve the experience with some kind of intelligence that is added on, which makes the experience much smoother for people. >>Well, Beth will give you last word. But first of all, thanks for helping us make our lives even even better and more convenient. But bring us home. What's the last word here? >>So the last word is, you know, we dio we do 12 million events a year right now, right? And if you if you like math, it's 35,000 day. It's 20 for every minute, you know. And the work that that Rajan team have done to make the scalable means we're ready to do the next 12 million on. Do you know we know. We know there are consumers out there having those events. We just want to be there for you, you know, take care of that frustrating event on get you back >>on the road. Well, it's just, you know, having you there and being able to push a button and talk to a device is just It's a game changer. So thank you guys for coming on the cube and sharing your story really interesting. Yeah. All right. Thanks for watching. Keep it right there. You're watching the cubes coverage of aws reinvent 2020. We'll be right back right after this short break

(upbeat music) >> Hi everyone, thank you for joining us at the Data Cloud Summit. The last couple of months have been an exciting time at Snowflake. And yet, what's even more compelling to all of us at Snowflake is what's ahead. Today I have the opportunity to share new product developments that will extend the reach and impact of our Data Cloud and improve the experience of Snowflake users. Our product strategy is focused on four major areas. First, Data Cloud content. In the Data Cloud silos are eliminated and our vision is to bring the world's data within reach of every organization. You'll hear about new data sets and data services available in our data marketplace and see how previous barriers to sourcing and unifying data are eliminated. Second, extensible data pipelines. As you gain frictionless access to a broader set of data through the Data Cloud, Snowflakes platform brings additional capabilities and extensibility to your data pipelines, simplifying data ingestion, and transformation. Third, data governance. The Data Cloud eliminates silos and breaks down barriers and in a world where data collaboration is the norm, the importance of data governance is ratified and elevated. We'll share new advancements to support how the world's most demanding organizations mobilize your data while maintaining high standards of compliance and governance. Finally, our fourth area focuses on platform performance and capabilities. We remain laser focused on continuing to lead with the most performant and capable data platform. We have some exciting news to share about the core engine of Snowflake. As always, we love showing you Snowflake in action, and we prepared some demos for you. Also, we'll keep coming back to the fact that one of the characteristics of Snowflake that we're proud as staff is that we offer a single platform from which you can operate all of your data workloads, across clouds and across regions, which workloads you may ask, specifically, data warehousing, data lake, data science, data engineering, data applications, and data sharing. Snowflake makes it possible to mobilize all your data in service of your business without the cost, complexity and overhead of managing multiple systems, tools and vendors. Let's dive in. As you heard from Frank, the Data Cloud offers a unique capability to connect organizations and create collaboration and innovation across industries fueled by data. The Snowflake data marketplace is the gateway to the Data Cloud, providing visibility for organizations to browse and discover data that can help them make better decisions. For data providers on the marketplace, there is a new opportunity to reach new customers, create new revenue streams, and radically decrease the effort and time to data delivery. Our marketplace dramatically reduces the friction of sharing and collaborating with data opening up new possibilities to all participants in the Data Cloud. We introduced the Snowflake data marketplace in 2019. And it is now home to over 100 data providers, with half of them having joined the marketplace in the last four months. Since our most recent product announcements in June, we have continued broadening the availability of the data marketplace, across regions and across clouds. Our data marketplace provides the opportunity for data providers to reach consumers across cloud and regional boundaries. A critical aspect of the Data Cloud is that we envisioned organizations collaborating not just in terms of data, but also data powered applications and services. Think of instances where a provider doesn't want to open access to the entirety of a data set, but wants to provide access to business logic that has access and leverages such data set. That is what we call data services. And we want Snowflake to be the platform of choice for developing discovering and consuming such rich building blocks. To see How the data marketplace comes to live, and in particular one of these data services, let's jump into a demo. For all of our demos today, we're going to put ourselves in the shoes of a fictional global insurance company. We've called it Insureco. Insurance is a data intensive and highly regulated industry. Having the right access control and insight from data is core to every insurance company's success. I'm going to turn it over to Prasanna to show how the Snowflake data marketplace can solve a data discoverability and access problem. >> Let's look at how Insureco can leverage data and data services from the Snowflake data marketplace and use it in conjunction with its own data in the Data Cloud to do three things, better detect fraudulent claims, arm its agents with the right information, and benchmark business health against competition. Let's start with detecting fraudulent claims. I'm an analyst in the Claims Department. I have auto claims data in my account. I can see there are 2000 auto claims, many of these submitted by auto body shops. I need to determine if they are valid and legitimate. In particular, could some of these be insurance fraud? By going to the Snowflake data marketplace where numerous data providers and data service providers can list their offerings, I find the quantifying data service. It uses a combination of external data sources and predictive risk typology models to inform the risk level of an organization. Quantifying external sources include sanctions and blacklists, negative news, social media, and real time search engine results. That's a wealth of data and models built on that data which we don't have internally. So I'd like to use Quantifind to determine a fraud risk score for each auto body shop that has submitted a claim. First, the Snowflake data marketplace made it really easy for me to discover a data service like this. Without the data marketplace, finding such a service would be a lengthy ad hoc process of doing web searches and asking around. Second, once I find Quantifind, I can use Quantifind service against my own data in three simple steps using data sharing. I create a table with the names and addresses of auto body shops that have submitted claims. I then share the table with Quantifind to start the risk assessment. Quantifind does the risk scoring and shares the data back with me. Quantifind uses external functions which we introduced in June to get results from their risk prediction models. Without Snowflake data sharing, we would have had to contact Quantifind to understand what format they wanted the data in, then extract this data into a file, FTP the file to Quantifind, wait for the results, then ingest the results back into our systems for them to be usable. Or I would have had to write code to call Quantifinds API. All of that would have taken days. In contrast, with data sharing, I can set this up in minutes. What's more, now that I have set this up, as new claims are added in the future, they will automatically leverage Quantifind's data service. I view the scores returned by Quantifind and see the two entities in my claims data have a high score for insurance fraud risk. I open up the link returned by Quantifind to read more, and find that this organization has been involved in an insurance crime ring. Looks like that is a claim that we won't be approving. Using the Quantifind data service through the Snowflake data marketplace gives me access to a risk scoring capability that we don't have in house without having to call custom APIs. For a provider like Quantifind this drives new leads and monetization opportunities. Now that I have identified potentially fraudulent claims, let's move on to the second part. I would like to share this fraud risk information with the agents who sold the corresponding policies. To do this, I need two things. First, I need to find the agents who sold these policies. Then I need to share with these agents the fraud risk information that we got from Quantifind. But I want to share it such that each agent only sees the fraud risk information corresponding to claims for policies that they wrote. To find agents who sold these policies, I need to look up our Salesforce data. I can find this easily within Insureco's internal data exchange. I see there's a listing with Salesforce data. Our sales Ops team has published this listing so I know it's our officially blessed data set, and I can immediately access it from my Snowflake account without copying any data or having to set up ETL. I can now join Salesforce data with my claims to identify the agents for the policies that were flagged to have fraudulent claims. I also have the Snowflake account information for each agent. Next, I create a secure view that joins on an entitlements table, such that each agent can only see the rows corresponding to policies that they have sold. I then share this directly with the agents. This share contains the secure view that I created with the names of the auto body shops, and the fraud risk identified by Quantifind. Finally, let's move on to the third and last part. Now that I have detected potentially fraudulent claims, I'm going to move on to building a dashboard that our executives have been asking for. They want to see how Insureco compares against other auto insurance companies on key metrics, like total claims paid out for the auto insurance line of business nationwide. I go to the Snowflake data marketplace and find SNL U.S. Insurance Statutory Data from SNP. This data is included with Insureco's existing subscription with SMP so when I request access to it, SMP can immediately share this data with me through Snowflake data sharing. I create a virtual database from the share, and I'm ready to query this data, no ETL needed. And since this is a virtual database, pointing to the original data in SNP Snowflake account, I have access to the latest data as it arrives in SNPs account. I see that the SNL U.S. Insurance Statutory Data from SNP has data on assets, premiums earned and claims paid out by each us insurance company in 2019. This data is broken up by line of business and geography and in many cases goes beyond the data that would be available from public financial filings. This is exactly the data I need. I identify a subset of comparable insurance companies whose net total assets are within 20% of Insureco's, and whose lines of business are similar to ours. I can now create a Snow site dashboard that compares Insureco against similar insurance companies on key metrics, like net earned premiums, and net claims paid out in 2019 for auto insurance. I can see that while we are below median our net earned premiums, we are doing better than our competition on total claims paid out in 2019, which could be a reflection of our improved claims handling and fraud detection. That's a good insight that I can share with our executives. In summary, the Data Cloud enabled me to do three key things. First, seamlessly fine data and data services that I need to do my job, be it an external data service like Quantifind and external data set from SNP or internal data from Insureco's data exchange. Second, get immediate live access to this data. And third, control and manage collaboration around this data. With Snowflake, I can mobilize data and data services across my business ecosystem in just minutes. >> Thank you Prasanna. Now I want to turn our focus to extensible data pipelines. We believe there are two different and important ways of making Snowflakes platform highly extensible. First, by enabling teams to leverage services or business logic that live outside of Snowflake interacting with data within Snowflake. We do this through a feature called external functions, a mechanism to conveniently bring data to where the computation is. We announced this feature for calling regional endpoints via AWS gateway in June, and it's currently available in public preview. We are also now in public preview supporting Azure API management and will soon support Google API gateway and AWS private endpoints. The second extensibility mechanism does the converse. It brings the computation to Snowflake to run closer to the data. We will do this by enabling the creation of functions and procedures in SQL, Java, Scala or Python ultimately providing choice based on the programming language preference for you or your organization. You will see Java, Scala and Python available through private and public previews in the future. The possibilities enabled by these extensibility features are broad and powerful. However, our commitment to being a great platform for data engineers, data scientists and developers goes far beyond programming language. Today, I am delighted to announce Snowpark a family of libraries that will bring a new experience to programming data in Snowflake. Snowpark enables you to write code directly against Snowflake in a way that is deeply integrated into the languages I mentioned earlier, using familiar concepts like DataFrames. But the most important aspect of Snowpark is that it has been designed and optimized to leverage the Snowflake engine with its main characteristics and benefits, performance, reliability, and scalability with near zero maintenance. Think of the power of a declarative SQL statements available through a well known API in Scala, Java or Python, all these against data governed in your core data platform. We believe Snowpark will be transformative for data programmability. I'd like to introduce Sri to showcase how our fictitious insurance company Insureco will be able to take advantage of the Snowpark API for data science workloads. >> Thanks Christian, hi, everyone? I'm Sri Chintala, a product manager at Snowflake focused on extensible data pipelines. And today, I'm very excited to show you a preview of Snowpark. In our first demo, we saw how Insureco could identify potentially fraudulent claims. Now, for all the valid claims InsureCo wants to ensure they're providing excellent customer service. To do that, they put in place a system to transcribe all of their customer calls, so they can look for patterns. A simple thing they'd like to do is detect the sentiment of each call so they can tell which calls were good and which were problematic. They can then better train their claim agents for challenging calls. Let's take a quick look at the work they've done so far. InsureCo's data science team use Snowflakes external functions to quickly and easily train a machine learning model in H2O AI. Snowflake has direct integrations with H2O and many other data science providers giving Insureco the flexibility to use a wide variety of data science libraries frameworks or tools to train their model. Now that the team has a custom trained sentiment model tailored to their specific claims data, let's see how a data engineer at Insureco can use Snowpark to build a data pipeline that scores customer call logs using the model hosted right inside of Snowflake. As you can see, we have the transcribed call logs stored in the customer call logs table inside Snowflake. Now, as a data engineer trained in Scala, and used to working with systems like Spark and Pandas, I want to use familiar programming concepts to build my pipeline. Snowpark solves for this by letting me use popular programming languages like Java or Scala. It also provides familiar concepts in APIs, such as the DataFrame abstraction, optimized to leverage and run natively on the Snowflake engine. So here I am in my ID, where I've written a simple scalar program using the Snowpark libraries. The first step in using the Snowpark API is establishing a session with Snowflake. I use the session builder object and specify the required details to connect. Now, I can create a DataFrame for the data in the transcripts column of the customer call logs table. As you can see, the Snowpark API provides native language constructs for data manipulation. Here, I use the Select method provided by the API to specify the column names to return rather than writing select transcripts as a string. By using the native language constructs provided by the API, I benefit from features like IntelliSense and type checking. Here you can see some of the other common methods that the DataFrame class offers like filters like join and others. Next, I define a get sentiment user defined function that will return a sentiment score for an input string by using our pre trained H2O model. From the UDF, we call the score method that initializes and runs the sentiment model. I've built this helper into a Java file, which along with the model object and license are added as dependencies that Snowpark will send to Snowflake for execution. As a developer, this is all programming that I'm familiar with. We can now call our get sentiment function on the transcripts column of the DataFrame and right back the results of the score transcripts to a new target table. Let's run this code and switch over to Snowflake to see the score data and also all the