>>Okay. And welcome back to the cubes live coverage here in San Francisco, California for 80 us summit 2022 Amazon web services summit 2020 New York city is coming up in the summer will be there. Check us out the cube.net. Our next guest here is Jim long. The CEO of dig also known as local. BTV a very interesting AWS customer doing some really progressive things around video and, uh, challenging the status quo in code cutting and all kinds of broadcast models. Jim, welcome to the cube. Great to see you. >>Thank you, John. Great to be here. Okay. >>So first of all, before we get into some of the disrupt option, take a minute to explain what is dig and local BTV. >>Uh, dig is all about, uh, providing, uh, edge video networking for broadcast television, basically modernizing local television and hopefully extending it to hyper local content like high schools and community government and community channels and things like that. So essentially free bringing, using the internet as an antenna to bring broadcast television to your phone, your laptop you're connected TVs. >>So if I understand it correctly, if I UN and I look at the, the materials of your site, you basically go into each market, Metro areas like New York Philly bay area, grab the tee signal out of the air. >>Yep. >>Local TV, and then open that up to everyone. Who's got, um, an >>Correct. And, uh, what, we've, where we're essentially building a hybrid network with AWS. Uh, I like to say we got all the smart and account stuff, you know, in the cloud at AWS. And we have all the dumb, fast stuff in the actual TV market. We have servers and transcoding there we work with, uh, of course, um, uh, AWS on that centrally as well. But basically that hybrid cloud allows us to be the fastest simplest and lowest cost way to get a local video. Any type could be an antenna or an IP stream to a local house. So we're, so are the local pickup and delivery people. We're not building a brand, we're not building content. We're delivering the local content to the local views. You >>Like the pipes. >>We are, we're essentially an infrastructure company. Um, we're right at that wonderful intersection of the, uh, the infrastructure and the content where I always like to play. >>I like, I love the store. I think the cost of that nature, how you're using Amazon, it's really impressive. Um, what are some of the cool things you're doing on AWS that you think's notable? >>Well, of course the, the standard issue stuff where you want to store all your data in the cloud. Right? So we, uh, and we use a quick site to, to get to that. And obviously we're using S3 and we're using media tailor, which we really like, which is cuz we first actual company on the planet. I believe that's inserting digital ads, impression based ads into local broadcast streams. So that's, that's fun because the advertisers, they like the fact that they could still do traditional TV buys and they could spice it up with digital impressions based, but ads on us. Yeah. And, and we're adding to it a real fun thing called clip it, which is user clipping. It's an app that's been running on AWS for years. It's had over half a million plays in social media. Yeah. We're combining those together and, and AWS makes it very simple to do that. >>Well, I've been using your app on my Firestick and uh, download local BTV on the app store. Um, I gotta say the calendar's awesome. And the performance is 10 times better than, than some of the other streaming apps because the other performance they crash all the time. The calendar's weird. So congratulations. Clearly you're running the cloud technology. I gotta ask you what's going on in the market? Netflix missed their earnings. The stock was down big time. Um, obviously competition what's up going on with Netflix? >>Well, what's, it's a big shift. >>What does it mean for the streaming market? >>Well, what it means is, is, is a consumer choice. It's really the golden age of consumer choice. Uh, originally back when I was a kid, it was all antenna TV. We didn't even have DBRS right. And then, uh, the cable companies and the satellite companies, the phone companies came in and took over and all of a sudden everyone started paying for TV for just linear TV. Right? And then the next thing, you know, streaming comes around, uh, Netflix shows up for, for VOD or, or SVOD, they call it cuz it's payt TV and uh, and the whole, uh, that ecosystem starts to melt down. And now you have a consumer choice market where you can pay, pay for VAD or pay for, for linear. And everyone does linear and everyone does VAD or you can use free TV. Now we correctly guessed that free TV was gonna have a huge comeback. You know, know what is it about free even obviously gen Z smarter than us boomers. They love free too. Uh, targeted advertising makes the ads less, uh, painful or less of a distraction. Uh, so we knew that free ad supported TV was gonna happen. Lots of stuff happened. And then, then the, uh, major media companies started doing their own subscription apps. Right? They're all cool. >>We like paramount plus >>Paramount plus Disney pluses, PN peacock, uh, time Warner's doing something. I mean, it's all cool, but you know, people only have so much of a big pocketbook. So what it's doing is pay TV has now become much more complicated, but also you, you know, you gotta trade off. So you saw it with Netflix, right? Yeah. Netflix