>> connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, everybody this is Dave Vellante of theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O our open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these great Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Starting the record, Dave in five, four, three. Hi, everybody this is Dave Vellante, theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SaaS model, the most popular SaaS model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O or open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these Greek Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is the CUBE Conversation. >> Hello everybody, welcome to this special CXO Series that I've been running over the past couple weeks, my name is Dave Vellante and what I've been doing is bringing in executives from around the industry to try to better understand how they're dealing with this COVID crisis, what some of their fundamental communications principles are, and I'm really pleased to invite in Sri Srinivasan, who's the Senior Vice President and GM of Cisco Collaborations. Sri, great to see you again. It seems like just a long time ago actually, but it was just January that we were in Barcelona together, wow, a lot has changed. >> A lot has changed, Dave. Dave, thanks for having me on the show, it's always a pleasure to see you and I'm so happy to see you safe and sound today. >> Yeah, ditto, we're all in this together, as they say so I want to go back to, I mean we were in January we were getting clenches of this thing. We were definitely a little bit worried but not really fully grasping the impact. At what point did you kind of realize that you were going to have to adjust, and how did you shift your priorities as a leader? >> Yeah, so, Dave we started seeing this right out of the Chinese New Year, coming out of the Chinese New Year, on February 11th, if my memory serves me right. Users out of China started increasing, connecting to their global sites by multiples. Like, they went up as much as 22 times, on the night of February 11th and right off the bat we started seeing it expanding to South Korea, Japan, Singapore, Australia, Malaysia, Vietnam, Thailand, and towards the end of February, we started seeing it going to Europe in terms of expanded volumes where people working from home. Europe has expanded nearly four times for us, Asia pack has expanded nearly three to four times in terms of total usage and from the second week of March, it's US, our biggest market, which has more than doubled and as you may have heard, this past month in March, we served 324 million attendees on our meeting platform. We provide a whole slew of collaboration capability set. The fundamental principle for us that we apply is, provide customers with business continuity, while keeping their employees and their families safe. That is the fundamental principle we apply and one of my engineers said it really well. He said, "for every WebEx engineer-hour spent, "we now keep people safe for 14 thousand hours, "or 583 days". That is the amount of time through virtual capability set we're able to bring people together safely and continue their businesses forward as is nearly normal. >> I mean, the numbers are unbelievable. Chuck Robbins, over a month ago, said you guys held, and this is early March, 3.2 million meetings and 5.5 billion minutes, and the numbers have just gone up from then. Guys, I wonder if you could bring up the chart, I want to set up this conversation and so, we along with our partner ETR, we're one of the first to report sort of the impact of COVID on IT Budgets and what this chart is showing is that, that gray bar says 35% of those CIO's that we talked to said they don't expect any change in spending for 2020. >> Sri: Mhm. >> Dave: The green side over 20% said they expect to spend more and then you can see the big red. So overall, we've taken the overall forecast at the beginning of the year was plus 4% was kind of the consensus for IT spend. We're now down to minus 4%. The point though is that it would be a lot worse Sri, were it not for that green, which is being driven by the work from home offset, and it's not just collaborations tools, its networking, its security, its VPN, it's all the infrastructure around that so I wonder if you could comment and add a little bit of color to what you're seeing in the space. >> I think we're seeing immense expansion of work from home capabilities. Work from home is new for so many people, like for people like me it's the norm but there's so many people who are coming into it cold for the very first time, it can be daunting and that requires investments from organizations, I think CIOs IT infrastructure heads are working to make sure they provide the best secure collaboration canvas for people to work in from home, understanding the challenges of last mile excellence, security challenges and things of that sort so there is a ton of investment going on, in speeding up that investment and I see something coming out of this, which is recognition that organizations are going to have to fix and modernize their digital infrastructure. Why is that important? I think environmental sustainability has something called LEED Certification. Very similar to that work from home is going to have some type of certification that says, an organization is ready for this type of a mass upheaval moment where their infrastructures keep their businesses alive, kicking and thriving through any situation and I think what we have seen is many organizations struggle getting to that first step. Now, technology allows them to move very fast these days but no organization wants to struggle through it in the future, whether it's public sector or commercial enterprise, it's one and the same. >> I think that's a great point, one of the things I wanted to ask you was about some of the things that you've learned and maybe some of the things that are going to be permanent and I think that, people didn't expect this obviously and so do you feel as though that organizations will kind of rethink and that portions of this will become permanent, maybe they'll sub-optimize, in the near-term profitability and try to optimize for business resilience and the flexibility to do things like work from home, your thoughts. >> So Dave, I do see some things becoming permanent, right? Do I expect the volumes of collaboration to go down? No, it's never going to go back to the same level. The world as we know it is going to change forever. We are going to have a Post-COVID era and that's going to be changed for the better. There's a number of employees who are being skeptical, reticent to working from home, who are suddenly going to say, work from home thing is not so bad after all so you're going to have that moment for sure and then you're going to also have a set of employers who are going to look at a much wider pool of applicants that are cross timezone, geography, language barriers, it's going to help an organization increase their diversity and inclusiveness ocean, making their products and services much better so I think we are opening up the surface area for innovation as a result and you will see a lot of the work from home technologies get better and better, we're being forced to be better because we now have to be relatable, discernible easy to a new class of worker that has never seen these technologies and it is across all kinds of barriers that technology has to adhere itself to so I do see a lot of goodness coming in and you know what, at the end of the day, it's really good for the environment too >> I want to ask you how you're supporting customers. The data partner that I mentioned ETR, the other day I sat in one of their CIO Roundtables and it's a private conversation with (mumbles) and CIOs and they were asking them like, who's helping you through this and who's not and they mentioned, for instance, back in the 2009 timeframe, there was one company they won't mention. It was doing audits right after the crisis. That was not a cool thing but I got to give Cisco some props it came up that they really were helping in three areas and one of the CIOs just really mentioned this and called it out. He said, collaboration tools, network, we're a Cisco customer so we're relying more on the network and then the security piece so specifically how are you supporting your customers in this crisis? >> So, towards the end of February, what we did is we opened up our collaboration technology and Chuck said something very profound to me. He basically said, "let's make sure we do right by our customers "and keep them safe through this exercise." What we came out with was a set of free offers. We expanded (mumbles) free offer by providing unlimited meeting time, up to a hundred participants toll dial-in into our meetings infrastructure in 52 countries. We didn't basically just say, hey, only in countries afflicted by the virus, we basically made it as global as we could make it possible and then we provided enterprise trials through our partner routes to market that is an enterprise could sign up for a 90-day thing, no strings attached. Just take on the collaboration platform and whether it's calling, meetings, our device infrastructure and just take advantage of it and in a secure fashion using our security portfolio using extensions of our network portfolio and just continue to operate so we've added close to north of 15 million users through our free offers to date that (mumbles) >> Wow. >> and no strings attached. We're not asking for a credit card or a contract at the end of it, if you like it, and we come out at the other end of it, we are happy that they're safe and if they stay a long-term customer of ours, we are happy about that too. >> I mean, that's awesome. We saw recently a lot of talk about big tech and a lot of attacks on big tech and you're seeing big tech really step up, so thank you for that. You know us. We're not gotcha media, but it's I feel it's really important to ask you this. Zoom has had some clear issues with security. Eric Yuan, was instrumental in developing WebEx so what assurances can you give (Sri coughs) your customers and our audience that you're not subjected to similar security gaps and flaws? >> So let's talk about our security principles, right? Our security principles are very clear, we are open and transparent about the issues we face, the investments we make and we will be very open in terms of our posture. Secondly, we will never rent or sell customers data. Thirdly, we have a growth mindset around security. It's a differentiator. You never get complacent about security, you keep on investing in it and to be honest with you, WebEx has come a very long way since some of the comments that were made in the press by some of our competitors. circa 2012 WebEx versus now there's so much innovation that has happened Dave. We've had over 100 major software updates so I would rather have our competitors focus on their issues rather than, give us kudos in public. Our promise to our customers is to be open, transparent and continuously invest in the space because the moment you take your eyes off it, you've opened yourself up for a set of attacks so we're not going to ever say we are fully secure. You just have to continually invest in the growing threat posture world we live in today. >> So I want to follow up on that because I mean, I'm not a security expert, but I've interviewed enough people to know that they will tell you, you can't just bolt on security, you got to build it in and it's a hard thing to do. Some of your security pros Gee Rittenhouse, TK Keanini would definitely second this so, >> Yeah. >> How, you're saying you've spent a lot of time obviously designing in and I'm inferring not bolting on so I wonder if you could add some color to the sort of types of things that you've done to really, assure your customers that you're secure. >> Yeah, so I think security is in the DNA of Cisco, pun intended in many ways. We pride ourselves in our craft and to be honest with you, security starts at the time of design for us and it's not a checkbox exercise at the end of the ship cycle. You build for security. You build for privacy and compliance and you build with one simple rule. It's your customers data, we are custodians and we need to be protectors of it all the way through. We do not sacrifice experience for security. We never will. We build high-grade experiences but we never give up on security capability set and whether it's free, whether it's premium, whether it's paid. We have the same levels of security, yes, we do have additional security add ons and finally, we have a culture where there are groups within Cisco that continually test us. They don't report to me, they report to chuck and the board and they pretty much are continuously measuring our threat posture. These are world class organizations that keep you on your toes and I'm so thankful for that. It helps our customers safe, it helps us be better. It helps us stay current with the threat postures and this is years of investment. This is not something you can do in 90 days or 30 days. You'd be doing lip service to it. This is something you've got to do, critical, intentional, deliberate investments that pay off in the long-term. >> Yeah, and things like penetration testing, it's not a one shot deal, you got to do it on an ongoing basis. I want to come back to productivity. There are some organizations that are concerned they're struggling a little bit with productivity, particularly with the work from home. What advice would you give to organizations in terms of being able to maintain that productivity? They might take a little bit of hit but what would you tell them? >> I think change is difficult. Change is not easy. I'll take my own story here. Dave, two years back when I joined Cisco work from home was a alien culture to me based on where I came from, for the first month I did struggle. I had my questions, I had my trepidations of is this really going to work? Am I going to be able to run thousands of engineers, multibillion dollar business from home or while traveling on a plane because we have so many development centers across the globe and I'm a remote worker. I really saw this as opening up new horizons for me starting the first month. I took it on with gusto so I think my guidance to organizations is help end users deal with that change. If you force it down their throats, it's not going to work. You've got to understand their pains, you've going to make it more pleasing. You've got to introduce things like a digital water cooler talk, you've got to make it easy on them, you've got to talk about improvements in a remote-work setting like providing them with a set of accessories that make it easier for you to work from home. One of the core principles we have and i espouse within my organization is by working from home, you're intruding into your family's space. I think it's so important to make sure you let your family in on your work and when kids walk into the door, today, when we work at Cisco, we actually share our family and we share our joy with the wider teams and we are so proud of such culture so be very open and make sure that you understand that you're intruding into somebody else's house when you're working from home. >> Yeah, we have dogs barking, we have kids playing games and crawling all over us, that's great. one of the... >> The dogs barking we have solved we have an AI technology that brings it down. >> Mutes the barking. That's good, I need one. >> Absolutely. >> So one of my big takeaways and you really underscored it here is we're not going back to 2019. The digital transformation that we talk about and that frankly many give lip service (mumbles) but it is now going to be accelerated and it's ironic, we're starting a new decade but this digital transformation is going to be accelerated and collaboration is going to be a key underpinning so I'll ask you to give us some final thoughts, will you please? >> Yeah, I think, people to people collaboration is so important in this day and age. As such, industry has been changing from a task-based hierarchy driven world to a group-outcome based synergistic, a bring people along type culture and that brings people along type cultures now, thanks to collaboration technology, becoming independent of timezone, you don't have to worry about language barriers anymore or cultural boundaries. Think of the type of ideation you can do by bringing people across the world together with a low carbon footprint and what this time has shown us is that businesses can still continue to operate and operate really well when you bring people together using these virtual technologies and capability sets. You're saving people some time by having them work from home like you don't have to travel 30 and 40 minutes to get to work. You're just doing doing your thing from wherever you are and that saves so much in cost, in capability sets and the concept of hoteliering and open spaces in different organizations is only going to sprout even further because not everybody is going to have a home office, have an office, a set office, in within the enterprise CEOs are going to see that as a cost saving opportunity that they can funnel back into the growth of the organization. Right? So I think it's a plethora of opportunity in front of us and that these technologies are going to get monumentally better in the months to come. >> We're definitely entering a new chapter. Sri, thanks so much for sharing your insights and some of your leadership principles and thanks to Cisco, for all that you guys are doing some of the pro bono work. I know some of the volunteerism that Chuck has talked about. Really appreciate your time. >> Thanks Dave. Always a pleasure, stay safe. >> And thank you for watching everybody. This is Dave Vellante for theCUBE, we'll see you next time. (upbeat music)

>>Ply from Barcelona, Spain pits the cube covering Cisco live 2020 Ratu by Cisco and its ecosystem partners. >>Hey, welcome back live to Cisco live in 2020 in Barcelona. We're in Europe, Barcelona. I'm John Ferrara, Dave Alante. We've got a great guest here and the whole theme of the show is not about the infrastructure is about the applications and the applications being powered by an infrastructure powered by Cisco. We've got a great guest, senior vice president, general manager, team collaboration, Shri Travaasa of Cisco. You run all the big products, WebEx on steroids, new announcements. You had a really killer announcements, the pack booth. We'll get into that. Welcome to the cube. Thanks for coming. Thank you for having me. What's the quick news? You're on stage giving the keynote quickly share the news. We can get into it. So we are obviously >>coming out with a set of updates to our great portfolio. We reach out to about 300 million users across the enterprise today who use us for all the way from meetings to team collaboration to calling to powering meeting rooms. So in a sense, what we have as a products that, uh, is either in the meeting room or on the desktop or on a mobile phone. So any one of those methods and mechanisms. And in the past couple of years we've seen massive adoption of video, uh, whether it'd be on the mobile phone, whether it be in your desktop or in a meeting room itself. >>So video is the key. You had an announcement with Mike, uh, Microsoft teams explain that because don't they? Don't they compete with you? >>Yes, we, we, so the best way to describe it as is it's compatibility and competition. So it's competitive to compete, um, for the sake of our end users. So end user choice pretty much drives, uh, the types of integrations we do these days. You can't leave it to an it organization to do that integration. You've got to make sure these products work. So we integrate quite a bit with our competitors, spar, Slack, Microsoft teams, zoom. We do integrate with all of those guys. And the Microsoft teams integration, um, is prefaced on providing the best real time media experience into the Microsoft ecosystem. So if a customer is using office three 65 for document collaboration and chooses us for real time collaboration, they get >>the best experience comes from. So this has been a sleepy space for awhile and then all of a sudden you've mentioned Slack, zoom comes out, big IPOs, high valuations, Microsoft kind of transitioning and gets, it's based to to teams. There's a lot of excitement all of a sudden. And I was thinking in the last year out, geez, I wonder if Cisco is asleep at the wheel, but today you had all these announcements, so obviously not asleep at the wheel. Describe what you see going on in the space and what excites you from a standpoint of what you've just announced. So I think >>over the past two years, rightfully so, there's been a ton of movement in this space and I think it's driven by, it's, it's important to talk about why it's driven by globalization of the workforce. So that globalization of the workforce has, has, has, has gotten caught steam in the past few years and you pretty much see folks being employed across the globe. Whoever has the skill gets employed in a sentence. And what we see within the confines of WebEx is an increase in user engagement. So the same user is using WebEx a lot more and we wonder why we're seeing basically cross time zone meetings go up and team collaboration as we know it is no longer across the table. It's actually across time zones, across geographies, across language boundaries. So you're seeing that happen and the power of team collaboration is not just bringing people together, it's the data in heading to within the conversation becomes the new currency. >>It's the new frontier. And you can do a whole bunch of analytics on that. You can provide information on that. You can basically bring what I would call uninterrupted work streams in the myths, which is, you know, how do you take a conversation, take a part of a set of action items out of it and basically take it all the way so that there's automation, there's least amount of transmission loss and transmission loss in a sense. So that's, that's what's causing, um, this, this industry to wake up because it's a productivity gain in knowledge worker population. >>I don't know why it's off the charts on these systems, you know, low denominator and it's so easy to justify. I mean to me this is the biggest way that people are kind of talking about, but not really specifically addressing it. And to me, I always like to look at the startup world because the startup world is ultimately the Canary in the coal mine. Cody cloud native was before cloud hit, the startups were in there wipe clean sheet of paper, all cloud. Now that's mainstream. I had a conversation with Mitchell, the founder of Hashi Corp and we were talking about the concept of virtual first. And his startup was all virtual. They didn't have an office, they could afford one, but their teams were remote. This is the new dynamic that works. And so I believe that this is going to be an enterprise requirement because this has been validated. >>You seeing people work virtually, development teams, marketing to any team, they're remote, they're at home. So this is a trend. This is real. And designing a product for virtual first versus saying, Oh, if your virtual uses Proctor was designed for this, this is really where it's coming to in my opinion. How are you guys addressing that? Because in that video is not easy. Totally not. You guys been doing video Cisco for a lot them. I know from the cable companies to make a deep packet inspection and managing packets, QoS and mean policy basis, the perfect storm for making video work better. So explain the whole virtual first and the video. Start by sharing a small little secret. I run this business and yet I'm a remote worker. Cisco's based in San, I live in Seattle. >>I live in a small town called mamasan. I'm, I'm a perfect example of who we are. It's all the. So without a doubt, what has also spurred this is the bandwidth to trust the globe, not just in the U S uh, I find that, you know, parts of Asia have very good connectivity. If you go into Korea, Singapore, it's just fantastic, right? If you go into the Western Europe, Scandinavian countries, it's just fabulous. So I think the, the fact of the matter is you, the act of working together across the table and the act of these collaboration tools bringing people together need to be the same. That's pretty much where we are all headed. We're all trying to achieve that Nirvana, making sure there's no dissonance when you bring people across video that's key. That requires not only the ability to see and hear people, but to be able to whiteboard, to be able to have a very rich and immersive conversation on biblical creation so that, you know, using like stickies on a whiteboard for example, how well can you do it? >>So those are the types of things that we are headed towards. Uh, and I w I would pretty much say you guys said it in your question. You have to design for a remote worker for a virtual work environment, which basically is all about optimizing for team collaboration and optimizing for information that's consistent across different communication types. Whether you pick up the phone, whether you are on a meeting in a persistent chat, all that transcription should look and feel the same. This is the convergence really of networking and software because software is where the action