work that Snowpark has done for us on the back end. If I do a select star from scored logs, we can see the sentiment score of each call right alongside the transcript. With Snowpark all the logic in my program is pushed down into Snowflake. I can see in the query history that Snowpark has created a temporary Java function to host the pre trained H20 model, and that the model is running right in my Snowflake warehouse. Snowpark has allowed us to do something completely new in Snowflake. Let's recap what we saw. With Snowpark, Insureco was able to use their preferred programming language, Scala and use the familiar DataFrame constructs to score data using a machine learning model. With support for Java UDFs, they were able to run a train model natively within Snowflake. And finally, we saw how Snowpark executed computationally intensive data science workloads right within Snowflake. This simplifies Insureco's data pipeline architecture, as it reduces the number of additional systems they have to manage. We hope that extensibility with Scala, Java and Snowpark will enable our users to work with Snowflake in their preferred way while keeping the architecture simple. We are very excited to see how you use Snowpark to extend your data pipelines. Thank you for watching and with that back to you, Christian. >> Thank you Sri. You saw how Sri could utilize Snowpark to efficiently perform advanced sentiment analysis. But of course, if this use case was important to your business, you don't want to fully automate this pipeline and analysis. Imagine being able to do all of the following in Snowflake, your pipeline could start far upstream of what you saw in the demo. By storing your actual customer care call recordings in Snowflake, you may notice that this is new for Snowflake. We'll come back to the idea of storing unstructured data in Snowflake at the end of my talk today. Once you have the data in Snowflake, you can use our streams and past capabilities to call an external function to transcribe these files. To simplify this flow even further, we plan to introduce a serverless execution model for tasks where Snowflake can automatically size and manage resources for you. After this step, you can use the same serverless task to execute sentiment scoring of your transcript as shown in the demo with incremental processing as each transcript is created. Finally, you can surface the sentiment score either via snow side, or through any tool you use to share insights throughout your organization. In this example, you see data being transformed from a raw asset into a higher level of information that can drive business action, all fully automated all in Snowflake. Turning back to Insureco, you know how important data governance is for any major enterprise but particularly for one in this industry. Insurance companies manage highly sensitive data about their customers, and have some of the strictest requirements for storing and tracking such data, as well as managing and governing it. At Snowflake, we think about governance as the ability to know your data, manage your data and collaborate with confidence. As you saw in our first demo, the Data Cloud enables seamless collaboration, control and access to data via the Snowflake data marketplace. And companies may set up their own data exchanges to create similar collaboration and control across their ecosystems. In future releases, we expect to deliver enhancements that create more visibility into who has access to what data and provide usage information of that data. Today, we are announcing a new capability to help Snowflake users better know and organize your data. This is our new tagging framework. Tagging in Snowflake will allow user defined metadata to be attached to a variety of objects. We built a broad and robust framework with powerful implications. Think of the ability to annotate warehouses with cost center information for tracking or think of annotating tables and columns with sensitivity classifications. Our tagging capability will enable the creation of companies specific business annotations for objects in Snowflakes platform. Another key aspect of data governance in Snowflake is our policy based framework where you specify what you want to be true about your data, and Snowflake enforces those policies. We announced one such policy earlier this year, our dynamic data masking capability, which is now available in public preview. Today, we are announcing a great complimentary a policy to achieve row level security to see how role level security can enhance InsureCo's ability to govern and secure data. I'll hand it over to Artin for a demo. >> Hello, I'm Martin Avanes, Director of Product Management for Snowflake. As Christian has already mentioned, the rise of the Data Cloud greatly accelerates the ability to access and share diverse data leading to greater data collaboration across teams and organizations. Controlling data access with ease and ensuring compliance at the same time is top of mind for users. Today, I'm thrilled to announce our new row access policies that will allow users to define various rules for accessing data in the Data Cloud. Let's check back in with Insureco to see some of these in action and highlight how those work with other existing policies one can define in Snowflake. Because Insureco is a multinational company, it has to take extra measures to ensure data across geographic boundaries is protected to meet a wide range of compliance requirements. The Insureco team has been asked to segment what data sales team members have access to based on where they are regionally. In order to make this possible, they will use Snowflakes row access policies to implement row level security. We are going to apply policies for three Insureco's sales team members with different roles. Alice, an executive must be able to view sales data from both North America and Europe. Alex in North America sales manager will be limited to access sales data from North America only. And Jordan, a Europe sales manager will be limited to access sales data from Europe only. As a first step, the security administrator needs to create a lookup table that will be used to determine which data is accessible based on each role. As you can see, the lookup table has the row and their associated region, both of which will be used to apply policies that we will now create. Row access policies are implemented using standard SQL syntax to make it easy for administrators to create policies like the one our administrators looking to implement. And similar to masking policies, row access policies are leveraging our flexible and expressive policy language. In this demo, our admin users to create a row access policy that uses the row and region of a user to determine what row level data they have access to when queries are executed. When users queries are executed against the table protected by such a row access policy, Snowflakes query engine will dynamically generate and apply the corresponding predicate to filter out rows the user is not supposed to see. With the policy now created, let's log in as our Sales Users and see if it worked. Recall that as a sales executive, Alice should have the ability to see all rows from North America and Europe. Sure enough, when she runs her query, she can see all rows so we know the policy is working for her. You may also have noticed that some columns are showing masked data. That's because our administrator's also using our previously announced data masking capabilities to protect these data attributes for everyone in sales. When we look at our other users, we should notice that the same columns are also masked for them. As you see, you can easily combine masking and row access policies on the same data sets. Now let's look at Alex, our North American sales manager. Alex runs to st Korea's Alice, row access policies leverage the lookup table to dynamically generate the corresponding predicates for this query. The result is we see that only the data for North America is visible. Notice too that the same columns are still masked. Finally, let's try Jordan, our European sales manager. Jordan runs the query and the result is only the data for Europe with the same columns also masked. And you reintroduced masking policies, today you saw row access policies in action. And similar to our masking policies, row access policies in Snowflake will be accepted Hands of capability integrated seamlessly across all of Snowflake everywhere you expect it to work it does. If you're accessing data stored in external tables, semi structured JSON data, or building data pipelines via streams or plan to leverage Snowflakes data sharing functionality, you will be able to implement complex row access policies for all these diverse use cases and workloads within Snowflake. And with Snowflakes unique replication feature, you can instantly apply these new policies consistently to all of your Snowflake accounts, ensuring governance across regions and even across different clouds. In the future, we plan to demonstrate how to combine our new tagging capabilities with Snowflakes policies, allowing advanced audit and enforcing those policies with ease. And with that, let's pass it back over to Christian. >> Thank you Artin. We look forward to making this new tagging and row level security capabilities available in private preview in the coming months. One last note on the broad area of data governance. A big aspect of the Data Cloud is the mobilization of data to be used across organizations. At the same time, privacy is an important consideration to ensure the protection of sensitive, personal or potentially identifying information. We're working on a set of product capabilities to simplify compliance with privacy related regulatory requirements, and simplify the process of collaborating with data while preserving privacy. Earlier this year, Snowflake acquired a company called Crypto Numerix to accelerate our efforts on this front, including the identification and anonymization of sensitive data. We look forward to sharing more details in the future. We've just shown you three demos of new and exciting ways to use Snowflake. However, I want to also remind you that our commitment to the core platform has never been greater. As you move workloads on to Snowflake, we know you expect exceptional price performance and continued delivery of new capabilities that benefit every workload. On price performance, we continue to drive performance improvements throughout the platform. Let me give you an example comparing an identical set of customers submitted queries that ran both in August of 2019, and August of 2020. If I look at the set of queries that took more than one second to compile 72% of those improved by at least 50%. When we make these improvements, execution time goes down. And by implication, the required compute time is also reduced. Based on our pricing model to charge for what you use, performance improvements not only deliver faster insights, but also translate into cost savings for you. In addition, we have two new major announcements on performance to share today. First, we announced our search optimization service during our June event. This service currently in public preview can be enabled on a table by table basis, and is able to dramatically accelerate lookup queries on any column, particularly those not used as clustering columns. We initially support equality comparisons only, and today we're announcing expanded support for searches in values, such as pattern matching within strings. This will unlock a number of additional use cases such as analytics on logs data for performance or security purposes. This expanded support is currently being validated by a few customers in private preview, and will be broadly available in the future. Second, I'd like to introduce a new service that will be in private preview in a future release. The query acceleration service. This new feature will automatically identify and scale out parts of a query that could benefit from additional resources and parallelization. This means that you will be able to realize dramatic improvements in performance. This is especially impactful for data science and other scan intensive workloads. Using this feature is pretty simple. You define a maximum amount of additional resources that can be recruited by a warehouse for acceleration, and the service decides when it would be beneficial to use them. Given enough resources, a query over a massive data set can see orders of magnitude performance improvement compared to the same query without acceleration enabled. In our own usage of Snowflake, we saw a common query go 15 times faster without changing the warehouse size. All of these performance enhancements are extremely exciting, and you will see continued improvements in the future. We love to innovate and continuously raise the bar on what's possible. More important, we love seeing our customers adopt and benefit from our new capabilities. In June, we announced a number of previews, and we continue to roll those features out and see tremendous adoption, even before reaching general availability. Two have those announcements were the introduction of our geospatial support and policies for dynamic data masking. Both of these features are currently in use by hundreds of customers. The number of tables using our new geography data type recently crossed the hundred thousand mark, and the number of columns with masking policies also recently crossed the same hundred thousand mark. This momentum and level of adoption since our announcements in June is phenomenal. I have one last announcement to highlight today. In 2014, Snowflake transformed the world of data management and analytics by providing a single platform with first class support for both structured and semi structured data. Today, we are announcing that Snowflake will be adding support for unstructured data on that same platform. Think of the abilities of Snowflake used to store access and share files. As an example, would you like to leverage the power of SQL to reason through a set of image files. We have a few customers as early adopters and we'll provide additional details in the future. With this, you will be able to leverage Snowflake to mobilize all your data in the Data Cloud. Our customers rely on Snowflake as the data platform for every part of their business. However, the vision and potential of Snowflake is actually much bigger than the four walls of any organization. Snowflake has created a Data Cloud a data connected network with a vision where any Snowflake customer can leverage and mobilize the world's data. Whether it's data sets, or data services from traditional data providers for SaaS vendors, our marketplace creates opportunities for you and raises the bar in terms of what is possible. As examples, you can unify data across your supply chain to accelerate your time and quality to market. You can build entirely new revenue streams, or collaborate with a consortium on data for good. The possibilities are endless. Every company has the opportunity to gain richer insights, build greater products and deliver better services by reaching beyond the data that he owns. Our vision is to enable every company to leverage the world's data through seamless and governing access. Snowflake is your window into this data network into this broader opportunity. Welcome to the Data Cloud. (upbeat music)

