(bright upbeat music) >> Hello and welcome to theCUBE's coverage of International Women's Day. I'm your host, John Furrier here in Palo Alto, California Studio and remoting is a great guest CUBE alumni, co-founder, technical co-founder and she's also the VP of Product at Platform9 Systems. It's a company pioneering Kubernetes infrastructure, been doing it for a long, long time. Madhura Maskasky, thanks for coming on theCUBE. Appreciate you. Thanks for coming on. >> Thank you for having me. Always exciting. >> So I always... I love interviewing you for many reasons. One, you're super smart, but also you're a co-founder, a technical co-founder, so entrepreneur, VP of product. It's hard to do startups. (John laughs) Okay, so everyone who started a company knows how hard it is. It really is and the rewarding too when you're successful. So I want to get your thoughts on what's it like being an entrepreneur, women in tech, some things you've done along the way. Let's get started. How did you get into your career in tech and what made you want to start a company? >> Yeah, so , you know, I got into tech long, long before I decided to start a company. And back when I got in tech it was very clear to me as a direction for my career that I'm never going to start a business. I was very explicit about that because my father was an entrepreneur and I'd seen how rough the journey can be. And then my brother was also and is an entrepreneur. And I think with both of them I'd seen the ups and downs and I had decided to myself and shared with my family that I really want a very well-structured sort of job at a large company type of path for my career. I think the tech path, tech was interesting to me, not because I was interested in programming, et cetera at that time, to be honest. When I picked computer science as a major for myself, it was because most of what you would consider, I guess most of the cool students were picking that as a major, let's just say that. And it sounded very interesting and cool. A lot of people were doing it and that was sort of the top, top choice for people and I decided to follow along. But I did discover after I picked computer science as my major, I remember when I started learning C++ the first time when I got exposure to it, it was just like a light bulb clicking in my head. I just absolutely loved the language, the lower level nature, the power of it, and what you can do with it, the algorithms. So I think it ended up being a really good fit for me. >> Yeah, so it clicked for you. You tried it, it was all the cool kids were doing it. I mean, I can relate, I did the same thing. Next big thing is computer science, you got to be in there, got to be smart. And then you get hooked on it. >> Yeah, exactly. >> What was the next level? Did you find any blockers in your way? Obviously male dominated, it must have been a lot of... How many females were in your class? What was the ratio at that time? >> Yeah, so the ratio was was pretty, pretty, I would say bleak when it comes to women to men. I think computer science at that time was still probably better compared to some of the other majors like mechanical engineering where I remember I had one friend, she was the single girl in an entire class of about at least 120, 130 students or so. So ratio was better for us. I think there were maybe 20, 25 girls in our class. It was a large class and maybe the number of men were maybe three X or four X number of women. So relatively better. Yeah. >> How about the job when you got into the structured big company? How did that go? >> Yeah, so, you know, I think that was a pretty smooth path I would say after, you know, you graduated from undergrad to grad school and then when I got into Oracle first and VMware, I think both companies had the ratios were still, you know, pretty off. And I think they still are to a very large extent in this industry, but I think this industry in my experience does a fantastic job of, you know, bringing everybody and kind of embracing them and treating them at the same level. That was definitely my experience. And so that makes it very easy for self-confidence, for setting up a path for yourself to thrive. So that was it. >> Okay, so you got an undergraduate degree, okay, in computer science and a master's from Stanford in databases and distributed systems. >> That's right. >> So two degrees. Was that part of your pathway or you just decided, "I want to go right into school?" Did it go right after each other? How did that work out? >> Yeah, so when I went into school, undergrad there was no special major and I didn't quite know if I liked a particular subject or set of subjects or not. Even through grad school, first year it wasn't clear to me, but I think in second year I did start realizing that in general I was a fan of backend systems. I was never a front-end person. The backend distributed systems really were of interest to me because there's a lot of complex problems to solve, and especially databases and large scale distributed systems design in the context of database systems, you know, really started becoming a topic of interest for me. And I think luckily enough at Stanford there were just fantastic professors like Mendel Rosenblum who offered operating system class there, then started VMware and later on I was able to join the company and I took his class while at school and it was one of the most fantastic classes I've ever taken. So they really had and probably I think still do a fantastic curriculum when it comes to distributor systems. And I think that probably helped stoke that interest. >> How do you talk to the younger girls out there in elementary school and through? What's the advice as they start to get into computer science, which is changing and still evolving? There's backend, there's front-end, there's AI, there's data science, there's no code, low code, there's cloud. What's your advice when they say what's the playbook? >> Yeah, so I think two things I always say, and I share this with anybody who's looking to get into computer science or engineering for that matter, right? I think one is that it's, you know, it's important to not worry about what that end specialization's going to be, whether it's AI or databases or backend or front-end. It does naturally evolve and you lend yourself to a path where you will understand, you know, which systems, which aspect you like better. But it's very critical to start with getting the fundamentals well, right? Meaning all of the key coursework around algorithm, systems design, architecture, networking, operating system. I think it is just so crucial to understand those well, even though at times you make question is this ever going to be relevant and useful to me later on in my career? It really does end up helping in ways beyond, you know, you can describe. It makes you a much better engineer. So I think that is the most important aspect of, you know, I would think any engineering stream, but definitely true for computer science. Because there's also been a trend more recently, I think, which I'm not a big fan of, of sort of limited scoped learning, which is you decide early on that you're going to be, let's say a front-end engineer, which is fine, you know. Understanding that is great, but if you... I don't think is ideal to let that limit the scope of your learning when you are an undergrad phrase or grad school. Because later on it comes back to sort of bite you in terms of you not being able to completely understand how the systems work. >> It's a systems kind of thinking. You got to have that mindset of, especially now with cloud, you got distributed systems paradigm going to the edge. You got 5G, Mobile World Congress recently happened, you got now all kinds of IOT devices out there, IP of devices at the edge. Distributed computing is only getting more distributed. >> That's right. Yeah, that's exactly right. But the other thing is also happens... That happens in computer science is that the abstraction layers keep raising things up and up and up. Where even if you're operating at a language like Java, which you know, during some of my times of programming there was a period when it was popular, it already abstracts you so far away from the underlying system. So it can become very easier if you're doing, you know, Java script or UI programming that you really have no understanding of what's happening behind the scenes. And I think that can be pretty difficult. >> Yeah. It's easy to lean in and rely too heavily on the abstractions. I want to get your thoughts on blockers. In your career, have you had situations where it's like, "Oh, you're a woman, okay seat at the table, sit on the side." Or maybe people misunderstood your role. How did you deal with that? Did you have any of that? >> Yeah. So, you know, I think... So there's something really kind of personal to me, which I like to share a few times, which I think I believe in pretty strongly. And which is for me, sort of my personal growth began at a very early phase because my dad and he passed away in 2012, but throughout the time when I was growing up, I was his special little girl. And every little thing that I did could be a simple test. You know, not very meaningful but the genuine pride and pleasure that he felt out of me getting great scores in those tests sort of et cetera, and that I could see that in him, and then I wanted to please him. And through him, I think I build that confidence in myself that I am good at things and I can do good. And I think that just set the building blocks for me for the rest of my life, right? So, I believe very strongly that, you know, yes, there are occasions of unfair treatment and et cetera, but for the most part, it comes from within. And if you are able to be a confident person who is kind of leveled and understands and believes in your capabilities, then for the most part, the right things happen around you. So, I believe very strongly in that kind of grounding and in finding a source to get that for yourself. And I think that many women suffer from the biggest challenge, which is not having enough self-confidence. And I've even, you know, with everything that I said, I've myself felt that, experienced that a few times. And then there's a methodical way to get around it. There's processes to, you know, explain to yourself that that's actually not true. That's a fake feeling. So, you know, I think that is the most important aspect for women. >> I love that. Get the confidence. Find the source for the confidence. We've also been hearing about curiosity and building, you mentioned engineering earlier, love that term. Engineering something, like building something. Curiosity, engineering, confidence. This brings me to my next question for you. What do you think the key skills and qualities are needed to succeed in a technical role? And how do you develop to maintain those skills over time? >> Yeah, so I think that it is so critical that you love that technology that you are part of. It is just so important. I mean, I remember as an example, at one point with one of my buddies before we started Platform9, one of my buddies, he's also a fantastic computer scientists from VMware and he loves video games. And so he said, "Hey, why don't we try to, you know, hack up a video game and see if we can take it somewhere?" And so, it sounded cool to me. And then so we started doing things, but you know, something I realized very quickly is that I as a person, I absolutely hate video games. I've never liked them. I don't think that's ever going to change. And so I was miserable. You know, I was trying to understand what's going on, how to build these systems, but I was not enjoying it. So, I'm glad that I decided to not pursue that. So it is just so important that you enjoy whatever aspect of technology that you decide to associate yourself with. I think that takes away 80, 90% of the work. And then I think it's important to inculcate a level of discipline that you are not going to get sort of... You're not going to get jaded or, you know, continue with happy path when doing the same things over and over again, but you're not necessarily challenging yourself, or pushing yourself, or putting yourself in uncomfortable situation. I think a combination of those typically I think works pretty well in any technical career. >> That's a great advice there. I think trying things when you're younger, or even just for play to understand whether you abandon that path is just as important as finding a good path because at least you know that skews the value in favor of the choices. Kind of like math probability. So, great call out there. So I have to ask you the next question, which is, how do you keep up to date given all the changes? You're in the middle of a world where you've seen personal change in the past 10 years from OpenStack to now. Remember those days when I first interviewed you at OpenStack, I think it was 2012 or something like that. Maybe 10 years ago. So much changed. How do you keep up with technologies in your field and resources that you rely on for personal development? >> Yeah, so I think when it comes to, you know, the field and what we are doing for example, I think one of the most important aspect and you know I am product manager and this is something I insist that all the other product managers in our team also do, is that you have to spend 50% of your time talking to prospects, customers, leads, and through those conversations they do a huge favor to you in that they make you aware of the other things that they're keeping an eye on as long as you're doing the right job of asking the right questions and not just, you know, listening in. So I think that to me ends up being one of the biggest sources where you get tidbits of information, new things, et cetera, and then you pursue. To me, that has worked to be a very effective source. And then the second is, you know, reading and keeping up with all of the publications. You guys, you know, create a lot of great material, you interview a lot of people, making sure you are watching those for us you know, and see there's a ton of activities, new projects keeps coming along every few months. So keeping up with that, listening to podcasts around those topics, all of that helps. But I think the first one I think goes in a big way in terms of being aware of what matters to your customers. >> Awesome. Let me ask you a question. What's the most rewarding aspect of your job right now? >> So, I think there are many. So I think I love... I've come to realize that I love, you know, the high that you get out of being an entrepreneur independent of, you know, there's... In terms of success and failure, there's always ups and downs as an entrepreneur, right? But there is this... There's something really alluring about being able to, you know, define, you know, path of your products and in a way that can potentially impact, you know, a number of companies that'll consume your products, employees that work with you. So that is, I think to me, always been the most satisfying path, is what kept me going. I think that is probably first and foremost. And then the projects. You know, there's always new exciting things that we are working on. Even just today, there are certain projects we are working on that I'm super excited about. So I think it's those two things. >> So now we didn't get into how you started. You said you didn't want to do a startup and you got the big company. Your dad, your brother were entrepreneurs. How did you get into it? >> Yeah, so, you know, it was kind of surprising to me as well, but I think I reached a point of VMware after spending about eight years or so where I definitely packed hold and I could have pushed myself by switching to a completely different company or a different organization within VMware. And I was trying all of those paths, interviewed at different companies, et cetera, but nothing felt different enough. And then I think I was very, very fortunate in that my co-founders, Sirish Raghuram, Roopak Parikh, you know, Bich, you've met them, they were kind of all at the same journey in their careers independently at the same time. And so we would all eat lunch together at VMware 'cause we were on the same team and then we just started brainstorming on different ideas during lunchtime. And that's kind of how... And we did that almost for a year. So by the time that the year long period went by, at the end it felt like the most logical, natural next step to leave our job and to, you know, to start off something together. But I think I wouldn't have done that had it not been for my co-founders. >> So you had comfort with the team as you knew each other at VMware, but you were kind of a little early, (laughing) you had a vision. It's kind of playing out now. How do you feel right now as the wave is hitting? Distributed computing, microservices, Kubernetes, I mean, stuff you guys did and were doing. I mean, it didn't play out exactly, but directionally you were right on the line there. How do you feel? >> Yeah. You know, I think that's kind of the challenge and the fun part with the startup