is, but the network controls the routes. So, you know, give you an example, we were doing a live broadcast in our studio in Palo Alto had Ken Jennings on from jeopardy and it was, I was so excited. It was a good interview. We had multiple guests on about AI and you know, and he was kind of our celebrity guests and he had terrible bandwidth with his house. >>I don't know, maybe his kids were playing games on it or he was downloading some Netflix, who knows, but he had a horrible visual. We couldn't control that. This is where the network optimization comes in. What are you guys doing there? You guys run the networks, you guys have access to some of the routes and looking for, you know, best route, best quality. So I think without a doubt, you know, the, your lowest common denominator leg in your network kind of decides the quality per se. Uh, but we, we continue to do things like a compression of bits on the wire so that you need the smallest amount of pipe. But at the end of the day for high Raz video, you still need a decent amount of bandwidth. And what ends up happening is it's not just bandwidth, it's uh, you know, understanding what kind of packet loss profile you have on that network. >>So what we are doing across nearly nearly every vendor today is figuring out how we can optimize for these Laci networks. So if you're talking to any collaboration engineer, um, the first interview question will inadvertently be, tell me your experience on Laci networks. What have you done, how many patents do you have? You know, that's kind of the, the discussion per se. So I think without a doubt the advent of 5g and its expansion will lead to Ken Jennings potentially having a much better experience. Right. Can you auto scale, not auto scale, but auto detect? Yes. That cause that's something that could be automated. And we, we automatically, we call it graceful degradation. So we start with aspiring for the 10 ADP. Then we'll bring it down to seven 2360 and no video. And that happens automatically and we let the end user know you're having a network blip and hence, uh, we have, we are degrading it or today's product. Yes. >>So years ago when you, there's video conferencing, you just have to show 15 minutes beforehand just to make sure everybody get on. Okay. So simplicity is another big adoption theme, whether it's one push phone calling or call me or whatever it is. At the same time, you've got to add functionality. You've had a transcription, you've had a translation, you've got the split screen. And when I stand up, the camera follows me. So are those counterpoints simplicity and functionality, how do you integrate those together? >>I think the, the, all of this is done in the quest to simplicity, right? Um, one of the key things we've done across the Cisco WebEx portfolio, we've been known as the stodgy characters. Um, you know guys who don't move fast, which is exactly the opposite, to be honest with you. We worked on making sure we get rid of, I'm going to use the word here, nerd knobs in the product optimized for the simple in a meeting, there are three things that matter. Three big use cases, scheduling, joining in, meeting quality. Those are the only three things matter. The rest doesn't matter, right? So if you look at our devices, if you look at everything, we have this consistent green button that shows up everywhere. Whether you bring up outlook, whether you bring up an iPhone calendar, whether you bring up a desktop in one of our devices, all of those things will have this consistent green bar. We don't, we never want the end user to miss it. See it hit it. It'll show up at the right time. Basically shows up between six minutes and the 40 minute Mark before the meeting. >>And by that in meeting quality, you mean the experience overall, how hard it is to share something or >>actually can you see that person? Can you hear that person, you know, things of that sort of, right. You know, how do you avoid echos in a meeting? Like, what if I turn on both audio multiple times in a particular echo, right. As I mentioned in our last interview, Sri about um, uh, the previous guests around, they want API APIs cause it was like API APIs. It's kind of a trend towards a thin, I won't say thin client cause that's some kind of an old, old word. But um, more efficient source code on the client side, not bloated >>software in the sense of having all these bells and whistles. I mean, I mean at some point you're going to use, right? It could be an advanced version. Maybe you have a tiered thing, but at the base set, how do you create software in this modern error so that you can have really fast software managing front end with the powerful backend. You think about, Hey Siri, you know, there's the front end, there's a back end. So you starting to see this kind of decoupling. How do you guys look at that as it changed the development thesis? Is that something that you guys are thinking about? What's your take on all that? >>Yeah, without a doubt. Right? So we, we, we constantly optimize media is a very different workload than for example, a commanding tool. Right? Yeah. Uh, and I don't mean to trivialize city or any other assistant media is hard when you're doing video. The app needs to have some intelligence to be able to disintegrate audio and video streams and content sharing, right? So these apps tend to have a bigger footprint on the desktop, on the mobile phone than other traditional apps. So there is a constant quest for that additional bit of optimization to reduce, you know, substantially reduce the juice you use out of the laptop. Uh, and with laptops becoming more and more powerful, mobile phones becoming more and more, more powerful, we are only able to bring more, more into that big tree. >>Yes. And the rich media is only getting more and more robust with video. Look at the gaming world. My kids got their rig set up, multiple monitors. I mean, it's a lifestyle experience, consumption of video. It's all, it put more pressure on you guys. It's hard. We know we do it. How, what's the, in your mind, what's your guiding principle for future innovation? Whether you're hiring, designing around video, what do you guys chasing that Nirvana? What is it? Is it the software, the hardware? It's a chips. >>I think it's a combination of them, right? If you look at Cisco, our inherent differentiation is we know, we know how to do software. We know a thing or two about networks. I mean no hardware. How do you bring these three together and there's a four to dimension, I'm going to call it quad. And it's security. You can't ignore security. You know, it's, it's something that you have to intrinsically think about. It's not a check by check box after you don't want somebody peeping Toms in their meeting. For example, everybody is simply >>back in the cams. Jeff Bezos has got hacked on video on his WhatsApp embedded malware. So are all kinds of weird things that come through. You don't know. >>I think it's, it's the amalgamation of all of these things. How do you maximize every single element of the pipe? Um, so we are working with, for example, our own DNA center methods and mechanisms by which we're saying based on our workload, how do we optimize the next look for our workload. When we find an issue within let's say WebEx, how do we automatically self heal the network? That is basically where we are headed. So we want to make sure we are constantly stack up and down the stairs, down the stack. And the other, you know you've talked about simplicity of use case. I'll give you an example. What we're doing with our devices now as it has face recognition, we don't store any, any images in the cloud. So as soon as you walk into a meeting room, we've got an IOT sensor that it recognizes your face. >>It says, Hey, let me pull up your meetings. It starts to track who all have joined your meeting. And then let's assume you forget to join the meeting. It wakes up and it says, would you like to join the meeting? Two of two of your colleagues have joined so you don't even have to hit the button. It is germaphobe friendly. So you don't have to touch. It binds you in basic automation. So that level of automation is coming in. So you're talking about the future. The future is about simplicity. That spans generations. So you're pretty much worn the human to come back and for the tech to fade away in the back of them. If you don't want them to be reliant on this app that you have to learn, right, it should be discernible, relatable, easy to use. >>Works like the movies in history. You're a rock star. I'm great to have you. In fact, now we know you live in Seattle. We're going to have you in our studio remotely and we're gonna make sure that bandwidth and that video is of highest quality., the SVP, senior vice president, general manager of the collaboration group of Cisco. Big part of the future of Cisco. This group is going to be really driving some of those network benefits. The applications are big part of the focus, changing the business models, business outcomes. This is the conversation is the cube coverage from Barcelona. We'll be right back after this short break.

(upbeat music) >> Announcer: From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a Cube conversation. >> Everyone, welcome to this special Cube conversation here in Palo Alto Cube studios. I'm John Furrier, host of the Cube. We have special breaking news here, with Sri Ambati who is the founder and CEO of H2O.ai with big funding news. Great to see you Cube alumni, hot startup, you got some hot funding news, share with us. >> We are very excited to announce our Series D. Goldman Sachs, one of our leading customers and Ping An from China are leading our round. It's a round of $72 million, and bringing our total fundraise to 147. This is an endorsement of their support of our mission to democratize AI and an endorsement of the amazing teamwork behind the company and its customer centricity. Customers have now come to lead two of our rounds. Last round was Series C led by Wells Fargo and NVIDIA and I think it just goes to say how critical a thing we are for their success in AI. >> Well congratulations, I've been watching you guys build this company from scratch, we've had many conversations going back to 2013, '14 on The Cube. You call it-- >> You covered us long before. >> You guys were always on the wave, and you really created a category, this is a new category that Cloud 2.0 is creating which is a DevOps mindset, entrepreneurial mindset, creating a category to enable people to have the kind of infrastructure and tooling and software to enable them to do all the heavy lifting of AI without doing the heavy lifting. As the quote for cloud is, that Amazon always quotes is you do all of the undifferentiated heavy lifting that's required to stand up stuff and then provide tooling for the heavy differentiated lifting to make it easy to use. This has been a key thing. Has that been the-- >> Customers have be core to our, company building. H2O is here to build an amazing piece of innovation and technology and innovation is not new for Silicon Valley, as you know. But I think innovation, with a purpose and with a focus of customer success is something we represent and that's been kind of the key north finder for us. In terms of making things simpler, when we started, it was a grassroots movement in open source and we wanted the mind share of millions of users worldwide and that mind share got us a lot of feedback. And that feedback is how we then built the second generation of the product lines, which is driverless AI. We are also announcing our mission to make every company an AI company, this funding will power that transformation of several businesses that can then go on to build the AI superpower. >> And certainly, cloud computing, more compute more elastic resources is always a great tailwind. What are you guys going to do with the funding in terms of focus? >> You mentioned cloud which is a great story. We're obviously going to make things easier for folks who are doing the cloud, but they are the largest players, as well, Google, Microsoft, Amazon. They're right there, trying to innovate. AI is at the center of every software moment because AI eating software, software is eating the world. And so, all the software players are right there, trying to build a large AI opportunity for the world and we think in ecosystems, not just empires. So our mission is to uplift the entire AI to the place where businesses can use it, verticalize it, build new products, globalize. We are building our sales and marketing efforts now with a much bigger, faster systems-- >> So a lot of, go to market expansion, more customer focus. More field sales and support kind of thing. >> Build our center for AI research in Prague, within the CND, now we are building it in Chennai and Ottawa, and so globalizing the operation, going to China, going to build focus in Asia as well. >> So nice step up on funding at 72 million, you said? >> 72.5 million. >> 72.5 million, that's almost double what you've raised to date, nice kickup. So global expansion, nice philosophy. That's important to you guys, isn't it? >> The world has become a small village. There's no changing that, and data is global. Things are a wide global trend, it's amazing to see that AI is not just transforming the US, it's also transforming China, it's also transforming India. It's transforming Africa. Pay through mobile is a very common theme worldwide and I think data is being collected globally. I think there is no way to unbox it and box it back to a small place, so our vision is very borderless and global and we want the AI companies of the valley to also compete in a global arena and I think that's kind of why we think it's important to be-- >> Love competition, that's certainly going to force everyone to be more open. I got to ask you about the role of the developer. I love the democratization, putting AI in the hands of everybody, it's a great mission. You guys do a lot of AI for Good efforts. So congratulations on that, but how does this change the nature of the developer, because you're seeing with cloud and DevOps, developers are becoming closer to the front lines, they're becoming kingmakers. They're becoming really, really important. So the role of the developer is important. How do you change that role, if any. How do you expand it, what happens? >> There are two important transformations happening right now in the tech world. One is the role of data scientists and the role of the software engineer. Right, so they're coming closer in many ways, in actually in some of the newer places, software engineers are deploying data science models, data scientists are deploying software engineering. So Python has been a good new language, the new languages that are coming up that help that happen more closely. Software engineering as we know it, which was looking at data creating the rules and the logic that runs a program is now being automated to a degree where that logic is being generated from data using data science. So that's where the brains behind how programs run how computers build is now being, is AI inside. And so that's where the world is transforming, software engineers now get to do a lot more with a lot less of tinkering on a daily basis for little modules. They can probably build a whole slew of an application what would take 18 months to build is now compressing into 18 weeks or 18 days. >> Sri, I love how you talk about software engineering and data scientists, very specific. I was having a debate with my young son around what is computer science was the question. Well, computer science is the study of computers the science of computers. It used to be if you were a CS or a comp sci major which is not cool to say anymore but, when you were a computer science major, you were really a software engineer, that was the discipline. Now, computer science as a field has spread so far and so broad, you've got software engineering you've got data science, you have newer roles are emerging. But that brings up the question I want to put to you which is, the whole idea of, I'm a full stack developer. Well, if what you're saying you're doing is true, you're essentially cutting the stack in half. So it's a half stack developer on one end and a data scientist that's got the other half. So the notion of the full stack developer kind of goes away with the idea of horizontally scalable infrastructure and vertically specialized data and AI. Your thoughts, what's your reaction to that? >> I think the most... I would say the most scarce resource in the world is empathy, right? When developers have empathy for their users, they now start building design that cares for the users. So the design becomes still the limiting factor where you can't really automate a lot of that design. So the full stack engineer is now going closer to the front and understanding their users and making applications that are perceptive of how the users are using them and building that empathy into the product. A lot of the full stack, we used to learn how to build up a kernel, deploy it on cloud, scale it on your own servers. All of that is coming together in reasonably easier ways. With cloud is helping there, AI is helping there, data is helping there, and lessons from the data. But I think what has not gone away is imagination, creativity, and how to power that creativity with AI and get it in the hands of someone quickly. Marketing has become easier in the new world. So it's not just enough to make products, you have to make markets for your products and then deliver and get that success for customers-- >> So what you're saying-- >> The developers become-- >> The consistency of the lower end of the stack of wiring together the plumbing and the kernel and everything else is done for you. So you can move up. >> Up the stack. >> So the stack's growing, so it's still kind of full. No one calls themselves a half stack developer. I haven't met anyone say "Yeah I'm a half stack developer." They're full stack developers, but the roles are changing. >> I think what-- >> There's more to do on the front end of creativity so the stack's extending. >> Creativity is changing, I think the one thing we have learned. We've gone past Moore's Law in the valley and people are innovating architectures to run AI faster. So AI is beginning to eat hardware. So you've seen the transformation in microprocessors as well I think once AI starts being part of the overall conversation, you'll see a much more richer coexistence with being how a human programmer and a computer programmer is going to be working closely. But I think this is just the beginning of a real richness when you talk about rich interactive applications, you're going to talk about rich interactive appliances, where you start seeing intelligence really spread around the form. >> Sri, if we really want to have some fun we can just talk about what a 10x engineer is. No I'm only kidding, we're not going to go there. It's always a good debate on Twitter what a 10x engineer is. Sri, congratulations on the funding. $72.5 million in finance for global expansion on the team side as well as in geographies, congratulations. >> Thank you. >> H2O.ai >> The full stack engineer of the future is, finishing up your full stack engineer conversation is going to get that courage and become a leader. Going from managers to leaders, developers to founders. I think it's become easier to democratize entrepreneurship now than ever before and part of our mission as a company is to democratize things, democratize AI, democratize H2O like in the AI for Good, democratize water. But also democratize the art of making more entrepreneurs and remove the common ways to fail and that's also a way to create more opportunity more ownership in the world and so-- >> And I think society will benefit from this globally because in the data is truth, in the data is the notion of being transparent, if it's all there and we're going to get to the data faster and that's where AI helps us. >> That's what it is. >> Sri, congratulations, $72 million of funding for H2O. We're here with the founder and CEO Sri Ambati. Great success story here in Silicon Valley and around the world. I'm John Furrier with the Cube, thanks for watching. >> Sri: Thank you. (upbeat music)

>> from our studios in the heart of Silicon Valley, Palo ALTO, California It is a cute conversation. >> Hello and welcome to this Special Cube conversation here in Palo Alto, California Cubes Studios Jon for your host of the Q. We retreat embodies the founder and CEO of H 20 dot ay, ay, Cuba Lem hot. Start up right in the action of all the machine learning artificial intelligence with the democratization, the role of data in the future, it's all happening with the cloud 2.0, Dev Ops 2.0, great to see you, The test. But the company What's going on, you guys air smoking hot? Congratulations. You got the right formally here with a I explain what's going on. It started about seven >> years ago on Dottie. I was was just a new fad that arrived into Silicon Valley. Today we have thousands of companies in the eye and we're very excited to be partners in making more companies becoming I first. And our region here is to democratize the eye and we've made simple are open source made it easy for people to start adapting data signs and machine learning and different functions inside their large and said the large organizations and apply that for different use cases across financial service is insurance healthcare. >> We leapfrog in 2016 and build our first closer. It's chronic traveler >> C I. We made it on GPS using the latest hardware software innovations Open source. I has funded the rice off automatic machine learning, which >> further reduces the need for >> extraordinary talent to build machine learning. >> No one has time >> today and then we're trying to really bring that automatic mission learning a very significant crunch. Time free, I so people can consuming. I better. >> You know, this is one of the things I love about the current state of the market right now. Entrepreneur Mark, as well as start of some growing companies Go public is that there's a new breed of entrepreneurship going on around large scale, standing up infrastructure, shortening the time it takes to do something like provisioning like the old eyes. I get a phD and we're seeing this in data science. I mean, you don't have to be a python coder. This democratisation is not just a tagline. It's actually the reality is of a business opportunity of whoever can provide the infrastructure and the systems four people to do. It is an opportunity. You guys were doing that. This is a real dynamic. This isn't a new way, a new kind of dynamic in the industry. The three real character >> sticks on ability to adopt. Hey, Iris Oneness Data >> is a team, a team sport, which means that you gotta bring different dimensions within your organization to be able to take advantage of data and the I and, um, you've got to bring in your domain. Scientists work closely with your data. Scientists were closely with your data. Engineers produce applications that can be deployed and then get your design on top of it. That can convince users are our strategist to make those decisions. That delays is showing up, so that takes a multi dimensional workforce to work closely together. So the rial problem, an adoption of the AI today is not just technology, it's also culture. And so we're kind of bringing those aspects together and form of products. One of our products, for example, explainable. Aye, aye. It's helping the data. Scientists tell a story that businesses can understand. Why is the model deciding? I need to take discretion. This'll direction. Why's this moral? Giving this particular nurse a high credit score? Even though she is, she has a very she doesn't have a high school graduation. That kind of figuring out those Democratic democratization goes all the way down there. It's wise, a mortal deciding what's deciding and explaining and breaking that down into English, which which building trust is a huge aspect in a >> well. I want to get to the the talent in the time and the trust equation on the next talk track, but I want to get the hard news out there. You guys are have some news driverless a eyes, your one of your core things. What's the hard Explain the news. What's the big news? >> The big news has Bean, that is, the money ball from business and money Ball, as it has been played out, has been. The experts >> were left out of the >> field and all garden is taking over and there is no participation between experts, the domain scientists and the data scientists and what we're bringing with the new product in travel see eyes, an ability for companies to take away I and become a I companies themselves. The rial air races not between the Googles and the Amazons and Microsoft's and other guy companies, software companies. The relay race is in the word pickles. And how can a company, which is a bank or an insurance giant or a health care company take a I platforms and become, take the data, monetize the data and become a I companies themselves? >> You know, that's a really profound state. I would agree with 100% on that. I think we saw that early on in the big data world round Doop doop kind of died by the wayside. But day Volonte and we keep on team have observed and they actually predicted that the most value was gonna come from practitioners, not the vendors, because