is suffering from there's too much pay TV. So where are you gonna put your money on Comcast? On YouTube TV paramount plus Netflix. >>Yeah. I mean, I love the free thing. I gotta bring up something. I wanna get your reaction to a company called low cast went under, they got sued out of their deal. They were the free TV. Are you guys have issues like them? What's the cast most people don't know got was, was >>Doing same. So we started before low cast and we're uh, what we would call a permissions based system, legal system. The broadcast Mar industry, uh, is, uh, is the wild wild west. I mean, I like to say antenna TV is a direct to consumer. The antenna is a direct to consumer device and it's controlled by the channel. People it's not controlled by a platform like Comcast, right? It's not controlled by a stick. >>When you say channel, do you mean like CBS or >>Yeah, CBS or the local Korean religious cooking channel or, uh, Spanish channels or local independent to television, which is really a national treasure for us. The United States really should be making sure that local content, local channels, uh, do well local businesses, you know, with targeted advertising, Janes nail salon can, can now advertise just in San Jose and not the entire San Francisco TV market. Um, so you ha you have, have all that going on and we recognize, you know, that, that local content, but you have to have permission from the channel stuff. It's not easy because you got channels on stations. You have syndicators, it's hard to keep track of. And sometimes you, you, uh, you, you know, you have to shift things around, but, uh, low cast, uh, like another kind before it just went hog wild, illegal, trying to use a loophole, uh, didn't quite work out for 'em and, uh, >>You see, they have put out of business by the networks, the names, the big names. Yes. Content people, >>Correct. I mean the big, the big guys, but I mean, because they weren't following the rules, um, >>The rules, meaning license, the content, right. >>Well correct. Or yes, >>Basically they, they were stealing the content in the eyes of the, >>Well, there is, there is, it is a little of, a bit of a gray area between the FCC and the copyright laws that Congress made. So, um, there are people certainly out there that think there is a path there, low cast, didn't find it. We're not trying to find it. Uh, we just want to get all the free TV, uh, the bottom line. And you've seen fast channels explode recently, Pluto, uh, Samsung TV. >>And what does that all mean? >>Well, what it means is people love free TV and the best free TV out there is your local TV. So putting that on the internet and those comp, but the media companies, they have trouble with this new stuff. What's, >>What's your >>They're overthinking it. What's >>Some of this CBS, NBC, all these big guys. >>Well, those guys have a little less trouble than the people that actually, uh, they're affiliates, right? So there's 210 TV markets and the, uh, your major networks, you know, they have their own stations. And in a bit, you know, in about 39% of the population, which is about 15 to 20, is it >>Cultural or is a system system problem? >>No, it's a, it's a problem of all the, the media companies are just having trouble moving towards the new technology and, and they're, I think they're siloing it. >>So why not? You gonna let 'em die. Are you trying to do deals with em? >>Oh no, no, absolutely. For us, if we don't make money, unless stations make money, we want local TV to, to flourish. It is local TV is Neilson, just report yesterday, you know, uh, that, uh, local TV is growing. We're taking advantage of that. And I think the station groups are having a little trouble realizing that they have the original, fast channels before Pluto, before Tubi did it in movies. And, and, and what >>Are people understanding in the, in the industry? I know NA's coming up a show. Yeah, >>That's right. >>National associated of broadcasters. What's going on in that industry right now. And you're, if you get to put it down the top three problems that are opportunities to be solved, what would they be? >>Well, I think, you know, I think the, the, the, the last, the, the best one that's left is what we're doing. I have to say it, uh, I think it's worth billions. >>You free TV over the air free and stream >>O TV. Oh yeah. Over the air TV that also works with the internet, right. Public internet connected to public television stations so that everybody, including homeless people, et cetera, that, you know, they don't have a TV, they don't have an antenna, they can't afford comp. They got an >>IPhone though. >>They an iPhone. For sure. And, and so it's, it's, uh, it's a wonderful thing. It's, you know, our national broadcasting and I don't think the station groups or the major networks are taking advantage of it they're as much as they should. Yeah. And, and I don't think, you know, obviously NBC and CBS with their new apps, they're sort of done with that. They did mergers, they got, they got the virtual pay guys. I mean, YouTube TV off the ground, the only thing left is suck another shitload of good, uh, eyeballs and, and advertising. >>Well, I mean, yeah, I think that, that, and what you said earlier around subscription fatigue, I mean, nobody wants to have 20 subscriptions. >>Well, that brings up a whole new other war. That's going on that, thank goodness. We're