journey, right? Which is you can never predict how things are going to go. When we kicked off we thought that OpenStack is going to really take over infrastructure management space and things kind of went differently, but things are going that way now with Kubernetes and distributed infrastructure. And so I think it's been interesting and in every path that you take that does end up not being successful teaches you so much more, right? So I think it's been a very interesting journey. >> Yeah, and I think the cloud, certainly AWS hit that growth right at 2013 through '17, kind of sucked all the oxygen out. But now as it reverts back to this abstraction layer essentially makes things look like private clouds, but they're just essentially DevOps. It's cloud operations, kind of the same thing. >> Yeah, absolutely. And then with the edge things are becoming way more distributed where having a single large cloud provider is becoming even less relevant in that space and having kind of the central SaaS based management model, which is what we pioneered, like you said, we were ahead of the game at that time, is becoming sort of the most obvious choice now. >> Now you look back at your role at Stanford, distributed systems, again, they have world class program there, neural networks, you name it. It's really, really awesome. As well as Cal Berkeley, there was in debates with each other, who's better? But that's a separate interview. Now you got the edge, what are some of the distributed computing challenges right now with now the distributed edge coming online, industrial 5G, data? What do you see as some of the key areas to solve from a problem statement standpoint with edge and as cloud goes on-premises to essentially data center at the edge, apps coming over the top AI enabled. What's your take on that? >> Yeah, so I think... And there's different flavors of edge and the one that we focus on is, you know, what we call thick edge, which is you have this problem of managing thousands of as we call it micro data centers, rather than managing maybe few tens or hundreds of large data centers where the problem just completely shifts on its head, right? And I think it is still an unsolved problem today where whether you are a retailer or a telecommunications vendor, et cetera, managing your footprints of tens of thousands of stores as a retailer is solved in a very archaic way today because the tool set, the traditional management tooling that's designed to manage, let's say your data centers is not quite, you know, it gets retrofitted to manage these environments and it's kind of (indistinct), you know, round hole kind of situation. So I think the top most challenges are being able to manage this large footprint of micro data centers in the most effective way, right? Where you have latency solved, you have the issue of a small footprint of resources at thousands of locations, and how do you fit in your containerized or virtualized or other workloads in the most effective way? To have that solved, you know, you need to have the security aspects around these environments. So there's a number of challenges that kind of go hand-in-hand, like what is the most effective storage which, you know, can still be deployed in that compact environment? And then cost becomes a related point. >> Costs are huge 'cause if you move data, you're going to have cost. If you move compute, it's not as much. If you have an operating system concept, is the data and state or stateless? These are huge problems. This is an operating system, don't you think? >> Yeah, yeah, absolutely. It's a distributed operating system where it's multiple layers, you know, of ways of solving that problem just in the context of data like you said having an intermediate caching layer so that you know, you still do just in time processing at those edge locations and then send some data back and that's where you can incorporate some AI or other technologies, et cetera. So, you know, just data itself is a multi-layer problem there. >> Well, it's great to have you on this program. Advice final question for you, for the folks watching technical degrees, most people are finding out in elementary school, in middle school, a lot more robotics programs, a lot more tech exposure, you know, not just in Silicon Valley, but all around, you're starting to see that. What's your advice for young girls and people who are getting either coming into the workforce re-skilled as they get enter, it's easy to enter now as they stay in and how do they stay in? What's your advice? >> Yeah, so, you know, I think it's the same goal. I have two little daughters and it's the same principle I try to follow with them, which is I want to give them as much exposure as possible without me having any predefined ideas about what you know, they should pursue. But it's I think that exposure that you need to find for yourself one way or the other, because you really never know. Like, you know, my husband landed into computer science through a very, very meandering path, and then he discovered later in his career that it's the absolute calling for him. It's something he's very good at, right? But so... You know, it's... You know, the reason why he thinks he didn't pick that path early is because he didn't quite have that exposure. So it's that exposure to various things, even things you think that you may not be interested in is the most important aspect. And then things just naturally lend themselves. >> Find your calling, superpower, strengths. Know what you don't want to do. (John chuckles) >> Yeah, exactly. >> Great advice. Thank you so much for coming on and contributing to our program for International Women's Day. Great to see you in this context. We'll see you on theCUBE. We'll talk more about Platform9 when we go KubeCon or some other time. But thank you for sharing your personal perspective and experiences for our audience. Thank you. >> Fantastic. Thanks for having me, John. Always great. >> This is theCUBE's coverage of International Women's Day, I'm John Furrier. We're talking to the leaders in the industry, from developers to the boardroom and everything in between and getting the stories out there making an impact. Thanks for watching. (bright upbeat music)

(upbeat music) >> Live from Las Vegas, it's theCUBE! Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back to theCUBE, everyone. We are in the middle of two days of coverage of Informatica World here in Las Vegas. I'm your host, Rebecca Knight, along with my cohost, John Furrier. We are joined by Ansa Sekharan, he is the Executive Vice President and Chief Customer Officer at Informatica. Thanks so much for coming on the Cube, Ansa. >> My pleasure to be back on theCUBE. >> Great to see you. >> Thank you. >> So, let's talk about your role as the Chief Customer Officer. Last year you announced this change from a customer service model to a customer success model. How has that been? How have you implemented it and how's it going? >> Now, we have a great opportunity ahead of us. You see a number of enterprises embarking on a data transformation journey. As we offer the best products, it was quite apparent we had to take the services to the next level. We had to take the services and connect them to customers' business values. So we are blurring the lines between the various services functions: support, professional services, university, customer success, we want to abstract them, along with their products, we want to offer the best value to the customers. It's very simple. We sign up a new customer. The first thing we want to do is to work with the customer and define the success plan. What does success mean to them? Success, in two words, business outcomes. It's not about go-lives. Are the business users adopting and realizing value? That's where Informatica is very different from other enterprises, and I think that's going to further fuel our growth in the future. >> Ansa, you've been in the industry a very long time, Informatica many many years, how many years? >> 23 and counting. >> So, I'd consider you a historian of Informatica. (speaks indistinctly) I never saw myself as a historian. You've seen the transformations. Talk about what's going on now because, and certainly going private affords a lot of good things, in the public eye anymore in terms of shot clock earnings, being on that treadmill. You guys really did a lot of digging in to innovate. Now four years later, you start to see the fruit coming off that tree in the form of good catalog decision with the catalog, cloud early, AI early, the horizontal scalability of the infrastructure now and one operating model. Interesting kind of tailwinds for you guys. What's going on? How do you talk to customers who have kind of living in a cave, I won't want to say living in a cave, but they've been not as on the front end as you guys have been. >> I think when you use the word innovation it's just not about products. As a company we have been innovating. Along with the products, we have been innovating on all fronts, being at the services. We have, used to have, a major release every four years on services. We have shortened the cycle to two years. As a company we are now offering all our products on the cloud. What does it mean? What does it mean in customer support? We are having to redefine the entire delivery model end to end. You heard in the conference eight trillion transactions we process in a month. That was grown 3X just in a year. We have so much data. It's all about what is the information we can glean from these transactions. We have over a billion interactions with the customers every year. How can we put these transactions and interactions, package it in the form of we have the best telemetry products? We are leveraging this data to better sell the customers so that we can drive them, accelerate the business outcomes. When I started off we were a one product portfolio company. We had power center. Now we are the leader in six categories, and our user base is now, not only IT business, it's a great opportunity for us. >> The other thing that's a perfect storm, at least for innovation that's also happening, is the absolute validation that SAS business models have agility benefits, meaning you can take risk using data, understanding data, to get big rewards if scaled properly with cloud, so the role of data in pure SAS has been proven. Enterprises are recognizing that. Not that easy but still that's the path that people are now seeing clear visibility to. You guys are going after that. What's your take on that? >> I think when it comes to SAS, I think customers realize they should be focusing more on their business processes, and push the technology aside to the vendor. Try to partner with the vendor on how they can leverage on the technology side. That's where Informatica has put in a number of programs around that. Imagine a scenario, I'll give you a quick scenario. There's always this risk of putting this data on the cloud. What if you were to say, and there's upgrades every quarter, we push a lot of features and there's always the worry is something going to break. We are going to come out of the program, it's going to guarantee that we're going to foolproof the upgrades. Your stuff will work better, faster with every upgrade. That's the kind of, what customers expect. >> Guarantee that it won't break, basically? >> That's the kind of programs we're going to offer to our customers. We're going to have them for a day at scale, MDM is coming on the cloud you saw the demos we showed yesterday. I think we are redefining our model and going to push the envelope further on. >> Are customers asking for that assurance or is it more of you guys going to make that a table stakes because it's an opportunity for you? >> Both. >> Okay. >> Within the company our philosophy is very simple. I'll say an equation, CS equal to IS, customer success is equal to Informatica success. In my humble opinion, we both need each other. >> Just like data and AI. A symbiotic relationship. So I want to get back to what you were saying in terms of how you are defining this kind of customer success. We're working together with customers to define the business outcome and then working to see, okay, how do we get there? You have a lot of great customers, many in the Fortune 500, 100. Tell us a little bit about what you've seen over the past year in terms of, maybe without naming names or name names if you want to, but in terms of how these companies have seen a difference since you've changed this model. >> We sell a platform. I think we're the only vendor which offers a platform for data management. There are a number of vendors with poor installations. Informatica is the only vendor which offers late inclusion data platforms. Customers buy into the vision because data is, everyone is looking to leverage the power of data. As they buy this platform, they work with us to see how should they approach. This blueprint needs to evolve. We need to define the building blocks. Should they start with the catalog, should they validate what they're assets are? Where are we trying to push the service's frontiers that's not around technology? How can we help on the business processes side, as well? It's a big journey we are going to undertake and I think that's going to pay off big. I can quote a number of examples. I was sitting in a meeting this morning with a large bank and meeting up with the Chief Data Officer, and she kind of laid out her data strategy and we discussed how Informatica is going to be player owned. They are depending on us, and now we are going to keep our commitment, we are going to deliver on that promise we have made to them. >> How many customers do you guys see really thinking about data location storage where on premise versus cloud or are they more thinking differently around knowing that they're probably going to store it everywhere or somewhere? Can you share any insight into what the trends are there with your customers? >> Informatica's uniquely position is, there's future workloads which go to the cloud. It's hard to change systems that working, there's always going to be data in the premises. That shift, if something is working, customers don't quickly shut it down. So we see future workloads going to the cloud, traditional workloads, even we have a number of large clients still on mainframes. We offer the best products on mainframes as well as, it does not get much press, but-- >> This is the end to ending benefits that you guys are-- >> Correct. We go all the way, we cover the entire gambit of the data spectrum. >> What's the key enabler to make that happen? Is it the catalog, what's the big-- >> Catalog was the big, I think, last year that was the turning point with the catalog coming in, and now through professional services we offer a lot of workshops at no cost to our customer on how they should put their strategy, as well. >> One of the things that I'm hearing from you is the importance of really understanding the business in addition to the technology. I'm interested to hear how you hire. Obviously we hear so much about the importance of technical talent, and the problem of the skills gap in Silicon Valley and beyond, but you obviously are looking for candidates who also really get the business. So, what are the kinds of things that you're looking for and what kind of problems do you see in terms of the candidates that you're getting for your open roles? >> Customer support could be a hard job. We really want to, we look for people who want to make a difference. And if you have that attitude you get plenty of opportunities to make a difference. Now, with so much talk about AI, service automation, Chadbot, robotics, you know at the end of the day employees are still the core of the apple tree. I think the current trainers don't forget the people. The technology is not going to replace the people overnight, so I think we have a fabulous team at Informatica of customer support professionals. Our average retention rate is the mid 90s. So, we hire the best people, and they stay with us because this is a great platform. They move around products, but as long as we can give them that spectrum to grow, over time as they sell customers they build that tribal knowledge, and they can sell them better. And so we look for, I mean, there's a lot of data scientists coming in. We look, we always hire from colleges, groom them. I started off that way, and still with the company 23 years. I want to give that chance for the rest of team, as well. >> So how many other folks in the company have been there that long? That's a long time. You've been there a very, very long time. >> You'd be surprised at the number of people who have been long-timers at Informatica. It's a great company. >> How do you maintain the startup mentality? You were there when it was three years old, and now it's... >> I think personally what drives me is the fear of failure. Having set the bar high, you have to push, and if you want to keep at the pace you need to have the startup mentality. We have a number of projects in flight, and some, you have to have that mindset, and now