they're the ones who have the data. And you mentioned verticals. This is another interesting point. I want to get more explanation from you on Is that APS are driven by data data needs domain specific information. So you can't just say I have data. Therefore, magic happens. It's really at the edge of the domain speak or the domain feature of the application. This is where the data is this kind of supports your idea that the eyes with the company's not that are using it, not the suppliers of the technology. >> Our vision has always being hosted by maker customer service for right to be focused on the customer, and through that we actually made customer one of the product managers inside the company. And the way that the doors that opened from working where it closed with some of our leading customers was that we need to get them to participate and take a eyes, algorithms and platforms that can tune automatically. The algorithms and the right hyper parameter organizations, right features and amend the right data sets that they have. There's a whole data lake around there on their data architecture today, which data sets them and not using in my current problem solving. That's a reasonable problem in looking at that combination of these Berries. Pieces have been automated in travel a, C I. A. And the new version that we're not bringing to market is able to allow them to create their own recipes, bring your own transformers and make that automatic fit for their particular race. Do you think about this as a rebuilt all the components of a race car. They're gonna take it and apply for that particular race to win. >> So that's where driverless comes in its travels in the sense of you don't really need a full operator. It kind of operates on its own. >> In some sense, it's driver less, which is in some there taking the data scientists giving them a power tool that historically before automatic machine learning your valises in the umbrella automatic machine learning they would find tune learning the nuances off the data and the problem, the problem at hand, what they're optimizing for and the right tweaks in the algorithm. So they have to understand how deep the streets are gonna be home, any layers off, off deep learning they need what particular variation and deploying. They should put in a natural language processing what context they need to the long term, short term memory. All these pieces, they have to learn themselves. And they were only a few Grand masters are big data scientist in the world who could come up with the right answer for different problems. >> So you're spreading the love of a I around. So you simplifying that you get the big brains to work on it and democratization. People can then participate in. The machines also can learn both humans and machines between >> our open source and the very maker centric culture we've been able to attract on the world's top data scientists, physicists and compiler engineers to bring in a form factor that businesses can use. And today it one data scientist in a company like Franklin Templeton can operate at the level of 10 or hundreds of them and then bring the best in data science in a form factor that they can plug in and play. >> I was having a cautious We can't Libby, who works with being our platform team. We have all this data with the Cube, and we were just talking. Wait higher data science and a eye specialist and you go out and look around. You get Google and Amazon all these big players, spending between 3 to $4,000,000 per machine learning engineer, and that might be someone under the age of 30. And with no experience or so the talent war is huge. I mean the cost to just hire these guys. We can't hire these people. It's a >> global war. >> There's no there's a talent shortage in China. There's talent shortage in India. There stand shortage in Europe and we have officers in in Europe and in India. The talent shortage in Toronto and Ottawa writes it is. It's a global shortage off physicists and mathematicians and data scientists. So that's where our tools can help. And we see that you see travelers say I as a wave you can drive to New York or you can fly to me >> off. I started my son the other days taking computer science classes in school. I'm like, Well, you know, the machine learning at a eyes kind like dog training. You have dog training. You train that dog to do some tricks that some tricks. Well, if you're a coder, you want to train the machines. This is the machine training. This is data science is what a. I possibilities that machines have to be taught. Something is a base in foot. Machines just aren't self learning on their own. So as you look at the science of a I, this becomes the question on the talent gap. Can the talent get be closed by machines and you got the time you want speed low, latent, see and trust. All these things are hard to do. All three. Balancing all three is extremely difficult. What's your thoughts on those three variables? >> So that's where we brought a I to help the day >> I travel A. C. I's concept that bringing a I to simplify it's an export system to do a I better so you can actually give it to the hands of a new data scientists so you can perform it the power off a Dead ones data centers if you're not disempowering. The data sent that he is a scientist, the park's still foreign data scientist, because he cannot be stopped with the confusion matrix, false positives, false negatives. That's something a data scientists can understand. What you're talking about featured engineering. That's something a data scientists understand. And what travelers say is really doing is helping him may like do that rapidly and automated on the latest hardware. That's what the time is coming into GPS that PTSD pews different form off clouds at cheaper, faster, cheaper and easier. That's the democratization aspect, but it's really targeted. Data Scientist to Prevent Excrement Letter in Science data sciences is a search for truth, but it's a lot of extra minutes to get the truth and law. If you can make the cost of excrement really simple, cheaper on dhe prevent over fitting. That's a common problem in our science. Prevent by us accidental bites that you introduced because the data is last right, trying to kind of prevent the common pitfalls and doing data science leakage. Usually your signal leaks. And how do you prevent those common those pieces? That's kind of weird, revolutionize coming at it. But if you put that in the box, what that really unlocks is imagination. The real hard problems in the world are still the same. >> Aye aye for creative people, for instance. They want infrastructure. They don't wanna have to be an expert. They wanted that value. That's the consumer ization, >> is really the co founder for someone who's highly imaginative and his courage right? And you don't have to look for founders to look for courage and imagination that a lot of intra preneurs in large companies were trying to bring change to that organization. >> You know, we always say that it's intellectual property game's changing from you know I got the protocol. This is locked and patented. Two. You could have a workflow innovation change. One little tweak of a process with data and powerful. Aye, aye, that's the new magic I P equation. It's in the workforce, in the applications, new opportunities. Do you agree with that? >> Absolutely. That the leapfrog from here is businesses will come up with new business processes that we looked at. Business process optimization and globalization can help there. But a I, as you rightfully said earlier, is training computers, not just programming them. Their schooling most of computers that can now with data, think almost at the same level as a go player. Right there was leading Go player. You can think at the same level off an expert in that space. And if that's happening now, I can transform. My business can run 24 by seven at the rate at which I can assembled machines and feed a data data creation becomes making new data becomes the real value that hey, I can >> h 20 today I announcing driverless Aye, aye. Part of their flagship problem product around recipes and democratization. Ay, ay, congratulations. Final point take a minute to explain for the folks just the product, how they buy it. What's it made of? What's the commitment? How did they engage with you >> guys? It's an annual license recruit. License this software license people condone load on our website, get a three week trial, try it on their own retrial. Pretrial recipes are open source, but 100 recipes built by then Masters have been made open source and they could be plugged and tried and taken. Customers, of course, don't have to make their software open source. They can take this, make it theirs. And our region here is to make every company in the eye company. And and that means that they have to embrace it. I learn it. Ticket. Participate some off. The leading conservation companies are giving it back so you can access in the open source. But the real vision here is to build that community off. A practitioners inside large formulations were here or teams air global. And we're here to support that transformation off some of the largest customers. >> So my problem of hiring an aye aye person You could help you solve that right today. Okay, So it was watching. Please get their stuff and come get a job opening here. That's the goal. But that's that's the dream. That is the dream. And we we want to be should one day. I have watched >> you over the last 10 years. You've been an entrepreneur. The fierce passion. We want the eye to be a partner so you can take your message to wider audience and build monetization or on the data you have created. Businesses are the largest after the big data warlords we have on data. Privacy is gonna come eventually. But I think I did. Businesses are the second largest owners of data. They just don't know how to monetize it. Unlock value from it. I will have >> Well, you know, we love day that we want to be data driven. We want to go faster. I love the driverless vision travel. Say I h 20 dot ay, ay here in the Cuban John for it. Breaking news here in Silicon Valley from that start of h 20 dot ay, ay, thanks for watching. Thank you.

(upbeat music) >> Woman Voiceover: From our studios in the heart of Silicon Valley, Palo Alto, California this is a CUBE Conversation. >> Hello and welcome to this special CUBE Conversation here in Palo Alto, California, CUBE Studios, I'm John Furrier, host of theCUBE, here with Sri Ambati. He's the founder and CEO of H20.ai. CUBE Alum, hot start up right in the action of all the machine learning, artificial intelligence, with democratization the role of data in the future, it's all happening with Cloud 2.0, DevOps 2.0, Sri, great to see you. Thanks for coming by. You're a neighbor, you're right down the street from us at our studio here. >> It's exciting to be at theCUBE Com. >> That's KubeCon, that's Kubernetes Con. CUBEcon, coming soon, not to be confused with KubeCon. Great to see you. So tell us about the company, what's going on, you guys are smoking hot, congratulations. You got the right formula here with AI. Explain what's going on. >> It started about seven years ago, and .ai was just a new fad that arrived that arrived in Silicon Valley. And today we have thousands of companies in AI, and we're very excited to be partners in making more companies become AI-first. And our vision here is to democratize AI, and we've made it simple with our open source, made it easy for people to start adapting data science and machine learning in different functions inside their large organizations. And apply that for different use cases across financial services, insurance, health care. We leapfrogged in 2016 and built our first closed source product, Driverless AI, we made it on GPUs using the latest hardware and software innovations. Open source AI has funded the rise of automatic machine learning, Which further reduces the need for extraordinary talent to fill the machine learning. No one has time today, and then we're trying to really bring that automatic machine learning at a very significant crunch time for AI, so people can consume AI better. >> You know, this is one of the things that I love about the current state of the market right now, the entrepreneur market as well as startups and growing companies that are going to go public. Is that there's a new breed of entrepreneurship going on around large scale, standing up infrastructure, shortening the time it takes to do something. Like provisioning. The old AIs, you got to be a PHD. And we're seeing this in data science, you don't have to be a python coder. This democratization is not just a tag line, actually the reality is of a business opportunity. Whoever can provide the infrastructure and the systems for people to do it. It is an opportunity, you guys are doing that. This is a real dynamic. This is a new way, a new kind of dynamic and an industry. >> The three real characteristics on ability to adopt AI, one is data is a team sport. Which means you've got to bring different dimensions within your organization to be able to take advantage of data and AI. And you've got to bring in your domain scientists, work closely with your data scientists, work closely with your data engineers, produce applications that can be deployed, and then get your design on top of it that can convince users or strategists to make those decisions that data is showing up So that takes a multi-dimensional workforce to work closely together. The real problem in adoption of AI today is not just technology, it's also culture. So we're kind of bringing those aspects together in formal products. One of our products, for example, Explainable AI. It's helping the data scientists tell a story that businesses can understand. Why is the model deciding I need to take this test in this direction? Why is this model giving this particular nurse a high credit score even though she doesn't have a high school graduation? That kind of figuring out those democratization goes all the way down. Why is the model deciding what it's deciding, and explaining and breaking that down into English. And building a trust is a huge aspect in AI right now. >> Well I want to get to the talent, and the time, and the trust equation on the next talk, but I want to get the hard news out there. You guys have some news, Driverless AI is one of your core things. Explain the news, what's the big news? >> The big news has been that... AI's a money ball for business, right? And money ball as it has been played out has been the experts were left out of the field, and algorithms taking over. And there is no participation between experts, the domain scientists, and the data scientists. And what we're bringing with the new product in Driverless AI, is an ability for companies to take our AI and become AI companies themselves. The real AI race is not between the Googles and the Amazons and the Microsofts and other AI companies, AI software companies. The real AI race is in the verticals and how can a company which is a bank, or an insurance giant, or a healthcare company take AI platforms and become, take the data and monetize the data and become AI companies themselves. >> Yeah, that's a really profound statement I would agree with 100% on that. I think we saw that early on in the big data world around Hadoop, well Hadoop kind of died by the wayside, but Dave Vellante and the WikiBon team have observed, and they actually predicted, that the most value was going to come from practitioners, not the vendors. 'Cause they're the ones who have the data. And you mentioned verticals, this is another interesting point I want to get more explanation from you on, is that apps are driven by data. Data needs domain-specific information. So you can't just say "I have data, therefore magic happens" it's really at the edge of the domain speak or the domain feature of the application. This is where the data is, so this kind of supports your idea that the AI's about the companies that are using it, not the suppliers of the technology. >> Our vision has always been how we make our customers satisfied. We focus on the customer, and through that we actually make customer one of the product managers inside the company. And the doors that open from working very closely with some of our leading customers is that we need to get them to participate and take AIs, algorithms, and platforms, that can tune automatically the algorithms, and have the right hyper parameter optimizations, the right features. And augment the right data sets that they have. There's a whole data lake around there, around data architecture today. Which data sets am I not using in my current problem I'm solving, that's a reasonable problem I'm looking at. That combination of these various pieces have been automated in Driverless AI. And the new version that we're now bringing to market is able to allow them to create their own recipes, bring their own transformers, and make an automatic fit for their particular race. So if you think about this as we built all the components of a race car, you're going to take it and apply it for that particular race to win. >> John: So that's the word driverless comes in. It's driverless in the sense of you don't really need a full operator, it kind of operates on its own. >> In some sense it's driverless. They're taking the data scientists, giving them a power tool. Historically, before automatic machine learning, driverless is in the umbrella of machine learning, they would fine tune, learning the nuances of the data, and the problem at hand, what they're optimizing for, and the right tweaks in the algorithm. So they have to understand how deep the streets are going to be, how many layers of deep learning they need, what variation of deep learning they should put, and in a natural language crossing, what context they need. Long term shot, memory, all these pieces they have to learn themselves. And there were only a few grand masters or big data scientists in the world who could come up with the right answer for different problems. >> So you're spreading the love of AI around. >> Simplifying that. >> You get the big brains to work on it, and democratization means people can participate and the machines also can learn. Both humans and machines. >> Between our open source and the very maker-centric culture, we've been able to attract some of the world's top data scientists, physicists, and compiler engineers. To bring in a form factor that businesses can use. One data scientist in a company like Franklin Templeton can operate at a level of ten or hundreds of them, and then bring the best in data science in a form factor that they can plug in and play. >> I was having a concert with Kent Libby, who works with me on our platform team. We have all this data with theCUBE, and we were just talking, we need to hire a data scientist and AI specialist. And you go out and look around, you've got Google, Amazon, all these big players spending between 3-4 million per machine learning engineer. And that might be someone under the age of 30 with no experience. So the talent bore is huge. The cost to just hire, we can't hire these people. >> It's a global war. There's talent shortage in China, there's talent shortage in India, there's talent shortage in Europe, and we have offices in Europe and India. There's a talent shortage in Toronto and Ottawa. So it's a global shortage of physicists and mathematicians and data scientists. So that's where our tools can help. And we see Driverless AI as, you can drive to New York or you can fly to New York. >> I was talking to my son the other day, he's taking computer science classes in night school. And it's like, well you know, the machine learning in AI is kind of like dog training. You have dog training, you train the dog to do some tricks, it does some tricks. Well, if you're a coder you want to train the machine. This is the machine training. This is data science, is what AI possibility is there. Machines have to be taught something. There's a base input, machines just aren't self-learning on their own. So as you look at the science of AI, this becomes the question on the talent gap. Can the talent gap be closed by machines? And you got the time, you want speed, low latency, and trust. All these things are hard to do. All three, balancing all three is extremely difficult. What's your thoughts on those three variables? >> So that's why we brought AI to help with AI. Driverless AI is a concept of bringing AI to simplify. It's an expert system to do AI better. So you can actually give to the hands of the new data scientists, so you can perform at the power of an advanced data scientist. We're not disempowering the data scientist, the part's still for a data scientist. When you start with a confusion matrix, false positives, false negatives, that's something a data scientist can understand. When you talk about feature engineering, that's something a data scientist can understand. And what Driverless AI is really doing is helping him do that rapidly, and automated on the latest hardware, that's where the time is coming into. GPUs, FPGAs, TPUs, different form of clouds. Cheaper, right. So faster, cheaper, easier, that's the democratization aspect. But it's really targeted at the data scientist to prevent experimental error. In science, the data science is a search for truth, but it's a lot of experiments to get to truth. If you can make the cost of experiments really simple, cheaper, and prevent over fitting. That's a common problem in our science. Prevent bias, accidental bias that you introduce because the data is biased, right. So trying to prevent the flaws in doing data science. Leakage, usually your signal leaks, and how do you prevent those common pieces. That's where Driverless AI is coming at it. But if you put that in a box, what that really unlocks is imagination. The real hard problems in the world are still the same. >> AI for creative people, for instance. They want infrastructure, they don't want to have to be an expert. They want that value. That's the consumerization. >> AI is really the co founder for someone who's highly imaginative and has courage, right. And you don't have to look for founders to look for courage and imagination. A lot of entrepreneurs in large companies, who are trying to bring change to their organizations. >> Yeah, we always say, the intellectual property game is changing from protocols, locked in, patented, to you could have a workflow innovation. Change one little tweak of a process with data and powerful AI, that's the new magic IP equation. It's in the workflow, it's in the application, it's new opportunities. Do you agree with that? >> Absolutely. The leapfrog from here is businesses will come up with new business processes. So we looked at business process optimization, and globalization's going to help there. But AI, as you rightfully said earlier, is training computers. Not just programming them, you're schooling them. A host of computers that can now, with data, think almost at the same level as a Go player. The world's leading Go player. They can think at the same level of an expert in that space. And if that's happening, now I can transform. My business can run 24 by 7 and the rate at which I can assemble machines and feed it data. Data creation becomes, making new data becomes, the real value that AI can- >> H20.ai announcing Driverless AI, part of their flagship product around recipes and democratizing AI. Congratulations. Final point, take a minute to explain to the folks just the product, how they buy it, what's it made of, what's the commitment, how do they engage with you guys? >> It's an annual license, a software license people can download on our website. Get a three week trial, try it on their own. >> Free trial? >> A free trial, our recipes are open-source. About a hundred recipes, built by grand masters have been made open source. And they can be plugged, and tried. Customers of course don't have to make their software open source. They can take this, make it theirs. And our vision here is to make every company an AI company. And that means that they have to embrace AI, learn it, tweak it, participate, some of the leading conservation companies are giving it back in the open source. But the real vision here is to build that community of AI practitioners inside large organizations. We are here, our teams are global, and we're here to support that transformation of some large customers. >> So my problem of hiring an AI person, you could help me solve that. >> Right today. >> Okay, so anyone who's watching, please get their stuff and come get an opening here. That's the goal. But that is the dream, we want AI in our system. >> I have watched you the last ten years, you've been an entrepreneur with a fierce passion, you want AI to be a partner so you can take your message to wider audience and build monetization around the data you have created. Businesses are the largest, after the big data warlords we have, and data privacy's going to come eventually, but I think businesses are the second largest owners of data they just don't know how to monetize it, unlock value from it, and AI will help. >> Well you know we love data, we want to be data-driven, we want to go faster. Love the driverless vision, Driverless AI, H20.ai. Here in theCUBE I'm John Furrier with breaking news here in Silicon Valley from hot startup H20.ai. Thanks for watching.