not part of it's the platforms versus the cable companies. Right. Versus whatever. Right. Everyone's trying to be your open garden or your closed garden. They're trying to get your subscriptions in bundle self bundling it's. But I mean, it's wonderful for consumers, if you can navigate through it. Uh, we wanna, we think we'll have one of the gems in any of that everyone's want local TV. And so we'll supply that we're already doing that. We're supplying it to a couple companies, uh, free cast as a company, uh, app, a universal streaming, you know, manager, your all, all your, uh, streaming, a streaming aggregation, put your paid stuff in, put your free stuff in. They do that. And, and as, as does Roku try trying to do that fire TV, Xfinity's trying to do it. So it's all, it's a new war for the platform and hopefully we'll be on everyone. >>Well, you've been in this industry for a long time, you know, the streaming market, you know, the TV market. Um, so it's, it's good. I think it's a new battle, the shift's happening. Um, what should people know about dig local? BTV what are some of your goals for the next year or two? What are you trying to do? >>Well, what we're really trying to do is make sure that local, uh, local television thrives so that it can support wider communities. It could support hyper local content. So if you're, if you're, and we love the old paradigm and channel change, right? Forget, you know, every other app has all these boxes going by on different rows and stuff. And, and yeah, you can search and find stuff, but there's nothing like just changing channels, whether a commercial's on or, or you, you wanna see what else is on. You know, you're gonna go from local television and maybe all of a sudden, you'll see the local high school play over on another part of the, of the spectrum. And, and what we're trying to do is get those communities together. And the local high school people come over and find the local, you know, uh, Spanish, uh, Nova channel or something like that. >>So local is the new hot. >>It is. Absolutely. And by the way, it's where this high CPMs are gonna go. And the more targeted you get >>Ad revenue, >>I mean, that's for us is, is, is our number one, re we have a number of revenue streams, but targeted ads are really great for local, right? And, and so we're, we're gonna make an announce. We've >>Lost that we've lost that local, I've seen local things that local Palo Alto paper, for instance, just shut down this local sports high school coverage, our youth sports, because they don't budget, right? There's no TV community channels, like some Comcast throwaway channel. Um, we lost, we, we lo we're losing >>Local. No, I think that's a real national shame. And so I think if we can strengthen local television, I think it'll strengthen all local media. So we expect to help local radio and local newspapers. That's a bigger part of the vision. Uh, but I it's gonna happen. There's >>An education angle here too. >>There is an education angle because the bottom line is you can use linear television as a way to augment. Uh, we have a really exciting project going on in New York, uh, uh, with, uh, some of the housing, uh, projects, uh, in Harlem and, and, and the Bronx, uh, their I idea is to have the, the homework channel and they can, and literally when you have a, and both swiping and everything you can have, I mean, literally you can have a hundred schools that, that have things well, >>We know zoom schooling sucks. I mean, that didn't work. So I think you're gonna see a lot of augmentation, right. >>Amazon. >>I was just talking to some people here, AI training, machine learning, training, all here could be online in linear format. >>Yeah. And exactly. And then I think about the linear format is it's discovery television, and you can also, um, you know, you can also record it. Yeah. Right. If you see a program and you want to record it, you sit >>Record. So final minute we have left. I want to just get your thoughts on this one thing and, and ask your question. Are you looking for content? Are you, I outreach at the content providers who, >>Well, we're, we're PRI our primary mission is to get more channel local channels on which really means station groups and independence. We have a number, I mean, basically 50% of the channels in any market. When we move into it are like, this is a no-brainer. I want more eyeballs. We're Nielsen, uh, RA, uh, rated mean we support. And so we, >>How many markets are you in right now? >>We're in 21 now. And we hope to be in, uh, over 50 by the end of the year, covering more than half the United States. >>So, all right, Jim, thanks for coming on the queue. Really appreciate it. >>My pleasure. Good luck >>Recognition. Very disruptive disrupting media, um, combination of over the air TV, local with I internet. Obviously we love that with a cube. We want a cube channel anywhere possible. I'm John furry host of the queue here at AWS summit. Highing all the big trends and technologies in cloud and media back with more coverage after this short break,

>> 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.