>> From theCUBE Studios in Palo Alto, in Boston connecting with alt leaders all around the world, This is a CUBE conversation. >> Hello everybody, this is Dave Vellante and welcome to this Breaking Analysis. I'm here with Erik Bradley, who's the managing director of ETR and runs their VEN program. Erik good to see you. >> Very nice to see you too Dave. Hope you're doing well. >> Yeah, I'm doing okay hanging in there. You know, you guys in New York are fighting the battle. Looks like we're making some progress here so, you know, all the best, you and your family and the wider community. I'm really excited to have you on today because I had the pleasure of sitting in on a CIO/ CISO panel last week. And we're going to explain sort of what that's all about, but one of the things ETR does that I really like is they go deeper with anecdotal information and it's almost like in-depth interviews in these round tables. So they compliment their quarterly surveys, and their other drill down surveys, with other anecdotal information for people in their community. So it's a tried and true survey practice that adds some color to the dataset. So guys if you bring up the agenda, I want to share with the audience what we're going to talk about today. So, we'll talk a little bit about, you know we just did intros, I want to ask Erik, what ETR VENN is and then we'll go through some of the guests, but if we go back to Erik, explain a little bit about VENN and the whole process and how you guys do that. >> Yeah sure, we should hire you for marketing. You just did a great job, actually, describing that, but about three years ago what we decided was, ETR does an amazing job collecting the data. It can tell you what's happening, who it's happening to and when it's happening. But it can't always tell you why it's happened. So leveraging a lot of my background in twenty-plus years in journalism and institutional Wall Street research, we decided to take the ETR community, the people that actually take the surveys, and start doing interviews with them and start doing events with them. And enable to doing that, we're basically just trying to compliment the survey findings and the data. So what we always say is that ETR will always give you the quantitative answer and VENN will give you the qualitative answer. >> Now guys, let's bring up the agenda slide again, let's take a look at the folks that participated in the round table. Now, for ETR's clients, they actually know the names and the titles and well the company that these guys work for. We've anonymized it for the public. But you had a CIO of a Global Auto Supplier, a CISO of a Diversified Holdings Firm, who actually had some hospitality exposure but also some government contract manufacturing exposure. Chief Architect of a Software ISV and a VP and CISO of a Global Hospitality Resort Chain. So you had three out of the for, Erik, were really in industries that are getting hit hard. Obviously the software company maybe a little bit better. But maybe you can add some color to that. >> Well actually the software company, unfortunately, was getting hit hard as well because they're a software ISV that actually plays into the manufacturing space as well. So, this particular panel of CIOs and CISOs were actually in a very hard hit industries. And are going to make sure we do two more follow-ups with different industry verticals to make sure we're getting a little bit of a wider berth and collect all of that information in a better way. But coming back to this particular call, the whole reason we did this, and as you know, you spoke to my colleague and friend, Sagar Kadakia, who is the Director of Research for ETR, and we were nimble enough to actually change our survey while it was in the field, to start collecting data on what the real-time impact was on the COVID-19 pandemic. We were able to take that information, extrapolate it, and then say okay let's start reading out to these people and dig deeper. Find out why it's happening and even more so, is it permanent? And which vendors are going to win and which vendors might lose from it. So that was the whole reason we set up the series of calls. We've only conducted on so far. We have another one this coming Tuesday as well with four entirely new panelists that are going to be from different industry verticals because, as you astutely pointed out, these verticals were very hard hit and not all of them are as hard as others. So it's important to get a wider cross-section. >> So, guys let's take a look at some of the budget impacts the anecdotal evidence that we gathered here. So let me just scan through it and then Erik, I'll ask you to comment. So, you know, like Erik said, some hard hit industries. All major projects, anything sort of next-generation, have been essentially shelved. That was the ISV. And then another one, we cut at least 70% of the big projects moving forward. He mentioned ServiceNow actually calls them out, but the ServiceNow is a SaaS company they'll probably, you know, weather the storm here. But he did say we've put that on hold. The best comment, you know, "As-a-service has Saved our SaaS." (Erik laughs) That one's great. And then we're going to get into some of the networking commentary. Some really interesting things about how to support the work from home. You know, kind of shifting from a hardened top into remote workers. And then a lot of commentary on security. So, you know, that's sort of a high level scan and there's just so much information here Erik, but maybe you could sort of summarize on some of that commentary. >> Yeah, we should definitely dig into each of those sectors a little more, but to summarize what we're seeing here was the real winners and losers are clear. Not everyone was prepared to have a work from home strategy. Not everyone was prepared to send their workers out. Their VPN wasn't, they didn't have enough bandwidth. So there was a real quick uptick in spending, but longer term we're starting to see that these changes will become more permanent. So the real winners and losers right now, we're going to see on the loser's side traditional networking. The MPLS networking is in a lot of trouble according to all the data and the commentary that we're seeing. It's expensive, it's difficult to ramp to up bandwidth as quickly as you need and it doesn't support remote. So we're seeing that lose out and the winners there are in the SD-WAN space. It's going to be impossible to ignore that going forward and some of our CIO and CISO panelists said that change will be permanent. Also, we're seeing, at the same time, what they were calling a "SaaS and Cloud". Now, we know these trends obviously were already happening but they're being exacerbated. They're happening even more quickly and more strong. And I don't see that changing any time soon. That, of course, is at the expense of network, I'm sorry, data centers. Whether it be your own or hosted. Which has huge ramifications on on-prem hardware. Even the firewall providers. So what we're seeing here is obviously we know things are going to be impacted by this situation. We didn't necessarily expect all of our community members and IT decision-makers to talk about them being possibly permanent. So that on a high level was something that was extremely interesting. And the last one that I would bring up is that as we make this shift towards working from home, towards remote access, you also have to align yourself with the security that can support that. And one of the things that we're seeing in our data side on ETR, is a widening bifurcation between the next-generation security vendors and the more traditional security or the legacy security players. That bifurcation just keeps getting wider and wider and this situation could be the last straw. >> So I want to follow up on a couple of those things. You're talking about sort of the network shift you know, towards the SD-WAN. What people have described to me is that they got a, you know, a hardened top. It's a hierarchical network. It's very well understood and it's safe, right? And now all of a sudden you got all those remote workers and so you've got to completely soft of rethink your whole network architecture. The other thing I want to drill into is your Cloud commentary. There's a comment that I saw, Erik, that really stood out. One of the folks said, "I would like to see the data centers "be completely deleted, if you will, or closed down." I think we're going to see, you know, a lot more of this obviously. Not only from the standpoint of, and you heard this a lot, the kind of paid by the drink. But just generally getting rid of all that sort of so-called non-differentiated heavy-lifting as we often hear about. >> That is a extreme comment. I don't think everyone feels that way. But, yes, the comment was made and we've heard the comment from other people. As you and I both know, the larger the enterprise the harder that is to go completely SaaS. But yeah, when a situation like this has and see the inflexibility of their on-prem infrastructure, yes it becomes something that really has to be addressed and it can become a permanent change. I was also shocked about that comment. That gentleman also stated that his executives outside of the ITs area, the CEO, the CFO, had never ever, ever wanted to discuss Cloud. They did not want to discuss work from home. They did not want to discuss remote access. He said that conversation has changed immediately and to the credit of the actual IT companies out there, the technology companies, they're doing everything they can with this opportunity to make that happen. >> Yeah, and so you're right the whole work from home conversation. To your point earlier, Erik, big chunks of COVID, the post-COVID world are going to remain permanent. Guys bring up the SaaS slide if you will. The SaaS commentary, "As-a-Service Saved our SaaS." "The wittiest quip award" going to the ETR. You know, but you had, what's very interesting to hear folks, in fact I think somebody even called out, "Hey," you know, "we expected Oracle to," you know, "be auditing us but they're actually being supportive "as is IBM." Salesforce was an interesting common, Erik. One of the folks said they would share accounts on-prem, but when they all do the work from home they had to actually buy some more. You also got Cisco with big props. Microsoft was called out. A lot of organizations actually allowing them to defer payments. So the SaaS vendors actually got very high marks didn't they? >> They really did and even I wrote that summary and it was difficult to write that about Oracle because we all know that they're infamous for auditing their own customers in 2009 right after we came out of financial crisis. They have notoriously been a-- I don't know if they found religion and they decided to be nice to their customers, but every-single person mentioned them as one of the vendors that was actually helping. That was very shocking. And we all know that when bad situations happen people become opportunistic. And right now it's really seeming that the SaaS vendors understand that they need a longterm relationship with these customers and they're being altruistic instead. Which is really nice. >> Yeah I think that anybody with a Cloud realizes that hey, we have an opportunity here that the lifetime value of that customer, whereas maybe in 2009 when Oracle didn't have a Cloud, they had to get people in a headlock to try to persevere their, you know, income statement. Let's go to the networking drill down guys, that next slide because Fortinet, some of the things we've been reporting on is the sort of divergence in evaluations between Fortinet and Palo Alto before this whole thing hit, Fortinet has done a really good job with its Cloud offerings. Palo Alto struggles a little bit with trying to figure out the sales compensation, is maybe a little bit behind. Although both companies got strong props and I've talked to a number of customers, Palo Alto is going to be in the mix. Fortinet, from a Cloud standpoint, seems to be doing quite well? Obviously networking, Cisco is the big gorilla there. But we also got call outs from guys like Trend Micro which was interesting, from some of the folks. So, your thoughts on this Erik. >> Yeah, I'll start on the networking side because this is something that I've really, I've dug into quite amount, in not only this panel, but a lot of interviews and it really seems as if as networking refresh starts to come up, and it's coming up with a lot of large enterprises, when your network refresh comes up people are going to do an RFP for SD-WAN. They are sick and tired of paying MPLS network vendors and they really want to look at something else. That was even prior to this situation. Now what we're hearing is this is a permanent change. I particularly had one person say, I wanted to find this quote real quickly if I can, but basically they basically saying that, "From a permanency perspective, the freedom from MTLS "will reduce our networks spend by over half "while more than doubling or tripling our bandwidth." You can't ignore that. You're going to save me money and triple my bandwidth, and hey by the way, my refresh is due. It's something that's coming and it's going to happen. And yes, you mentioned the few right? There's Viptela, there's Velocloud, there's some big players like Cisco. The Palo Alto just acquired CloudGenix in the midst of all of this. They just went and got an SD-WAN player themselves. And they just keep acquiring a portfolio to shift from their on-prem to next-generation. It's going to take some time, because 70% plus of their revenues is still on-prem hardware, but I do believe that their portfolio that they're creating is the way the world is moving. And that's just one comment on the traditional networking versus the next-generation SD-WAN. >> And the customers have indicated, you know it's not easy just to get off of their MPLS network. I mean it takes time, it's like slowly pulling of the bandaid. But, like many things, COVID-19 is sort of accelerating that. We haven't talked about digital transformation. That came up as a maybe more strategic initiative. But one that very clearly has legs. >> You know, David, it's very simple. You just said it. People, when things are going well and they're comfortable, they don't change. And that's the same for an enterpriser company. Hey, everything's great, our revenue's fine. Why would we do this? We'll worry about that next year. Then something like this happens and you realize wow, we've been dragging our feet. That digital transformation that we've been talking about, and we've been a little bit slow to accept, we need to accept it, we need to move now. And yes, it was another one of the major themes and it sounds silly for researchers like you and I because we know this is a theme. We know Clouded option is there, we know digital transformation is there. But, there are still a lot of people that haven't moved as quickly as they should and this is going to be that final catalyst to get them there, without a doubt. Quickly on your point of Fortinet, I was actually very impressed with the commentary that came from that because Fortinet is sometimes one of those names that you think of that maybe plays in a smaller pool or isn't as big as some of the 800 pound gorillas out there. But in other other interviews besides this I've heard the phrase coined of "Forti-everything". So through RND and through acquisition, Fortinet has really expanded the portfolio and right now is their time to shine because when you have smaller satellite, you know, offices and branches that you need to connect, they're really, really good at it. And you don't always want to call a Palo Alto and pay that price when you have smaller branch offices. And I actually, I was glad you brought up Fortinet because it's not a name that we get to herald that often and it was deserving from this panel. >> Yeah and, you know, companies that can secure gateways, secure endpoints, obviously going to have momentum. Zscaler came up, you know I think that, and I'll tell ya, looking at, I've done a couple of breaking analysis on security and Fortinet has been strong in two dimensions. You know ETR is, as our audience is I think getting to know. We really look at two key metrics. One is net score, which is a measure of spending momentum, and the other is market share, which is a measure of pervasiveness. And companies like Fortinet, in security, show up on both of those dimensions so it's notable. >> Yes, it certainly is, it is. And I'm glad you brought up Zscaler too. Very recently by client request, we did a very in-depth research on Zscaler versus Palo Alto Prisma Access and they were very interested. This was before all this happened, you know. Does Palo Alto have a chance of catching up, taking share from Zscaler. And I've had the pleasure, myself, personally hosting Jay the CEO of Zscaler at an event in New York City. And I have nothing but incredible respect for the company. But what we found out through this research is Zscaler, at the moment, their technology is still ahead, according to their answers. There's no doubt. However, there doesn't seem to be any real secret sauce that will stop Palo Alto from catching up. So we do believe the parody of feature set will shrink over time. And then it will come down to Palo Alto obviously has a wider and user base. Now, what's happening today might change that. Because if I had to make a decision right now, for my company on secure web gateway, I'm still probably going to go to Zscaler. It's the name. If I had to choose that in a year from now, Palo Alto might have had a better chance. So in this panel, as you brought up, Zscaler was mentioned numerous times as just the wave of the future. Along with CASB brokers right? Whether you're talking about a Netskoper or Forcepointer. All those people that also play in CASB space to secure your access. Zero trust is no longer a marketing-hype term. It is real and it is becoming more real by the week. >> And so, I want to kind of end on one of the other comments that really struck me because we're constantly talking about okay, do you go with a portfolio of a suite of services or do you go with best of breed? What about startups? Are startups more risky in a crisis like this? And one of your panelists, I just love this comment, he said, "One of things that I've always done," he said, "You always hear about the guy, "oh we're going to go to the gardener, we're going to "check out the magic water, we'll pick out three guys "in the upper right hand corner and test them out." He says, "One of the things I always like to do, "I'll pick two from the upper right "and I'll take one from the lower left." One of the emerging, text, "And I'll give em a shot." It won't win every time, but then he called out FireEye as one of the organizations that he found early that gave them competitive advantage. >> Right. >> Love that comment. >> It's a great comment. And honestly if you're in charge of procurement you'd be stupid not to do that. Not only just to see what the technology is, but now I can play you off the big guys because I have negotiating leverage and I can say oh, well I could always just take their contract. So it's silly not to do it from a business perspective. But from technology perspective, what we kept hearing from these people with the smaller vendors. My partner Peter Steube, my colleague and I, we did the host together, we asked this question really believing that the financial insecurity of the moment and the times would make smaller vendors not viable. We heard the exact opposite. What our panelists said was, "No, I'd be happy "to work with a smaller vendor right now "because they're going to give me pricing flexibility, "they're going to work with me right now. "I don't need to pay them upfront "because we're seeing a permanent shift from CapEx to OpEX, "and the smaller vendors are willing to work with me and I can pay them later." So we were actually surprised to hear that and glad to hear it because, to connect to your other point, the other person who was talking about security and the platform approach versus best of breed, he said "Listen, platform approaches you're already "with the vendor, you can bundle a little bit. "But the problem is, if you're just going to acquire "a new technology every time there's a new threat, "the bad guys are just going to switch the threat. "And you can't acquire indefinitely. "So therefore, best of breed with security "will always beat platform." And that's kind of a message to Palo Alto and Cisco, in my opinion, because they seem to be the ones fighting that out. Even Microsoft now, trying to say they're a platform approach in security. >> Well and this says to me the security business, as we predicted, is going to stay fragmented because you're still going to get that best of breed. You know, just like Cloud is going to be fragmented and it's, you know, multiple vendors. Ever since I've been in this business people are trying to consolidate the number of vendors, but technology moves so quickly, it gives competitive advantage. Erik, awesome! Thank you so much for joining us. I'm looking forward to next Tuesday with the next vendor and love to have you back and talk about it anytime. You're a great guest, thanks so much. >> Certainly, I'll do my best to get a better AV connection the next time guys, I apologize for that. But it was great talking to you tonight. >> Hey we're all learning, you know so, thank you everybody for watching, this is Dave Vellante for theCUBE and we'll see you next time. (upbeat music)