>> Live, from Las Vegas, it's theCUBE. Covering AWS re:Invent, 2018. Brought to you by Amazon web services, Intel, and their ecosystem partners. >> Well, we are nearly two days strong into our coverage, here at AWS re:Invent. If you look behind us, here on the set, this show floor is still jam packed, still a lot of activity, as 40,000 plus have made their way to Las Vegas, for this year's show. Along with Justin Warren, and I'm John Walls. we're joined now by Josh Yulish, who's the CEO of TmaxSoft, and Sri Akula, who's the CIO of HealthPlan Services. Gentlemen, welcome to theCUBE, glad to have you >> Thank you for having us. >> Thank you for having us. Well, first, let's just share the story at home, a little bit, about TmaxSoft, and HealthPlan. What your core functions are, and then we'll get into why your here. >> Sure, great question. So, TmaxSoft one of the key things we're doing right now, is helping companies take their old, legacy mainframe applications, and moving them into the future, running them on the cloud. Enabling that digital transformation, of taking the old, integrating it in, with the new. >> And you're one of those companies, I assume. >> Yes, we are on of those companies, and we're a technology solutions company, in health care. And, we're the market leaders in providing the platform for the archive business. And then we have a group, and other health care solutions, as well. >> Alright, so you've got to get rid of the old, at some point, you've got to move over to the new, at some point, you can't do it all at once. How do you start making those decisions about, what legacy, what are we moving, what aren't we, what are we going to redo? I assume a lot of it's budget, but there've got to be other implications, and other considerations, as well. >> Yeah, you some of the systems evolve, over the last two decades, especially in health care. And, I think it's fair to say health care has been lagging in adapting the cloud technology. Whether it be PII, or PHI, or HIPAA regulations, but now starting to embrace cloud more. And that opens up the opportunity for us to take investments, which was them, and move to the cloud, so that we can get agility into our systems, and get some efficiency, so that we can double up the modern technologies, and get more to our customers, our members. >> Yeah. That's often a challenge, about how you choose which ones to do, at what time. Because, I mean, IT projects don't have a great track record of being completed successfully. So, when you decide to move something to cloud, you are taking on a bit of risk, there, so you need to be able to manage that risk, reward. How do you consider which projects you should be running, so that I can get a bit of short term gain, now, but also to make those more strategic decisions about, well, we actually wanted to have this happen over a longer period of time, and we're willing to take a little bit more. How do you balance that risk, reward ratio? >> I think there's multiple lenses we apply together. A, first you need the right technology, to get up on the mainframe. And then you need the right partner, not just the technology, but who understands the nuances of software, you well over the gates, to get to that. >> Yeah. >> And, also, you know, if the change is less, it's working, don't fix something that's not broken. But, still bring the agility, and then leverage the cloud, what cloud has to offer. I think that's where Tmax comes into picture, where, helps us from a technology, and as a partner, to kind of guide us through this journey. Identify the path, you can't do this in isolation. You've got to have the right technology, and the right partner to help us to get to a better place. >> Yeah. And, Josh, with customers who are running through, there's plenty of customers out there, I'm sure, who are considering this, and struggle with this themselves. What are some of the behaviors you see, from people who do this well? So, when you've seen people who are succeeding at this transformation journey, what are some of the key things you look at and say, these are the markers of someone who really understands how to do this well? >> Sure, that's a great question. So, everybody wants to do this. Nobody really wants to stay in the past. The people that do it successfully, are the people that have a change agent mentality. That understand if I ignore the problem, my business, not just my IT, but my business is going to suffer. And, the IT leaders, and the CIO's that can see that vision, are the one's that enable the business to move forward, and give them a competitive differentiator. So that, to us, is what we really see as the differentiator for who's successful, faster, versus who isn't. >> You know, Justin was talking about the long view, right, and having a firm strategy, and taking a much deeper perspective. But, how do you do that when you know, whatever course you're going to take, is going to change. Because there's going to be a new technology, there's going to be a wrinkle that's going to come along, and it's going to upset the apple cart. And it might happen in six months. >> Amazon will announce something this afternoon. (laughs) >> So how do you have that long view strategy, both of you, when you know that, whatever road we're going on right now, a year from now it's probably not going to look like this. >> I'll take a stab at it. Probably Josh has, looking at other customers, may have more insights into it. The only list I see is, the member experience is the key, right now. The digital journey is all revolving around member experience. You take any vertical, client facing, client touching, is changing, evolving much faster, but your core systems don't need to change that fast. So, if you take the core systems, which are legacy, and move to a modern, and then embed with, maybe a mobile native, in a multi only channel, digital, and there, probably you don't want to modernize it. The most systems, they seem to stay there, anyway. So, take something core, data change is low, move them to cloud, and monetize, and get the most ROI, out of the investment you made. At least, that's what we are looking at it. >> Yeah, I think that we see the same thing with most of our customers. So, when they look at, how do I get off the mainframe? These things have been around 20, 30, 40 years. If it was easy, they would have done it. It's not, it's a very closed, difficult to disrupt system. And, when they think about, we'll re-write our application, or we'll do something else, that's a five to 10 year journey, on average. So, then your disruption, or the new thing comes out in a year, it impacts that project for them, and it makes it very difficult. So, we've helped them move off six to 12 months, on average. So now you can more rapidly, solve your initial pain, and then look at your longer term journey in a way that allows you to do it all at once, and over time. >> I think, being able to react to what's coming new, as you said, John, but also have that longer term vision, that's a tricky thing to be able to do, but it is so important to be able to balance that short term benefits, that then actually support the longer term vision. >> That's spot on. So, get the TCO under control now, and then that gives us the flexibility to re-write which is going to be more forward looking, and not necessarily re-writing the same old, in a new way. >> And it builds credibility with the rest, because then we're customers as well. If you were to go and try something like this, that actually disrupts the business, and disrupts customers, then they're probably not going to trust you when you try to do this again. But if you've got a few wins on the board, then they're going to trust you with a slightly bigger project, and you can actually get further with it, I think. >> Yeah, absolutely. And, I think what we've seen, and we're working with HealthPlan Services, and all of our customers in the same way, is they can free up cash, from their operational spend, and IT, very quickly. That they can then invest into innovation. And, everything that you see here at the show, they can now go do those things. Where, before, most of there money is stuck in operating, just keeping the lights on. Keeping the lights on, on something 40 years old. Now, you can invest in innovation, without disrupting the customer, the experience is the same. Performance is the same, all of those things are the same, but they get that value of, we can make it new as well. >> I know there's plenty CFO's that'd be happy to hear that. Because they go, you want me to invest how much of new money? Oh, no, no, we found plenty of money, it was just sort of lying over here. We were setting it on fire, for some reason. (laughs) Let's not do that. >> And it's an easy conversation, as a CIO, getting to the CEO saying, hey, I'm going to take the cost out, invest back into the product. That's an easy conversation to have. >> Josh talks about this, I guess, multi-faceted process that you're going to go through, right. How do you decide, on the customer side, how do you prioritize, particularly in your space, health care, what's going to go first, what's going to go second, and then what can we put off long enough, there's probably going to be something else coming, that we can adopt a different approach. So, who are your stakeholders, who do you answer to, how do you come up with that? >> Multiple ways to look at it. Especially in our domain, at least the technology Tmax has to offer, they're taking out most of the risk for us. Because, it's a lot of lift, and shift, and the technology was so much. They are minimizing the risk. And, the timeline's also shrinking. Because the longer it takes, by the time you realize the ROI, and the projects move on, the changes, I think that's where the technology maturity's coming in. It's really helping us a lot. And, again, we look at more member experience, we do ground up building. Take the core assets, do a lift, and shift, and shrink the time. But, again, Josh if there is one thing I look at, it's the timeline before the shrink. I know, it used to be two years, 18 months, 24 months, coming to nine months to 12 months. I would like to see that more, and more, happen, maybe six to nine months. And, that gives us more leverage, and more confidence, to customers. The longer the project stays, the failure rate goes up, higher, and higher. >> Right. >> Yes. >> We all want it now. (laughs) So, Josh, go deliver, would you please? >> That's the goal. >> You've got the mission. Thank you for the time, we appreciate it. And, wish you both success down the road. >> Thank you. >> Thank you, very much >> Thank you. >> We're back with more, we're live at AWS re:Invent, in Las Vegas, Nevada. (mellow music)

>> Announcer: Live from Washington, DC, it's CUBEConversations with John Furrier. (techy music playing) >> Welcome back everyone, here to a special CUBEConversation in Washington, DC. We're actually in Arlington, Virginia, at Amazon Web Services Public Sector Headquarters. We're here with Sri Vasireddy, who is with REAN Cloud and recently won a big award for $950 million for the Department of Defense contract to partner with Amazon Web Services, really kind of changing the game in the cloud space with Amazon, among other partners. Thanks for joining me today. >> Thank you. >> So, obviously we love cloud. I mean, we actually, we have all of our stuff in Amazon, so we're kind of a little bit biased, but we're open minded to any cloud that we don't provision any infrastructure, so we love the idea of horizontally disrupting markets. We're just kind of doing it on a media business. You're taking an approach with REAN Cloud that's different. What's different about what you guys are doing and why are you winning so much? >> Yeah, I mean, I guess that is, you know, the key word being disruption. You know, I'm hearing more and more as this news spreads out about why, you know, we've disrupted, so they're proven the disruption, and when I mean disruption, you know, I'll explain what the disruption, you know, we're creating in the service industry is if you take a typical, like a services company-- >> John: Mm-hm. >> They integrate products using people to integrate products to solve a problem, but in the cloud world you can create those integrations with programmatic or APIs, so we can create turnkey solutions. With that, what we're able to do is really sell outcome based. We go to the customer and say it's not time and material, it's not fixed price, it's pure outcome based. So, to give you an example, let's say if you went to a theme park and while you're on a ride somebody just takes a picture, and then after you're done with the ride they put a picture in front of you and say, "Do you want to buy this?" And if you don't buy it they throw it away, so we literally have the ability to create those outcomes on the fly like that, and that's the disruption because that kind of outcome based allows customers to meet their goals much quicker. So, one of the secrets to do that, if I can get this right, is you have to have a really software driven, data driven environment. >> Sri: Absolutely. >> So, that's fundamental, so I want to explore how you do that, and then what does it mean for the customers because what you're essentially doing is kind of giving a little predictive solution management to them. Say you want to connect to this service-- >> Sri: Yeah. >> Is that microservices, is this where it's going to be wired, take us through how that works, because there's tech involved. I'm not saying you don't want to throw anything away, but if it's digital (chuckling) what does it mean to turn it on or off, so is this what people are referring to with microservices and cloud? >> Yeah, so I'll get to the microservices part. The disruption, the way, you know... The innovation that we created is if you take 20 years ago, when you look at people transforming to the internet, right, so their first time they're going on the internet, at the time they were paying a HTML developer that would develop a webpage. >> Mm-hm. >> You know, hundreds of dollars an hour, right, and today high school kids can create their own webpages. That's the outcome focus, because the technology matured to a point where it auto-generates those HTML pages. So, fast forward 20 years, today people are looking for devops engineer as a talent, and whatever that devops engineer produces, we've figured out a way to outcome base. We can drag and drop and create my architectures and we are to produce that code, right. That's what makes us very unique. Now, coming to your question about microservices, when we are going to large customers we're taking this phased approach, right. First they will do lift and shift based-- >> John: Mm-hm. >> Move to cloud, which actually doesn't even give them a lot of their features. It doesn't give them better response. It doesn't optimize for cloud and give the benefits. Say they put in the effort to apply devops to become very responsive to customers. Say if I'm a bank I have my checking business and savings business, and each line of business got very efficient by using cloud, but they have not disrupted an industry because they have not created a platform across lines of business. >> John: Mm-hm. >> Right, so what they really need to do is to take these services they are providing across lines of business and create a platform of microservices. >> So, you basically provide an automation layer for things that are automated, but you allow glue to bring them together. >> Absolutely. >> That then kicks off microservices on top of it. >> Absolutely, right. >> So, very innovative, so you essentially, it's devops in a box. (laughing) >> That's it and what-- >> Or in the cloud. >> Yeah, what normally takes three years, so most of our customers when they tell this story they tell us, "Oh, that's five years down the road." So, we knock out three years off the mark, right. There are companies that, for example, DOD is one of our customers. >> Mm-hm. >> There are some other companies that have been working with DOD for the last two, three years and they have not been able to accomplish what we accomplished in three months. >> You guys see a more holistic approach. I can imagine just you basically break it down, automate it, put it in a library, use the overlay to drag and drop. >> Exactly, plug and play and that's it. >> So, question for you, so this makes sense in hardened environments like DOD, probably locked and solid, pretty solid but what about unknown, new processes. How do you guys look at that, do you take them as they come or use AI, so if you have unknown processes that can morph out of this, how do you deal with that use case? >> So, yeah, those unfortunately, you know, so what... There's this notion of co-creation-- >> John: Yeah. >> So, there's unknown processes where we put out best engineers is what drives to this commoditization or legos that-- >> So, you're always feeding the system with new, if you will, recipes. I use that word as more of a chef thing, but you know, more-- >> Sri: Exactly. >> Modules, if you will. >> Sri: Yeah. >> As a bit of an automated way, so it's really push button cloud. >> Absolutely. >> So, no integration, you don't have to hire coders to do anything. >> No. >> At best hit a rest API-- >> Sri: Yeah. >> Or initiate a microservice. >> Yeah, so what, I mean, the company started with Amazon.com as a, sorry Amazon Web Services as our first customer, and they retained us for software companies like Microsoft, SAP, and they went to Amazon and said, "We want to create a turnkey solution," like email as a solution, for example, for Microsoft, exchanging software. Email as a solution is spam filters plus, you know, four or five other things that we have to click button and launch, and Amazon, then we were servicing Amazon to create these turnkey solutions. >> So, talk about the DOD deal, because now this is interesting because I can see how they could like this. What does it mean for the customer, your customer, in this case the DOD, when you won this new contract was announced a couple days ago, how'd that go down? >> Yeah, so you know, I think we're super happy. Actually, again, 2010-- >> All your friends calling you and saying, "Hey, that $950 million check clear yet?" (laughing) That doesn't work that way, does it? >> It doesn't, it doesn't quite work that way, but although, you know, just some history, 10 years ago I had to choose between joining as a lead cloud architect for DISA versus first architect for Amazon Web Services, and I made the choice to go to Amazon Web Services, although I really loved servicing DOD because I think DOD's very mature in what you're calling microservices. >> John: Mm-hm. Back in the day, they had to be on the forefront of net-centric enterprise services, modern day microservices, because the Information Sharing Act required them to create so many services across the department, right, but there wasn't a technology like Amazon Web Services to make them so successful. >> John: Yeah. >> So, we're coming back now and we're able to do this, and I was with a company called MITRE at the time-- >> John: Yeah. >> And we, you know, I was the lead on the first infrastructure as a service BPA. If I compare to what that infrastructure as a service BPA was, the blanket purchase agreement, to what this OTA I think it's a night and day difference. >> What's OTA? >> OTA stands for other transaction agreements. >> Okay, got it. >> Which is how-- >> It's a contract thing. >> It's a contract thing, it's outside of federal acquisition regulation. >> Okay, got it. >> Which is beautiful, by the way, because unlike if you are doing such a deal, $950 million deal, probably companies that spend millions of dollars to write paper to win the deal, OTA's a little different. DIUx, who has the charter for the OTA, they need to find a real customer and a real problem to bring commercial entities and the commercial innovation to solve a theory problem, and then we have to prove ourselves. Thereabout, I'm told 29 companies competed and we, you know, we won the first phase, but there were two consequent phases where we have to provide our services, our platform, to the customer's satisfaction, and the OTA can only be the services we already provide. So, it's a very proven technology. >> John: Mm-hm. >> And as I see some of the social media responses, I look at those responses that people are talking about, you know, small companies winning this big deal and somebody was responding like, okay, we spent, you know, hundreds of millions on large companies, did nothing, and this small company already did a lot with $6 million. >> Well, that's the flattening of the world we're living in. You're doing with devops, you've automated away a lot of their inefficiencies. >> Absolutely, yeah. >> And this is really what cloud's about. That's the promise that you're getting to the DOD. >> Sri: Yeah, absolutely. >> So, the question for you is, okay, now as you go into this, and they could've added another $50 million just to get a nice billion dollar, get a unicorn feature in there, but congratulations. >> Sri: Thank you. >> You got to go in and automate. How do you roll this out, how big is the company, what are your plans, are you... Where do you go from here? >> Our company today is, you know, about 300 plus people, but we're not rolling this out on a people basis, obviously, right. You know, usually we have at least 10x more productivity than a normal company because especially servicing someone like DOD, it's very interesting because they do follow standards set by DISA. >> Mm-hm. >> So, what that means is if I'm building applications or microservices, which is a collection of instances, I have, DISA has something called STIG. You know, it's security guidelines, so everybody is using these STIG components. Now we create this drag and drop package of those components, and at that point it's variations of, you know, those components that you drag and drop and create, right, and the best thing is you get very consistent quality, secure, you know, deployment. >> I mean, you and I are on the same page on this whole devops valuation, and certainly Mark and Teresa wrote that seminal common about the 10x engineer. >> Sri: Yeah. >> This is really the scale we're talking about here. >> Sri: Absolutely. >> You know, so for the folks that don't get this, how do you explain to them that they, like what Oracle and IBM and the other guys are trying to do there. All the old processes are like they got stacks of binders of paper, they have their strategies to go win the deals, and then they're scratching their heads saying, "Why didn't we win?" What are they missing, what are the competitors that failed in the bid, what are they missing with cloud in your opinion? Is it the architecture, is it the automation, is it the microservices, or are they just missing the boat on the sales motion? >> Yeah, I think the biggest thing that people need to know is being on their toes. When Andy talks about being on the toes, when companies like Amazon at scale being on their toes, which means gone are those days where you can have roadmaps that you plan year, you know, year from now and you know, you do it, you're away from the customer by then, right, but if you're constantly focusing on the customer and innovating every day, right, we have a vision and a backlog. We don't have a roadmap, right. What we work on is what our next customer needs. >> John: Mm-hm. >> Right, and you're constantly servicing customers and you have stories to tell about customers being successful. >> What's your backlog look like? (laughing) >> Backlog could be a zillion things. Like what-- >> Features. >> Yeah, exactly. >> Feature requests or just whatever the customer might need. >> Feature requests, user stories, really understanding the why part of it. We try to emphasize the why of, you know, why you're doing and whose pain are you solving type of things, but the important thing is, you know, are we focusing on what matters to the customer next. >> How hard is multi-cloud to do, because if you take devops and you have this abstraction layer that you're providing on top of elastic resources, like say Amazon Web Services, when you start taking multi-cloud, isn't that just an API call or does it kind of change because you have, Amazon's got S3 and EC2 and a variety of other services, Azure and Google have their own file system. How hard is it code-based-wise to do what you're doing across multiple clouds? >> It's not at all difficult because every cloud has their infrastructure as code language, just like I talked about, you know, HTML to be generated to get a webpage. We use a technology called Terraform-- >> Mm-hm. >> That is inherently multi-cloud, so when we generate that cord I could change the provider and make it, you know, another cloud, right. >> Just a whole nother language conversion. >> Sri: A whole nother language, yes, exactly. >> So, you guys, do you have to do that heavy lifting upfront? >> Again, we don't, and it so happened that it will look at our platform that automates all these-- >> Yeah. >> The Amazon part of it grew so much because of what I just said. Like, the customer demand, even the enterprise customers that do have a multi-cloud strategy-- >> Mm-hm. >> You know, they end up more of what is good. >> Yeah. >> Sri: Right, so we end up building more of what is good. >> So, the lesson is, besides be on your toes, which I would agree with Andy on that one, is to be devops, automate, connect via APIs. >> Yeah. >> Anything else you would add to that? >> Devops is a, it's a principle of being very agile, experimenting in small batches, being very responsive to customers, right. It is all principles that, you know, that we embody and just call it devops, it's a culture. >> Managing partner of REAN Cloud. Sri, thanks so much for coming in. Congratulations on your $950 million, this close to a billion, almost, and congratulations on your success. Infrastructures, code, devops, going to the next level is all about automation and really making things connect and easily driven by software and data. It's theCUBE bringing you the data here in Washington, DC, here in Arlington, Virginia, AWS's Public Sector World Headquarters. I'm John Furrier, thanks for watching. (techy music playing)