>>Locke from Las Vegas. It's the cube covering KWS executive sub brought to you by extension. >>Welcome back everyone to the cubes live coverage of the Accenture executive summit here at AWS reinvent. I'm your host, Rebecca Knight. We have three guests for this segment. We have Dan Sheeran, the director of global accounts at AWS. Thank you so much for coming on the show. We have Ken Schwartz, vice president, enterprise analytics at health first. Welcome Ken and shale Jane lead data business group in North America. Accenture. Thank you so much. I am glad to have you all here. Good to be here. Yes. So we're talking today about driving digital transformation via data and analytics. I'm going to start with, you can tell us our viewers a little bit about health first as a business. >>Sure. Health first is the largest not-for-profit health plan in New York city. It's a 26 year old company. It's owned by 15 sponsor hospitals. So the business model is a little different than most health plans. The sponsor hospitals who own us, we actually share risk with the sponsor hospitals. So if our members obtain their medical services at sponsor hospitals, we have the same goal of keeping them out of the hospital essentially. And we, the revenue stays within the health healthcare delivery system. So it's a little bit different business model. We've been very successful. We're very local plan, so we have a big footprint in the communities, the very diverse communities in New York city. We're kind of part of the fabric of New York city and that's really very much part of our brand. >>So your patient population is mostly, I mean who, who, who are cuckoo prizes? >>1.4 million members, 1.4 million people mostly in New York city. So we like to say if you ride the subway in New York city, it's very likely that one in eight people are health first members, a one in three if you're in the Bronx, mostly underserved populations in a lot of cases. And people that really, like I said, sort of the, the real fabric of communities in the city. >>So what were the reasons that health works? Health first embarked on this data transformation. >>Really just again, a 26 year old company kind of outgrowing its infrastructure and really wanting to make sure that we can keep up with growth. We've been lucky to grow steadily over our entire history and at a certain point in time the legacy systems and legacy data systems don't support the new ways to do things. Prescriptive, predictive analytics, some of the great new capabilities that you can do in the cloud. So it became really important to get off the legacy hardware, get off the legacy approaches and big people change management to make that happen. I mean that's kind of what we've been living for about the last three years. >>So what were some of the goals? >>The goals are just to be able to do things at scale for in the legacy systems. I think we really didn't support analytics across our entire membership and our entire 30 million claims a year. 1.4 million members, 37,000 providers. So just being able initially just being able to query and do sort of business intelligence at scale across that, that much data, the old infrastructure just didn't support it from there. We've gone into launching our data science platform and things like that. So like I said, just, just being able to keep up with the times and provide more information, get to know everything we can possibly know about our members so that we can reach out to them in better and more effective ways. >>So shale, I want to bring you in here a little bit. How was, how did Accenture partner with health first and helping it achieve this goal? >>Yeah, so, um, we work with companies like health verse all the time and you almost have to embark on a journey that starts with a concept, almost the imagination, if you will. And then you take it into a test mode, the pilot mode in the scale up mode. And we were fortunate enough to actually to be involved in, in the journey that health first has had all throughout that, those stages, if you will. Um, and it's been, it's been a very rewarding experience because health first is one of those companies that actually took a very early lead on moving to the cloud, moving to the new data architectures and actually trying new technologies such as we recently finished a, uh, a knowledge graph project with them as well, which is relatively new in this space. So it's been a rewarding experience for us as well. >>So what are kind of, what are some of the challenges that you faced along this journey? Organization of lead technically and how did you overcome them? >>I think early on it's, it's whole new roles and new new technical paths that just didn't exist at the company. So Accenture being partner, good support from AWS really helped us. So we didn't have machine learning engineers and data engineers and cloud practitioners. So you don't grow that overnight. So having professionals come on graph as well. We oftentimes you start off with the use case and you have somebody just download things and get going. Right. And that's great, but that doesn't really land it. So getting professionals who have done things in the new environments on board to help us out was, was really key in the challenges side. I really think the people change management can be really hard. Again, if you're a sort of a brand new company or startup and you're just, you have to do your business on the cloud and it's dependent on that from day one. >>It's a lot different than we have a lot of people. Our company has been successful for 26 years. We have to look to the future to make these changes, but we've been doing