we are a distributor team. We have to keep that spirit going throughout. And like I said, coming back to my equation, customer success equals Informatica success. That's what we believe as the company. >> You said CS is IS, customer success is. I mean, right? >> There you go. You made it sound even better. >> So just getting back to that, one of the biggest problems in the technology industry is the skills gap. Are you finding enough people to fill the roles you have? >> We do not have a problem hiring. The ramp up time, we have a good enablement program, which is good. Take the space of big data. The whole industry landscape changes every six months, so it's that mindset you need to have. Even I have that mindset today. I come in thinking I'm going to learn something new. Learning never stops. So you've just got to keep learning everyday. And I'm not setting expectations, we're going to groom them. I want people who learn on their own. They have to, they have to keep pace with the current technology. >> Any skills in school, kids in school that might, or parents watching with their kids, in high school or elementary school, what disciplines can they turn up, turn down, you think would make them successful in the future of how the data is going to impact society? There's a lot of new jobs coming out that don't have degrees for. Cal Berkeley just graduated their first inaugural class in data analytics. It's just a tell sign of how early it is, so still, you go back to sixth grade, you go back at the high school. Kids are looking to, they're gamers. They're into tech. They want to dial up some-- >> When I went to high school in 1984 I was the first batch of computer science, and we learned basic programming, things have really changed. My girls don't want to do computers, but it is something which we have to evolve constantly right, but-- >> Any classes right now that jump out at you that think, that's important? >> Data science is hard now, you know? >> A hard one. >> Yeah, it's hard. And with all the emphasis, we have a number of initiatives within support that will leverage AI, ML, as well. And I talked about it in the last year's program, but there could be some skills gap in some pockets, always you fill that that's going to be out of their pocket. You just got to be constantly pushing at it. >> Ansa, thank you so much for coming on theCUBE. >> It's a pleasure being on here, thank you. >> Thank you. >> Thank you, great job. >> I'm Rebecca Knight, for John Furrier, you are watching theCUBE's live coverage of Informatica World. Stay tuned. (upbeat music)

>> Live from Las Vegas, it's theCUBE Covering Informatica World 2019 Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019 here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We are joined by Ariel Kelman. He is the VP, Worldwide Marketing at AWS. Thank you so much for coming on theCUBE. >> Thanks so much for having me on today. >> So let's start out just at ten thousand feet and talk a little bit about what you're seeing as the major cloud and AI trends and what your customers are telling you. >> Yeah, so I mean, clearly, machine learning and AI is really the forefront of a lot of discussions in enterprise IT and there's massive interest but it's still really early. And one of the things that we're seeing companies really focused on now is just getting all their data ready to do the machine learning training. And as opposed to also, in addition I mean, training up all their people to be able to use these new skills. But we're seeing tons of interest, it's still very early, but you know one of the reasons here at Informatica World is that getting all the data imported and ready is, you know, it's almost doubled or tripled in importance as it was when people were just trying to do analytics. Now they're doing machine learning as well. You know, we're seeing huge interest in that. >> I want to get into some of the cloud trends with your business, but first, what's the relationship with Informatica, and you know we see them certainly at re:Invent. Why are you here? Was there an announcement? What's the big story? >> I mean, we've been working together for a long time and it's very complementary products and number varies. I think the relationship really started deepening when we released Redshift in 2013, and having so many customers that wanted to get data into the cloud to do data we're housing, we're already using Informatica in, to help get the data loaded and cleansed and so really they're one of the great partners that's fueling moving data into the cloud and helping our customers be more successful with Redshift. >> Yeah, one of the things I really admire about you guys is that you're very customer centric. We've been following Amazon as you know since their, actually second reinvent, Cube's been there every time, and just watching the growth, you know, Cloud certainly has been a power source for innovation, SAS companies that are born in the cloud have exponentially scaled faster than most enterprises because they use data. And so data's been a heart of all the successful SAS businesses, that's why start ups gravitated to the Cloud right away. But now that you guys got enterprise adoption, you guys have been customer centric and as you listen to customers, what are you guys hearing from that? Because the data on premises, you've got more compliance, you've got more regulation, you've got-- news today-- more privacy and now you've got regions, countries with different laws. So the complexity around even just regulatory, nevermind tech complexity, how are you guys helping customers when they say, you know what, I want to get to the cloud, love Amazon, love the cloud, but I've got my, I've got to clean up my on param house. >> Yeah, I would say like a lot, if you look at a lot of the professional services work that we do, a lot of it is around getting the company prepared and organized with all their data before they move to the cloud: segmenting it, understanding the different security regulatory requirements, coming up with a plan of what they need, what data they're going to maybe abstract up, before they load it, and there's a lot of work there. And, you know, we've been focused on trying to help customers.. >> And is there a part in you're helping migrate to the cloud, is that.. >> Yeah, there's technology pieces, companies like Informatica helping to extract and transform and load the data and on data governance policies. But then also, for a lot of our systems integrator partners, Cognizant, Accenture, Deloitte-- they're very involved in these projects. There's a lot of work that goes on; a lot of people don't talk about just before you can even start doing the machine learning, and a lot of that's getting your data ready. >> So how, what are some of the best practices that have emerged in working with companies that, as you said, there's a lot of pre-work that needs to be done and they need to be very thoughtful about about sort of getting their data sorted. >> Well I think the number one thing that I see and I recommend is to actually first take a step back from the data and to focus on what are the business requirements of, what questions are you trying to answer, let's say with machine learning, or with data science advanced analytics, and then back out the data from that. What we see a lot of, you know companies sometimes will have it be a data science driven project. Okay, here's all the data that we have, let's put it in one place, when you may not be spending time proportionate to the value of the data. And so that's one of the key things that we see, and to come up-- just come up with a strong plan around what answers you're, what business questions you're trying to answer. >> On the growth of Amazon, you guys certainly have had great record numbers, growth, even in the double digit kind of growth you're seeing on top of your baseline has been phenomenal. Clearly number one on the cloud. Enterprise has been a big focus. I noticed that on the NHL, your logo's on the ice during the playoffs; you've got the Statcast. You guys are creating a lot of aware-- I see a lot of billboards everywhere, a lot of TV ads. Is that part of the strategy is to get you guys more brand awareness? What's the.. >> We're trying, you know, it's part of our overall brand awareness strategy. What we're trying to do is to help, we're trying to communicate to the world how our customers are being successful using our technology, specifically machine learning and AI. It's one of these things where so many companies want to do it but they say, well, what am I supposed to use it for? And so, you know, one of, if you dumb down what marketing is at AWS, it's inspiring people about what they can run in the cloud with AWS, what use cases they should consider us for, and then we spend a lot of energy giving them the technical education and enablement so they can be successful using our products. At the end of the day, we make money when our customers are successful using our products. >> One of the hot products was SageMaker, we see in that group, AI's gone mainstream. That's a great tail wind for you guys because it kind of encapsulates or kind of doesn't have to get all nerdy about cloud, you know, infrastructure and SAS. AI kind of speaks to many people. It's one of the hottest curriculums and topics in the world. >> Yeah, and with SageMaker, we're trying to address a problem that we see in most of our customers where the everyday developer is not, does not have expertise in machine learning. They want to learn it, so we think that anything we can do to make it easier for every developer to ramp up on machine learning the better. So that's why we came up with SageMaker as a platform to really make all three stages of machine learning easier: getting your data prepared for training, training in optimized models, and then running inference to make the predictions and incorporate that into people's applications. >> One of the themes that's really emerging in this conversation is the need to make sure developers are ready and that your people are skilled up and know what they need to know. How are, how is AWS thinking about the skills gap, and what are you doing to remedy it? >> Yeah, a couple things. I mean, we're really, like a lot of things we do, we'll say what are all the ways we can attack the problem and let's try and help. So, we have free training that we've been creating online. We've been partnering with large online training firms like Udacity and Coursera. We have an ML solutions lab that help companies prototype, we have a pretty significant professional services team, and then we're working with all of out systems integrators partners to build up their machine learning practices. It's a new area for a lot of them and we've been pushing them to add more people so they can help their customers. >> Talk about the conferences, you have re:Invent, the CORE conference, we've been theCUBE there. We've just also covered London, Amazon's Web Services summit, and 22,000 registered, 14,000 showed up. Got huge global reach now. How do you keep up with this? I mean it's a... >> Well we're trying to help our customers keep up with all the technology. I mean, really, we have about, maybe 25 or so of these summits around the world-- usually around two days, several thousand people, free conferences. And what we're trying to do is >> They're free? >> The summits are free and it's like, we introduce so much new technology, new services, deeper functionality within our exiting services, and our customers are very hungry to learn the latest best practices and how they can use these, and so we're trying to be in all the major areas to come in and provide deep educational content to help our customers be more successful. >> And re:Invent's coming around the corner. Any themes there early on, numbers wise? Last year you had, again, record numbers. I mean at some point, is Vegas too small >> Yeah, we had over 50,000 people. We're going to have even more, and we've been expanding to more and more locations around Las Vegas and you know we're going to keep growing. There's a lot of demand. I mean, we want to be able to provide the re:Invent experience for as many people as want to attend. >> What's the biggest skill set, you know the folks graduating this month, my daughter's graduating from Cal Berkeley, and a lot of others are graduating >> Congratulations >> high school. Everyone wants to either jump into some sort of data related field, doesn't have to be computer science, those numbers are up. What's your view of skill sets that are needed right now that weren't in curriculum, or what pieces of curriculum should people be learning to be successful if machine learning continues to grow from helping videos surface to collecting customer data. Machine learning's going to be feeding the AI applications and SAS businesses. >> Yeah, I mean look, you just forget about machine learning, you go to a higher level. There's not enough good developers. I mean, we're in a world now where any enterprise that is going to be successful is going to have their own software developers. They're going to be writing their own software. That's not how the world was 15 years ago. But if you're a large corporation and you're outsourcing your technology, you're going to get disrupted by someone else who does believe in custom software and developers. So the demand for really good software engineers, I mean we deal with all the time, we're hiring. It is always going to outstrip supply. And so, for young people, I would encourage them to start coding and to not be over reliant on the university curriculums, which don't always keep pace with, you know, with the latest trends. >> And you guys got a ton of material online too, you can always go to your site. Okay, on the next question around, as someone figures out, okay, enterprise versus pure SAS, you guys have proven with the Cloud that start ups can grow very fast and then the list goes on: AirBnB, Pinterest, Zoom Communications, disrupting existing big, mature markets by having access to the data. So how do you talk about customers when you say, hey, you know, I want to be like a SAS company, like a consumer company, leverage data, but I've got a lot of stuff on premise. So how do I not make that data constrained? How do you guys feel about that conversation because that seems to be the top conversation here, is you know, it's not to say be consumer, it's consumer-like. Leveraging data, cause if data's not into AI, there's no, AI doesn't work, right? So >> Right >> It can't be constrained by anything. >> Well, you know, you talk to all these companies and at first they don't even know what they don't know in terms of what is that data? And where is it? And what are the pieces that are important? And so, you know, we encourage people to do a good amount of strategy work before they even start to move bits up to the cloud. And of course, then we have a lot of ways we can help them, from our Snowball machines that they can plug in, all the way to our Snowmobile, which is the semi truck that you can drive up to your data center and offload very large amounts of data and drive it over to our data centers. >> One of the things that is trending-- we had Ali from Data Bricks talk about, he absolutely believes a lot of the same philosophies you guys do-- data in the cloud. And one of his arguments was is that there's a lot of data sets in these marketplaces now where you can really leverage other people's data, and we see that on cybersecurity where people are starting to share data, and Cloud is a better model for that than trying to ship drives around, and there's a time for Snowball, I get that, and Snowmobile, the big trucks for large ingestion into the cloud, but the enterprise, this is a new phenomenon. No one really shared a lot in the old days. This is a new dynamic. Talk about that, is it-- >> I mean, sharing, selling, monetizing data. If there's something that is important, there will be a market for it. And I think we're seeing that just the hunger, everything from enterprises to startups, that want more data, whether it's for machine learning to train their models, or it's just to run analytics and compare against their data sets. So I think the commercial opportunity is pretty large. >> I think you're right on that. I think that's a great insight. I mean, no one ever thought about data as a service from our data set standpoint, 'cause data sets feed machine learning. All right, so let's do, give the plug on what's going on with AWS. What's new, what's on your plate, what's notable. I mean I love the NHL, I couldn't resist that plug for you being a hockey fan. But what's new in your world? >> Um, you know, we're, we're in early planning stages on our re:Invent conference, our engineers are hard at work on a lot of new technology that we're going to have ready between now and our re:Invent show. You know, also we're, my team's been doing a lot of work with the sports organizations. We've had some interesting machine learning work with major league baseball. They rolled out this year a new machine