>> Announcer: Live, from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2017. Brought to you by Hortonworks. (electronic music fading) >> Hi everybody, this is George Gilbert. We're watching theCUBE. We're at DataWorks 2017 with my good friend Sri Raghavan from Teradata, and Sri, let's kick this off. Tell us, bring us up to date with what Teradata's been doing in the era of big data and advanced analytics. >> First of all, George, it's always great to be back with you. I've done this before with you, and it's a pleasure coming back, and I always have fun doing this. So thanks for having me and Teradata on theCUBE. So, a lot of things have been going on at Teradata. As you know, we are the pioneer in the enterprise data warehouse space. We've been so for the past 25 plus years, and, you know, we've got an incredible amount of goodwill in the marketplace with a lot of our key customers and all that. And as you also know, in the last, you know, five or seven years or so, between five and seven years, we've actually expanded our portfolio significantly to go well beyond the enterprise data warehouse into advanced analytics. We've got solutions for the quote-unquote the big data, advanced analytics space. We've acquired organizations which have significant amount of core competence with enormous numbers of years of experience of people who can deliver us solutions and services. So it's fair to say, as an understatement, that we have, we've come a long way in terms of being a very formidable competitor in the marketplace with the kinds of, not only our core enterprise data warehouse solutions, but also advanced analytics solutions, both as products and solutions and services that we have developed over time. >> So I was at the Influencer Summit, not this year but the year before, and the thing, what struck me was you guys articulated very consistently and clearly the solutions that people build with the technology as opposed to just the technology. Let's pick one, like Customer Journey that I remember that was used last year. >> Sri: Right. >> And tell us, sort of, what are the components in it, and, sort of, what are the outcomes you get using it? >> Sure. First of all, thanks for picking on that point because it's a very important point that you mentioned, right? It's not- in today's world, it can't just be about the technology. We just can't go on and articulate things around our technology and the core competence, but we also have to make a very legitimate case for delivering solutions to the business. So, our, in fact, our motto is: Business solutions that are technology-enabled. We have a strong technology underpinning to be able to deliver solutions like Customer Journey. Let me give you a view into what Customer Journey is all about, right? So the idea of the Customer Journey, it's actually pretty straightforward. It's about being able to determine the kind of experience a customer is having as she or he engages with you across the various channels that they do business with you at. So it could be directly they come into the store, it could be online, it could be through snail mail, email, what have you. The point is not to look at Customer Journey as a set of disparate channels through which they interact with you, but to look at it holistically. Across the various areas of encounters they have with you and engagements they have with you, how do you determine what their overall experience is, and, more importantly, once you determine what their overall experience is, how can you have certain kinds of treatments that are very specific to the different parts of the experience and make their experience and engagement even better? >> Okay, so let me jump in for a second there. >> We've seen a lot of marketing automation companies come by and say, you know, or come and go having said over many generations, "We can help you track that." And they all seem to, like, target either ads or email. >> Correct. >> There's like, the touchpoints are constrained. How do you capture a broader, you know, a broader journey? >> Yeah, to me it's not just the touchpoints being constrained, although all the touchpoints are constrained. To me, it's almost as if those touchpoints are looked at very independently, and it's very orthogonal too, right? I look at only my online experience versus a store experience versus something else, right? And the assumption in most cases is that they're all not related. You know, sometimes, I may not come directly to the store, right, but the reason why I'm not coming to the store is because, to buy things, because, you know, I have seen an advertisement somewhere which says, "Look, go online and purchase a product." So whatever the case might be, the point is each part of the journey is very interrelated, and you need to understand this is as well. Now, the question that you asked is, "How do you, for instance, collect all this information? "Where do you store it?" >> George: And how do you relate it ... >> And, exactly, and how do you connect the various points of interaction, right? So for one thing, and let me just, sort of, go a little bit tangential and go into some architecture, the marchitecture, if you will that allows us to be able to, first of all, access all of this data. As you can imagine, the types and the sources of data are quite a bit, are pretty disparate, particularly as the number of channels by which you can engage with me as an organization has expanded, so do the number of sources. So, you know, we have to go to place A, where there's a lot of CRM information for instance, or place B, where it's a lot of online information, weblogs and web servers and what have you, right? So, we have to go to, for instance, some of these guys would have put all this information in a big data lake. Or they could have stored it in an EDW, in an enterprise data warehouse. So we've put in place a technology, an architecture, which allows us to be able to connect to all these various sources, be it Teradata products, or non-Terada- third-party sources, we don't care. We have the capability to connect all to, to these different data sources to be able to access information. So that's number one. Number two is how do you normalize all of this information? So as you can well imagine, right, webs logs servers are very different in their data makeup as apposed to CRM solutions, highly structured information. So we need a way to be able to bring them together, to connect a singular user ID across the different sources, so we have filtering, you know, data filters in place that extracts information from weblogs, let's say it's a XML file. So we extract all that information, and we connect it. We, ultimately, all of that information comes to you in a structured manner. >> And can it, can it be realtime reactive? In other words when- >> Sri: Absolutely. >> someone comes to- >> Sri: Absolutely. >> you know, a channel where you need to anticipate and influence. >> Very good question. In fact, I think we will be doing a big disservice to our customers if we did not have realtime decisioning in place. I mean, the whole idea is for us to be able to provide certain treatments based on what we anticipate your reactions are going to be to certain, let's say if it's a retail store, let's say to certain product coupons we've placed, which says, you know, come online, and basically behavior we think there's a 90% chance that tomorrow morning you're going to come back, you know, through our online portal and buy the products. And because of the fact that our analytics allows us to be able to predict your behavior tomorrow morning, as soon as you land on the online portal, we will be able to provide certain treatment to you that takes advantage of that. Absolutely. >> Techy question: because you're anticipating, does that mean you've done the prediction runs, batch, >> Sri: Absolutely. >> And so you're just serving up the answer. >> Yeah, the business level answer is absolutely. In fact, we have, as part of our advanced analytics solution, we have pre-built algorithms that take all this information that I've talked to you about, where it's connected all that information across the different sources, and we apply algorithms on top of that to be able to deliver predictive models. Now, these models, once they are actually applied as and when the data comes in, you know, you can operationalize them. So the thing to be very clear here, a key part of the Teradata story, is that not only are we in a position to be able to provide the infrastructure which allows you to be able to collect all the information, but we provide the analytic capabilities to be able to connect all of the data across the various sources and at scale, to do the analytics on top of all that disparate data, to deliver the model, and, as an important point, to operationalize that model, and then to connect it back in the feedback loop. We do the whole thing. >> That's, there's a lot to unpack in there, and I called our last guest dense. What I was actually trying to say, we had to unpack a dense answer, so it didn't come out quite that, quite right. So I won't make that mistake. >> Sri: That's a very backhanded compliment there. (George laughing) >> So, explain to me though, the, I know from all the folks who are trying to embed predictive analytics in their solutions, the operationalizing of the model is very difficult, you know, to integrate it with the system of record. >> Yeah, yeah, yeah. >> How do, you know, how do you guys do that? >> So a good point. There are two ways by which we do it. One is we have something called the AppCenter. It's called Teradata AppCenter. The AppCenter is a core capability of some of the work we've done so far, in fact we've had it for the last, I don't know, four years or so. We've actually expanded it across, uh, to include a lot of the apps. So the idea behind the AppCenter is that it's a framework for us to be able to develop very specific apps for us to be able to deliver the model so that next time, as and when realtime data comes in, when you connect to a database for instance. So the way the app works is that you set up the app. There's a code that we've created, it's all prebuilt code that he put behind that app, and it runs, the app runs. Every time the data is refreshed, you can run the app, and it automatically comes up with visualizations which allow you to be able to see what's happening with your customers in realtime. So that's one way to operationalize. In fact, you know, if you come by to our booth, we can show you a demo as to how the AppCenter works. The other say by which we've done it is to develop a software development kit where we actually have created an operationalization. So, as an, I'll give you an example, right? We developed an app, a realtime operationalization app where the folks in the call center are assessing whether you should be given a loan to buy a certain kind of car, a used car, brand new car, what have you the case might be. So what happens is the call center person takes information from you, gets information about, you know, what your income level is, you know, how long you've been working in your existing job, what have you. And those are parameters that are passed into the screen- >> By the way, I should just say, on the income level, it's way too low for my taste. >> Those are, um, those are comments I'll take, uh, later. >> Off slide. >> But, I mean, you got a brand new Armani suit, so you're not doing badly. But, uh, so what happens is, you know, as and when the data goes into the parameters, right, the call center person just clicks on the button, and the model which sits behind the app picks up all the parameters, runs it, and spews out a likelihood score saying that this person is 88% likely- >> So an AppCenter is not just a full end to end app, it also can be a model. >> AppCenter can include the model which can be used to operationalize as and when the data comes in. >> George: Okay. >> It's a very core part of our offering. In fact, AppCenter is, I can't stress how important, I can't stress enough how important it is to our ability to operationalize our various analytic models. >> Okay, one more techy question in terms of how that's supported. Is the AppCenter running on Aster or the models, are they running on Aster, uh, the old Aster database or Teradata? >> Well, just to be clear, right, so the Aster solution is called Aster Analytics of which one foreign factor contains a database, but you have Aster which is in Hadoop, you have Aster in the Cloud, you have Aster software only, so there's a lot of difference between these two, right? So AppCenter sits on Aster, but right now, it's not just the Aster AppCenter. It's called the Teradata AppCenter which sits on, with the idea is that it will sit on Teradata products as well. >> George: Okay. >> So again, it's a really core part of our evolution that we've come up with. We're very proud of it. >> On that note, we have to wrap it up for today, but to be continued. >> Sri: Time flies when you're having fun. >> Yes. So this is George Gilbert. I am with Sri Raghavan from Teradata. We are at DataWorks 2017 in San Jose, and we will be back tomorrow with a whole lineup of exciting new guests. Tune in tomorrow morning. Thanks. (electronic music)

(gentle music) >> Announcer: "theCube's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) (logo whooshing) >> Hey, everyone, welcome back to "theCube's" coverage of day one, MWC23 live from Barcelona, Lisa Martin here with Dave Vellante. Dave, we've got some great conversations so far This is the biggest, most packed show I've been to in years. About 80,000 people here so far. >> Yeah, down from its peak of 108, but still pretty good. You know, a lot of folks from China come to this show, but with the COVID situation in China, that's impacted the attendance, but still quite amazing. >> Amazing for sure. We're going to be talking about trends and mobility, and all sorts of great things. We have a couple of guests joining us for the first time on "theCUBE." Please welcome Dr. Srinivas Mukkamala or Sri, chief product officer at Ivanti. And Dave Shepherd, VP Ivanti. Guys, welcome to "theCUBE." Great to have you here. >> Thank you. >> So, day one of the conference, Sri, we'll go to you first. Talk about some of the trends that you're seeing in mobility. Obviously, the conference renamed from Mobile World Congress to MWC mobility being part of it, but what are some of the big trends? >> It's interesting, right? I mean, I was catching up with Dave. The first thing is from the keynotes, it took 45 minutes to talk about security. I mean, it's quite interesting when you look at the shore floor. We're talking about Edge, we're talking about 5G, the whole evolution. And there's also the concept of are we going into the Cloud? Are we coming back from the Cloud, back to the Edge? They're really two different things. Edge is all decentralized while you recompute. And one thing I observed here is they're talking about near real-time reality. When you look at automobiles, when you look at medical, when you look at robotics, you can't have things processed in the Cloud. It'll be too late. Because you got to make millisecond-based stations. That's a big trend for me. When I look at staff... Okay, the compute it takes to process in the Cloud versus what needs to happen on-prem, on device, is going to revolutionize the way we think about mobility. >> Revolutionize. David, what are some of the things that you're saying? Do you concur? >> Yeah, 100%. I mean, look, just reading some of the press recently, they're predicting 22 billion IoT devices by 2024. Everything Sri just talked about there. It's growing exponentially. You know, problems we have today are a snapshot. We're probably in the slowest place we are today. Everything's just going to get faster and faster and faster. So it's a, yeah, 100% concur with that. >> You know, Sri, on your point, so Jose Maria Alvarez, the CEO of Telefonica, said there are three pillars of the future of telco, low latency, programmable networks, and Cloud and Edge. So, as to your point, Cloud and low latency haven't gone hand in hand. But the Cloud guys are saying, "All right, we're going to bring the Cloud to the Edge." That's sort of an interesting dynamic. We're going to bypass them. We heard somebody, another speaker say, "You know, Cloud can't do it alone." You know? (chuckles) And so, it's like these worlds need each other in a way, don't they? >> Definitely right. So that's a fantastic way to look at it. The Cloud guys can say, "We're going to come closer to where the computer is." And if you really take a look at it with data localization, where are we going to put the Cloud in, right? I mean, so the data sovereignty becomes a very interesting thing. The localization becomes a very interesting thing. And when it comes to security, it gets completely different. I mean, we talked about moving everything to a centralized compute, really have massive processing, and give you the addition back wherever you are. Whereas when you're localized, I have to process everything within the local environment. So there's already a conflict right there. How are we going to address that? >> Yeah. So another statement, I think, it was the CEO of Ericsson, he was kind of talking about how the OTT guys have heard, "We can't let that happen again. And we're going to find new ways to charge for the network." Basically, he's talking about monetizing the API access. But I'm interested in what you're hearing from customers, right? 'Cause our mindset is, what value you're going to give to customers that they're going to pay for, versus, "I got this data I'm going to charge developers for." But what are you hearing from customers? >> It's amazing, Dave, the way you're looking at it, right? So if we take a look at what we were used to perpetual, and we said we're going to move to a subscription, right? I mean, everybody talks about subscription economy. Telcos on the other hand, had subscription economy for a long time, right? They were always based on usage, right? It's a usage economy. But today, we are basically realizing on compute. We haven't even started charging for compute. If you go to AWS, go to Azure, go to GCP, they still don't quite charge you for actual compute, right? It's kind of, they're still leaning on it. So think about API-based, we're going to break the bank. What people don't realize is, we do millions of API calls for any high transaction environment. A consumer can't afford that. What people don't realize is... I don't know how you're going to monetize. Even if you charge a cent a call, that is still going to be hundreds and thousands of dollars a day. And that's where, if you look at what you call low-code no-code motion? You see a plethora of companies being built on that. They're saying, "Hey, you don't have to write code. I'll give you authentication as a service. What that means is, Every single time you call my API to authenticate a user, I'm going to charge you." So just imagine how many times we authenticate on a single day. You're talking a few dozen times. And if I have to pay every single time I authenticate... >> Real friction in the marketplace, David. >> Yeah, and I tell you what. It's a big topic, right? And it's a topic that we haven't had to deal with at the Edge before, and we hear it probably daily really, complexity. The complexity's growing all the time. That means that we need to start to get insight, visibility. You know? I think a part of... Something that came out of the EU actually this week, stated, you know, there's a cyber attack every 11 seconds. That's fast, right? 2016, that was 40 seconds. So actually that speed I talked about earlier, everything Sri says that's coming down to the Edge, we want to embrace the Edge and that is the way we're going to move. But customers are mindful of the complexity that's involved in that. And that, you know, lens thought to how are we going to deal with those complexities. >> I was just going to ask you, how are you planning to deal with those complexities? You mentioned one ransomware attack every 11 seconds. That's down considerably from just a few years ago. Ransomware is a household word. It's no longer, "Are we going to get attacked?" It's when, it's to what extent, it's how much. So how is Ivanti helping customers deal with some of the complexities, and the changes in the security landscape? >> Yeah. Shall I start on that one first? Yeah, look, we want to give all our customers and perspective customers full visibility of their environment. You know, devices that are attached to the environment. Where are they? What are they doing? How often are we going to look for those devices? Not only when we find those devices. What applications are they running? Are those applications secure? How are we going to manage those applications moving forward? And overall, wrapping it round, what kind of service are we going to do? What processes are we going to put in place? To Sri's point, the low-code no-code angle. How do we build processes that protect our organization? But probably a point where I'll pass to Sri in a moment is how do we add a level of automation to that? How do we add a level of intelligence that doesn't always require a human to be fixing or remediating a problem? >> To Sri, you mentioned... You're right, the keynote, it took 45 minutes before it even mentioned security. And I suppose it's because they've historically, had this hardened stack. Everything's controlled and it's a safe environment. And now that's changing. So what would you add? >> You know, great point, right? If you look at telcos, they're used to a perimeter-based network. >> Yep. >> I mean, that's what we are. Boxed, we knew our perimeter. Today, our perimeter is extended to our home, everywhere work, right? >> Yeah- >> We don't have a definition of a perimeter. Your browser is the new perimeter. And a good example, segueing to that, what we have seen is horizontal-based security. What we haven't seen is verticalization, especially in mobile. We haven't seen vertical mobile security solutions, right? Yes, you hear a little bit about automobile, you hear a little bit about healthcare, but what we haven't seen is, what about food sector? What about the frontline in food? What about supply chain? What security are we really doing? And I'll give you a simple example. You brought up ransomware. Last night, Dole was attacked with ransomware. We have seen the beef producer colonial pipeline. Now, if we have seen agritech being hit, what does it mean? We are starting to hit humanity. If you can't really put food on the table, you're starting to really disrupt the supply chain, right? In a massive way. So you got to start thinking about that. Why is Dole related to mobility? Think about that. They don't carry service and computers. What they carry is mobile devices. that's where the supply chain works. And then that's where you have to start thinking about it. And the evolution of ransomware, rather than a single-trick pony, you see them using multiple vulnerabilities. And Pegasus was the best example. Spyware across all politicians, right? And CEOs. It is six or seven vulnerabilities put together that actually was constructed to do an attack. >> Yeah. How does AI kind of change this? Where does it fit in? The attackers are going to have AI, but we could use AI to defend. But attackers are always ahead, right? (chuckles) So what's your... Do you have a point of view on that? 'Cause everybody's crazy about ChatGPT, right? The banks have all banned it. Certain universities in the United States have banned it. Another one's forcing his students to learn how to use ChatGPT to prompt it. It's all over the place. You have a point of view on this? >> So definitely, Dave, it's a great point. First, we all have to have our own generative AI. I mean, I look at it as your digital assistant, right? So when you had calculators, you can't function without a calculator today. It's not harmful. It's not going to take you away from doing multiplication, right? So we'll still teach arithmetic in school. You'll still use your calculator. So to me, AI will become an integral part. That's one beautiful thing I've seen on the short floor. Every little thing there is a AI-based solution I've seen, right? So ChatGPT is well played from multiple perspective. I would rather up level it and say, generated AI is the way to go. So there are three things. There is human intense triaging, where humans keep doing easy work, minimal work. You can use ML and AI to do that. There is human designing that you need to do. That's when you need to use AI. >> But, I would say this, in the Enterprise, that the quality of the AI has to be better than what we've seen so far out of ChatGPT, even though I love ChatGPT, it's amazing. But what we've seen from being... It's got to be... Is it true that... Don't you think it has to be cleaner, more accurate? It can't make up stuff. If I'm going to be automating my network with AI. >> I'll answer that question. It comes down to three fundamentals. The reason ChatGPT is giving addresses, it's not trained on the latest data. So for any AI and ML method, you got to look at three things. It's your data, it's your domain expertise, who is training it, and your data model. In ChatGPT, it's older data, it's biased to the people that trained it, right? >> Mm-hmm. >> And then, the data model is it's going to spit out what it's trained on. That's a precursor of any GPT, right? It's pre-trained transformation. >> So if we narrow that, right? Train it better for the specific use case, that AI has huge potential. >> You flip that to what the Enterprise customers talk about to us is, insight is invaluable. >> Right. >> But then too much insight too quickly all the time means we go remediation crazy. So we haven't got enough humans to be fixing all the problems. Sri's point with the ChatGPT data, some of that data we are looking at there could be old. So we're trying to triage something that may still be an issue, but it might have been superseded by something else as well. So that's my overriding when I'm talking to customers and we talk ChatGPT, it's in the news all the time. It's very topical. >> It's fun. >> It is. I even said to my 13-year-old son yesterday, your homework's out a date. 