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Welcome back to Vegas, Lisa Martin with John farrier. We are live at AWS reinvent in the expo hall at the sands convention center. There's tons of people in here. You could probably hear some of the background AWS expecting 65,000 or so folks. John, how many of those 65,000 and have you talked to in the last two days? >>Well, I can hear all the conversations happening at once. It's about hybrid cloud, IOT edge data, machine learning. my head's going to come. >>I was going to say lots of cool stuff. John and I are pleased to be joined by Greg Coralville, the VP of products and strategy for Blackberry Q. Next group. Welcome to the program >>to be here with 65,000 of our closest friends. >>His friends. Exactly. So Blackberry, cute X. What's it all about? >>What's it all about? Well, we do software. We do embedded software for mission critical systems at this event, at the AWS reinvent over showing a software and a really cool car, a karma, and we're connecting it to the AWS IOT backend services and showing some really, really cool use cases. Some of which are near term summer, which are a bit longer term are pretty exciting. Take a quick minute to describe Kunis. Is background acquired by Blackberry system history legacy? Exactly. Just take a quick minute to explain that. So we were founded in 1980 and then developing software for mission critical devices and medical, industrial. And then we started developing software for automotive in 1998 so we've been in automotive for about 20 years and developing originally an infotainment and then digital instrument clusters, telematic systems, gateways, safety systems, acoustics systems, pretty much becoming the software platform in the car because in the car, the car, the software is to be reliable, safe, secure. >>So we're trusted to deliver that. In automotive, we were acquired by Blackberry in 2010 and we're bringing the best of Blackberry and automotive and all of our other markets. So Lisa and I always talk about IOT is RPA automation. All this stuff's going on. But one of the things that comes up is we're trying to grok what's the software development environment in the cloud, in the car, and a Amazon one by having great API APIs. Yep. That was one of their core design principles. Is there a similar design principle from a car standpoint? Because if I'm an app developer, I just love, I have my mobile app sit on the car, right? But I don't want to have to become an expert on all the nuances of is there a connector? So is there going to be multiple platforms? What's the, what's the principle? Can you explain that a great question and great observation. >>So cars traditionally have been proprietary, pretty much closed systems and started open up with CarPlay and Android auto or all of a sudden you saw your mobile device being able to communicate with the car and now I could run Android apps, I could run iOS apps and started to open it up a bit. And now what you've seen is cars are becoming more connected, they're becoming more automated, eventually autonomous. Um, they're definitely, and what you're seeing in the car is in order for that car to really evolve and to offer connected services and shared mobility and the electrification that's occurring, the automotive industry is going through a disruption. We've all heard that and it really is true. So to the point where the electronics in the car, the networks in the car, the software in the car, it's getting completely redesigned and you're seeing a lot more high end processors. >>You're seeing safety critical systems, which have always been in cars, but now you're seeing a lot more complexity. And that speaks to exactly what we do. So where that car's going, if you think about it, is moving to more of a software platform. You have applications and mobile devices. Why? Because you've got Android and you've got iOS. That car is moving to that sort of a common platform where with the help of AWS connected services, the cubix Blackberry Punic software platform in the car, all of a sudden that'll open the door to that kind of environment to applications, to connected services. And that's exactly where it's going. So connectivities, it's here and it's going to be predominant through a pretty much all the vehicles coming off the line in the coming years. So you're going to see the connectivity and now we can bring the services and the apps to that vehicle. But at the same time you got to keep it safe, got to keep it secure. Gotta keep it reliable. You know, it's the classic mobile device, bingo literal device on wheels, right of two ton mobile device on wheels. >>Doc disruption sounds really cool and it's consumers. We just had this expectation that we can have whatever I want, the whole experience I want. And obviously as everything evolves, we want it to be safer and safer. And as there's laws and regulations that govern, Hey, you're going to get hefty fines if you're seeing with this device and you're driving. But disruption is really challenging, right? We talked, we got some great examples yesterday on stage with Andy Jassy of Goldman Sachs, right? How many years old are they and how they have leveraged disruption to revolutionize their consumer business or healthcare revolutionizing. I'd love to get your perspective on what are some of the automakers that are bleeding edge going, we get it. We want to work with you guys so that they understand that this the, you know, the, the mobile devices, the connected device on wheels is going to be transformative for their business. >>Good point. So first of all, every automaker we work with and we work, we work with almost 50 auto makers and we're over a hundred. We're in over 150 million vehicles and multiple systems in the cars. They're all putting safety first. That's never really changed. But that remains primary, primary objective. And to your point is how do you maintain that safety net reliability while at the same time opening the door to connectivity, making sure that vehicle is secure and resilient to attacks and whatnot. And you've seen some of those attacks in the past. And the industry is learning. Um, but that's, that's exactly what, that's what speaks to us and what we do. Same thing with AWS. If you think about what we do, we're plumbers. We, we build plumbing in the car, AWL splits, plumbing in the cloud. And I've had that call, those conversations with AWS and they're like, yeah, we're plumbers. >>And I said, so are we, we're going to get along great. But to your point, we have to keep our eye on security. Our definitely our eye on privacy and safety. And that's exactly what we do. As much as we all want the consumer apps and the connected experience at the same time, we can't compromise on that. So the good thing in automotive is there's a automotive safety standards, ISO two, six, two, six, two and whatnot, which we've certified our products to and we're going to keep doing that and keep delivering that software in the car. But that's awesome for 0.2 ton mobile device on wheels. So we got to always be aware of that. Great opportunity. People want more conduct and safety too. And that's a huge thing. Security and safety. I want to get to that in a second, but I got to ask you, um, what is the relationship that you guys have with Amazon? >>Could you explain that? And what are you guys doing at reinvent this year? Is your leg a presentation demo? Take a minute to explain the relationship between queen Nixon and Amazon web services and what you're showing here. Well, we're in the connected home exhibit. In fact, we're in the quote unquote garage where we've got a vehicle, a beautiful karma Rivero GT. And I was told it's the first time there's actually a car at reinvent. So that was pretty cool. And it's a cool car if you get a chance, come on over. And what we've done is we've taken the karma vehicle and we've actually connected it to AWS IOT. So if you think about what we do, we do software in the car, as I was saying earlier. And then we worked with the Amazon team, with the AWS team to say, okay, what can we do? So one of the things we're doing is we're doing battery monitoring and prediction in terms of the life of the battery. >>That's one of the things that we're doing. The other thing we're doing is personalized cockpit, which is, which is pretty exciting. And, and the last thing we're doing is kind of a business to business demonstration, um, where it's data orchestrations. If you think about the vehicle, there's a lot of sensors on the vehicle, a lot of information available on the vehicle. And what we're doing with AWS is pulling information from the vehicle, putting it in the cloud. And then we've got a few examples that we're using. So one of them is an application for an auto detailing company where they might want, you might want to have your vehicle detailed where we can make the position of your vehicle available, GPS, the VIN number. So the identify the identification of the vehicle. Um, and then you could actually contract with that expert detailings what we called them to come to your vehicle, clean the vehicle, detail your vehicle within a finite period of time securely. >>And then you'll get notified when it's done and whatnot. We're doing facial recognition in the vehicle and we also put some ML in machine learning in the car. We're actually showing gesture recognition where I can fold the mirrors with a, with a peace sign or victory signs. I could have the mirrors fold in. Uh, I can, I can interact with the infotainment system. I can personalize the music and whatnot. So really personalizing the cockpit. But all through the power of AWS. Sorry, what are we going to have to the car flying cars? Come on Jetsons flyers. I love this coming. Maybe not the flying carpet. Wow. Okay. Flying cars. Fine. I mean, I always say anything else that's in star Trek or star Wars will be invented. So I'm respecting some flying vehicles. All fun aside. Yeah. Now the serious conversation is safety and security. >>Worst case scenario, my car is hacked. Take over. This is a fear. Again, it's the worst. It's a doom season here. Those stories are straight. All IOT device. It's a car. How do you guys view the security posture? Um, good question. This is concerned. It might be on people's mind. Yeah. And that's what really speaks to where our company has been for almost four decades now. You know, when people would ask me, Hey, where would I find Punic software? Blackberry Punic software, I'd say almost everywhere, but the desktop. So where things have to be reliable, safe, secure work all the time. That's where you'll find our software. So factory floor, we're in laser eye surgery. Machines are in patient monitoring devices, MRI machines. And so essentially those areas which are safety critical, where safety, security and reliability, you know, our top real really industrial IOT thing, big time, big time. >>And that's the cool thing about walking around reinvent. There's all kinds of industrial devices and control. So if you go to the car now, if you think about the vehicle, same fundamental needs, reliability, safety, security, and we're trusted to deliver an automotive. So security is one of those things. It's not static. So when you, when you, when you make something that's secure, you're really building something that's resilient to attacks. So you'd be as resilient as possible to prevent attacks. And then you do whatever you can to prevent any malicious act or actions on that. So we will monitor what's going on in the system. We'll monitor any communications going to the car, for instance. So the minute we detect something a bit of normal, we can take action based on that. So that, that's absolutely key, especially given the cars connected and more and more becoming connected. >>What's the opportunity is in a trucking industry, when I think of the number of sensors on trucks, the regulations that you know for drivers safety in terms of how many hours they actually have to be able to can drive. What's the opportunity there for Q next? >>Good question. So everything we're doing in the car, which I should generalize and say a vehicle applies to trucks. So if you think about trucking or vehicles or drones or anything like that, you have multiple sensors that you have to interact with. You have to interpret that information, you have to take action based on that information. So if we look at trucking specifically, everybody knows a major shortage of truck truck, truck drivers. So when people ask me about autonomous cars and Hey, when are we going to see autonomy's vehicles? I always look at trucking and we're working with companies, trucking companies that are using our technology. And one of the first use cases that they're putting forward is something called platooning, where you'll actually have the first truck on the road with a driver and any other trucks on the road. We'll be operating autonomously essentially following like a train if you want on a highway, and then they'll have a starting location and a drop off location and that all of a sudden becomes a real world scenario, which makes use of the same sensors, LIDAR, radar cameras, et cetera. >>So from a trucking perspective, we look at it very similar to a car and automotive perspective because they need the same fundamental technologies. So pretty exciting. Like I said, what we do applies all over the place and again, all going to be connected. But grant, thanks for coming on. I really appreciate, I want to get your final thoughts, at least from my perspective on developers. When you see deep racer, you see that trend. It's kind of, they've got LIDAR, it's kind of a toy, but people geeking out on this. And so I would imagine that we're going to see an emergence of a software development environment where as a controlled sandboxes, cause yeah, they've got the concern with the industrial equipment. Exactly. Yeah. How do you balance that old school industrial mindset of, you know, IOT with the new rapid agile product development? Yeah. And to your point, we're going through that transition now. >>So this is where things like Sage maker come into play where I can develop out and develop and refine machine learning models in the cloud. You still have those tight control loops that you need and there's tools for that. So that's the deeply embedded stuff that's controlling actuators and whatnot. You still need that. But to your point, you need to be more iterative. You need to be more agile, need to develop according to the safety standards and the various industries that they might be in. So it's that is evolving and it's evolving at exactly the right pace. Really glad to see that evolution. But to your point, all of these devices are going to become interconnected. There's going to be new opportunities. And from a developer perspective, you know, we can't hire enough developers. No one can. It's really exciting whether it's IOT cloud developers or embedded developers. >>There's such an exciting future ahead. And I got to ask, this is just popped in my head. So I want to ask, cause I'm curious, um, spectrum and RF power is great, but you need connectivity to make an IOT device work, right? How do you guys, how does the car folks look at conductivity? Just when they get to a spot they can connect. So is it managing the spectrum? How are cars thinking about the connectivity? So we work very closely with the modem vendors. For instance, in today in cars you'll see Bluetooth, you'll see wifi, you'll see 4g. Obviously there's the emergence of 5g. Um, vehicle to vehicle communications is through something called DSRC. Essentially wifi 5g is going to come along, so now you're going to be able to have throughput and also what's called low latency. So quick turn around on your messages and the information being exchanged. >>So that too is evolving from a, from a QA software perspective, we'll make use of whatever modems there. But to your point, we also have to deal with the cases where I've lost connectivity. I still need that V vehicle to operate safely. And especially if you consider that the systems might be, um, uh, the systems might be connected or we don't want to make, make it such that they're dependent on that connectivity. So you have to have fail over scenarios and whatnot, but cars will become connected, devices will become connected. We're going to take advantage of that connectivity, but not be dependent on that connectivity. >>Well, Greg, please let me know when that, uh, personalized service is available so that my car can be found and detailed. They'd find it right in my driveway going lady, please. It's been a pleasure, a really cool stuff. Blackberry Kunis thank you for joining John. We'll be, we'll have to go check out that car for John furrier. I'm Lisa Martin. You're watching the cube live in Vegas at AWS. Reinvent 19. Thanks for watching.