pretty well sort of on our legacy platforms and things like that. So it's not always easy to just get people to change streams and say like, Hey, you really should be be doing this differently. So I think the people change management realizing you have to kind of sometimes lead with use cases, lead with pilots, lead people by the hands to get from point a to point B was kind of surprising. But we've, we've learned that that's true. >>So Dan, he you had a nice shout out from Ken here by giving you some prompts buddy in the U S and what you bring to the value you bring to the table. What do you, what do you make of what he said about the people change and how that is in a lot of ways the hardest >>couldn't agree more. In fact, that was the first point that Andy Jesse led off with this morning in his keynote that it's any of these projects, if you don't start with leadership that is both committed to the change and coordinated among themselves, then you've got no chance of success. Now that's, that's a necessary condition. It's not sufficient. You do need to drive that change through the organization and this, the scenario that Ken described is very common in what we see in that you start with enthusiasts typically that will, we often call builders who are going to be at a department who are playing around with tools because one of the advantages of course of AWS is it's all self-serve. You can get started very easily create your own account. But it is tricky to make sure that before that gets too far along that an enterprise wide architecture and strategy is agreed upon or else you can get sort of half pregnant with an approach that really is not going to serve the longterm objectives. And that's the reason why working with Accenture, getting the reference architecture for a data Lake really agreed on early on in this project was essential and that's what allowed once that foundation was in place. All these other benefits to accrue pretty quickly. >>So on a project like this, how closely are you all working together in teams to get the job done? I mean, and what is the collaboration, what is the process and what does it look like? >>Well, you know, I'm sure that each of us is going to have an answer to that, but our perspective on that at AWS is to always be customer led. We have some customers who themselves want to use a journey like this to become a builder organization. And one of their strategic objectives is that their developers are the ones who are really at the controls longterm building out a lot of new features. We have other customers who really want to be principally buyers. They'll have some enthusiasts here and there in their organization, but they really want to principally define the objectives, participate in the architecture, but then really lean on somebody like an Accenture to implement it >>and to also stand behind it afterwards. So in this case, Accenture played a central role, but we really think that the very first meeting needs to be sit down and listen to what the customer wants. Yeah. I'd say we're builders but with guidance that against them we want people who have, who have hit their heads on things and kind of learn from that and that's, that can be a force multiplier instead of having, and we definitely jumped into use cases that we wanted to just build. Like I said in a year later, we're a little bit spinning our wheels. It's not really hurting anything cause it's not necessarily anything anybody else's for anyway is standing up a graph database. It's just something we wanted to do. Right. So having these guys come in as force multiplier has been really useful. So we reach out to AWS, have really good support from AWS when we need it. AWS also has great online training, the loft in lower Manhattan or in Soho we go to things as well so we can help ourselves. And the next venture is just really been embedded with us too. We have seven or eight data engineers that have really walked pretty much every mile with us so far on this journey. So >>yeah, the only thing I would, I would add to it is that, you know, we have a very strong relationship with AWS and as such we become privy to a lot of the things that are coming down the pike, if you will. So that can add value. At the same time, we have very good access to some of the top technologists within AWS as well, so we can bring that to bear so that that all kind of works really well together. Having a partnership with AWS and then with our, we have different parts of the organization. They can also bring not just the technology skills but also domain skills as well. So we can add to some of the thinking behind the use cases as well. So that's another part of the collaboration that happens including in the security model. Right. And if we don't have that right from the beginning, then very true. Nothing else becomes possible. And there's a lot of domain expertise within Accenture. It helps us scale. >>One of the things that we, that I've heard a lot today at the Accenture executive summit is this idea of thinking differently about failure. And this is an idea that's in Silicon Valley, failed, fail better, fail happier, fail up all these things. Fail fast. Exactly. But all of them do. How do you, how but how does a co does a nonprofit in New York city, how does it embrace that? I mean, as we've talked about a lot here just now is the people are, are the