'Cause I knew he was doing some summary stuff on ChatGPT. So a little wind up that's out of date just to make that emphasis around the model. And that's where we, with our Neurons platform Ivanti, that's what we want to give the customers all the time, which is the real-time snapshot. So they can make a priority or a decision based on what that information is telling them. >> And we've kind of learned, I think, over the last couple of years, that access to real-time data, real-time AI, is no longer nice to have. It's a massive competitive advantage for organizations, but it's going to enable the on-demand, everything that we expect in our consumer lives, in our business lives. This is going to be table stakes for organizations, I think, in every industry going forward. >> Yeah. >> But assumes 5G, right? Is going to actually happen and somebody's going to- >> Going to absolutely. >> Somebody's going to make some money off it at some point. When are they going to make money off of 5G, do you think? (all laughing) >> No. And then you asked a very good question, Dave. I want to answer that question. Will bad guys use AI? >> Yeah. Yeah. >> Offensive AI is a very big thing. We have to pay attention to it. It's got to create an asymmetric war. If you look at the president of the United States, he said, "If somebody's going to attack us on cyber, we are going to retaliate." For the first time, US is willing to launch a cyber war. What that really means is, we're going to use AI for offensive reasons as well. And we as citizens have to pay attention to that. And that's where I'm worried about, right? AI bias, whether it's data, or domain expertise, or algorithmic bias, is going to be a big thing. And offensive AI is something everybody have to pay attention to. >> To your point, Sri, earlier about critical infrastructure getting hacked, I had this conversation with Dr. Robert Gates several years ago, and I said, "Yeah, but don't we have the best offensive, you know, technology in cyber?" And he said, "Yeah, but we got the most to lose too." >> Yeah, 100%. >> We're the wealthiest nation of the United States. The wealthiest is. So you got to be careful. But to your point, the president of the United States saying, "We'll retaliate," right? Not necessarily start the war, but who started it? >> But that's the thing, right? Attribution is the hardest part. And then you talked about a very interesting thing, rich nations, right? There's emerging nations. There are nations left behind. One thing I've seen on the show floor today is, digital inequality. Digital poverty is a big thing. While we have this amazing technology, 90% of the world doesn't have access to this. >> Right. >> What we have done is we have created an inequality across, and especially in mobility and cyber, if this technology doesn't reach to the last mile, which is emerging nations, I think we are creating a crater back again and putting societies a few miles back. >> And at much greater risk. >> 100%, right? >> Yeah. >> Because those are the guys. In cyber, all you need is a laptop and a brain to attack. >> Yeah. Yeah. >> If I don't have it, that's where the civil war is going to start again. >> Yeah. What are some of the things in our last minute or so, guys, David, we'll start with you and then Sri go to you, that you're looking forward to at this MWC? The theme is velocity. We're talking about so much transformation and evolution in the telecom industry. What are you excited to hear and learn in the next couple of days? >> Just getting a complete picture. One is actually being out after the last couple of years, so you learn a lot. But just walking around and seeing, from my perspective, some vendor names that I haven't seen before, but seeing what they're doing and bringing to the market. But I think goes back to the point made earlier around APIs and integration. Everybody's talking about how can we kind of do this together in a way. So integrations, those smart things is what I'm kind of looking for as well, and how we plug into that as well. >> Excellent, and Sri? >> So for us, there is a lot to offer, right? So while I'm enjoying what I'm seeing here, I'm seeing at an opportunity. We have an amazing portfolio of what we can do. We are into mobile device management. We are the last (indistinct) company. When people find problems, somebody has to go remediators. We are the world's largest patch management company. And what I'm finding is, yes, all these people are embedding software, pumping it like nobody's business. As you find one ability, somebody has to go fix them, and we want to be the (indistinct) company. We had the last smile. And I find an amazing opportunity, not only we can do device management, but do mobile threat defense and give them a risk prioritization on what needs to be remediated, and manage all that in our ITSM. So I look at this as an amazing, amazing opportunity. >> Right. >> Which is exponential than what I've seen before. >> So last question then. Speaking of opportunities, Sri, for you, what are some of the things that customers can go to? Obviously, you guys talk to customers all the time. In terms of learning what Ivanti is going to enable them to do, to take advantage of these opportunities. Any webinars, any events coming up that we want people to know about? >> Absolutely, ivanti.com is the best place to go because we keep everything there. Of course, "theCUBE" interview. >> Of course. >> You should definitely watch that. (all laughing) No. So we have quite a few industry events we do. And especially there's a lot of learning. And we just raised the ransomware report that actually talks about ransomware from a global index perspective. So one thing what we have done is, rather than just looking at vulnerabilities, we showed them the weaknesses that led to the vulnerabilities, and how attackers are using them. And we even talked about DHS, how behind they are in disseminating the information and how it's actually being used by nation states. >> Wow. >> And we did cover mobility as a part of that as well. So there's a quite a bit we did in our report and it actually came out very well. >> I have to check that out. Ransomware is such a fascinating topic. Guys, thank you so much for joining Dave and me on the program today, sharing what's going on at Ivanti, the changes that you're seeing in mobile, and the opportunities that are there for your customers. We appreciate your time. >> Thank you >> Thank you. >> Yes. Thanks, guys. >> Thanks, guys. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching "theCUBE" live from MWC23 in Barcelona. As you know, "theCUBE" is the leader in live tech coverage. Dave and I will be right back with our next guest. (gentle upbeat music)

(upbeat music) >> Good morning or good afternoon, or good evening depending on where you are, and welcome to this session: Reduce claims fraud with data. Very excited to have you all here. My name is Monique Hesseling and I'm Cloudera's managing director for the insurance vertical. First and foremost, we want to let you know that we know insurance. We have done it for a long time collectively, personally, I've done it for over 30 years. And, you know, as a proof of that, we want to let you know that we insure, we insure as well as we do data management work for the top global companies in the world, in north America, over property casualty, general insurance, health, and life and annuities. But besides that, we also take care of the data needs for some smaller insurance companies and specialty companies. So if you're not one of the huge glomar, conglomerates in the world, you are still perfectly fine with us. So why are we having this topic today? Really digital claims and digital claims management is accelerating. And that's based on a couple of things. First and foremost, customers are asking for it. Customers are used to doing their work more digitally over the last decennium or two. And secondly, with the last year or almost two, by now with the changes that we made in our work processes and the society at large around Covid, both regulators, as well as companies, have enabled digital processing and a digital journey to a degree that they've never done before. Now that had some really good impacts for claims handling. It did meant that customers were more satisfied. They felt they have more control over their processes in the claims, in the claims experience. It also reduced in a lot of cases, both in commercial lines, as well as in personal lines, the time periods that it took to settle on a claim. However, it, the more digital you go, it, it opened up more access points for fraudulence activities. So unfortunately we saw indicators of fraud, and fraud attempts, you know, creeping up over the last time period. So we thought it was a good moment to look at, you know, some use cases and some approaches insurers can take to manage that even better than they already are. And this is how we plan to do that. And this is how we see this in action. On the left side, you see progress of data analytics and data utilization around in this case, we're talking about claims fraud, but it's a generic picture. And really what it means is most companies that start with data efforts pretty much started around data warehousing and preliminary analytics and all around BI and reporting, which pretty much is understanding what we know, right? The data that we already have utilizing that to understand better what we know already. Now, when we move to the middle blue color, we get into different types of analytics. We get into exploratory data science, we get to predictions and we start getting in the space of describing what we can learn from what we know, but also start moving slowly into predicting. So first of all, learn and gather insights of what we already know, and then start augmenting with that with other data sets and other findings, so that we can start predicting for the future, what might happen. And that's the point where we get to AI, artificial intelligence and machine learning, which will help us predict which of our situations and claims are most likely to have a potential fraud or abuse scenario attached to it. So that's the path that insurers and other companies take in their data management and analytics environments. Now, if you look at the right side of this slide, you see data complexity per use cases in this case in fraud. So the bubbles represent the types of data that are being used, or the specific faces that we discussed on the left side. So for reporting, we used a DBA data policy verification, claims, files, staff data, that it tends to be heavily structured and already within the company itself. And when you go to the middle to the more descriptive basis, you start getting into unstructured data, you see a lot of unstructured text there, and we do a use case around that later. And this really enables us to better understand what the scenarios are that we're looking at and where the risks are around, in our example today, fraud, abuse and issues of resources. And then the more you go to the upper right corner, you see the outside of the baseball field, people refer to it, you see new unstructured data sources that are being used. You tend to see the more complex use cases. And we're looking at picture analysis, we're looking at voice analysis there. We're looking at geolocation. That's quite often the first one we look at. So this slide actually shows you the progress and the path in complexity and in utilization of data and analytical tool sets to manage data fraud, fraud use cases optimally. Now how we do that and how we look at that at Cloudera is actually not as complicated as this slide might want to, to, to give you an impression. So let's start at the left side, at the left side, you see the enterprise data, which is data that you as an organization have, or that you have access to. It doesn't have to be internal data, but quite often it is. Now that data goes into a data journey, right? It gets collected first. It gets manipulated and engineered so that people can do something with it. It gets stored something, you know, people need to have access to it. And then they get into analytical capabilities for insight gathering and utilization. Now, especially for insurance companies that all needs to be underpinned by a very, very strong security and governance environment. Because if not the most regulated industry in the world, insurance is awfully close. And if it's not the most regulated one, it's a close second. So it's critically important that insurers know where the data is, who has access to it, for what reason, what is being used for, so terms like lineage, transparency are crucial, crucially important for insurance. And we manage that in the shared data experience that goes over the whole Cloudera platform and every application, or tool, or experience, you use within Cloudera. And on the right side, you see the use cases that tend to be deployed around claims and claims fraud, claims fraud management. So over the last year or so, we've seen a lot of use cases around upcoding, people get one treatment or one fix on a car, but it gets coded as a more expensive one. That's a fraud scenario, right? We see also the more classical fraud things and we see anti-money laundering. So those are the types of use cases on the right side that we are supporting on the platform around claims fraud. And this is an example of how that actually looks like. Now, this is a one that it's actually a live one of a company that had claims that dealt with health situations and pain killers. So that obviously is relevant for health insurers, but you also see it in, in auto claims and car claims, right? You know, accidents. There are a lot of different claims scenarios, that have health risks associated with it. And what we did in this one is, we joined tables in a complex schema. So you have to look at the claimant, the physician, the hospital, all the providers that are involved, procedures that are being deployed medically, medicines has been utilized to uncover the full picture. Now that is a hard effort in itself, just for one claim at one scenario. But if you want to see if people are abusing, for example, painkillers in this scenario, you need to do that over every instant that this member, this claimant has, you know, with different doctors, with different hospitals, with different pharmacies or whatever, That classically it's a very complicated and complex the and costly data operations. So nowadays that tends to be done by graph databasing, right? So you put fraud rings within a graph database and walk the graph. And if you look at it here in that, you can see that in this case, that is a member that was shopping around for painkillers and went to different systems, and different providers to get multiple of the same big LR stat. You know, obviously we don't know what he or she did with it, but that's not the intent of the system. And that was actually a fraud and abuse case. So I want to share some customer success stories and recent AML and fraud use cases. And we have a couple of them and I'm not going to go in an awful lot of detail about them because we have some time to spend on one of them immediately after this. But one of them, for example, is voice analytics, which is a really interesting one. And on the baseball slide that I showed you earlier, that would be a right upper corner one. And what happened there is that an insurance company utilized the, the divorce records. They got from the customer service people, to try to predict which one were potentially fraudulence. And they did it in two ways. They look at actually the contents of what was being said. So they looked at certain words that were being used, certain trigger words, but they also were looking at tone of voice, pitch of voice, speed of talking. So they try to see trends there, and hear trends that would, that would ping them for a potential bad situation. Now, good and bad news of this proof of concept was, it's, we learned that it's very difficult, just because every human is different to get an indicator for bad behavior out of the pitch or the tone or the voice, you know, or those types of nonverbal communication in voice. But we did learn that it was easier to, to predict if a specific conversation needed to be transferred to somebody else based on emotion. You know, obviously as we all understand, life and health situations tend to come with emotions. Also, people either got very sad or they got very angry or, so the proof of concept didn't really get us to affirm understanding of potential fraudulence situation, but it did get us to a much better understanding of workflow around claims escalation in customer service, to route people, to the right person, depending on, you know, what they need, in that specific time. Another really interesting one, was around social media, geo open source, all sorts of data that we put together. And we linked to the second one that I listed on the slide here that was an on-prem deployment. And that was actually an analysis that regulators were asking for in a couple of countries for anti-money laundering scams, because there were some plots out there that networks of criminals would all buy low value policies, surrender them a couple of years later. And in that way, got criminal money into the regular amount of monetary system, whitewashed the money and this needed some very specific and very, very complex link analysis because there were fairly large networks of criminals that all needed to be tied together with the actions, with their policies to figure out where potential pin points were. And that also obviously included ecosystems, such as lawyers, administrative offices, all the other things. Now, but most, you know, exciting, I think that we see happening at the moment and we, we, you know, our partner, of analytics just went live with this with a large insurer, is that by looking at different types, that insurers already have, unstructured data, their claims notes, reports, claims filings, statements, voice records, augmented with information that they have access to, but that's not theirs. So it's just geo information obituary, social media, deployed on the cloud, and we can analyze claims much more effectively and efficiently, for fraud and litigation than ever before. And the first results over the last year or two, showcasing a significant decrease, significant decrease in claims expenses and, and an increase at the right moment of what a right amount in claims payments, which is obviously a good thing for insurers. Right? So having said all of that, I really would like to give Sri Ramaswamy, the CEO of Infinilytics, the opportunity to walk you through this use case, and actually show you how this looks like in real life. So Sri, here you go. >> So insurers often ask us this question, can AI help insurance companies, lower loss expenses, litigation, and help manage reserves better? We all know that insurance industry is majority, majority of it is unstructured data. Can AI analyze all of this historically, and look for patterns and trends to help workflows and improve process efficiencies. This is exactly why we brought together industry experts at Infinilytics to create the industry's very first pre-trained and pre-built insights engine called Charlee. Charlee basically summarizes all of the data, structured and unstructured. And when I say unstructured, I go back to what Monique, basically traded, you know, it is including documents, reports, third party, it reports and investigation, interviews, statements, claim notes included as well, and any third party enrichment that we can legally get our hands on, anything that helps the adjudicate, the claims better. That is all something that we can include as part of the analysis. And what Charlee does is takes all of this data and very neatly summarizes all of this, after the analysis into insights within a dashboard. Our proprietary natural language processing semantic models adds the explanation to our predictions and insights, which is the key element that makes all of our insights action. So let's just get into understanding what these steps are and how Charlie can help, you know, with the insights from the historical patterns in this case. So when the claim comes in, it comes with a lot of unstructured data and documents that the, the claims operations team have to utilize to adjudicate, to understand and adjudicate the claim in an efficient manner. You are looking at a lot of documents, correspondences reports, third party reports, and also statements that are recorded within the claim notes. What Charlee basically does is crunches all, all of this data, removes the noise from that and brings together five key elements, locations, texts, sentiments, entities, and timelines. In the next step. In the next step, we are basically utilizing Charlee's built-in proprietary natural language processing models to semantically understand and interpret all of that information and bring together those key elements into curated insights. And the way we do that is by building knowledge, graphs, and ontologies and dictionaries that can help understand the domain language and convert them into insights and predictions that we can display on the dashboard. And if you look at what is being presented in the dashboard, these are KPIs and metrics that are very interesting for a management staff or even the operations. So the management team can basically look at the dashboard and start with the summarized data and start to then dig deeper into each of the problematic areas and look at patterns at that point. And these patterns that we learn, from not only from what the system can provide, but also from the historic data, can help understand and uncover some of these patterns in the newer claims that are coming in. So important to learn from the historic learnings and apply those learnings in the new claims that are coming in. Let's just take a very quick example of what this is going to look like for a claims manager. So here the claims manager discovers from the summarized information that there are some problems in the claims that basically have an attorney involved. They have not even gone into litigation and they still are, you know, experiencing a very large average amount of claim loss when they compare to the benchmark. So this is where the manager wants to dig deeper and understand the patterns behind it from the historic data. And this has to look at the wealth of information that is sitting in the unstructured data. So Charlee basically pulls together all these topics, and summarizes these topics that are very specific to certain losses combined with entities and timelines and sentiments, and very quickly be able to show to the manager where the problematic areas are and what are those patterns leading to high, severe claims, whether it's litigation or whether it's just high, severe indemnity payments. And this is where the managers can adjust their workflows, based on what we can predict using those patterns that we have learned and predict the new claims. The operations team can also leverage Charlee's deep level insights, claim level insights, in the form of red flags, alerts and recommendations. They can also be trained using these recommendations, and the operations team can mitigate the claims much more effectively and proactively, using these kind of deep level insights that need to look at unstructured data. So at the, at the end, I would like to say that it is possible for us to achieve financial benefits, leveraging artificial intelligence platforms like Charlee and help the insurers learn from their historic data and being able to apply that to the new claims, to work, to adjust their workflows efficiently. >> Thank you very much Sri. That was very enlightening as always. And it's great to see that actually, some of the technology that we all work so hard on together, comes to fruition in, in cost savings and efficiencies and, and help insurers manage potential bad situations, such as claims fraud better, right? So to close this session out as a next step, we would really urge you to assess your available data sources and advanced or predictive fraud prevention capabilities, aligned with your digital initiatives to digital initiatives that we all embarked on, over the last year are creating a lot of new data that we can use to learn more. So that's a great thing. If you need to learn more, want to learn more about Cloudera and our insurance work and our insurance efforts call me, I'm very excited to talk about this forever. So if you want to give me a call or find a place to meet, when that's possible again, and schedule a meeting with us. And again, we love insurance. We'll gladly talk to you until SDC and parts of the United States, the cows come home about it. And we're done. I want to thank you all for attending this session, and hanging in there with us for about half an hour. And I hope you have a wonderful rest of the day.