learning model to do stolen base predictions. So, you can see on some of the broadcasts, as a runner goes past first base, we'll have a ticker that will show what the probability is that they'll be successful stealing second base if they choose to run. Trying to make a little more entertaining all those scenes we've seen in the past of the pitcher throwing the ball back to first, trying to use AI machine leaning to give a little bit more insight into what's going on. >> And that's the Statcast. Part of that's the Statcast >> That's Statcast, yeah >> And you got anything new coming around that besides that new.. >> Yeah, I think that yeah, major league baseball is hard at work on some new models that I think will be announced fairly soon. >> All right, to wrap up Informatica real quick, an announcement here, news coming I hear. How are you guys working with Informatica in the field? Is there any, can you share more about relationship >> Yeah I mean I think we're going to have an announcement a little bit later today, I mean it's around the subject we've been talking about: making it easier for customers to, you know, be successful moving their data to the Cloud so that they can start to benefit from the agility, the speed and the cost savings of data analytics and machine learning in the Cloud. >> And so when you're working with customers, I mean, because this is the thing about Amazon. It is a famously innovative, cutting edge company, and when you talk about the hunger that you describe, that these customers, isn't it just that they want to be around Amazon and kind of rub shoulders with this really creative, thinking four steps ahead kind of company. I mean how do you let your innovation rub off on these customers? >> I mean there's a couple ways We do, one of the things we've done recently is these innovation workshops. We have this thing we talk about a lot this working backwards process where we force the engineers to write a press release before we'll green light the product because we feel like if you can't clearly articulate the customer benefit, then we probably shouldn't start investing, right? And so we, that's one of the processes that we use to help us innovate better, more effectively and so we've been walk-- we walk customers through this. We have them come, you know there's an international company that I was, part of one of the efforts we did in Palo Alto last year where we had a bunch of their leadership team out for two days of workshops where we worked a bunch of ideas through, through our process. And so we do some of that but the other area is we try and capture area where we think that we've innovated in some interesting way into a service that then customers can use. Like Amazon Connect I think is a good example of it. This is our contact center call routing technology and you know, one of the things Amazon's consumer business is known for is having great customer support, customer service, and they spent a lot of time and energy making sure that calls get routed intelligently to the right people, that you don't sit on hold forever, and so we figure we're probably not the only company that could benefit from that. Kind of like with AWS, when we figure out how to run infrastructure securely and high performance and availability, and so we turn that into a service and it's become a very successful service for us. A lot of companies have similar contact center problems. >> As a customer, I can attest to being on hold a lot. Ariel, thank you so much for coming on theCUBE. It's been great talking to you. >> I appreciate it. Thank you. >> Thanks for coming out, appreciate it. >> I'm Rebecca Knight, for John Furrier. You are watching theCUBE. Stay tuned. (upbeat music)

>> Live from Las Vegas, it's theCUBE covering Informatica World 2019. Brought to you by Informatica. >> Welcome everyone, you are watching theCUBE. We are kicking off a two-day event here at Informatica World 2019 in Las Vegas. I'm your host, and I'm co-hosting along with John Furrier. It's great to have you. Great to be here. >> Great to see you again. >> So, Informatica is really sitting in the sweet spot of a fast-growing area of technology, cloud and big data. I want to ask you a big question. Where is the market? What do you see happening in this sweet spot area? >> Well we're here in Informatica World. I think it's our fourth Cube coverage. We've been following these guys since they've gone private two years ago in depth. Interesting changeover. They went private just like Michael Dell did with Dell Technologies. And then they went public in great performance. We said at that time, if they can go private with the product skills that they have in their senior leadership, they could do well. And they've been on the same trend line, which has been really positive data. Now data is the hottest thing on the planet. This is the theme of the industry. Data is everything. Machine learning needs data. Data feeds machine learning. Machine learning feeds AI. This is a core innovator. Now the challenge is on the enterprise side is that data is structured. It's in all these different databases. So in an enterprise, data's kind of has all these legacy structures and legacy systems. And the cloud for instance. Cloud is where SaaS wins. And SaaS winners like Zoom Communications, Air BNB, you name all those successful cloud data companies. Data's at the heart of their value proposition. And data is unencumbered. There's no restrictions. They use data, data as analysis. They look at customer behavior, AB testing. So data is the heart of innovation. This is Informatica's plan here. CLAIRE is their AI product. Their theme is kind of clever. CLAIRE starts here. And this is really the focus for Informatica. Their opportunity is to be that independent vendor supplier, the Switzerland as it has been called, the neutral third party to bring data together On Premise and Cloud. That's what they're saying. That's their opportunity. The challenges are high. The data business is being regulated. We talk about it last time. You know, privacy, GDPR one-year anniversary, Microsoft's calling for more privacy. As more regulation comes in, that puts more restrictions on data. That requires more software. That creates overhead. Overhead is not good for SaaS business models. And that is where the conflict is. This is the opportunity, and if they can overcome that as a supplier, then they can do well. And data growth is just massive. Cloud, IoT Edge, you name it. Data is the center of the value proposition. >> Well, and we're going to have a lot of great guests on the program this week, in particular we're going to have Sally Jenkins talking about these four customer journeys that the customers are going on. And in fact data governance and privacy is one of the big tenants. So, they are making, they are saying this is our wheelhouse. We can do this. We can help you do this. >> Well, the thing is we're going to ask every guest the question of the week is What's the skill gaps? Because digital transformation although very relevant is only as good as the people and the culture that's behind it. And that's a theme that we hear all throughout our different CUBE events. If people have the culture for it, they could do it. DevOps is another word that has been kicked around. But ultimately if you don't have the people and just machines, it's really going to be a tough balance to strike. You need the machines, you need the data, you need the people. And this is where the challenge is in the industry. I think the skill gaps is a huge problem for digital transformation. It's to me the big blocker in seeing innovation accelerate. So customers are now having that journey. They're starting, they really think about how to architect their enterprise with an On Premise, with a Legacy and Cloud Native with full SaaS. And the companies that can get to a SaaS business model, managing the On-Premise's legacy will have a winning shot at taking new market share or top one down incumbents in leadership positions. >> I'm really excited about this idea. Asking people about the skill gap and where the next generation of jobs are going to be in big data. I saw a statistic, a survey from Google, 94% of IT managers can't find qualified candidates for open Cloud roles. That is-that's astonishing. I also saw an interesting quote from Tim Cook, who recently said that half of Apple's new hires are not going to have a college degree this year. He said when our own founder didn't have one. It kind of really shows you what you can do. >> It's really early. >> You might not need this degree. >> First of all, it's really, first of all I agree that degrees don't really matter. In some cases, old degrees might not apply to the new jobs. I'll give you an example. My daughter just graduated from Cal Berkeley this week. And they had the inaugural class of data, data science, data analytics. For the first time, first graduating class. That's a tell-sign that we're at the early, early stages. But data science can come from anyone. You could be, you know, anthropologist, you could be any any skill. You can solve a problem, you're good at math. You can see the big picture. You're seeing data science really becoming a career. And again, there's just not enough job openings. And data science isn't just for the data jockeys out there who just want to do data. There's cyber security, huge data-driven. Everything is data-driven. The big growth area in the enterprise is the IoT, the Edge. As devices come online for manufacturing to oil rigs to wind farms. The edge computing is a huge thing. And that's a data problem. Everything is a data problem. So this is where the industry is focused I think Informatica was really on it early. And now everyone's jumping in. You got Amazon, Google, Microsoft, the big cloud players, and you got all the existing incumbent enterprise suppliers all putting data at the center-value proposition. You know you got a lot of competition now for Informatica, and they have to make some good moves here. And what I'm going to be looking for here, Rebecca, is how they transform as a company. Because I think that they have to be an integration company. They want to be that Switzerland. They got to integrate to all the clouds. They got to integrate to all the different platforms and environments on the enterprise and create that one operating model. And this is something they say they want to do, and we're going to ask them. >> And you not only called them Switzerland, they've called themselves Switzerland. And so I think that they are. They do want that. They want that for themselves. They want they are having these partnerships with all of the major cloud providers. So, you said this is what you're going to be asking. This is what you're going to be looking for. What is it that you think will set them apart? >> I think ultimately I think Informatica's got a great management team when it comes to product and engineering. One of the things I've been impressed with is they get the product around data. The only thing I think that could be a headwind for them as a challenge is this regulatory environment. I brought that up earlier. I think this could be a challenge and an opportunity, and it could be the difference maker because there's no question that their value proposition or how they're dealing with data management, their deals we're going to hear about with the cloud and all of the new innovation they have with CLAIRE and AI. Certainly that's good. But if you don't have data-feeding machine learning, and the data's hard to get at, and it's regulated, you got clouds with geographies and countries have new regulations. This is a complicated problem. If they could create software to make that easier and create an abstraction layer and use the power of the cloud, I think they could have a winning formula. So to me, that's a killer opportunity. And then making data work for SaaS-oriented business models, On-Premise and in the cloud. >> I think you're absolutely right and we heard Anil Chakravarthy say this today. Data needs the machine learning an AI, AI machine learning need data. And any application of AI and machine learning is only as good as the data that's been collected. So, the other big challenge is what I think is going to be really exciting about for this show is seeing all of these use cases. In industry after industry we are seeing applications of AI and machine learning transforming business models and approaches and leadership and big ideas around these important game-changers in our industry. >> Yeah, one of the things that's interesting I had an interview with in the city of Howie Xu, who's formally VMWare engineer, entrepreneur, sold his company to Zscaler. He's an AI guy, and we talked about the SaaS business model. And one of the things that's key is if you don't have the data feeding the SaaS, it's not going to work, so to me if they could get that data back in to the system quicker with all that regulation, that's going to be a game changer. And I think they got to start thinking how they can show the customer proof points. That's going to be interesting when the customers start adapting in that scale. >> And as we've also said many times on theCUBE the governance is kind of a mess itself. I mean Washington doesn't quite know what to do with this and how to regulate it. How do you think that these technology companies should be working with Washington on this? >> Well that's a loaded question. First of all, I think the government is not the bellwether for technology innovation. In fact, I think innovation is stifled by too much regulation. There's got to have a balance there. One of the things that's positive is in the cyber-security area you see private, public partnerships go on where there's some joint sharing. I think cloud is going to be a catalyst. We're going to have the VP of marketing from Amazon web services on, I'm going to ask him that direct question. This is where the action is. So I think this notion of collaboration the enterprise and cloud players is going to be key because if you look at like just how search engines used to work back in the old days, if it was not encumbered by all this legacy infrastructure in the enterprise, it works great. The more you add complexity to things, the more you need software. The more you need software, you need horsepower to compute. You need more storage. So all these things are creating a different environment than it was just three years ago. So, you know can they adjust, can the industry shape itself out? I think the industry needs to lead here, not the government. >> What about the idea of Informatica working together with customers and making sure that they are in fact deriving value? Because I mean I think that's the other thing is that all of these companies know they need to have an AI strategy, they need to be using more machine learning. It's very complicated as you said. But then there's this question of am I really going to see a return of investment on this? >> Well, I think Informatica can do a good job working with cloud architecture and looking at because you got again IoT edge is coming around the corner. But if they can nail the architecture On-Premises and Cloud, that is a great start. The second thing that Informatica can help customers at, and this is a customer challenge, is where do you store the data? Because moving data around is very expensive. So this scenario is where you want it all on the cloud. This scenario is where you want it all On Premise. And this scenario is where you want it on both locations. And then with the edge, you want to move data I mean compute to where the data is. So, data becomes a very critical piece of the overall architecture and whoever can build this operating system's mindset will have a winning formula, and again being neutral is a critical strategy. And the more Informatica can help enterprise be more like consumer companies, the better. If you look at Slack for instance, it's an IPO candidate coming out very popular. It's just a chat kind of message board app. What made Slack successful is that they built connectors and APIs into all different tools. If Informatica could do that, that would be a winning formula because they want to be data brokering, they want to be data connecting, and they want to feed the applications and machine learning data. If they can't get data to the machine learning and AI, the AI will not be sufficient. And that will be a problem. >> Well, this is all the things we are going to be talking about over these next two days. John, I look forward to it. I'm Rebecca Knight, you are watching theCUBE. (lighthearted techno music)