>>live from San Francisco. It's the Q covering Micron Insight 2019 to You by Micron. >>Welcome back, everybody. We hear a Pier 27 in San Francisco. Beautiful day. David Floor is my co host on Day Volante, and this is Micron Inside. 2019. Jeff Baylor is here. He's the corporate vice president of the embedded business unit at Micron. Jeff, great to see you again. >>Thank you. Nice to be here >>so love to talk about autos. I o. T Edge. Use cases to talk about the focus of your team. Let's start there. Yeah, >>sure. So the embedded business to point. It's absolutely focused on the automotive industry's way. Call industrial markets. So factory automation, surveillance and stolen a swell as a consumer electronics businesses on we're really in across all those sort of focused on how connectivity and compute is changing inside of those. And, of course, how that drives memory. >>I mean, yeah, memory and storage. They hide in places that we use every day. You don't see them, but if they weren't there, you wouldn't be able to use all these devices. They wouldn't be as life changing as they are. So you know you mentioned some of the consumer stuff. You know what the big trends that are driving your business? Well, I do >>think it is absolutely. That's sort of the ubiquity of connectivity. First of all, and then, sort of the ubiquity of compute has enabled all of these what used to be sort of isolated applications to now be connected and doing a whole lot more analytics inside that machine. Do you think about intelligence in your thermostat on the wall? You think about intelligence, obviously, in the automotive business, where safety features and so on are using so much more electron ICs and a I machine learning. And that's happening really in every application, whether it's the smart speakers at home, voice control on your TV and so on and so forth. All of those drive more intelligence, more connectivity and then more memory and storage behind that. >>When people talk about automotive, of course, everybody wants to talk about autonomous vehicles. I love to talk about autonomous vehicles, but there's so much action going on in today's vehicles dozens and dozens of microprocessors throwing off all kinds of data. So give us the update on the automotive industry. >>Yeah, you're exactly right. I mean, autonomous gets the headlines and it will for several more years just be headlines more or less right? And the real story is what we call eight ass or advanced driver assistant system. So things like lane departure warning, lane departure, keeping things like auto emergency braking those those sort of much simpler, easier problems to solve are still very compute intensive on. So are driving a huge growth and electronics on memory of storage inside the car. The other major part of the car market in the automotive market is what we call infotainment, sort of the center console. More and more large screens going into that more high function capabilities being integrated in that whether it's navigation or streaming media service is and all of those air driving again a much richer mix that's required >>for those applications. I was at the arm conference and they were talking about automotive and some of the challenges, one of the most fascinating areas they were talking about. How do you make something that will last for 20 years in the car on make it such that if it does go wrong that it that it could recover seamless less. Can you talk about some of the technologies that >>are sort of two parts to that? Unpack a little bit? First through? What does it take to succeed in automotive? First of all, it's all about quality. Yeah, right. It is quality, quality, quality location, location, location. It's quality. It's it's reducing and eliminating defense fundamentally at the end of the day and so inside of our process. Design inside of our technology designed our product designs. Our product manufacturing flows are all designed to sort of fundamentally improve and continue to improve the quality level because at the end of the day, that is what what makes or breaks you in the car. As soon as you solve that, you know, small problem. Next problem is longevity and stability of that solution, because the design cycle itself is shortening and automotive. But it's a very long design cycle, and then the life cycle in automotive is still very, very long. I mean, the average car on the road in the U. S. Is 12 or 15 years old, right, and that needs to both continue to be viable but also often need toe continue shipping that product. It's gonna shipment volumes or have spares and replace. So So we have a strategy that sort of focused on both bringing those leading edge technologies that Micron has into automotive as soon as possible and that timeline is shrinking. But then also having a very long life manufacturing strategy to continue to provide those for so long. >>So you're certainly a leader in automotive. You might even be the leader. I'm not sure I have the data, but what is it you mentioned? You know, quality and those other factors. What is it that's allowing you to do so well in automotive? >>So So we are the beater for sure. We're about 40% market share, which is a little more than three times as big as the nearest competitors, right, So leader by far, really an automotive. And it's been a very long time that we're in this industry and very focused on. So it is. It is about the product mix and bringing in particular lately leading edge technology into that story. You know, we are at the very beginnings of LP five, the low power GDR five generation, where the very beginnings of that rolling out into mobile applications, its primary markets at the same time, almost literally the same time. Way air sampling and providing that into our automotive customers and our automotive partners to start beginning building their systems around L P. Five. So that time to adopt leading edge technology is rowing is shrinking very rapidly. And so we're able to provide that leading edge Tech started, coupled with that long life solution and then one of the areas, when you think about being in a 40% market share position, way air investing tremendously in sort of partnering with the customers around, essentially defining and driving the innovation that they need to deliver So way have a number of labs that we've established customer facing labs that were able to bring customers and even our customers customers. So the Auto am is directly into those labs to start looking at usage models and architectural sort of feasibility and optimization kinds of things that we could then plan into our road map to follow two or three years later. After that, >>a lot of domain expertise there, so tremendous I said the Derrick Dicker that Micron has a very large observation space. You sell to a lot of different channels and I want to ask you about industrial I ot David night. We spent a lot of time in the Enterprise and we see a lot of I t company saying, Hey, here's a box. We're gonna throw it over. We're gonna go dominate the edge anywhere you talkto operations, technology, professions there like No, we're talking about machines and equipment and it's like this whole different parlance and language. So what are you seeing? Just in terms of the ecosystem, how it's developing the sort of analog going to digital And that whole explosion? Yeah, >>again, Industrial is extremely broad market, and it means a 1,000,000,000 things toe people. Right? So So, one of the first things we have to do is sort of narrow the field a little bit, at least into specific verticals and specific areas. Way have the right product mix and opportunity, right? So, for example, in the in the space of factory automation, it's a little bit what you're just saying the operational technology guys are trying to figure out how they're gonna drive efficiency, drive productivity inside a factory on, and that is often a question of instrument ing, and putting in my crown is doing a lot of this sort of smart manufacturing deployment. Putting this sensor network multiple cameras, multiple high resolution cameras, audio sensors, accelerometers, sort of sensors and capturing all of that sensor data to Dr Things like better predictive maintenance, better sort of yield detection or excursion detection kind of capability. So you could tell this machine, you know, seven days, five days out of the week Sounds like this. But last night at 10 o'clock, it started sounding different way. Don't know what it means necessarily, but we can detect that. And that's where all of the A I and Machine Learning is now being applied to say. And that means it's due for a P M. About this particular portion of >>what about security at the edge, obviously a hot topic in the Enterprise on every C. I ose mind what's happening with security in Io ti industrial out in the edge. Yeah, I think >>to some extent, security in the I. O. T. I think is, is why I ot is where it is in the hype cycles. Maybe it's sort of still at the bottom of one of these types cycles, meaning solving that increasing security problem, that cyber security problem that the edge is really a big problem. You saw you know the hacks a few years back of the Jeep charity. You saw the hack two years back on surveillance cameras. All these cameras moving toe i p surveillance cameras means they're now connected and open to the world. Dispersed. He just announced last week in a report that basically showed I ot specific hacks up seven fold or seven fold this year after being up tenfold last year. So it's absolutely a growing problem for people thinking about deploying again. Connectivity is a great tool in a great weapon, Depending. And I was so so. One of my crown is doing is is way. >>Have a >>solution called authentic, which is essentially a cybersecurity, is a secure element built into the non volatile memory that goes in each one of these systems. So today, security is not a one chip problem. It is a full and and system problem. And so what we're tryingto build with that is the capability at a very sort of lowest level in the system right where the code is right where the four part of the system is to protect that in the memory itself and sort of a test that that is safe and secure. And then the system can build out about around that. And that sort of simple boot device, in the case of a nor device or Anand device is in every embedded application >>right in the world, >>right? I mean, you think about you go back a long way, Stuxnet. You know, 10 plus years ago with a seaman's controller, which was the and now you think about fast forward, how much Maur infrastructure is out there? How much more complicated it is, It's ah, it's a scary situation is Oh, it is so that we think that's a >>big opportunity. And we're making the announcement later, uh, later in the show today, on an extension of what we're doing already in that space. >>I know you're working with other vendors. People like >>me are worry with Yes, >>it is really >>an end to end. >>This is really an end to an an ecosystem >>activity, for sure, because again, arm is a great example. You know, all of the S o. C. Vendors. You know, everybody in this industry has some slice of the of the rules. Let's say to figure out how they're going to secure this system and we're tryingto build a basic building block that they can then build on >>that when we started this morning was really quiet. But the crowd is rolling in. Now there's a buzz that you can hear, hear. The key was excited to be here, Jeff. Thanks very much for coming on. The king here to see you again. >>Very much nicer here. >>All right. Keep it right to everybody. We're gonna be taking a short break. We'll be back. Day long coverage wall to Wall of Micron inside. 2019. You're watching the cube.

>>live from Miami, Florida It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, everybody. We're here at the Intercontinental Hotel. You're watching the Cube? The leader and I live tech covered set. Daubert is here. He's the vice president of data and I and a I and the chief data officer of cloud and cognitive software. And I'd be upset too. Good to see you again. >>Good. See, Dave, thanks for having me >>here. The data in a I form hashtag data. I I It's amazing here. 1700 people. Everybody's gonna hands on appetite for learning. Yeah. What do you see out in the marketplace? You know what's new since we last talked. >>Well, so I think if you look at some of the things that are really need in the marketplace, it's really been around filling the skill shortage. And how do you operationalize and and industrialize? You're a I. And so there's been a real need for things ways to get more productivity out of your data. Scientists not necessarily replace them. But how do you get more productivity? And we just released a few months ago, something called Auto A I, which really is, is probably the only tool out there that automates the end end pipeline automates 80% of the work on the Indian pipeline, but isn't a black box. It actually kicks out code. So your data scientists can then take it, optimize it further and understand it, and really feel more comfortable about it. >>He's got a eye for a eyes. That's >>exactly what is a eye for an eye. >>So how's that work? So you're applying machine intelligence Two data to make? Aye. Aye, more productive pick algorithms. Best fit. >>Yeah, So it does. Basically, you feed it your data and it identifies the features that are important. It does feature engineering for you. It does model selection for you. It does hyper parameter tuning and optimization, and it does deployment and also met monitors for bias. >>So what's the date of scientists do? >>Data scientist takes the code out the back end. And really, there's some tweaks that you know, the model, maybe the auto. Aye, aye. Maybe not. Get it perfect, Um, and really customize it for the business and the needs of the business. that the that the auto A I so they not understand >>the data scientist, then can can he or she can apply it in a way that is unique to their business that essentially becomes their I p. It's not like generic. Aye, aye for everybody. It's it's customized by And that's where data science to complain that I have the time to do this. Wrangling data >>exactly. And it was built in a combination from IBM Research since a great assets at IBM Research plus some cattle masters at work here at IBM that really designed and optimize the algorithm selection and things like that. And then at the keynote today, uh, wonderment Thompson was up there talking, and this is probably one of the most impactful use cases of auto. Aye, aye to date. And it was also, you know, my former team, the data science elite team, was engaged, but wonderment Thompson had this problem where they had, like, 17,000 features in their data sets, and what they wanted to do was they wanted to be able to have a custom solution for their customers. And so every time they get a customer that have to have a data scientist that would sit down and figure out what the right features and how the engineer for this customer. It was an intractable problem for them. You know, the person from wonderment Thompson have prevented presented today said he's been trying to solve this problem for eight years. Auto Way I, plus the data science elite team solve the form in two months, and after that two months, it went right into production. So in this case, oughta way. I isn't doing the whole pipeline. It's helping them identify the features and engineering the features that are important and giving them a head start on the model. >>What's the, uh, what's the acquisition bottle for all the way as a It's a license software product. Is it assassin part >>of Cloudpack for data, and it's available on IBM Cloud. So it's on IBM Cloud. You can use it paper use so you get a license as part of watching studio on IBM Cloud. If you invest in Cloudpack for data, it could be a perpetual license or committed term license, which essentially assassin, >>it's essentially a feature at dawn of Cloudpack for data. >>It's part of Cloudpack per day and you're >>saying it can be usage based. So that's key. >>Consumption based hot pack for data is all consumption based, >>so people want to use a eye for competitive advantage. I said by my open that you know, we're not marching to the cadence of Moore's Law in this industry anymore. It's