>>Good morning or good afternoon or good evening, depending on where you are and welcome to this session, reduce claims, fraud, we're data, very excited to have you all here. My name is Winnie castling and I'm Cloudera as managing director for the insurance vertical. First and foremost, we want to let you know that we know insurance. We have done it for a long time. Collectively, personally, I've done it for over 30 years. And, you know, as a proof of that, we want to let you know that we insure, we insure as well as we do data management work for the top global companies in the world, in north America, over property casualty, general insurance health, and, um, life and annuities. But besides that, we also take care of the data needs for some smaller insurance companies and specialty companies. So if you're not one of the huge Glomar conglomerates in the world, you are still perfectly fine with us. >>So >>Why are we having this topic today? Really digital claims and digital claims management is accelerating. And that's based on a couple of things. First and foremost, customers are asking for it. Customers are used to doing their work more digitally over the last descending year or two. And secondly, with the last year or almost two, by now with the changes that we made in our work processes and in society at large around cuvettes, uh, both regulators, as well as companies have enabled digital processing and the digital journey to a degree that they've never done before. Now that had some really good impacts for claims handling. It did meant that customers were more satisfied. They felt they have more control over their processes in the cloud and the claims experience. It also reduced in a lot of cases, both in commercial lines, as well as in personal lines, the, um, the, the time periods that it took to settle on a claim. However, um, the more digital you go, it, it opened up more access points for fraud, illicit activities. So unfortunately we saw indicators of fraud and fraud attempts, you know, creeping up over the last time period. So we thought it was a good moment to look at, you know, some use cases and some approaches insurers can take to manage that even better than they already >>Are. >>And this is how we plan to do that. And this is how we see this in action. On the left side, you see progress of data analytics and data utilization, um, around, in this case, we're talking about claims fraud, but it's a generic picture. And really what it means is most companies that start with data affords pretty much start around data warehousing and we eliminate analytics and all around BI and reporting, which pretty much is understanding what we know, right? The data that we already have utilizing data to understand better what we know already. Now, when we move to the middle blue collar, we get into different types of analytics. We get into exploratory data science, we get to predictions and we start getting in the space of describing what we can learn from what we know, but also start moving slowly into predicting. So first of all, learn and gather insights of what we already know, and then start augmenting with that with other data sets and other findings, so that we can start predicting for the future, what might happen. >>And that's the point where we get to AI, artificial intelligence and machine learning, which will help us predict which of our situations and claims are most likely to have a potential fraud or abuse scenario attached to it. So that's the path that insurers and other companies take in their data management and analytics environments. Now, if you look at the right side of this light, you see data complexity per use cases in this case in fraud. So the bubbles represent the types of data that are being used, or the specific faces that we discussed on the left side. So for reporting, we used a TPA data, policy verification, um, claims file staff data, that it tends to be heavily structured and already within the company itself. And when you go to the middle to the more descriptive basis, you start getting into unstructured data, you see a lot of instructor texts there, and we do a use case around that later. >>And this really enables us to better understand what the scenarios are that we're looking at and where the risks are around. In our example today, fraud, abuse and issues of resources. And then the more you go to the upper right corner, you see the outside of the baseball field, people refer to it, you see new unstructured data sources that are being used. You tend to see the more complex use cases. And we're looking at picture analysis, we're looking at voice analysis there. We're looking at geolocation. That's quite often the first one we look at. So this slide actually shows you the progress and the path in complexity and in utilization of data and analytical tool sets to manage data fraud, fraud, use cases, optimally. >>Now how we do that and how we look at at a Cloudera is actually not as complicated as, as this slight might want to, um, to, to give you an impression. So let's start at the left side at the left side, you see the enterprise data, which is data that you as an organization have, or that you have access to. It doesn't have to be internal data, but quite often it is now that data goes into a data journey, right? It gets collected first. It gets manipulated and engineered so that people can do something with it. It gets stored something, you know, people need to have access to it. And then they get into analytical capabilities who are inside gathering and utilization. Now, especially for insurance companies that all needs to be underpinned by a very, very strong security and governance, uh, environment. Because if not the most regulated industry in the world, insurance is awfully close. >>And if it's not the most regulated one, it's a close second. So it's critically important that insurers know, um, where the data is, who has access to it for Rodriguez, uh, what is being used for so terms like lineage, transparency are crucial, crucially important for insurance. And we manage that in the shared data experience. So it goes over the whole Cloudera platform and every application or tool or experience you use would include Dao. And on the right side, you see the use cases that tend to be deployed around claims and claims fraud, claims, fraud management. So over the last year or so, we've seen a lot of use cases around upcoding people get one treatment or one fix on a car, but it gets coded as a more expensive one. That's a fraud scenario, right? We see also the more classical fraud things and we see anti money laundering. So those are the types of use cases on the right side that we are supporting, um, on the platform, uh, around, um, claims fraud. >>And this is an example of how that actually looks like now, this is a one that it's actually a live one of, uh, a company that had, um, claims that dealt with health situations and being killers. So that obviously is relevant for health insurers, but you also see it in, um, in auto claims and counterclaims, right, you know, accidents. There are a lot of different claims scenarios that have health risks associated with it. And what we did in this one is we joined tables in a complex schema. So we have to look at the claimant, the physician, the hospital, all the providers that are involved procedures that are being deployed. Medically medicines has been utilized to uncover the full picture. Now that is a hard effort in itself, just for one claim and one scenario. But if you want to see if people are abusing, for example, painkillers in this scenario, you need to do that over every instant that is member. >>This claimant has, you know, with different doctors, with different hospitals, with different pharmacies or whatever that classically it's a very complicated and complex, um, the and costly data operation. So nowadays that tends to be done by graph databases, right? So you put fraud rings within a graph database and walk the graph. And if you look at it here in batch, you can see that in this case, that is a member that was shopping around for being killers and went through different systems and different providers to get, um, multiple of the same big LR stat. You know, obviously we don't know what he or she did with it, but that's not the intent of the system. And that was actually a fraud and abuse case. >>So I want to share some customer success stories and recent, uh, AML and fraud use cases. And we have a couple of them and I'm not going to go in an awful lot of detail, um, about them because we have some time to spend on one of them immediately after this. But one of them for example, is voice analytics, which is a really interesting one. And on the baseball slide that I showed you earlier, that would be a right upper corner one. And what happened there is that an insurance company utilized the, uh, the voice records they got from the customer service people to try to predict which one were potentially fraud list. And they did it in two ways. They look at actually the contents of what was being said. So they looked at certain words that were being used certain trigger words, but they also were looking at tone of voice pitch of voice, uh, speed of talking. >>So they try to see trends there and hear trends that would, um, that would bring them for a potential bad situation. Now good and bad news of this proof of concept was it's. We learned that it's very difficult just because every human is different to get an indicator for bad behavior out of the pitch or the tone or the voice, you know, or those types of nonverbal communication in voice. But we did learn that it was easier to, to predict if a specific conversation needed to be transferred to somebody else based on emotion. You know, obviously as we all understand life and health situations tend to come with emotions, or so people either got very sad or they got very angry or so the proof of concept didn't really get us to a firm understanding of potential driverless situation, but it did get us to a much better understanding of workflow around, um, claims escalation, um, in customer service to route people, to the right person, depending on what they need. >>And that specific time, another really interesting one was around social media, geo open source, all sorts of data that we put together. And we linked to the second one that I listed on slide here that was an on-prem deployment. And that was actually an analysis that regulators were asking for in a couple of countries, uh, for anti money laundering scams, because there were some plots out there that networks of criminals would all buy the low value policies, surrendered them a couple of years later. And in that way, God criminal money into the regular amount of monetary system whitewashed the money and this needed some very specific and very, very complex link analysis because there were fairly large networks of criminals that all needed to be tied together, um, with the actions, with the policies to figure out where potential pain points were. And that also obviously included ecosystems, such as lawyers, administrative offices, all the other things, no, but most, you know, exciting. >>I think that we see happening at the moment and we, we, you know, our partner, if analytics just went live with this with a large insurer, is that by looking at different types that insurers already have, um, unstructured data, um, um, their claims nodes, um, repour its claims, filings, um, statements, voice records, augmented with information that they have access to, but that's not their ours such as geo information obituary, social media Boyd on the cloud. And we can analyze claims much more effectively and efficiently for fraud and litigation and alpha before. And the first results over the last year or two showcasing a significant degree is significant degrees in claims expenses and, um, and an increase at the right moment of what a right amount in claims payments, which is obviously a good thing for insurers. Right? So having said all of that, I really would like to give Sri Ramaswami, the CEO of infinite Lytics, the opportunity to walk you through this use case and actually show you how this looks like in real life. So Sheree, here >>You go. So >>Insurers often ask us this question, can AI help insurance companies, lower loss expenses, litigation, and help manage reserves better? We all know that insurance industry is majority. Majority of it is unstructured data. Can AI analyze all of this historically and look for patterns and trends to help workflows and improve process efficiencies. This is exactly why we brought together industry experts at infill lyrics to create the industries where very first pre-trained and prebuilt insights engine called Charlie, Charlie basically summarizes all of the data structured and unstructured. And when I say unstructured, I go back to what money basically traded. You know, it is including documents, reports, third-party, um, it reports and investigation, uh, interviews, statements, claim notes included as well at any third party enrichment that we can legally get our hands on anything that helps the adjudicate, the claims better. That is all something that we can include as part of the analysis. And what Charlie does is takes all of this data and very neatly summarizes all of this. After the analysis into insights within our dashboard, our proprietary naturally language processing semantic models adds the explanation to our predictions and insights, which is the key element that makes all of our insights >>Actually. So >>Let's just get into, um, standing what these steps are and how Charlie can help, um, you know, with the insights from the historical patterns in this case. So when the claim comes in, it comes with a lot of unstructured data and documents that the, uh, the claims operations team have to utilize to adjudicate, to understand and adjudicate the claim in an efficient manner. You are looking at a lot of documents, correspondences reports, third party reports, and also statements that are recorded within the claim notes. What Charlie basically does is crunches all, all of this data removes the noise from that and brings together five key elements, locations, texts, sentiments, entities, and timelines in the next step. >>In the next step, we are basically utilizing Charlie's built-in proprietary, natural language processing models to semantically understand and interpret all of that information and bring together those key elements into curated insights. And the way we do that is by building knowledge, graphs, and ontologies and dictionaries that can help understand the domain language and convert them into insights and predictions that we can display on the dash. Cool. And if you look at what has been presented in the dashboard, these are KPIs and metrics that are very interesting for a management staff or even the operations. So the management team can basically look at the dashboard and start with the summarized data and start to then dig deeper into each of the problematic areas and look at patterns at that point. And these patterns that we learn from not only from what the system can provide, but also from the historic data can help understand and uncover some of these patterns in the newer claims that are coming in so important to learn from the historic learnings and apply those learnings in the new claims that are coming in. >>Let's just take a very quick example of what this is going to look like a claims manager. So here the claims manager discovers from the summarized information that there are some problems in the claims that basically have an attorney involved. They have not even gone into litigation and they still are, you know, I'm experiencing a very large, um, average amount of claim loss when they compare to the benchmark. So this is where the manager wants to dig deeper and understand the patterns behind it from the historic data. And this has to look at the wealth of information that is sitting in the unstructured data. So Charlie basically pulls together all these topics and summarizes these topics that are very specific to certain losses combined with entities and timelines and sentiments, and very quickly be able to show to the manager where the problematic areas are and what are those patterns leading to high, severe claims, whether it's litigation or whether it's just high, severe indemnity payments. >>And this is where the managers can adjust their workflows based on what we can predict using those patterns that we have learned and predict the new claims, the operations team can also leverage Charlie's deep level insights, claim level insights, uh, in the form of red flags, alerts and recommendations. They can also be trained using these recommendations and the operations team can mitigate the claims much more effectively and proactively using these kind of deep level insights that need to look at unstructured data. So at the, at the end, I would like to say that it is possible for us to achieve financial benefits, leveraging artificial intelligence platforms like Charlie and help the insurers learn from their historic data and being able to apply that to the new claims, to work, to adjust their workflows efficiently. >>Thank you very much for you. That was very enlightening as always. And it's great to see that actually, some of the technology that we all work so hard on together, uh, comes to fruition in, in cost savings and efficiencies and, and help insurers manage potential bad situations, such as claims fraud batter, right? So to close this session out as a next step, we would really urge you to a Sasha available data sources and advanced or predictive fraud prevention capabilities aligned with your digital initiatives to digital initiatives that we all embarked on over the last year are creating a lot of new data that we can use to learn more. So that's a great thing. If you need to learn more at one to learn more about Cloudera and our insurance work and our insurance efforts, um, you to call me, uh, I'm very excited to talk about this forever. So if you want to give me a call or find a place to meet when that's possible again, and schedule a meeting with us, and again, we love insurance. We'll gladly talk to anyone until they say in parts of the United States, the cows come home about it. And we're dad. I want to thank you all for attending this session and hanging in there with us for about half an hour. And I hope you have a wonderful rest of the day. >>Good afternoon, I'm wanting or evening depending on where you are and welcome to this breakout session around insurance, improve underwriting with better insights. >>So first and >>Foremost, let's summarize very quickly, um, who we're with and what we're talking about today. My name is goonie castling, and I'm the managing director at Cloudera for the insurance vertical. And we have a sizeable presence in insurance. We have been working with insurance companies for a long time now, over 10 years, which in terms of insurance, it's maybe not that long, but for technology, it really is. And we're working with, as you can see some of the largest companies in the world and in the continents of the world. However, we also do a significant amount of work with smaller insurance companies, especially around specialty exposures and the regionals, the mutuals in property, casualty, general insurance, life, annuity, and health. So we have a vast experience of working with insurers. And, um, we'd like to talk a little bit today about what we're seeing recently in the underwriting space and what we can do to support the insurance industry in there. >>So >>Recently what we have been seeing, and it's actually accelerated as a result of the recent pandemic that we all have been going through. We see that insurers are putting even more emphasis on accounting for every individual customers with lotta be a commercial clients or a personal person, personal insurance risk in a dynamic and a B spoke way. And what I mean with that is in a dynamic, it means that risks and risk assessments change very regularly, right? Companies go into different business situations. People behave differently. Risks are changing all the time and the changing per person they're not changing the narrow generically my risk at a certain point of time in travel, for example, it might be very different than any of your risks, right? So what technology has started to enable is underwrite and assess those risks at those very specific individual levels. And you can see that insurers are investing in that capability. The value of, um, artificial intelligence and underwriting is growing dramatically. As you see from some of those quotes here and also risks that were historically very difficult to assess such as networks, uh, vendors, global supply chains, um, works workers' compensation that has a lot of moving parts to it all the time and anything that deals with rapidly changing risks, exposures and people, and businesses have been supported more and more by technology such as ours to help, uh, gone for that. >>And this is a bit of a difficult slide. So bear with me for a second here. What this slide shows specifically for underwriting is how data-driven insights help manage underwriting. And what you see on the left side of this slide is the progress in make in analytical capabilities. And quite often the first steps are around reporting and that tends to be run from a data warehouse, operational data store, Starsky, Matt, um, data, uh, models and reporting really is, uh, quite often as a BI function, of course, a business intelligence function. And it really, you know, at a regular basis informs the company of what has been taken place now in the second phase, the middle dark, the middle color blue. The next step that is shore stage is to get into descriptive analytics. And what descriptive analytics really do is they try to describe what we're learning in reporting. >>So we're seeing sorts and events and sorts and findings and sorts of numbers and certain trends happening in reporting. And in the descriptive phase, we describe what this means and you know why this is happening. And then ultimately, and this is the holy grill, the end goal we like to get through predictive analytics. So we like to try to predict what is going to happen, uh, which risk is a good one to underwrite, you know, watch next policy, a customer might need or wants water claims as we discuss it. And not a session today, uh, might become fraud or lists or a which one we can move straight through because they're not supposed to be any issues with it, both on the underwriting and the claims side. So that's where every insurer is shooting for right now. But most of them are not there yet. >>Totally. Right. So on the right side of this slide specifically for underwriting, we would, we like to show what types of data generally are being used in use cases around underwriting, in the different faces of maturity and analytics that I just described. So you will see that on the reporting side, in the beginning, we start with rates, information, quotes, information, submission information, bounding information. Um, then if you go to the descriptive phase, we start to add risk engineering information, risk reports, um, schedules of assets on the commercial side, because some are profiles, uh, as a descriptions, move into some sort of an unstructured data environment, um, notes, diaries, claims notes, underwriting notes, risk engineering notes, transcripts of customer service calls, and then totally to the other side of this baseball field looking slide, right? You will see the relatively new data sources that can add tremendous value. >>Um, but I'm not Whitely integrated yet. So I will walk through some use cases around these specifically. So think about sensors, wearables, you know, sensors on people's bodies, sensors, moving assets for transportation, drone images for underwriting. It's not necessary anymore to send, uh, an inspection person and inspector or risk, risk inspector or engineer to every building, you know, be insurers now, fly drones over it, to look at the roofs, et cetera, photos. You know, we see it a lot in claims first notice of loss, but we also see it for underwriting purposes that policies out there. Now that pretty much say sent me pictures of your five most valuable assets in your home and we'll price your home and all its contents for you. So we start seeing more and more movements towards those, as I mentioned earlier, dynamic and bespoke types of underwriting. >>So this is how Cloudera supports those initiatives. So on the left side, you see data coming into your insurance company. There are all sorts of different data. There are, some of them are managed and controlled by you. Some orders you get from third parties, and we'll talk about Della medics in a little bit. It's one of the use cases. They move into the data life cycle, the data journey. So the data is coming into your organization. You collected, you store it, you make it ready for utilization. You plop it either in an operational environment for processing or in an analytical environment for analysis. And then you close on the loop and adjusted from the beginning if necessary, no specifically for insurance, which is if not the most regulated industry in the world it's coming awfully close, and it will come in as a, a very admirable second or third. >>Um, it's critically important that that data is controlled and managed in the correct way on the old, the different regulations that, that we are subject to. So we do that in the cloud era Sharon's data experiment experience, which is where we make sure that the data is accessed by the right people. And that we always can track who did watch to any point in time to that data. Um, and that's all part of the Cloudera data platform. Now that whole environment that we run on premise as well as in the cloud or in multiple clouds or in hybrids, most insurers run hybrid models, which are part of the data on premise and part of the data and use cases and workloads in the clouds. We support enterprise use cases around on the writing in risk selection, individualized pricing, digital submissions, quote processing, the whole quote, quote bound process, digitally fraud and compliance evaluations and network analysis around, um, service providers. So I want to walk you to some of the use cases that we've seen in action recently that showcases how this work in real life. >>First one >>Is to seize that group plus Cloudera, um, uh, full disclosure. This is obviously for the people that know a Dutch health insurer. I did not pick the one because I happen to be dodged is just happens to be a fantastic use case and what they were struggling with as many, many insurance companies is that they had a legacy infrastructure that made it very difficult to combine data sets and get a full view of the customer and its needs. Um, as any insurer, customer demands and needs are rapidly changing competition is changing. So C-SAT decided that they needed to do something about it. And they built a data platform on Cloudera that helps them do a couple of things. It helps them support customers better or proactively. So they got really good in pinging customers on what potential steps they need to take to improve on their health in a preventative way. >>But also they sped up rapidly their, uh, approvals of medical procedures, et cetera. And so that was the original intent, right? It's like serve the customers better or retain the customers, make sure what they have the right access to the right services when they need it in a proactive way. As a side effect of this, um, data platform. They also got much better in, um, preventing and predicting fraud and abuse, which is, um, the topic of the other session we're running today. So it really was a good success and they're very happy with it. And they're actually starting to see a significant uptick in their customer service, KPIs and results. The other one that I wanted to quickly mention is Octo. As most of you know, Optune is a very, very large telemedics provider, telematics data provider globally. It's been with Cloudera for quite some time. >>This one I want to showcase because it showcases what we can do with data in mass amounts. So for Octo, we, um, analyze on Cloudera 5 million connected cars, ongoing with 11 billion data points. And really what they're doing is the creating the algorithms and the models and insurers use to, um, to, um, run, um, tell them insurance, telematics programs made to pay as you drive pay when you drive, pay, how you drive. And this whole telemedics part of insurance is actually growing very fast too, in, in, still in sort of a proof of concept mini projects, kind of initiatives. But, um, what we're succeeding is that companies are starting to offer more and more services around it. So they become preventative and predictive too. So now you got to the program staff being me as a driver saying, Monique, you're hopping in the car for two hours. >>Now, maybe it's time you take a break. Um, we see that there's a Starbucks coming up on the ride or any coffee shop. That's part of a bigger chain. Uh, we know because you have that app on your phone, that you are a Starbucks user. So if you stop there, we'll give you a 50 cents discount on your regular coffee. So we start seeing these types of programs coming through to, again, keep people safe and keep cars safe, but primarily of course the people in it, and those are the types of use cases that we start seeing in that telematic space. >>This looks more complicated than it is. So bear with me for a second. This is a commercial example because we see a data work. A lot of data were going on in commercial insurance. It's not Leah personal insurance thing. Commercial is near and dear to my heart. That's where I started. I actually, for a long time, worked in global energy insurance. So what this one wheelie explains is how we can use sensors on people's outfits and people's clothes to manage risks and underwrite risks better. So there are programs now for manufacturing companies and for oil and gas, where the people that work in those places are having sensors as part of their work outfits. And it does a couple of things. It helps in workers' comp underwriting and claims because you can actually see where people are moving, what they are doing, how long they're working. >>Some of them even tracks some very basic health-related information like blood pressure and heartbeat and stuff like that, temperature. Um, so those are all good things. The other thing that had to us, it helps, um, it helps collect data on the specific risks and exposures. Again, we're getting more and more to individual underwriting or individual risk underwriting, who insurance companies that, that ensure these, these, um, commercial, commercial, um, enterprises. So they started giving discounts if the workers were sensors and ultimately if there is an unfortunate event and it like a big accident or big loss, it helps, uh, first responders very quickly identify where those workers are. And, and, and if, and how they're moving, which is all very important to figure out who to help first in case something bad happens. Right? So these are the type of data that quite often got implements in one specific use case, and then get broadly moved to other use cases or deployed into other use cases to help price risks, betters better, and keep, you know, risks, better control, manage, and provide preventative care. Right? >>So these were some of the use cases that we run in the underwriting space that are very excited to talk about. So as a next step, what we would like you to do is considered opportunities in your own companies to advance risk assessment specific to your individual customer's need. And again, customers can be people they can be enterprises to can be other any, any insurable entity, right? The please physical dera.com solutions insurance, where you will find all our documentation assets and thought leadership around the topic. And if you ever want to chat about this, please give me a call or schedule a meeting with us. I get very passionate about this topic. I'll gladly talk to you forever. If you happen to be based in the us and you ever need somebody to filibuster on insurance, please give me a call. I'll easily fit 24 hours on this one. Um, so please schedule a call with me. I promise to keep it short. So thank you very much for joining this session. And as a last thing, I would like to remind all of you read our blogs, read our tweets. We'd our thought leadership around insurance. And as we all know, insurance is sexy.