(lively classical music) >> Hello everyone, welcome to this special Cube conversation, we are here at the Power Panel Conversation. I'm John Furrier, in Palo Alto, California, theCUBE studies we have remote on the line here, talk about the cloud technology's impact on entrepreneurship and startups and overall ecosystem is Jim Long, who's the CEO of Didja, which is a startup around disrupting digital TV, also has been an investor and a serial entrepreneur, Sarbjeet Johal, who's the in-cloud influencer of strategy and investor out of Berkeley, California, The Batchery, and also Joseph Jacks, CUBE alumni, actually you guys are all CUBE alumni, so great to have you on. Joseph Jacks is the founder and general partner of OSS Capital, Open Source Software Capital, a new fund that's been raised specifically to commercialize and fund startups around open source software. Guys, we got a great panel here of experts, thanks for joining us, appreciate it. >> Go Bears! >> Nice to be here. >> So we have a distinguished panel, it's the Power Panel, we're on cloud technos, first I'd like to get you guys' reaction you know, you're to seeing a lot of negative news around what Facebook has become, essentially their own hyper-scale cloud with their application. They were called the digital, you know, renegades, or digital gangsters in the UK by the Parliament, which was built on open source software. Amazon's continuing to win, Azure's doing their thing, bundling Office 365, making it look like they've got more revenue with their catching up, Google, and then you got IBM and Oracle, and then you got an ecosystem that's impacted by this large scale, so I want to get your thoughts on first point here. Is there room for more clouds? There's a big buzzword around multiple clouds. Are we going to see specialty clouds? 'Causes Salesforce is a cloud, so is there room for more cloud? Jim, why don't you start? >> Well, I sure hope so. You know, the internet has unfortunately become sort of the internet of monopolies, and that doesn't do anyone any good. In fact, you bring up an interesting point, it'd be kind of interesting to see if Facebook created a social cloud for certain types of applications to use. I've no idea whether that makes any sense, but Amazon's clearly been the big gorilla now, and done an amazing job, we love using them, but we also love seeing, trying out different services that they have and then figuring out whether we want to develop them ourselves or use a specialty service, and I think that's going to be interesting, particularly in the AI area, stuff like that. So I sure hope more clouds are around for all of us to take advantage of. >> Joseph, I want you to weigh in here, 'cause you were close to the Kubernetes trend, in fact we were at a OpenStack event when you started Kismatic, which is the movement that became KubeCon Cloud Native, many many years ago, now you're investing in open source. The world's built on open source, there's got to be room for more clouds. Your thoughts on the opportunities? >> Yeah, thanks for having me on, John. I think we need a new kind of open collaborative cloud, and to date, we haven't really seen any of the existing major sort of large critical mass cloud providers participate in that type of model. Arguably, Google has probably participated and contributed the most in the open source ecosystem, contributing TensorFlow and Kubernetes and Go, lots of different open source projects, but they're ultimately focused on gravitating huge amounts of compute and storage cycles to their cloud platform. So I think one of the big missing links in the industry is, as we continue to see the rise of these large vertically integrated proprietary control planes for computing and storage and applications and services, I think as the open source community and the open source ecosystem continues to grow and explode, we'll need a third sort of provider, one that isn't based on monopoly or based on a traditional proprietary software business like Microsoft kind of transitioning their enterprise customers to services, sort of Amazon in the first camp vertically integrated many a buffet of all these different compute, storage, networking services, application, middleware. Microsoft focused on sort of building managed services of their software portfolio. I think we need a third model where we have sort of an open set of interfaces and an open standards based cloud provider that might be a pure software company, it might be a company that builds on the rails and the infrastructure that Amazon has laid down, spending tens of billions in cap ex, or it could be something based on a project like Kubernetes or built from the community ecosystem. So I think we need something like that just to sort of provide, speed the innovation, and disaggregate the services away from a monolithic kind of closed vendor like Amazon or Azure. >> I want to come back to that whole startup opportunity, but I want to get Sarbjeet in here, because we've been in the B2B area with just last week at IBM Think 2019. Obviously they're trying to get back into the cloud game, but this digital transformation that has been the cliche for almost a couple of years now, if not five or plus. Business has got to move to the cloud, so there's a whole new ball game of complete cultural shift. They need stability. So I want to talk more about this open cloud, which I love that conversation, but give me the blocking and tackling capabilities first, 'cause I got to get out of that old cap ex model, move to an operating model, transform my business, whether it's multi clouds. So Sarbjeet, what's your take on the cloud market for say, the enterprise? >> Yeah, I think for the enterprise... you're just sitting in that data center and moving those to cloud, it's a cumbersome task. For that to work, they actually don't need all the bells and whistles which Amazon has in the periphery, if you will. They need just core things like compute, network, and storage, and some other sort of services, maybe database, maybe data share and stuff like that, but they just want to move those applications as is to start with, with some replatforming and with some changes. Like, they won't make changes to first when they start moving those applications, but our minds are polluted by this thinking. When we see a Facebook being formed by a couple of people, or a company of six people sold for a billion dollars, it just messes up with our mind on the enterprise side, hey we can do that too, we can move that fast and so forth, but it's sort of tragic that we think that way. Well, having said that, and I think we have talked about this in the past. If you are doing anything in the way of systems innovation, if your building those at, even at the enterprise, I think cloud is the way to go. To your original question, if there's room for newer cloud players, I think there is, provided that we can detach the platforms from the environments they are sitting on. So the proprietariness has to kinda, it has to be lowered, the degree of proprietariness has to be lower. It can be through open source I think mainly, it can be from open technologies, they don't have to be open source, but portable. >> JJ was mentioning that, I think that's a big point. Jim Long, you're an entrepreneur, you've been a VC, you know all the VCs, been around for a while, you're also, you're an entrepreneur, you're a serial entrepreneur, starting out at Cal Berkeley back in the day. You know, small ideas can move fast, and you're building on Amazon, and you've got a media kind of thing going on, there's a cloud opportunity for you, 'cause you are cloud native, 'cause you're built in the cloud. How do you see it playing out? 'Cause you're scaling with Amazon. >> Well, so we obviously, as a new startup, don't have the issues the enterprise folks have, and I could really see the enterprise customers, what we used to call the Fortune 500, for example, getting together and insisting on at least a base set of APIs that Amazon and Microsoft et cetera adopt, and for a startup, it's really about moving fast with your own solution that solves a problem. So you don't necessarily care too much that you're tied into Amazon completely because you know that if you need to, you can make a change some day. But they do such a good job for us, and their costs, while they can certainly be lower, and we certainly would like more volume discounts, they're pretty darn amazing across the network, across the internet, we do try to price out other folks just for the heck of it, been doing that recently with CDNs, for example. But for us, we're actually creating a hybrid cloud, if you will, a purpose-built cloud to support local television stations, and we do think that's going to be, along with using Amazon, a unique cloud with our own APIs that we will hopefully have lots of different TV apps use our hybrid cloud for part of their application to service local TV. So it's kind of a interesting play for us, the B2B part of it, we're hoping to be pretty successful as well, and we hope to maybe have multiple cloud vendors in our mix, you know. Not that our users will know who's behind us, maybe Amazon, for something, Limelight for another, or whatever, for example. >> Well you got to be concerned about lock-in as you become in the cloud, that's something that everybody's worried about. JJ, I want to get back to you on the investment thesis, because you have a cutting edge business model around investing in open source software, and there's two schools of thought in the open source community, you know, free contribution's great, and let tha.t be organic, and then there's now commercialization. There's real value being created in open source. You had put together a chart with your team about the billions of dollars in exits from open source companies. So what are you investing in, what do you see as opportunities for entrepreneurs like Jim and others that are out there looking at scaling their business? How do you look at success, what's your advice, what do you see as leading indicators? >> I think I'll broadly answer your question with a model that we've been thinking a lot about. We're going to start writing publicly about it and probably eventually maybe publish a book or two on it, and it's around the sort of fundamental perspective of creating value and capturing value. So if you model a famous investor and entrepreneur in Silicon Valley who has commonly modeled these things using two different letter variables, X and Y, but I'll give you the sort of perspective of modeling value creation and value capture around open source, as compared to closed source or proprietary software. So if you look at value creation modeled as X, and value capture modeled as Y, where X and Y are two independent variables with a fully proprietary software company based approach, whether you're building a cloud service or a proprietary software product or whatever, just a software company, your value creation exponent is typically bounded by two things. Capital and fundraising into the entity creating the software, and the centralization of research and development, meaning engineering output for producing the software. And so those two things are tightly coupled to and bounded to the company. With commercial open source software, the exact opposite is true. So value creation is decoupled and independent from funding, and value creation is also decentralized in terms of the research and development aspect. So you have a sort of decentralized, community-based, crowd-sourced, or sort of internet, global phenomena of contributing to a code base that isn't necessarily owned or fully controlled by a single entity, and those two properties are sort of decoupled from funding and decentralized R and D, are fundamentally changing the value creation kind of exponent. Now let's look at the value capture variable. With proprietary software company, or proprietary technology company, you're primarily looking at two constituents capturing value, people who pay for accessing the service or the software, and people who create the software. And so those two constituents capture all the value, they capture, you know, the vendor selling the software captures maybe 10 or 20% of the value, and the rest of the value, I would would express it say as the customer is capturing the rest of the value. Most economists don't express value capture as capturable by an end user or a customer. I think that's a mistake. >> Jim, you're-- >> So now... >> Okay, Jim, your reaction to that, because there's an article went around this weekend from Motherboard. "The internet was built on free labor "of open source developers. "Is that sustainable?" So Jim, what's your reaction to JJ's comments about the interactions and the dynamic between value creation, value capture, free versus sustainable funding? >> Well if you can sort of mix both together, that's what I would like, I haven't really ever figured out how to make open source work in our business model, but I haven't really tried that hard. It's an intriguing concept for sure, particularly if we come up with APIs that are specific to say, local television or something like that, and maybe some special processes that do things that are of interest to the wider community. So it's something I do plan to look at because I do agree that if you, I mean we use open source, we use this thing called FFmpeg, and several other things, and we're really happy that there's people out there adding value to them, et cetera, and we have our own versions, et cetera, so we'd like to contribute to the community if we could figure out how. >> Sarbjeet, your reactions to JJ's thesis there? >> I think two things. I will comment on two different aspects. One is the lack of standards, and then open source becoming the standard, right. I think open source kind of projects take birth and life in its own, because we have lack of standard, 'cause these different vendors can't agree on standards. So remember we used to have service-oriented architecture, we have Microsoft pushing some standards from one side and IBM pushing from other, SOAP versus xCBL and XML, different sort of paradigms, right, but then REST API became the de facto standard, right, it just took over, I think what REST has done for software in last about 10 years or so, nothing has done that for us. >> well Kubernetes is right now looking pretty good. So if you look at JJ, Kubernetes, the movement you were really were pioneering on, it's having similar dynamic, I mean Kubernetes is becoming a forcing function for solidarity in the community of cloud native, as well as an actual interoperable orchestration layer for multiple clouds and other services. So JJ, your thoughts on how open source continues as some of these new technologies, like Kubernetes, continue to hit the scene. Is there any trajectory change in open source that you see, that you could share, I'd love to get your insights on what's next behind, you know, the rise of Kubernetes is happening, what's next? >> I think more abstractly from Kubernetes, we believe that if you just look at the rate of innovation as a primary factor for progress and forward change in the world, open source software has the highest rate of innovation of any technology creation phenomena, and as a consequence, we're seeing more standards emerge from the open source ecosystem, we're seeing more disruption happen from the open source ecosystem, we're seeing more new technology companies and new paradigms and shifts happen from the open source ecosystem, and kind of all progress across the largest, most difficult sort of compound, sensitive problems, influenced and kind of sourced from the open source ecosystem and the open source world overall. Whether it's chip design, machine learning or computing innovations or new types of architectures, or new types of developer paradigms, you know, biological breakthroughs, there's kind of things up and down the technology spectrum that have a lot to sort of thank open source for. We think that the future of technology and the future of software is really that open source is at the core, as opposed to the periphery or the edges, and so today, every software technology company, and cloud providers included, have closed proprietary cores, meaning that where the core is, the data path, the runtime, the core business logic of the company, today that core is proprietary software or closed source software, and yet what is also true, is at the edges, the wrappers, the sort of crust, the periphery of every technology company, we have lots of open source, we have client libraries and bindings and languages and integrations, configuration, UIs and so on, but the cores are proprietary. We think the following will happen over the next few decades. We think the future will gradually shift from closed proprietary cores to open cores, where instead of a proprietary core, an open core is where you have core open source software project, as the fundamental building block for the company. So for example, Hadoop caused the creation of MapR and Cloudera and Hortonworks, Spark caused the creation of Databricks, Kafka caused the creation of Confluent, Git caused the creation of GitHub and GitLab, and this type of commercial open source software model, where there's a core open source project as the kernel building block for the company, and then an extension of intellectual property or wrappers around that open source project, where you can derive value capture and charge for licensed product with the company, and impress customer, we think that model is