hardest part that then that's a really different mindset in a really big change for an organization like health first. >>But the, the, the business model of working with AWS to is pay as you go and everything. It's like failing cheapest, very possible. You know, we're not putting out huge upfront costs to turn something on. We can turn it on for pennies sometimes and do a use case. So it really does support experimentation. We've been, one of our successes I think is we really just try a lot of things. So we've, we've had to learn how to do that and learn how to sort of either pull in more experienced people to help us or just just cut it off kind of in some cases. So yeah, the cloud patterns and AWS is business model just makes it really easy. >>And it's also key of course, to have some quick wins that are highly visible. So to my understanding that in the case of health first there was, you know, whether it's reimbursement claims or there's potential fraud that can be detected, that is a lot easier to start doing once you got your data into a common data Lake and you've got world-class analytics tools that are available directly to the business analysts. Instead of requiring lots of hand holding and passing datasets around, when you get those initial quick wins that builds the kind of enthusiasm that allows you to then take this from being a project that people are skeptical about to people really seeing the value >>and people get excited about it too. So talk about some of the benefits that your members have seen from this. >>Sure. So again, we have 1.4 million members. So just something pretty simple. Every health plan wants to prevent readmissions. So someone's been in the hospital and then they have to go right back with the same condition. That's bad for the member or bad for the plan. Bad for everybody, right? So just just being able to take a data science model on our own data, train it up for predicting readmissions. Again, we have large care management community. Many nurses go out in the field every day and meet members, but now that we can give them a list of the 500 most important members and it's also self-service, it's, it's in a dashboard that's running in red shift and people can go and just get their lists. I mean that's really profoundly satisfying and important to change our members health outcomes. You know, that's only one example. That was kind of the first model we've built, but we have models for people being adherent to their medication. Just a lot of things that we can do. Targeted interventions instead of kind of having a bunch of business rules. Kind of in your head of who you think you should reach out to. This is the data's telling us who's most at risk and sometimes empowering the call center personnel >>when you can give them access to data that allows them to really personalize that, that phone call experience with somebody. It's a, it's a relatively low cost way to surprise and delight the patient or the health plan member. And that then drives customer satisfaction scores, which are very important in the healthcare industry for all sorts of reasons related to accreditation are related to reimbursement. And also frankly just related to enrollment and retention. >>I speak from experience when I say the best, the companies are the ones with the good call centers that you just are happy and you get off the phone, you don't want to slam it down, you're, you're happy to talk to them. So final pieces of advice for companies that are, that are trying to drive change through data analytics. What, what is a best practice? Best piece of advice? Well, because you looked at me, I'll let you go first. >>Um, we always, it sounds obvious, but it's surprisingly often not the case. Once you get past the initial five minutes of a conversation, really stress are we actually focused on a real problem as opposed to something that sounds cool or fun to go experiment with. Because these tools, as Ken said, these are, it's fun to play with these self-service AI tools. You can predict all sorts of things. Isn't an actual pain point for either an internal customer or an external customer. >>Yeah, I think you hit it on the head as well. That's advice to starting this as get, get some wins, get some early wins and then don't be afraid to experiment and don't be afraid to think outside the box. I think I would say there are two pieces of advice. One is focused on strategy like Dan was talking about before, because with tools like AWS where you can literally use your credit card to get started, you can lose sight of the big picture. So have a data strategy that is directly tied to your business strategy is very important. And the second is instead of thinking about building a data pipeline for a specific use case, think about building a platform, a data platform that can serve the need of today and tomorrow as well in a, in an architecture that is, that is fit for purpose architecture like Andy Jesse talked about today. So don't go for a Swiss army knife approach. Go for fit for purpose platforms, products, models, if you will, that can allow you to build that platform that can serve the need of the future as well. >>Excellent. Thank you so much shale. Ken and Dan, thanks for coming on the cube. Thank you. Thanks. Thank you. I'm Rebecca Knight. Stay tuned for more of the cubes live coverage of the Accenture executive summit.