a combination of data and then cloud for scale. So so people want competitive advantage. You've talked about some things that folks are doing to gain that competitive advantage. But the same time we heard from Rob Thomas that only about 4 to 10% penetration for a I. What? What are the key blockers that you see and how you're knocking them >>down? Well, I think there's. There's a number of key blockers, so one is of access to data, right? Cos have tons of data, but being able to even know what data is, they're being able to pull it all together and being able to do it in a way that is compliant with regulation because you got you can't do a I in a vacuum. You have to do it in the context of ever increasing regulation like GDP R and C, C, P A and all these other regulator privacy regulations that are popping up. So so that's that's really too so access to data and regulation can be blockers. The 2nd 1 or the 3rd 1 is really access to appropriate skills, which we talked a little bit about. Andi, how do you retrain, or how do you up skill, the talent you have? And then how do you actually bring in new talent that can execute what you want on then? Sometimes in some cos it's a lack of strategy with appropriate measurement, right? So what is your A II strategy, and how are you gonna measure success? And you and I have talked about this on Cuban on Cube before, where it's gotta measure your success in dollars and cents right cost savings, net new revenue. That's really all your CFO is care about. That's how you have to be able to measure and monitor your success. >>Yes. Oh, it's so that's that Last one is probably were where most organizations start. Let's prioritize the use cases of the give us the best bang for the buck, and then business guys probably get really excited and say Okay, let's go. But to up to truly operationalize that you gotta worry about these other things. You know, the compliance issues and you gotta have the skill sets. Yeah, it's a scale. >>And sometimes that's actually the first thing you said is sometimes a mistake. So focusing on the one that's got the most bang for the buck is not necessarily the best place to start for a couple of reasons. So one is you may not have the right data. It may not be available. It may not be governed properly. Number one, number two the business that you're building it for, may not be ready to consume it right. They may not be either bought in or the processes need to change so much or something like that, that it's not gonna get used. And you can build the best a I in the world. If it doesn't get used, it creates zero value, right? And so you really want to focus on for the first couple of projects? What are the one that we can deliver the best value, not Sarah, the most value, but the best value in the shortest amount of time and ensure that it gets into production because especially when you're starting off, if you don't show adoption, people are gonna lose interest. >>What are you >>seeing in terms of experimentation now in the customer base? You know, when you talk to buyers and you talk about, you know, you look at the I T. Spending service. People are concerned about tariffs. The trade will hurt the 2020 election. They're being a little bit cautious. But in the last two or three years have been a lot of experimentation going on. And a big part of that is a I and machine learning. What are you seeing in terms of that experimentation turning into actually production project that we can learn from and maybe do some new experiments? >>Yeah, and I think it depends on how you're doing the experiments. There's, I think there's kind of academic experimentation where you have data science, Sistine Data science teams that come work on cool stuff that may or may not have business value and may or may not be implemented right. They just kind of latch on. The business isn't really involved. They latch on, they do projects, and that's I think that's actually bad experimentation if you let it that run your program. The good experimentation is when you start identity having a strategy. You identify the use cases you want to go after and you experiment by leveraging, agile to deliver these methodologies. You deliver value in two weeks prints, and you can start delivering value quickly. You know, in the case of wonderment, Thompson again 88 weeks, four sprints. They got value. That was an experiment, right? That was an experiment because it was done. Agile methodologies using good coding practices using good, you know, kind of design up front practices. They were able to take that and put it right into production. If you're doing experimentation, you have to rewrite your code at the end. And it's a waste of time >>T to your earlier point. The moon shots are oftentimes could be too risky. And if you blow it on a moon shot, it could set you back years. So you got to be careful. Pick your spots, picked ones that maybe representative, but our lower maybe, maybe lower risk. Apply agile methodologies, get a quick return, learn, develop those skills, and then then build up to the moon ship >>or you break that moon shot down its consumable pieces. Right, Because the moon shot may take you two years to get to. But maybe there are sub components of that moon shot that you could deliver in 34 months and you start delivering knows, and you work up to the moon shot. >>I always like to ask the dog food in people. And I said, like that. Call it sipping your own champagne. What do you guys done internally? When we first met, it was and I think, a snowy day in Boston, right at the spark. Some it years ago. And you did a big career switch, and it's obviously working out for you, But But what are some of the things? And you were in part, brought in to help IBM internally as well as Interpol Help IBM really become data driven internally? Yeah. How has that gone? What have you learned? And how are you taking that to customers? >>Yeah, so I was hired three years ago now believe it was that long toe lead. Our internal transformation over the last couple of years, I got I don't want to say distracted there were really important business things I need to focus on, like gpr and helping our customers get up and running with with data science, and I build a data science elite team. So as of a couple months ago, I'm back, you know, almost entirely focused on her internal transformation. And, you know, it's really about making sure that we use data and a I to make appropriate decisions on DSO. Now we have. You know, we have an app on her phone that leverages Cognos analytics, where at any point, Ginny Rometty or Rob Thomas or Arvin Krishna can pull up and look in what we call E P M. Which is enterprise performance management and understand where the business is, right? What what do we do in third quarter, which just wrapped up what was what's the pipeline for fourth quarter? And it's at your fingertips. We're working on revamping our planning cycle. So today planning has been done in Excel. We're leveraging Planning Analytics, which is a great planning and scenario planning tool that with the tip of a button, really let a click of a button really let you understand how your business can perform in the future and what things need to do to get it perform. We're also looking across all of cloud and cognitive software, which data and A I sits in and within each business unit and cloud and cognitive software. The sales teams do a great job of cross sell upsell. But there's a huge opportunity of how do we cross sell up sell across the five different businesses that live inside of cloud and cognitive software. So did an aye aye hybrid cloud integration, IBM Cloud cognitive Applications and IBM Security. There's a lot of potential interplay that our customers do across there and providing a I that helps the sales people understand when they can create more value. Excuse me for our customers. >>It's interesting. This is the 10th year of doing the Cube, and when we first started, it was sort of the beginning of the the big data craze, and a lot of people said, Oh, okay, here's the disruption, crossing the chasm. Innovator's dilemma. All that old stuff going away, all the new stuff coming in. But you mentioned Cognos on mobile, and that's this is the thing we learned is that the key ingredients to data strategies. Comprised the existing systems. Yes. Throw those out. Those of the systems of record that were the single version of the truth, if you will, that people trusted you, go back to trust and all this other stuff built up around it. Which kind of created dissidents. Yeah. And so it sounds like one of the initiatives that you you're an IBM I've been working on is really bringing in the new pieces, modernizing sort of the existing so that you've got sort of consistent data sets that people could work. And one of the >>capabilities that really has enabled this transformation in the last six months for us internally and for our clients inside a cloud pack for data, we have this capability called IBM data virtualization, which we have all these independent sources of truth to stomach, you know? And then we have all these other data sources that may or may not be as trusted, but to be able to bring them together literally. With the click of a button, you drop your data sources in the Aye. Aye, within data. Virtualization actually identifies keys across the different things so you can link your data. You look at it, you check it, and it really enables you to do this at scale. And all you need to do is say, pointed out the data. Here's the I. P. Address of where the data lives, and it will bring that in and help you connect it. >>So you mentioned variances in data quality and consumer of the data has to have trust in that data. Can you use machine intelligence and a I to sort of give you a data confidence meter, if you will. Yeah. So there's two things >>that we use for data confidence. I call it dodging this factor, right. Understanding what the dodging this factor is of the data. So we definitely leverage. Aye. Aye. So a I If you have a date, a dictionary and you have metadata, the I can understand eight equality. And it can also look at what your data stewards do, and it can do some of the remediation of the data quality issues. But we all in Watson Knowledge catalog, which again is an in cloudpack for data. We also have the ability to vote up and vote down data. So as much as the team is using data internally. If there's a data set that had a you know, we had a hive data quality score, but it wasn't really valuable. It'll get voted down, and it will help. When you search for data in the system, it will sort it kind of like you do a search on the Internet and it'll it'll down rank that one, depending on how many down votes they got. >>So it's a wisdom of the crowd type of. >>It's a crowd sourcing combined with the I >>as that, in your experience at all, changed the dynamics of politics within organizations. In other words, I'm sure we've all been a lot of meetings where somebody puts foursome data. And if the most senior person in the room doesn't like the data, it doesn't like the implication he or she will attack the data source, and then the meeting's over and it might not necessarily be the best decision for the organization. So So I think it's maybe >>not the up, voting down voting that does that, but it's things like the E PM tool that I said we have here. You know there is a single source of truth for our finance data. It's on everyone's phone. Who needs access to it? Right? When you have a conversation about how the company or the division or the business unit is performing financially, it comes from E. P M. Whether it's in the Cognos app or whether it's in a dashboard, a separate dashboard and Cognos or is being fed into an aye aye, that we're building. This is the source of truth. Similarly, for product data, our individual products before me it comes from here's so the conversation at the senior senior meetings are no longer your data is different from my data. I don't believe it. You've eliminated that conversation. This is the data. This is the only data. Now you can have a conversation about what's really important >>in adult conversation. Okay, Now what are we going to do? It? It's >>not a bickering about my data versus your data. >>So what's next for you on? You know, you're you've been pulled in a lot of different places again. You started at IBM as an internal transformation change agent. You got pulled into a lot of customer situations because yeah, you know, you're doing so. Sales guys want to drag you along and help facilitate activity with clients. What's new? What's what's next for you. >>So really, you know, I've only been refocused on the internal transformation for a couple months now. So really extending IBM struck our cloud and cognitive software a data and a I strategy and starting to quickly implement some of these products, just like project. So, like, just like I just said, you know, we're starting project without even knowing what the prioritized list is. Intuitively, this one's important. The team's going to start working on it, and one of them is an aye aye project, which is around cross sell upsell that I mentioned across the portfolio and the other one we just got done talking about how in the senior leadership meeting for Claude Incognito software, how do we all work from a Cognos dashboard instead of Excel data data that's been exported put into Excel? The challenge with that is not that people don't trust the data. It's that if there's a question you can't drill down. So if there's a question about an Excel document or a power point that's up there, you will get back next meeting in a month or in two weeks, we'll have an e mail conversation about it. If it's presented in a really live dashboard, you can drill down and you can actually answer questions in real time. The value of that is immense, because now you as a leadership team, you can make a decision at that point and decide what direction you're going to do. Based on data, >>I said last time I have one more questions. You're CDO but you're a polymath on. So my question is, what should people look for in a chief data officer? What sort of the characteristics in the attributes, given your >>experience, that's kind of a loaded question, because there is. There is no good job, single job description for a chief date officer. I think there's a good solid set of skill sets, the fine for a cheap date officer and actually, as part of the chief data officer summits that you you know, you guys attend. We had were having sessions with the chief date officers, kind of defining a curriculum for cheap date officers with our clients so that we can help build the chief. That officer in the future. But if you look a quality so cheap, date officer is also a chief disruption officer. So it needs to be someone who is really good at and really good at driving change and really good at disrupting processes and getting people excited about it changes hard. People don't like change. How do you do? You need someone who can get people excited about change. So that's one thing. On depending on what industry you're in, it's got to be. It could be if you're in financial or heavy regulated industry, you want someone that understands governance. And that's kind of what Gardner and other analysts call a defensive CDO very governance Focus. And then you also have some CDOs, which I I fit into this bucket, which is, um, or offensive CDO, which is how do you create value from data? How do you caught save money? How do you create net new revenue? How do you create new business models, leveraging data and a I? And now there's kind of 1/3 type of CDO emerging, which is CDO not as a cost center but a studio as a p N l. How do you generate revenue for the business directly from your CDO office. >>I like that framework, right? >>I can't take credit for it. That's Gartner. >>Its governance, they call it. We say he called defensive and offensive. And then first time I met Interpol. He said, Look, you start with how does data affect the monetization of my organization? And that means making money or saving money. Seth, thanks so much for coming on. The Cube is great to see you >>again. Thanks for having me >>again. All right, Keep it right to everybody. We'll be back at the IBM data in a I form from Miami. You're watching the Cube?