where the future is headed, and this includes cloud providers, basically selling proprietary services that could be based on a mixture of open source projects, but perhaps not fundamentally on a core open source project. Now we think generally, like abstractly, with maybe somewhat of a reductionist explanation there, but that open core future is very likely, fundamentally because of the rate of innovation being the highest with the open source model in general. >> All right, that's great stuff. Jim, you're a historian of tech, you've lived it. Your thoughts on some of the emerging trends around cloud, because you're disrupting linear TV with Didja, in a new way using cloud technology. How do you see cloud evolving? >> Well, I think the long lines we discussed, certainly I think that's a really interesting model, and having the open source be the center of the universe, then figure out how to have maybe some proprietary stuff, if I can use that word, around it, that other people can take advantage of, but maybe you get the value capture and build a business on that, that makes a lot of sense, and could certainly fit in the TV industry if you will from where I sit... Bring services to businesses and consumers, so it's not like there's some reason it wouldn't work, you know, it's bound to, it's bound to figure out a way, and if you can get a whole mass of people around the world working on the core technology and if it is sort of unique to what mission of, or at least the marketplace you're going after, that could be pretty interesting, and that would be great to see a lot of different new mini-clouds, if you will, develop around that stuff would be pretty cool. >> Sarbjeet, I want you to talk about scale, because you also have experience working with Rackspace. Rackspace was early on, they were trying to build the cloud, and OpenStack came out of that, and guess what, the world was moving so fast, Amazon was a bullet train just flying down the tracks, and it just felt like Rackspace and their cloud, you know OpenStack, just couldn't keep up. So is scale an issue, and how do people compete against scale in your mind? >> I think scale is an issue, and software chops is an issue, so there's some patterns, right? So one pattern is that we tend to see that open source is now not very good at the application side. You will hardly see any applications being built as open source. And also on the extreme side, open source is pretty sort of lame if you will, at very core of the things, like OpenStack failed for that reason, right? But it's pretty good in the middle as Joseph said, right? So building pipes, building some platforms based on open source, so the hooks, integration, is pretty good there, actually. I think that pattern will continue. Hopefully it will go deeper into the core, which we want to see. The other pattern is I think the software chops, like one vendor has to lead the project for certain amount of time. If that project goes into sort of open, like anybody can grab it, lot of people contribute and sort of jump in very quickly, it tends to fail. That's what happened to, I think, OpenStack, and there were many other reasons behind that, but I think that was the main reason, and because we were smaller, and we didn't have that much software chops, I hate to say that, but then IBM could control like hundred parties a week, at the project >> They did, and look where they are. >> And so does HP, right? >> And look where they are. All right, so I'd love to have a Power Panel on open source, certainly JJ's been in the thick of it as well as other folks in the community. I want to just kind of end on lightweight question for you guys. What have you guys learned? Go down the line, start with Jim, Sarbjeet, and then JJ we'll finish with you. Share something that you've learned over the past three months that moved you or that people should know about in tech or cloud trends that's notable. What's something new that you've learned? >> In my case, it was really just spending some time in the last few months getting to know our end users a little bit better, consumers, and some of the impact that having free internet television has on their lives, and that's really motivating... (distorted speech) Something as simple as you might take for granted, but lower income people don't necessarily have a TV that works or a hotel room that has a TV that works, or heaven forbid they're homeless and all that, so it's really gratifying to me to see people sort of tuning back into their local media through television, just by offering it on their phone and laptops. >> And what are you going to do as a result of that? Take a different action, what's the next step for you, what's the action item? >> Well we're hoping, once our product gets filled out with the major networks, et cetera, that we actually provide a community attachment to it, so that we have over-the-air television channels is the main part of the app, and then a side part of the app could be any IP stream, from city council meetings to high schools, to colleges, to local community groups, local, even religious situations or festivals or whatever, and really try to tie that in. We'd really like to use local television as a way to strengthening all local media and local communities, that's the vision at least. >> It's a great mission you guys have at Didja, thanks for sharing that. Sarbjeet, what have learned over the past quarter, three months that was notable for you and the impact and something that changed you a little bit? >> What actually I have gravitated towards in last three to six months is the blockchain, actually. I was light on that, like what it can do for us, and is there really a thing behind it, and can we leverage it. I've seen more and more actually usage of that, and sort of full SCM, supply chain management and healthcare and some other sort of use cases if you will. I'm intrigued by it, and there's a lot of activity there. I think there's some legs behind it, so I'm excited about that. >> And are doing a blockchain project as a result, or are you still tire-kicking? >> No actually, I will play with it, I'm a practitioner, I play with it, I write code and play with it and see (Jim laughs) what does that level of effort it takes to do that, and as you know, I wrote the Alexa scale couple of weeks back, and play with AI and stuff like that. So I try to do that myself before I-- >> We're hoping blockchain helps even out the TV ad economy and gets rid of middle men and makes more trusting transactions between local businesses and stuff. At least I say that, I don't really know what I'm talking about. >> It sounds good though. You get yourself a new round of funding on that sound byte alone. JJ, what have you learned in the past couple months that's new to you and changed you or made you do something different? >> I've learned over the last few months, OSS Capital is a few months and change old, and so just kind of getting started on that, and it's really, I think potentially more than one decade, probably multi-decade kind of mostly consensus building effort. There's such a huge lack of consensus and agreement in the industry. It's a fascinatingly polarizing area, the sort of general topic of open source technology, economics, value creation, value capture. So my learnings over the past few months have just intensified in terms of the lack of consensus I've seen in the industry. So I'm trying to write a little bit more about observations there and sort of put thoughts out, and that's kind of been the biggest takeaway over the last few months for me. >> I'm sure you learned about all the lawyer conversations, setting up a fund, learnings there probably too right, (Jim laughs) I mean all the detail. All right, JJ, thanks so much, Sarbjeet, Jim, thanks for joining me on this Power Panel, cloud conversation impact, to entrepreneurship, open source. Jim Long, Sarbjeet Johal and Joseph Jacks, JJ, thanks for joining us, theCUBE Conversation here in Palo Alto, I'm John Furrier, thanks for watching. >> Thanks John. (lively classical music)

>> Announcer: Live from New York, it's theCUBE. Covering machine learning everywhere. Build your ladder to AI. Brought to you by IBM. Welcome back to New York City. Along with Dave Vellante, I'm John Walls. We continue our coverage here on theCUBE of machine learning everywhere. Build your ladder to AI, IBM our host here today. We put together, occasionally at these events, a panel of esteemed experts with deep perspectives on a particular subject. Today our influencer panel is comprised of three well-known and respected authorities in this space. Glad to have Colin Sumpter here with us. He's the man with the mic, by the way. He's going to talk first. But, Colin is an IT architect with CrowdMole. Thank you for being with us, Colin. Jennifer Shin, those of you on theCUBE, you're very familiar with Jennifer, a long time Cuber. Founded 8 Path Solutions, on the faculty at NYU and Cal Berkeley, and also with us is Craig Brown, a big data consultant. And a home game for all of you guys, right, more or less here we are in the city. So, thanks for having us, we appreciate the time. First off, let's just talk about the title of the event, Build Your Path... Or Your Ladder, excuse me, to AI. What are those steps on that ladder, Colin? The fundamental steps that you've got to jump on, or step on, in order to get to that true AI environment? >> In order to get to that true AI environment, John, is a matter of mastering or organizing your information well enough to perform analytics. That'll give you two choices to do either linear regression or supervised classification, and then you actually have enough organized data to talk to your team and organize your team around that data to begin that ladder to successively benefit from your data science program. >> Want to take a stab at it, Jennifer? >> So, I would say, compute, right? You need to have the right processing, or at least the ability to scale out to be able to process the algorithm fast enough to be able to find value in your data. I think the other thing is, of course, the data source itself. Do you have right data to answer the questions you want to answer? So, I think, without those two things, you'll either have a lot of great data that you can't process in time, or you'll have a great process or a great algorithm that has no real information, so your output is useless. I think those are the fundamental things you really do need to have any sort of AI solution built. >> I'll take a stab at it from the business side. They have to adopt it first. They have to believe that this is going to benefit them and that the effort that's necessary in order to build into the various aspects of algorithms and data subjects is there, so I think adopting the concept of machine learning and the development aspects that it takes to do that is a key component to building the ladder. >> So this just isn't toe in the water, right? You got to dive in the deep end, right? >> Craig: Right. >> It gets to culture. If you look at most organizations, not the big five market capped companies, but most organizations, data is not at their core. Humans are at their core, human expertise and data is sort of bolted on, but that has to change, or they're going to get disrupted. Data has to be at the core, maybe the human expertise leverages that data. What do you guys seeing with end customers in terms of their readiness for this transformation? >> What I'm seeing customers spending time right now is getting out of the silos. So, when you speak culture, that's primarily what the culture surrounds. They develop applications with functionality as a silo, and data specific to that functionality is the component in which they look at data. They have to get out of that mindset and look at the data holistically, and ultimately, in these events, looking at it as an asset. >> The data is a shared resource. >> Craig: Right, correct. >> Okay, and again, with the exception of the... Whether it's Google, Facebook, obviously, but the Ubers, the AirBNB's, etc... With the exception of those guys, most customers aren't there. Still, the data is in silos, they've got myriad infrastructure. Your thoughts, Jennifer? >> I'm also seeing sort of a disconnect between the operationalizing team, the team that runs these codes, or has a real business need for it, and sometimes you'll see corporations with research teams, and there's sort of a disconnect between what the researchers do and what these operations, or marketing, whatever domain it is, what they're doing in terms of a day to day operation. So, for instance, a researcher will look really deep into these algorithms, and may know a lot about deep learning in theory, in theoretical world, and might publish a paper that's really interesting. But, that application part where they're actually being used every day, there's this difference there, where you really shouldn't have that difference. There should be more alignment. I think actually aligning those resources... I think companies are struggling with that. >> So, Colin, we were talking off camera about RPA, Robotic Process Automation. Where's the play for machine intelligence and RPA? Maybe, first of all, you could explain RPA. >> David, RPA stands for Robotic Process Automation. That's going to enable you to grow and scale a digital workforce. Typically, it's done in the cloud. The way RPA and Robotic Process Automation plays into machine learning and data science, is that it allows you to outsource business processes to compensate for the lack of human expertise that's available in the marketplace, because you need competency to enable the technology to take advantage of these new benefits coming in the market. And, when you start automating some of these processes, you can keep pace with the innovation in the marketplace and allow the human expertise to gradually grow into these new data science technologies. >> So, I was mentioning some of the big guys before. Top five market capped companies: Google, Amazon, Apple, Facebook, Microsoft, all digital. Microsoft you can argue, but still, pretty digital, pretty data oriented. My question is about closing that gap. In your view, can companies close that gap? How can they close that gap? Are you guys helping companies close that gap? It's a wide chasm, it seems. Thoughts? >> The thought on closing the chasm is... presenting the technology to the decision-makers. What we've learned is that... you don't know what you don't know, so it's impossible to find the new technologies if you don't have the vocabulary to just begin a simple research of these new technologies. And, to close that gap, it really comes down to the awareness, events like theCUBE, webinars, different educational opportunities that are available to line of business owners, directors, VP's of systems and services, to begin that awareness process, finding consultants... begin that pipeline enablement to begin allowing the business to take advantage and harness data science, machine learning and what's coming. >> One of the things I've noticed is that there's a lot of information out there, like everyone a webinar, everyone has tutorials, but there's a lot of overlap. There aren't that many very sophisticated documents you can find about how to implement it in real world conditions. They all tend to use the same core data set, a lot of these machine learning tutorials you'll find, which is hilarious because the data set's actually very small. And I know where it comes from, just from having the expertise, but it's not something I'd ever use in the real world. The level of skill you need to be able to do any of these methodologies. But that's what's out there. So, there's a lot of information, but they're kind of at a rudimentary level. They're not really at that sophisticated level where you're going to learn enough to deploy in real world conditions. One of the things I'm noticing is, with the technical teams, with the data science team, machine learning teams, they're kind of using the same methodologies I used maybe 10 years ago. Because the management who manage these teams are not technical enough. They're business people, so they don't understand how to guide them, how to explain hey maybe you shouldn't do that with your code, because that's actually going to cause a problem. You should use parallel code, you should make sure everything is running in parallel so compute's faster. But, if these younger teams are actually learning for the first time, they make the same mistakes you made 10 years ago. So, I think, what I'm noticing is that lack of leadership is partly one of the reasons, and also the assumption that a non-technical person can lead the technical team. >> So, it's just not skillset on the worker level, if you will. It's also knowledge base on the decision-maker level. That's a bad place to be, right? So, how do you get into the door to a business like that? Obviously, and we've talked about this a little bit today, that some companies say, "We're not data companies, we're not digital companies, we sell widgets." Well, yeah but you sell widgets