>>Mhm Yes. Welcome to this cube conversation. I'm john Kerry host of the cube here in Palo alto California. We've got two great guests all the way from Ohio and here in the bay area with sequence securities is our focus on cloud growth companies. Sri and met a co founder and CTO of sequence security and Jason Kent hacker in residence at sequence security. We're gonna find out what that actually means in the second but this is a really important company in the sense of A P. I. S. As they are starting to be the connective tissue between systems and and data. Um you're starting to see more vulnerabilities, more risk but also more upside. So risk, reward is high. And anyone who's doing things in the cloud obviously deals with the A. P. I. So Trey and Jason. Thanks for let's keep conversation. >>Happy to be here >>guys. Let's let's talk about A P. I. Security. And but first before we get there trans what does sequence security do? What do you guys specifically build? And what do you sell >>sequences in the business protecting your web and um A P. I. S from various kinds of attacks? Uh We protect from business logic attacks, A P. I. Uh do your api inventory, uh also the detect and defend against things like a town taker. Where's fake account creation, scraping pretty much anything and everything. An application on a PDA is exposed to from from the Attackers. >>Jason. What do you what do you do there as hacker and residents? I also want to get your perspective on api security from the point of view of, you know, uh attack standpoint from a vector. How are people doing it? So first explain what you do and uh love the title hacker and residents. But also what does that actually mean from a security standpoint? >>Yeah. So we can't be in the business that we're in without having an adversarial approach to where our customers are deployed and how we look at them. So a lot of times I spend my time trying to be on the client's backdoors and and try to hit their A. P. I. S. With as many kinds of attacks that I can. It helps us understand how an attacker is going to approach a specific client as well as helps us tune for our machine learning models to make sure that we can defend against those kinds of things. Um as a hacker and residents, my mostly my position is client facing. But I do spend an awful lot of time being research and looking for the next api threat that's out there. >>You gotta stay ahead of the bad guys. But let's bring up some kind of cutting edge relevant topics. One is all over the news cycle. You heard peloton, very highly visible company, It represents that new breed of digital companies that have a new approach and it's absolutely doing very, very well. The new consumers like this product and you're seeing a lot more peloton, like companies out there that are leveraging technology, so they're fully integrated, they had an A. P. I. Issue recently. Um what does it mean? Is that, is that something we're gonna see more of these kind of leaks in these kind of vulnerabilities? What do you guys think about this political thing, >>You know, from an attacker's perspective as a really boring attack? Um, but it led to a huge amount of data leaking out. Same with, you know, the news has been been right with this lately, right, john Deere got hit. Um We've seen yet another credit bureau got hit right. Um and these attacks are coming off as fairly simple attacks that are dumping huge amounts of data, just proving that the FBI attack surface is really a great place to get a rich amount of data, but you have to have a good understanding of how the application works so you can spend a little bit of time on it. But once you've taken a look at how the data flows, you end up with, you know, pretty rich data set as an attacker. I go after them just by simply utilizing their products, utilizing the programs and understanding how they work. And then I drag out all the pieces that I think are going to be interesting and start plucking away at it. If I see a like a profile, for instance, that I can edit, I wonder can I edit someone else's profile. And this is how the peloton attack work. I'm logged in, I'm allowed to see my things, what other things can I see? And it turns out they can see everything. >>So we also saw a hack with clubhouse, which is the hot app now I think just opened up to android users, but they were simply calling it back and Agora, which is, you know, I've seen china, but once you've understood that the tokens work, once you understood what they were doing, you could essentially go in and figure things out. There seems to be like pretty like trivial stuff, but it gets exposed. No one kind of thinks it through. How does someone protect themselves against these things? Because that's the real issue, like just make it less secure. Our Api is gonna be more secure in the future. What can customers do about what do you guys to think about this? >>Yeah, but the reality is, I mean that's just uh too many babies out there. I mean if you see the transition that is happening and that is the transformation where it used to be like a one app or two apps before and now there are like hundreds and thousands of applications driven by the devops world, a child development and and what matters is, I mean the starting point really is you cannot protect what, you cannot see what used to be. Uh an up hosted in your data center is now being hosted in the cloud environments, in the virtual environments, in several less environments and coordinators, you name it, they're out there. So the key is really to understand your attack surface, that's your starting point. So you're you're tooling your applications need to uh I need to be able to provide that visibility that that that is needed to protect these applications and you can't rely just on your developers to do this for you. So you need a right tool that can secure these applications, >>Jason what's the steps that an attacker takes to uncover vulnerabilities? What goes through the mind of the attacker? Um I mean the old days you used to just do port scans and try to penetrate you get through the perimeter. Now with this no perimeter mindset, the surface area Schramm was talking about is huge. What what's going on the mind of the attacker here and the A P I S and vulnerabilities. >>So the very first thing that we do is we sign up for an account, we use the thing, right? We look at all the different endpoints. Um I've got scripts running in my attack tools that do things like show me comments uh in case the developer left some comments in there to tell me where things are. Um I basically I'm just going to poke around using it like a regular user, but in that I'm going to look for places. That makes sense to try to do an attack. So the login screen is a really easy thing. Everybody understands that you put in a user name, you put in a password, you can't go. What I'm gonna do is put in a bad username and a bad password. I'm gonna put in a good user name and a bad password and I'm gonna see what changes, what are the different things that your application is telling me. And so when we look at an application for flaws and ways to get to the data on the back end, all we're doing is seeing what data do you present me on standard use. And then I'm going to look at, well, how can I change these parameters or what are the things that I can change in my requests to get a different response? So in the early phases of an attack, Attackers are very difficult to a seat. Right. They just look like a regular user just doing regular things. It's when we decide. All right. I've found something that starts to get actually interesting and we start to try to pull data out. >>What are some of the common vulnerabilities and risks that you guys see in the A. P. I is when you look when you poke at them that people are are doing is that they're not really doing their homework. Doing good. Security designers are just more of tech risk. What's the most common vulnerabilities and risks? >>Well, so for me, I I've noticed a lot of the OAS KPI top 10, the first couple of things you see them on almost all applications, so broken object level authorization is the first one. It's mouthful. Um but basically all it is is I log onto the platform, I'm authorized to be there, but I can see someone else's stuff and that's exactly what happened in peloton. Um that and what we call insecure direct object reference where I don't have to be logged in, I can just make the request without any authentication and get information back. So those are pretty common areas um that you know people need to focus on, but there's a few others that are outside the top 10 that really make a lot more sense as a defender strains probably has a little better answer to me. >>Yeah. So um I'm like like we said um creating that inventories is key, but where are they being hostess? Another another aspect of things. So so when when Jason spoke about um like hackers are actually probing, trying to figure out what are the different entry points? It could be your production environment, it could be your QA environment staging environment and you're not even aware of, but once you've actually figured out those entry points, the next step of attack was like at peloton and and other places is really eggs filtering. Exfiltrate ng that that information. Right. Is it, is it the O P II information, ph I information um and and you don't want to exfiltrate as a hacker, just one person's information. You you're automating that business logic that is behind it ability to protect and defend against those kinds of attacks, giving that visibility, even though you might not have instrumented that application for for that kind of visibility is key. Once you are bubbling up those behaviors, then you can go ahead and and and protect from these kinds of attacks. And it could be about just simply enumerating through I. D. S. Uh that paladin might have or uh experience might have and just enumerate through that and exfiltrate the information behind it. So the tools need to be able to protect from those kinds of attacks out there. >>Yeah, I think I was actually on clubhouse when um that went down that hole enumerating through the I. D. S. Room I. D. S. And then the people just querying once they got an I. D. They essentially just sucked all the content out because they were just calling the back end. It was just like the most dumbest thing I've ever seen, but they didn't think about, I mean, you know, they were just rushing really fast. So So the question I have for transit and on a defense basis, people are going first party um with a P. I. S. A. P. I. First strategies because it's just some benefits there as we were talking about what do I need to do to protect myself? So I don't have that clubhouse problem or the pelton problem. Is there a Is there a playbook or is their software tools that I could use? How do I build? My apologies from day one and my principles around it to be good hygiene or good design? What's the what's the >>yeah. So aPI security is sort of a looking uh less known given that it's constantly evolving and changing. And the adoption of A P. S. Have gone up significantly. So what you need to start with effectively is the runtime security aspect of things. When a an aPI is live, how do I actually protected? And it ranges from simple syntactic protection things around people. Can can go ahead and break these ap is by providing sort of uh going after endpoints that you don't think exist anymore or going after certain functions by giving large values that they're not sort of coded to accept and so on so forth. Once you've done that runtime protection from a syntactic aspect, you also need to protect from a business logic aspect. I mean, mps will will expose uh information, interact with the customers and partners, what what business logic are they actually exposing and how can it be abused? Understanding that is another big aspects and then you can go ahead and protect from a runtime uh from a long time security perspective, once you've done that and understood that, well then you can start shifting lap things, invest in your uh sort of uh Dass tools or static analysis tools which can catch these things early so that they don't bubble up all the way, but none of them are actually silver bullets, right? So that you have a good uh time security tools, so I don't need to invest in dust or assessed whatever I have invested in my shift left aspect of things and uh and nothing will flow through. So you you need to start shifting left uh but covered all your bases properly, >>you can't shift left, there's nothing to shift from. I mean if you don't have that baseline foundation, what does that even mean to shift left and get that built into the Ci cd pipeline? So that's a great point. How does how does someone and some companies and teams set that foundation with the run time? Do you think it's a critical problem right now or most people are do a good job or they just get get lazy or just lose track of it or you know what, what's what's the common um, use case? Do you see behavior behaviorally inside these enterprises? >>Yeah. So what, what we're seeing is adoption of new technologies and environments um, and they're not um, well suited for the traditional way of doing that time. Security. Like if if you have an app running in your kubernetes environment, if you have an app running in in in a serval less environment, how do you actually protected with the traditional appliance based approach? So I think being able to get that visibility into these environments, understanding the the user behavior, how these applications are interacted with being able to differentiate from that uh, normal human behavior or even sometimes legitimate automation uh from from the malicious intents or or the the probing and the business logic attacks is key to understanding and defending these applications. >>Before we wrap up, I want to just get your expert opinion since you guys are both here around, you know, the next level of of innovation. Also you got cloud public cloud showed us a P. I. S are great. Now you're starting to see cloud operations, they call day two operations or whatever you call it A IOP. There's all kinds of buzz words are for it, but hybrid cloud and multi cloud, Edge five G. These are all basically pointing to distributed computing systems, basically distributed cloud. So that means more A P. I. Is gonna be out there. Um So in a way the surface area of a piece is increasing. What's your what's your view on this as a market? I mean, early days developing fast and what's, what's the, what's the landscape look like? What do you guys see from a attack and defense standpoint? >>Well, just from the attacker's perspective, you know, I see a lot more traffic going, what we call east west traffic, where it's traveling inside the application, it's a P is feeding a ps more data. Um, but what is really happening is we're trying to figure out how to hook third parties into our api is more and more. The john Deere attack was just simply their development api platform that they open up for other organizations to integrate with them. Um, you know, it's, it's very beneficial for John Deere to be able to say I planted this seed at an inch and a half of depth and later, uh, I harvested 280 bushels of corn off that acres. So I know that's perfect. I can feed that back to my seed guy. Well that kind of data flow that's going around from AP to AP means that there's far more attack surface and we're going to see it more and more. I I don't think that we're going to have less Ap is communicating in the near future. I think this is the foundation that we're building for what it's gonna look like for almost every business in the near term. >>I mean this is the plumbing of integration. I mean as people work with each other data transfer, data knowledge format, you mentioned syntax and all these basic things in computer science are coming to A PS which was supposed to be just a dumb pipe or just, you know, rest api those glory days now it's not there. They're basically, it's basically connections. >>Yeah. You're absolutely right. John, I mean like what Jason mentioned earlier, uh, in terms of the way the A. P. I. S are going to grow and the bad guys are going to go after it. You need to think like a bad guy, what are they going to go after? Uh, these assets that are going to be in the cloud, in your hybrid environment, in in your own prem environment. And, and it's, it's a flip of a switch where an internal API can be externally exposed or, or just a new api getting rolled out. So all those things you need to be able to protect, um, and get that visibility first and then being then protect these environments. >>That's awesome. You guys represent the new kind of company that's going to take advantage of the cloud scale and as people shift to the new structural change and people are re factoring security, This is an area that's going to be explosive in development. Obviously the upside is huge. Um Quickly before to end, you guys take a minute to give a plug for the company. Um This is pretty cool. I love love what you guys do. I think it's very relevant and cool at the same time. So sequence security. What are you guys doing funding hiring? What's the plug? Tell folks about it. >>Yeah. So uh we we we started about six years ago but we like starting in the the body defense space by focusing on obscenity ice. And from then we we've grown and we've grown significantly in terms of our customer base, the verticals that we're going after in financial retail social media, you name it, we are there because pretty much all these these uh articles depends on A. P. I. S. To interact with their customers. Uh We've we've raised our cities we last year we've we've grown our customer base. Uh Just in the last year when there was a lockdown people were all these retailers were transforming from brick and mortar to online. Social media also also grew and we grew with them. So >>Jason your thoughts. >>I think that sequence is his ability to scale out to any size environment. We've got a customer that does a billion and a half transactions a month. Um That are ap is from 1000 other clients of theirs. Being able to protect environments that are confusing and cloudy like that. Um Is really it makes what we do shine. We use a lot of machine learning models and ai in order to surface real problems. And we have a lot of great humans behind all of that, making sure that the bad guy maybe they're right now, but they're going away and we're going to keep them away. >>It's super, super awesome. I think it's a combination of more connections, distributed computing at large scale with a data problem. That's, that's playing out. You guys are solving great stuff and hey, you know when the cube studio ap I gets built, we're gonna need to call you guys up to to help us secure the cube data. >>Absolutely right. Absolutely. >>Hey, thanks for coming on the q Great uh, great insight and thanks for sharing about sequence. Appreciate you coming on, >>appreciate the time. >>Okay. It's a cube conversation here in Palo alto with remote guests. I'm john for your host. Thanks for watching. Yeah.