>> fly from San Francisco. It's the Cube covering Google Club next nineteen Tio by Google Cloud and its ecosystem partners. >> Hey, welcome back. Everyone live here in San Francisco, California is the cubes coverage of Google Cloud Next twenty nineteen star Third day of three days of wall to wall coverage. John for a maiko stupid demon devil on things out around the floor. Getting stories, getting scoops. Of course, we're here with Sadeer has Bay. Who's the director of product management? Google Cloud. So great to see you again. Go on Back on last year, I'LL see Big Query was a big product that we love. We thought the fifty many times about database with geek out on the databases. But it's not just about the databases. We talked about this yesterday, all morning on our kickoff. There is going to be database explosion everywhere. Okay, it's not. There's no one database anymore. It's a lot of databases, so that means data in whatever database format document relational, Unstructured. What you want to call it is gonna be coming into analytical tools. Yes, this's really important. It's also complex. Yeah, these be made easier. You guys have made their seers announcements Let's get to the hard news. What's the big news from your group around Big Queria Mail Auto ml Some of the news share >> the news. Perfect, I think not. Just databases are growing, but also applications. There's an explosion off different applications. Every organization is using hundreds of them, right from sales force to work today. So many of them, and so having a centralized place where you can bring all the data together, analyze it and make decisions. It's critical. So in that realm to break the data silos, we have announced a few important things that they went. One is clouded effusion, making it easy for customers to bring in data from different sources on Prum Ices in Cloud so that you can go out and as you bring the data and transform and visually just go out and move the data into Big query for for analysis, the whole idea is the board and have Dragon drop called free environment for customers to easily bring daytime. So we have, like, you know, a lot of customers, just bringing in all the data from their compromise. The system's oracle, my sequel whatever and then moving that into into big Query as they analyze. So that's one big thing. Super excited about it. A lot of attraction, lot of good feedback from our customers that they went. The second thing is Big Query, which is our Cloud Skill Data warehouse. We have customers from few terabytes to hundreds of terabytes with it. Way also have an inline experience for customers, like a data analyst who want to analyze data, Let's say from sales force work, they are from some other tools like that if you want to do that. Three. I have made hundred less connectors to all these different sense applications available to our partners. Like five Grand Super Metrics in Macquarie five four Barrel Box out of the box for two five clicks, >> you'LL be able to cloud but not above, but I guess that's afraid. But it's important. Connectors. Integration points are critical table stakes. Now you guys are making that a table stakes, not an ad on service the paid. You >> just basically go in and do five clicks. You can get the data, and you can use one of the partners connectors for making all the decisions. And also that's there. and we also announced Migration Service to migrate from candidate that shift those things. So just making it easy to get data into recipe so that you can unlock the value of the data is the first thing >> this has become the big story here. From the Cube standpoint on DH student, I've been talking about day all week. Data migration has been a pain in the butt, and it's critical linchpin that some say it could be the tell sign of how well Google Cloud will do in the Enterprise because it's not an easy solution. It's not just, oh, just move stuff over And the prizes have unique requirements. There's all kinds of governance, all kinds of weird deal things going on. So how are you guys making it easy? I guess that's the question. How you gonna make migrating in good for the enterprise? >> I think the one thing I'll tell you just before I had a customer tell me one pain. You have the best highways, but you're on grams to the highway. Is that a challenge? Can you pick that on? I'm like here are afraid. Analogy. Yeah, it's great. And so last year or so we have been focused on making the migration really easy for customers. We know a lot of customers want to move to cloud. And as they moved to cloud, we want to make sure that it's easy drag, drop, click and go for migration. So we're making that >> holding the on ramps basically get to get the data in the big challenge. What's the big learnings? What's the big accomplishment? >> I think the biggest thing has Bean in past. People have to write a lot ofthe court to go ahead and do these kind of activities. Now it is becoming Click and go, make it really cold free environment for customers. Make it highly reliable. And so that's one area. But that's just the first part of the process, right? What customers want is not just to get data into cloud into the query. They want to go out and get a lot of value out off it. And within that context, what we have done is way made some announcements and, uh, in the in that area. One big thing is the B I engine, because he'd be a engine. It's basically an acceleration on top of the query you get, like subsequently, agency response times for interactive dash boarding, interactive now reporting. So that's their butt in with that. What we're also announced is connected sheets, so connected sheets is basically going to give you spreadsheet experience on top ofthe big credit data sets. You can analyze two hundred ten billion rose off data and macquarie directly with drag drop weakened upriver tables again. Do visualizations customers love spreadsheets in general? >> Yeah, City area. I'm glad you brought it out. We run a lot of our business on sheep's way of so many of the pieces there and write if those the highways, we're using our data. You know what's the first step out of the starts? What are some of the big use cases that you see with that? >> So I think Andy, she is a good example of so air. Isha has a lot of their users operational users. You needed to have access to data on DH, so they basically first challenge was they really have ah subsequently agency so that they can actually do interact with access to the data and also be an engine is helping with that. They used their story on top. Off half now Big Quit it, Gordon. Make it accessible. Be engine will vote with all the other partner tooling too. But on the other side, they also needed to have spread sheet like really complex analysis of the business that they can improve operation. Last year we announced they have saved almost five to ten percent on operational costs, and in the airline, that's pretty massive. So basically they were able to go out and use our connective sheets experience. They have bean early Alfa customer to go out and use it to go in and analyse the business, optimize it and also so that's what customers are able to do with connected sheets. Take massive amounts of data off the business and analyze it and make better. How >> do we use that? So, for a cost, pretend way want to be a customer? We have so many tweets and data points from our media. I think fifty million people are in our kind of Twitter network that we've thought indexed over the years I tried to download on the C S V. It's horrible. So we use sheets, but also this They've had limitations on the han that client. So do we just go to Big Query? How would we work >> that you can use data fusion with you? Clicks move later into Big Query wants you now have it in big query in sheets. You will have an option from data connectors Macquarie. And once you go there, if you're in extended al far, you should get infection. Alfa. And then when you click on that, it will allow you to pick any table in bickering. And once you link the sheets to be query table, it's literally the spreadsheet is a >> run in >> front and got through the whole big query. So when you're doing a favour tables when you're saying Hey, aggregate, by this and all, it actually is internally calling big credit to do those activities. So you remove the barrier off doing something in the in the presentation layer and move that to the engine that actually can do the lot skill. >> Is this shipping? Now you mention it. Extended beta. What's the product? >> It's an extended out far for connected sheets. Okay, so it's like we're working with few customers early on board and >> make sure guys doing lighthouse accounts classic classic Early. >> If customers are already G sweet customer, we would love to get get >> more criteria on the connected sheets of Alfa sending bait after Now What's what's the criteria? >> I think nothing. If customers are ready to go ahead and give us feedback, that's what we care of. Okay, so you want to start with, like, twenty twenty five customers and then expanded over this year and expand it, >> maybe making available to people watching. Let us let us know what the hell what do they go? >> Throw it to me and then I can go with that. Folks, >> sit here. One of the other announcements saw this week I'm curious. How it connects into your pieces is a lot of the open source databases and Google offering those service maybe even expand as because we know, as John said in the open there, the proliferation of databases is only gonna increase. >> I think open source way announced lot of partnerships on the databases. Customers need different types of operational databases on. This is a great, great opportunity for us to partner with some of our partners and providing that, and it's not just data basis. We also announced announced Partnership with Confident. I've been working with the confident team for last one place here, working on the relationship, making sure our customers haven't. I believe customers should always have choice. And we have our native service with Cloud pops up. A lot of customers liked after they're familiar with CAFTA. So with our relationship with Khan fluent and what we announced now, customers will get native experience with CAFTA on Jessie P. I'm looking forward to that, making sure our customers are happy and especially in the streaming analytic space where you can get real time streams of data you want to be, Oh, directly analytics on top of it. That is a really high value add for us, So that's great. And so so that's the That's what I'm looking forward to his customers being able to go out and use all of these open source databases as well as messaging systems to go ahead and and do newer scenarios for with us. >> Okay, so you got big Big query. ML was announced in G. A big query also has auto support Auto ml tables. What does that mean? What's going what's going on today? >> So we announced aquarium L at Kew Blast next invader. So we're going Ta be that because PML is basically a sequel interface to creating machine learning models at scale. So if you have all your data and query, you can write two lines ofthe sequel and go ahead and create a model tow with, Let's say, clustering. We announced plastering. Now we announced Matrix factory ization. One great example I will give you is booking dot com booking dot com, one of the largest travel portals in the in the world. They have a challenge where all the hotel rooms have different kinds off criteria which says they have a TV. I have a ll the different things available and their problem was data quality. There was a lot of challenges with the quality of data they were getting. They were able to use clustering algorithm in sequel in Macquarie so that they could say, Hey, what are the anomalies in this data? Sets and identify their hotel rooms. That would say I'm a satellite TV, but no TV available. So those claims direct Lansing stuff. They were easily able to do with a data analyst sequel experience so that's that. >> That's a great example of automation. Yeah, humans would have to come in, clean the data that manually and or write scripts, >> so that's there. But on the other side, we also have, Ah, amazing technology in Auto Emma. So we had our primal table are normal vision off thermal available for customers to use on different technologies. But we realized a lot of problems in enterprise. Customers are structured data problems, So I have attained equerry. I want to be able to go in and use the same technology like neural networks. It will create models on top of that data. So with auto Emel tables, what we're enabling is customers can literally go in auto Emel Table Portal say, Here is a big query table. I want to be able to go out and create a model on. Here is the column that I want to predict from. Based on that data, and just three click a button will create an automated the best model possible. You'LL get really high accuracy with it, and then you will be able to go out and do predictions through an FBI or U can do bulk predictions out and started back into Aquarian also. So that's the whole thing when making machine learning accessible to everyone in the organization. That's our goal on with that, with a better product to exactly it should be in built into the product. >> So we know you've got a lot of great tech. But you also talk to a lot of customers. Wonder if you might have any good, you know, one example toe to really highlight. Thie updates that you >> think booking dot com is a good example. Our scent. Twentieth Century Fox last year shared their experience off how they could do segmentation of customers and target customers based on their past movies, that they're watched and now they could go out and protect. We have customers like News UK. They're doing subscription prediction like which customers are more likely to subscribe to their newspapers. Which ones are trying may turn out s o those He examples off how machine learning is helping customers like basically to go out and target better customers and make better decisions. >> So, do you talk about the ecosystem? Because one of things we were riffing on yesterday and I was giving a monologue, Dave, about we had a little argument, but I was saying that the old way was a lot of people are seeing an opportunity to make more margin as a system integrated or global less I, for instance. So if you're in the ecosystem dealing with Google, there's a margin opportunity because you guys lower the cost and increase the capability on the analytic side. Mention streaming analytics. So there's a business model moneymaking opportunity for partners that have to be kind of figured out. >> I was the >> equation there. Can you share that? Because there's actually an opportunity, because if you don't spend a lot of time analyzing the content from the data, talk aboutthe >> money means that there's a huge opportunity that, like global system integrators, to come in and help our customers. I think the big challenges more than the margin, there is lot of value in data that customers can get out off. There's a lot of interesting insights, not a good decision making they can do, and a lot of customers do need help in ramping up and making sure they can get value out of that. And it's a great opportunity for our global Asai partners and I've been meeting a lot of them at the show to come in and help organizations accelerate the whole process off, getting insights from from their data, making better decisions, do no more machine learning, leverage all of that. And I think there is a huge opportunity for them to come in. Help accelerate. What's the >> play about what some other low hanging fruit opportunities I'LL see that on ramping or the data ingestion is one >> one loving fruit? Yes, I think no hanging is just moving migration. Earlier, he said. Break the data silos. Get the data into DCP. There's a huge opportunity for customers to be like, you know, get a lot of value. By that migration is a huge opportunity. A lot of customers want to move to cloud, then they don't want to invest more and more and infrastructure on them so that they can begin level Is the benefits off loud? And I think helping customers my great migrations is going to be a huge Obviously, we actually announced the migration program also like a weak back also way. We will give training credits to our customers. We will fund some of the initial input, initial investment and migration activities without a side partners and all, so that that should help there. So I think that's one area. And the second area, I would say, is once the data is in the platform getting value out ofit with aquarium in auto ml, how do you help us? It must be done. I think that would be a huge opportunity. >> So you feel good too, dear. But, you know, build an ecosystem. Yeah. You feel good about that? >> Yeah, way feel very strongly about our technology partners, which are like folks like looker like tableau like, uh, talent confluence, tri factor for data prep All of those that partner ecosystem is there great and also the side partner ecosystem but for delivery so that we can provide great service to our customers >> will be given good logos on that slide. I got to say, Try facts and all the other ones were pretty good etcetera. Okay, so what's the top story for you in the show here, besides your crew out on the date aside for your area was a top story. And then generally, in your opinion, what's the most important story here in Google Cloud next. >> I think two things in general. The biggest news, I think, is open source partnership that we have announced. I'm looking forward to that. It's a great thing. It's a good thing both for the organizations as well as us on DH. Then generally, you'LL see lot off examples of enterprise customers betting on us from HSBC ends at bank that was there with mean in the session. They talked about how they're getting value out ofthe outof our data platform in general, it's amazing to see a lot more enterprises adopting and coming here telling their stories, sharing it with force. >> Okay, thanks so much for joining us. Look, you appreciate it. Good to see you again. Congratulations. Perfect fusion ingesting on ramps into the into the superhighway of Big Query Big engine. They're they're large scale data. Whereas I'm Jeffers dipping them in. We'LL stay with you for more coverage after this short break