and you need this to sell more widgets. And so, how do you get into the door and talk about this problem that Jennifer just cited? You're signing the checks, man. You're going to have to get up to speed on this otherwise you're not going to have checks to sign in three to five years, you're done! >> I think that speaks to use cases. I think that, and what I'm actually saying at customers, is that there's a disconnect and an understanding from the executive teams and the low-level technical teams on what the use case actually means to the business. Some of the use cases are operational in nature. Some of the use cases are data in nature. There's no real conformity on what does the use case mean across the organization, and that understanding isn't there. And so, the CIO's, the CEO's, the CTO's think that, "Okay, we're going to achieve a certain level of capability if we do a variety of technological things," and the business is looking to effectively improve some or bring some efficiency to business processes. At each level within the organization, the understanding is at the level at which the discussions are being made. And so, I'm in these meetings with senior executives and we have lots of ideas on how we can bring efficiencies and some operational productivity with technology. And then we get in a meeting with the data stewards and "What are these guys talking about? They don't understand what's going on at the data level and what data we have." And then that's where the data quality challenges come into the conversation, so I think that, to close that cataclysm, we have to figure out who needs to be in the room to effectively help us build the right understanding around the use cases and then bring the technology to those use cases then actually see within the organization how we're affecting that. >> So, to change the questioning here... I want you guys to think about how capable can we make machines in the near term, let's talk next decade near term. Let's say next decade. How capable can we make machines and are there limits to what we should do? >> That's a tough one. Although you want to go next decade, we're still faced with some of the challenges today in terms of, again, that adoption, the use case scenarios, and then what my colleagues are saying here about the various data challenges and dev ops and things. So, there's a number of things that we have to overcome, but if we can get past those areas in the next decade, I don't think there's going to be much of a limit, in my opinion, as to what the technology can do and what we can ask the machines to produce for us. As Colin mentioned, with RPA, I think that the capability is there, right? But, can we also ultimately, as humans, leverage that capability effectively? >> I get this question a lot. People are really worried about AI and robots taking over, and all of that. And I go... Well, let's think about the example. We've all been online, probably over the weekend, maybe it's 3 or 4 AM, checking your bank account, and you get an error message your password is wrong. And we swear... And I've been there where I'm like, "No, no my password's right." And it keeps saying that the password is wrong. Of course, then I change it, and it's still wrong. Then, the next day when I login, I can login, same password, because they didn't put a great error message there. They just defaulted to wrong password when it's probably a server that's down. So, there are these basics or processes that we could be improving which no one's improving. So you think in that example, how many customer service reps are going to be contacted to try to address that? How many IT teams? So, for every one of these bad technologies that are out there, or technologies that are not being run efficiently or run in a way that makes sense, you actually have maybe three people that are going to be contacted to try to resolve an issue that actually maybe could have been avoided to begin with. I feel like it's optimistic to say that robots are going to take over, because you're probably going to need more people to put band-aids on bad technology and bad engineering, frankly. And I think that's the reality of it. If we had hoverboards, that would be great, you know? For a while, we thought we did, right? But we found out, oh it's not quite hoverboards. I feel like that might be what happens with AI. We might think we have it, and then go oh wait, it's not really what we thought it was. >> So there are real limits, certainly in the near to mid to maybe even long term, that are imposed. But you're an optimist. >> Yeah. Well, not so much with AI but everything else, sure. (laughing) AI, I'm a little bit like, "Well, it would be great, but I'd like basic things to be taken care of every day." So, I think the usefulness of technology is not something anyone's talking about. They're talking about this advancement, that advancement, things people don't understand, don't know even how to use in their life. Great, great is an idea. But, what about useful things we can actually use in our real life? >> So block and tackle first, and then put some reverses in later, if you will, to switch over to football. We were talking about it earlier, just about basics. Fundamentals, get your fundamentals right and then you can complement on that with supplementary technologies. Craig, Colin? >> Jen made some really good points and brought up some very good points, and so has... >> John: Craig. >> Craig, I'm sorry. (laughing) >> Craig: It's alright. >> 10 years out, Jen and Craig spoke to false positives. And false positives create a lot of inefficiency in businesses. So, when you start using machine learning and AI 10 years from now, maybe there's reduced false positives that have been scored in real time, allowing teams not to have their time consumed and their business resources consumed trying to resolve false positives. These false positives have a business value that, today, some businesses might not be able to record. In financial services, banks count money not lended. But, in every day business, a lot of businesses aren't counting the monetary consequences of false positives and the drag it has on their operational ability and capacity. >> I want to ask you guys about disruption. If you look at where the disruption, the digital disruptions, have taken place, obviously retail, certainly advertising, certainly content businesses... There are some industries that haven't been highly disruptive: financial services, insurance, we were talking earlier about aerospace, defense rather. Is any business, any industry, safe from digital disruption? >> There are. Certain industries are just highly regulated: healthcare, financial services, real estate, transactional law... These are very extremely regulated technologies, or businesses, that are... I don't want to say susceptible to technology, but they can be disrupted at a basic level, operational efficiency, to make these things happen, these business processes happen more rapidly, more accurately. >> So you guys buy that? There's some... I'd like to get a little debate going here. >> So, I work with the government, and the government's trying to change things. I feel like that's kind of a sign because they tend to be a little bit slower than, say, other private industries, or private companies. They have data, they're trying to actually put it into a system, meaning like if they have files... I think that, at some point, I got contacted about putting files that they found, like birth records, right, marriage records, that they found from 100-plus years ago and trying to put that into the system. By the way, I did look into it, there was no way to use AI for that, because there was no standardization across these files, so they have half a million files, but someone's probably going to manually have to enter that in. The reality is, I think because there's a demand for having things be digital, we aren't likely to see a decrease in that. We're not going to have one industry that goes, "Oh, your files aren't digital." Probably because they also want to be digital. The companies themselves, the employees themselves, want to see that change. So, I think there's going to be this continuous move toward it, but there's the question of, "Are we doing it better?" It is better than, say, having it on paper sometimes? Because sometimes I just feel like it's easier on paper than to have to look through my phone, look through the app. There's so many apps now! >> (laughing) I got my index cards cards still, Jennifer! Dave's got his notebook! >> I'm not sure I want my ledger to be on paper... >> Right! So I think that's going to be an interesting thing when people take a step back and go like, "Is this really better? Is this actually an improvement?" Because I don't think all things are better digital. >> That's a great question. Will the world be a better, more prosperous place... Uncertain. Your thoughts? >> I think the competition is probably the driver as to who has to this now, who's not safe. The organizations that are heavily regulated or compliance-driven can actually use that as the reasoning for not jumping into the barrel right now, and letting it happen in other areas first, watching the technology mature-- >> Dave: Let's wait. >> Yeah, let's wait, because that's traditionally how they-- >> Dave: Good strategy in your opinion? >> It depends on the entity but I think there's nothing wrong with being safe. There's nothing wrong with waiting for a variety of innovations to mature. What level of maturity, I think, is the perspective that probably is another discussion for another day, but I think that it's okay. I don't think that everyone should jump in. Get some lessons learned, watch how the other guys do it. I think that safety is in the eyes of the beholder, right? But some organizations are just competition fierce and they need a competitive edge and this is where they get it. >> When you say safety, do you mean safety in making decisions, or do you mean safety in protecting data? How are you defining safety? >> Safety in terms of when they need to launch, and look into these new technologies as a basis for change within the organization. >> What about the other side of that point? There's so much more data about it, so much more behavior about it, so many more attitudes, so on and so forth. And there is privacy issues and security issues and all that... Those are real challenges for any company, and becoming exponentially more important as more is at stake. So, how do companies address that? That's got to be absolutely part of their equation, as they decide what these future deployments are, because they're going to have great, vast reams of data, but that's a lot of vulnerability too, isn't it? >> It's as vulnerable as they... So, from an organizational standpoint, they're accustomed to these... These challenges aren't new, right? We still see data breaches. >> They're bigger now, right? >> They're bigger, but we still see occasionally data breaches in organizations where we don't expect to see them. I think that, from that perspective, it's the experiences of the organizations that determine the risks they want to take on, to a certain degree. And then, based on those risks, and how they handle adversity within those risks, from an experience standpoint they know ultimately how to handle it, and get themselves to a place where they can figure out what happened and then fix the issues. And then the others watch while these risk-takers take on these types of scenarios. >> I want to underscore this whole disruption thing and ask... We don't have much time, I know we're going a little over. I want to ask you to pull out your Hubble telescopes. Let's make a 20 to 30 year view, so we're safe, because we know we're going to be wrong. I want a sort of scale of 1 to 10, high likelihood being 10, low being 1. Maybe sort of rapid fire. Do you think large retail stores are going to mostly disappear? What do you guys think? >> I think the way that they are structured, the way that they interact with their customers might change, but you're still going to need them because there are going to be times where you need to buy something. >> So, six, seven, something like that? Is that kind of consensus, or do you feel differently Colin? >> I feel retail's going to be around, especially fashion because certain people, and myself included, I need to try my clothes on. So, you need a location to go to, a physical location to actually feel the material, experience the material. >> Alright, so we kind of have a consensus there. It's probably no. How about driving-- >> I was going to say, Amazon opened a book store. Just saying, it's kind of funny because they got... And they opened the book store, so you know, I think what happens is people forget over time, they go, "It's a new idea." It's not so much a new idea. >> I heard a rumor the other day that their next big acquisition was going to be, not Neiman Marcus. What's the other high end retailer? >> Nordstrom? >> Nordstrom, yeah. And my wife said, "Bad idea, they'll ruin it." Will driving and owning your own car become an exception? >> Driving and owning your own car... >> Dave: 30 years now, we're talking. >> 30 years... Sure, I think the concept is there. I think that we're looking at that. IOT is moving us in that direction. 5G is around the corner. So, I think the makings of it is there. So, since I can dare to be wrong, yeah I think-- >> We'll be on 10G by then anyway, so-- >> Automobiles really haven't been disrupted, the car industry. But you're forecasting, I would tend to agree. Do you guys agree or no, or do you think that culturally I want to drive my own car? >> Yeah, I think people, I think a couple of things. How well engineered is it? Because if it's badly engineered, people are not going to want to use it. For instance, there are people who could take public transportation. It's the same idea, right? Everything's autonomous, you'd have to follow in line. There's going to be some system, some order to it. And you might go-- >> Dave: Good example, yeah. >> You might go, "Oh, I want it to be faster. I don't want to be in line with that autonomous vehicle. I want to get there faster, get there sooner." And there are people who want to have that control over their lives, but they're not subject to things like schedules all the time and that's their constraint. So, I think if the engineering is bad, you're going to have more problems and people are probably going to go away from wanting to be autonomous. >> Alright, Colin, one for you. Will robots and maybe 3D printing, for example RPA, will it reverse the trend toward offshore manufacturing? >> 30 years from now, yes. I think robotic process engineering, eventually you're going to be at your cubicle or your desk, or whatever it is, and you're going to be able to print office supplies. >> Do you guys think machines will make better diagnoses than doctors? Ohhhhh. >> I'll take that one. >> Alright, alright. >> I think yes, to a certain degree, because if you look at the... problems with diagnosis, right now they miss it and I don't know how people, even 30 years from now, will be different from that perspective, where machines can look at quite a bit of data about a patient in split seconds and say, "Hey, the likelihood of you recurring this disease is nil to none, because here's what I'm basing it on." I don't think doctors will be able to do that. Now, again, daring to be wrong! (laughing) >> Jennifer: Yeah so--6 >> Don't tell your own doctor either. (laughing) >> That's true. If anything happens, we know, we all know. I think it depends. So maybe 80%, some middle percentage might be the case. I think extreme outliers, maybe not so much. You think about anything that's programmed into an algorithm, someone probably identified that disease, a human being identified that as a disease, made that connection, and then it gets put into the algorithm. I think what w6ll happen is that, for the 20% that isn't being done well by machine, you'll have people who are more specialized being able to identify the outlier cases from, say, the standard. Normally, if you have certain symptoms, you have a cold, those are kind of standard ones. If you have this weird sort of thing where there's n6w variables, environmental variables for instance, your environment can actually lead to you having cancer. So, there's othe6 factors other than just your body and your health that's going to actually be important to think about wh6n diagnosing someone. >> John: Colin, go ahead. >> I think machines aren't going to out-decision doctors. I think doctors are going to work well the machine learning. For instance, there's a published document of Watson doing the research of a team of four in 10 minutes, when it normally takes a month. So, those doctors,6to bring up Jen and Craig's point, are going to have more time to focus in on what the actual symptoms are, to resolve the outcome of patient care and patient services in a way that benefits humanity. >> I just wish that, Dave, that you would have picked a shorter horizon that... 30 years, 20 I feel good about our chances of seeing that. 30 I'm just not so sure, I mean... For the two old guys on the panel here. >> The consensus is 20 years, not so much. But beyond 10 years, a lot's going to change. >> Well, thank you all for joining this. I always enjoy the discussions. Craig, Jennifer and Colin, thanks for being here with us here on theCUBE, we appreciate the time. Back with more here from New York right after this. You're watching theCUBE. (upbeat digital music)

>> Narrator: Live, from Washington, DC, it's the Cube. Covering AWS Public Sector Summit 2017. Brought to you by Amazon Web Services, and its partner ecosystem. >> Well welcome back to our nations' capitol, Washington, DC, hosting this week's AWS Public Sector Summit 2017. You're live, here on the Cube, which of course is the flagship broadcast of the Siliconangle TV, where my partner in crime John Fourier always likes to say, we extract the single from the noise, don't we John? >> That's right, we're here. >> Yeah, we are. >> In D.C. >> In DC and it's a little warm, it's a little toasty inside but outside especially. 95 and humidity, Jeff Raleigh could attest to that. He just pulled into town from Columbus, Ohio. Jeff, good to see you, the Senior VP and GM of Cloud at Ellucian, so thank you for being with us Jeff. >> Absolutely, John and John, happy to be here. >> You bet, so Ellucian, a leader in higher education software, we've talked a little bit about the company. 2,400 institutions around the world with which you work. Most of those, about 2,000 here in the US. Let's talk about that work, the kind of nature of the work first and then we'll jump into a little bit about how they're playing in the Cloud these days. >> Sure absolutely happy to, so the Ellucian's got a sole focus in higher education. So it's really the only industry that we serve. We serve the industry really from a software, enterprise software prospective. So that's really helping from an ERP perspective, HR finance, but really our bread and butter is the student system and it's really the systems around helping students achieve success. As they, go to a community college or go to a four year public or four year private. It's really about helping those students drive success. And actually get the successful outcomes. And we do that with registration, with advisement, with recruiting systems, so there's a full breadth of software that an institution needs in order to help a student successfully go through that process of getting a degree and ultimately getting a job. >> Well John and I can both relate to that. He's got a daughter who's transferring over to Cal Berkeley. Going to be going to school there. I've got a niece starting at UNC Wilmington that I'm helping out, I love the registration help. So, you and I need to talk about it. >> Absolutely. >> A question is how do you get the kids into the schools they want, is there a back door Trojan horse? >> We can't manipulate that much. But you talk about your company does data rich inside pour, which I thought that was an interesting way to kind of look at things. Like we have this huge treasure trove of information and data but yet maybe there's somewhat of a disconnect in interpreting that data and then putting it value, putting it to use. What do you see with regard to that in the higher education space? >> You know, I think John, that's a great question. That's actually a really big focus of ours in terms of unlocking that data. If you think about the systems that have been on campus for 30 years right. You've got all kinds of information about the students that have attended, the classes that they take and how well they've succeeded, the types of advising that they needed. But how do you unlock all of the rich information so that you can take that information, drive some insight and then just drive better outcomes? We've been working on a platform, we call it Ethos and what we basically built is a new data model for higher education where we've looked at all of those different systems and we've basically harmonized to a new data model that really sits above all of those systems. And we begin now to extract all of that information out from those systems, into a data model that's really designed around bringing role based or persona based insight. And we call it role based analytics. That basically is designed around answering the top five to seven questions that all of the people that are on campus have. So if you're a registrar and you want to know what classes should I be adding that I need extras of. Well, that's a tough question to answer, we unlock the answer to that through the Ethos platform and the new persona based analytics that we're developing. Cause when we sit down and we talk to presidents of a school or we talk to the provost, one of the things that they want is they want to know that the people that they have working on campus for student outcomes are getting access to the information that they need to do their jobs better. And so that's been a clear mandate from our customers to help them do a better job of using the information that they're collecting. >> How do you guys deal with the data science side of this Because it's interesting is that you're using data aggressively, Cloud's perfect for that. You got a lot of compute available, how are you guys taking that legacy environment and kind of putting overlay on top of a really high, functional analytic system? >> That's a great question John. So what we do is we enable all of our software, whether it be on premise software, most of our customers still run a lot of their software on premise. And what we've built for those systems is a set of restful APIs that we deliver wherever that software runs to push that data into an AWS cloud environment where we begin putting that data in the columnar databases that are really built and constructed to help get insight very, very quickly from that data. The most important part of doing that is really sitting down and talking to the person that has the question to understand, what's the question that you're trying to answer that you haven't been able to answer? And then building the visualization that they need that actually helps them answer that question. But we took it one step further, and what we did is we basically said, we know through our research that that first question really just always yields another question. Which then yields another question and so what we did is we built a heuristic capability into the analytic platform that based on the user, based on who they are, based on the role that they had at a school and based on other people that look like them and act like them and have that role. The system begins to learn the questions that are being asked and then where are they navigating to? What are the next questions, so that we actually begin presenting the users not just with the answer to the first question that they have, but actually to, we believe that now that you've got the answer to this, that this is where you're going to go next from an inside perspective. The next types of questions so we begin to guide the users and that's really where that guided nature comes from. >> So what's the next question John's going to ask then? >> This brings up the whole cognitive computing thing. The idea that predictive analytics are one thing, you've got prescription analytics also you've got the notion of recommendation engines. All kinds of cool things that are just sitting out there waiting to be applied, the question is how do you get the data, that's the number one problem. >> That's a good one, so we've got, one of the solutions that we have in our CR Import folio is called Advise. And what we do with that product is we actually bring all of the student data, so we bring their attendance data, we bring their health records, we bring all of the grades that they have. And we then build cohorts where we have like students. And what we begin to do is we begin to build a predictive model to find students that are at risk. That based on these attendance patters in these classes, we know that this set of students is likely to have a poor outcome. And so what we want to do is not just identify that, well, now they're at risk but it's the predictive side of, well what should you do, what is the actual intervention that you need to take that's going to drive a better outcome? So the solution actually takes all the data and does two things. First, it identifies who are the students that we want to be working with, could be at risk, could be hypos right, could by high potential students that we want to accelerate. But then it's about driving the actual actions and the interactions with those students. It is not just about identifying well, Johnny's going to be in trouble, it's well, okay, what should we do for Johnny to help him get out of trouble? And so it's both sides of that. So, it is about polling all of the data which means you need to understand where the data lives. We have an advantage there over, pretty much everyone else in higher education because those 2,400 institutions that we have, they are running a massive amount of our software from a portfolio perspective. So we know where the data is, so we know how to go out and get it. And then if you look at our partner, ecosystem we have over 130 partners that also serve higher education with us. And we know what data they have and we are enabling all of those partners to leverage the Ethos platform. To be able to share that data, both from an integration and interoperability perspective. But also to feed that cloud analytic solution as well. >> One of the cool things you're doing with AWS, I'll say, they pretty much run the table on public cloud, we see the numbers there. They're in the chapter of their company or divisions, like the way a company, I call the team period. I call it the enterprise years. Govnow is like really going, it's like reinvent size. It's getting to that level, what's the impact that that's having and what are some of the things that you're doing with AWS inside the public sector that's notable. >> That's a great question, I think one of the big things is we have a really, really strong go to market partnership with AWS. And I say the go to market side because we've had a really strong technical partnership with them for many years. Where we've been working with them as they've developed new services and we've been able to leverage those services to build micro applications, to build elastic applications, all of that. And that's great form a technical perspective but now it's about bringing all of that to market. We have a very strong joint partnership with. >> John: How many years has it gone back? >> About two and a half, three years. So our enterprise agreement is two and half years old. We were doing work with them before that. But it's about two and a half years old and when I look at that, we deploy all of our cloud applications solely on AWS. So they are the sole cloud provider for us. We've expanded our cloud offering outside of the United States, we're in Dublin, we have a data center in Sydney, Australia. And we are just expanded into their new data center in the eastern Canada area in Montreal. And that's helped us from a go to market because what they bring for us, is they bring that credibility of delivering cloud infrastructure. We bring credibility of delivering higher education solutions that solve specific problems that only exist in higher education. It's that combination when you go to market to basically say the world's leading infrastructure cloud provider partnered with the world's leading solution provider in higher education. That's an unbeatable solution for us. >> So I got to ask you the question that people might ask. Hey, I've not been following AWS public sector. I see the Wall Street Journal articles, they're killing it. How would you describe their current state of innovation, their current presence in the public sector market as of right now? >> I think the lens that I really have is really around that higher education, so community colleges, public four year schools and they are highly focused on it. They have a team of dedicated people that are just focused on higher education. They work with us kind of from a joint perspective and I know that my cloud business that I'm responsible for, it is the fastest growing part of Ellucian today. So in June of 2016, we actually surpassed, form a growth perspective we started growing much faster than the on premise side of our business. And that's in large part because of what AWS has enabled us to do, so from a training perspective, from a sales motion perspective, from a marketing and positioning perspective. It's a big focus for them. >> Would you consider them, like the perception of them would be they're getting traction, they've cleared the runway, they're at cruising altitude. Where are they in the mind share of higher eds? >> I definitely think, they've cleared the runway. They are clearly going past that 10,000 foot and up there. For us, one of the main reasons we chose AWS was that factor, they already had traction. They were well known and well understood and that really helps us. Prior to that, we were doing a co location where we were managing a bunch of infrastructure, that was a hard sell, cause let's face it, we're software people, not infrastructure people. When we started bringing AWS to the table and basically talking about that's where we deploy. That took a lot of questions around scale, security, elasticity and it basically put it all to rest. So we no longer have to contend with those questions because AWS is well known in the higher education space. So it really worked well for us. >> So when you sit down with a new client or new perspective client, the two of you, you come in with this great resume and I think is where it's kind of interesting to me, universities are these fountains of innovation and creative thinking. IT, maybe not so much, because it's very institutional. There's a lot of legacy baggage they're bringing along. So what are the impediments that you run into in terms of talking to folks who might be, not doubters, but maybe a little resistant to change or maybe have a little change aversion. I mean how do you go about bringing them along on that journey? >> What's interesting in terms of higher education is there's actually a couple things that are happening that really help us with that, that are happening. But to answer the first question John which was when we get into that, not really a battle. But when we get into that dialogue, where they're like well I'm not really sure that moving to the cloud is the right thing. There's an analyst that covers higher education and she's made a statement that basically is, by 2020, a no cloud policy on campus is going to be much like a no internet policy on campus. Just not going to be a thing. And a lot of that is because a lot of providers are only building cloud solutions. That's all you're going to have access to. One of the things that's happening in higher education is in the IT space particularly, they're having a hard time finding those IT professionals. Because higher education isn't seen IT wise as a sexy place to go. And so a lot of those people that have been working in higher education for 25, 30 years, they're reaching that retirement age, and so. >> John: The main frame guys. >> Right, the main frame guys, the Unix guys. And where do you go find replacements for those. And so, they're recognizing that, okay, well that's going to be a problem for us. And right there's a lot of the infrastructure, on premise infrastructure is getting old. So does it make sense to put that capital investment into infrastructure or I got other capital investment for research and research equipment that I'd much rather put, if I'm a president, I'd much rather put the money there. That also leads to an easier conversation around that journey to the cloud, that journey of taking your enterprise systems and moving them to cloud environments. The other thing that we find is, the conversation is never really around cost savings. What it's really around is the redeployment of those IT resources to be better business partners, to be business analysts, to be people that can actually be change agents at the university to bring about change cause they're no longer managing operating systems or writing network patches or security patches. They've offloaded that to us and we've offloaded part of that work to AWS. >> Well, we appreciate the perspective. Like you said, it sounds like you've got quite a corner on the market, 2,400 partners, if you will out there. Many of those overseas, so congratulations on that front. >> Thank you. >> And I wish you continued success and thanks for joining us on The Cube, first time I think right? >> Yep, first time. >> We have rookies across the board. >> But you're now a Cube alumni. >> I appreciate it. >> Look forward to having you back. >> Thanks John and John, appreciate it. >> Back with more from Washington, DC at the AWS Public Sector Summit, 2017. You're watching live on the Cube. (upbeat music)