>> Announcer: Live from Miami Beach, Florida It's theCUBE! Covering UiPathForward Americas. Brought to you by UiPath. >> Welcome back to South Beach everybody. You are watching theCUBE, the leader in live tech coverage. I'm Dave Vellante, Stu Miniman is here. This is UiPathForward Americas. UiPath does these shows all around the world and they've done, I don't know how many. But they've reached 14,000 customers this year. But Bobby Patrick knows, he's the CMO of UiPath. Bobby, great to see you again. >> It's great to be on again. >> So, how many of these events have you done in the last 12 months? >> We've probably done a dozen, all major cities. We still have Beijing and Dubai coming up. Over 14,000 people at our events alone. We go to a lot of other industry events obviously, but yeah, at our own events, every single event we break our records. We're always undersizing our events, it drives everyone nuts. >> You're always riding the wave, Bobby. You hit Cloud, right as the wave was building. How did you find this company? >> Yeah, so I was the HP of Cloud, they were, split assets off and took a little time, got a call and robotic process automation. Of course, I thought of physical robots. I look online and say wow that's interesting. I did some search terms on it and I saw RPA kind of sky rocketing in search and my background is actually in integration, data integration before Cloud. And then I met Daniel and I fell in love with Daniel and this was a year ago. I was employee 270, right? We'll have 2,000 by the end of the year. So, it's been everything I expected which was a rocket ship, has completely, constantly I've underestimated, it's amazing. >> So, you're the one who turned me onto this whole space. You sent me the Forrester Wave, >> Bobby: Right >> Where it was last year's and you guys were third this year, you leapfrogged into first. >> Bobby: Right. >> And then we said wow that's kind of cool. Let's download this and play with it. And we tried to download the other ones but we couldn't. You, know it was kind of too complicated. They wanted us to talk to resellers and, it was like, no no no. you guys were, like, really open. >> Bobby: It's part of our culture. >> And we found it super simple to use. It was, one of our guys wasn't a coder. Smart dude, but it was low code, no code type of situation. You were explaining to me at Legal Seafoods last week that you actually have written some automations. So, it's pretty simple to get started but there's a spectrum, right, and it's pretty powerful too. >> Yeah, it's an epiphany that hits everybody. This is the part where I see it, even in myself, when I realized every morning I was getting up and going to Google Trends and I was looking at us versus Automation Anywhere versus Blue Prism and we're pulling away. It's great, I'll get happy in the morning and I'll screen shot it and then I'll go to Slack and send it to the comp team. Why am I doing this? So, in 20 minutes now I have a robot everyday, every morning that does it for me. And I get a text and I get an email. We have, in marketing, a dozen of these. I've got one that does our Google Ad Words around the world. I've got one that takes all of our 30,000 inbound new contacts a month, in different languages, translates, finds out what country they are in, and routes them to the right country. These are simpler examples, but once you realize that anything you do that's routine and mundane that a robot can do for you. It brings, it makes you happy first of all, right? And you realize the vision we have for a robot for every person, its a very realistic vision and its two, three years out. >> Bobby, one on the things that has really interested me today is talking about what this means for jobs and careers. Dave and I were at Splunk earlier this week, talking about Splunkers, data is at the center of what they do and everybody comes to them, how do I leverage my data? I did operations for a bunch of my career and I'd spend lots of time with my team saying, what do you hate doing, what are you manually doing? What can you get rid of and there's a collaboration between, I hear, that your customers. It's not just oh some consultancy comes in and they cut something away and they took it away from you. Oh no wait, you're actually involved with this, it seems like an ongoing process and you're making people's jobs better. Can you talk a little about that dynamics of how this transforms a company? The vision for, I hear from UiPath, is that you're going to change the world. >> Yeah, so you have to sit in, you're talking about the future of work, or digital, you have to sit in a conference room and watch a bunch of workers sit around and I'll give you an example. At DISA, big federal government agency, federal government has lifetime workers, right? In the room, where 30 workers, who everyday download assets and then they compile them and then they analyze them. They have their best, fastest kind of human go against the UiPath robot that they automated. In 15 minutes, the human downloaded two assets or archives and the robot did 17. The entire room of 30 cheered! Cheered. No longer do we have to do that crap ever again. And this is, we see this in every industry. It's so much fun because you see just, people just radiating with excitement, right? Because, I was out with a customer today that says they can't even fulfill today with the humans they have, the 25% of the work they got. So, your robots are creating capacity, they're filling the void. You probably heard about Japan, right, and the aging population? And RPA and UiPath addressing suicide rates. This about making society better. This is about robots doing the work that we hate, right? One of our great customers, Holly Uhl from State Auto, said on stage that, you know, robots do the work nobody misses. And, I think that's trivial. Now what about job impacts, right? So, we worry everyday about what this means, right? So, we spend a lot of time on our academy, making it easier to train people, build digital era skills. We announced our academic alliance, right? We hired an amazing Chief of Learning Officer. You saw Tom Clancy. You know him and his team. We're going to train a million students in three years. You know, we're worried about the middle class. We're worried about people who are farther along in their careers and helping them re-skill. So, we take that as a part of our job as a company to figure out how to up-skill people and make them a part of this. And I'm really excited because a year ago when I joined, everybody said, the big problem you have is people going to worry about taking away jobs. I don't hear that from the 1500 customers in here today. >> Well, isn't a part of that re-skilling? Learning how to apply automation, maybe even learning how to apply RPA? Maybe even doing some automation? >> Yeah, so obviously there is-- World Economic Forum came out two weeks ago with a study that said, automation will add net 60 million jobs, I think that was for the people that losses, it will two x gains in jobs. Now those are different jobs in some cases. Some of those jobs are digital era skills, some of those jobs are AI, data science. So, I think that there's... But there are some cubicle jobs that will be affected, right? There are some swivel chair jobs that will be affected, but no different than when they automated toll booths, right? Or automated different parts of mundane work that we've all seen throughout our lives, right? So I think the speed at which this is happening is what worries people. Unlike, in the past, it took a little longer for automation or industrialization to impact jobs. But we're focused on this, right? We're going to put money towards this and we're just not seeing that today. Maybe it's because the economy is doing so great. People have a workforce shortage, but we're just not hearing it. >> Well, I mean, maybe a number of factors. I mean, there's no question, machines have always replaced humans. This is the first time in history of replacing humans in cognitive functions. >> Bobby: Augmenting >> Yes, absolutely, but It does suggest that there's opportunities for whether it's for education, you guys are investing there, training, and re-skilling whether it's around creativity and that's really where the discussion, in our view anyway, should be. Not about, okay lets protect our future, the past from the future. You don't want to just repave the cow path and use another bromide. You got to move forward and education is a key part of that. And you guys are putting your money where your mouth is. >> Yeah, we are and I think our academy that we launched a little over a year and a half ago has a quarter of a million people in it. They are already diplomas on LinkedIn. I watch everyday, people post their new diplomas, the different skills they've earned, right? Go through the courses, it's free. Democratization runs at the heart of this company, it's why we're growing so much faster than at automation anywhere, right? It's why we are a different kind of company. They're a very commercial minded kind of company. They're a marketplace, you have to be a customer. If your URL when you type in your email isn't a customer, you can't go to their store and do anything. We're free, open, share your automations and it's a very different mindset and community runs at our heart. If you're a small business, you know, under a million dollars, you get to use our software for free. And you can run your robots and we have one of our orchestrators run a manager. So, I think all of this is helping get companies and people more comfortable with our technology. There are kids and students now, we had University of Maryland up here. The professor, he's building whole classes now at the University of Maryland. All in the business school, all using our technology. Every student should have a robot, through their entire career, through their entire time at University of Maryland. That's every university, this is going to go so fast, Dave and Stu, so fast. And when I think back again, a year ago, I mean next year when we do this again, right? At our big flagship event, at three or four thousand people, you'll have felt that progression but the year I've been here, it's night and day already. >> Alright, so Bobby you know we're big fans of community. The open source stuff, you've for a long background in that. Help us put together some of these stats here. When I looked in your keynote, you said there's 114,000 certified RPA developers out there across the globe. 139 countries, 250,000 people have downloaded. You've only got at UiPath about 2,000 customers. So, you know, we talk business model and how your business grows, the industry grows, you know? Help us understand that dynamic. >> These are going to go exponential. So, we have large companies now that are committing to deploy UiPath to every employee. Every employee becomes a user then, so you're going to see that user number go like this. While the enterprise customer number goes like this. We're adding six new customers a day right now. The real opportunity for us is every one of our customers, very few are down their journey like an SMBC is. SMBC, RPA is in their annual reports, right? They say 500 million dollars already, right? It's a societal thing. They actually in Japan share together, to help each company. Here, in the U.S., we're a little competitive, right? Banks don't share with other banks typically, right? But, this is kind of what we're driving. It's, when you make an automation at UiPath. While we're not open source as a platform, the automation is open source. You put it on go, I can take that, you can take that. I had the same kind of problem. Put in the studio right away, modify it a bit and you're good to go. Now you've sped your implementation which is already fast by 70, 80, 90%. This is, we're just getting started. So, you're going to see companies adopting across HR, across supply chain, contact centers, you know. Today we're, for the most of our customers we're in one division. So, the opportunity to grow within a company, where we were barely 5% penetrated in our biggest client. >> And you've seen my prediction. A lot of the market forecast are under counting this space. >> Bobby: Right. >> There is a labor shortage, a skilled labor shortage There's more jobs than there are people to fill them. They don't have the right skills today. There is a productivity problem >> Bobby: Right. >> Productivity line is flat. RPA is going to become a fundamental component of digital transformations. It's about a billion dollar business today. I got it pegged at 10X by 2023. >> Craig at Forestry upped his guidance today, he may have told you all, to a 3.3 billion dollar market in 2021. Now I was a little disappointed, it was 2.9 before. I think he's still way under shooting it. But nevertheless, to grow 10% in one year, in his mind, is still pretty big. >> Yeah, a lot of those market forecasts are kind of linear. You're going to see, you know, an S curve, like growth in this market. I think there's no question about it. Just, in speaking to the customers today, we've seen this before in other major industry trends. We certainly saw it at ServiceNow, we saw it at Splunk, we saw it at Tableau. UiPath feels like a very similar vibe here. In Tenex, when we did the show here. I just feel an explosion coming, I already see it. It's palpable. >> One other reason for the explosion which is a little different than say most of the open source tech companies is that they were in IT sales. You don't have to use code to automate your tasks, right? The best developers for us are actually the subject matter experts in finance, in supply chain, in HR. So suddenly we've empowered them. Because IT everywhere is constrained, right? They're dealing with keeping systems current. So suddenly this these tools of software is available to any employee to go learn and automate what they do. The friction we've removed between business have to go to IT, IT be understaffed, IT have to get the requirements. All that's gone! So you create robots overnight, over the weekend. And make your life better. Again, most of the world still does not understand what's going on. I mean you can feel it now. But it's an epiphany for anyone when they see it. >> Well the open mindset that Daniel talked about today, he said, you know our competitors are doing what we do and that's okay. The rising tide lifts all boats kind of thing. That puts pressure on you guys to stay ahead of the pack. Big part of what Tom Clancy is doing is the training piece. That's huge. Free training. So you got to move faster than the market. You're confident you can do that. What gives you confidence? >> I think, one, is our product is simpler to use. So I think, you know, you go to Automation Anywhere and you need the code, right? You don't have to code with our design tool. We're told, we're about 40% faster to implement. And that's, look at the numbers. We shared our numbers again today. 100 million we announced in July 1st, for our first half of in ARR, 140 now, right? We are telling our numbers, we're open and transparent. Our competitors, well Blue Prism is public, right? We know they're growing slower. Another difference is the market, requirements are not created equal. Blue Prism only works in an unattended robot fashion, only in the back office. So, if you have front office automation, with call centers and customer service, they don't have the concept of an attended robot. You know, this idea of so, they lack the ability to serve all the requirements of a customer. I, think, it's just architecturally, I think what we're seeing in terms of simplicity and openness. And then market coverage very different then either Automation Anywhere or BluePrism. >> Alright Bobby, let me poke at something. So, if I look at, you came out this morning and said accelerate everything. One of the concerns I have is say okay, if I take existing processes, a lot of the time if you look at them, they're not ideal. They were manual in nature, it's great to do that but, how much do you need to wait and revisit and get consultants in to kind of fix things rather than just say oh okay. Faster is better for some things but not necessarily for all things unless you can make some adjustments first. >> You don't want to automate a bad process, right? So, we're not encouraging anyone to do that. So, you see a combination of... One thing about RPA is which great, is you don't have to go in and say, I'm going to go do procure to pay like Traditional IT guy. And so you can go into that process and say, oh look at all these errors, these tasks, these sub processes, these tasks. Where this huge friction and you can go automate that and get huge value. >> Almost like micro services. >> Yes, exactly. You're able to go in and that's really what people are doing. On the more ambitious projects, they're saying I'm also going to go optimize my process, think differently. But the reality is, people are going in, they're finding these few parts of a bigger process, automating it, getting immediate outcomes, immediate outcomes. And paying back that entire project in six months, including the fees on extension or PWC or other. That doesn't exist anywhere in technology. That kind of, you know, speed to an outcome and then payback period. It just doesn't exist. >> Well, the fact that the SIs are here. Yeah, we heard 15 day payback today. Super fast, ROI. The fact that the big SIs are here, especially given the relatively early days says a lot about the potential market size. I always joke, those guys like to eat at the trough. This is big business and it's important for you guys because they're strategic, they're at the board level. You need the top down support, at the same time, it sounds like there's a lot of bottom up activity. >> Bobby: Right. >> And that's where the innovations going to come from. What's next for you guys, you taking this show on the road again? >> Right, so the next Forward is in London. So, we had one in Europe and one in the U.S. We do what we call togethers, which is more intimate. Or all around the world, which are country specific or industry. I mean, we're going to go and call it the Automation First Tour. And we're going to go start our next tours up all through next year. Hit all the cities again, probably three times this size, each city. You know, I looked at Washington D.C. with federal government, we started federal government in January. Federal government for us next year should be a 60 million software business. For our partners, give them 6, 8, 10X on services on top of that. That's meaningful, that's why you see them here. That same calculation exists in every vertical and in every country. And so it's good for our partners. It's great, we want them to focus on building their skills though. Getting good skills and quality. So, we do a lot with them. We host a partner Forward yesterday with 500 partners, focusing on them. Look, we are investing in you, but you got to deliver quality, right? So, I think we amplify everything we did this year because it worked for us well. We amplify it big time and Forward in a year from now, whether it's Vegas or Orlando or we'll announce it soon, willl be substantially larger. >> Well, any company that's digitally transforming is going to put RPA as part of that digital transformation. It's not without its challenges but it's a tailwind. You better hop on that wave or you going to end up driftwood as Pat Gelsinger likes to say. Bobby, thanks so much. >> Bobby: Thank you Dave. >> Thanks for having us here. This has been a fantastic experience and congratulations and good luck going forward. >> Thank you. >> Alright guys, that's a wrap from here. This is theCUBE. Check out theCUBE.net Check out SiliconeANGLE.com for all the news. Cube.net's where all the videos are, wikimon.com for all the research. We are busy Stu, we're on the road a lot. So again, look at the upcoming events. Thanks for watching everybody. We'll see you next time.