>>Hello, buddy. And welcome back to the cubes. Coverage of Snowflake Data Cloud Summer 2020. We're tracking the rise of the data cloud and fresh off the keynotes. Hear Frank's Luqman, the chairman and CEO of Snowflake, and Anita Lynch, the vice president of data governance at Disney Streaming Services. Folks. Welcome E Need a Disney plus. Awesome. You know, we signed up early. Watched all the Marvel movies. Hamilton, the new Pixar movie Soul. I haven't gotten to the man DeLorean yet. Your favorite, but I really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the data cloud because I never liked the term Enterprise Data Warehouse. What you're doing is is so different from the sort of that legacy world that I've known all these years. But start with why the data cloud? What problems are you trying to solve? And maybe some of the harder challenges you're seeing? >>Yeah. You know, we have We've come a long way in terms of workload, execution, right? In terms of scale and performance and concurrent execution. We really taking the lid off. Sort of the physical constraints that that have existed on these types of operations. But there's one problem, uh, that were not yet, uh solving. And that is the silo ing and bunkering of data essentially in the data is locked in applications. It's locked in data centers. It's locked in cloud cloud regions incredibly hard for for data science teams to really unlock the true value of data when you when you can address patterns that that exists across data set. So we're perpetuate, uh, status we've had for for ever since the beginning off computing. If we don't start Thio, crack that problem now we have that opportunity. But the notion of a data cloud is like basically saying, Look, folks, you know, we we have to start inside, lowing and unlocking the data on bring it into a place where we can access it. Uh, you know, across all these parameters and boundaries that have historically existed, it's very much a step level function. Customers have always looked at things won't workload at that time. That mentality really has to go. You really have to have a data club mentality as well as a workload orientation towards towards managing data. >>Anita is great here in your role at Disney and you're in your keynote and the work you're doing the governance work and you're you're serving a great number of stakeholders, enabling things like data sharing. You got really laser focused on trust, compliance, privacy. This idea of a data clean room is really interesting. You know, maybe you can expand on some of these initiatives here and share what you're seeing as some of the biggest challenges to success. And, of course, the opportunities that you're unlocking. >>Sure, I mean, in my role leading data to governance, it's really critical to make sure that all of our stakeholders not only know what data is available and accessible to them, they can also understand really easily and quickly whether or not the data that they're using is for the appropriate use case. And so that's a big part of how we scale data governance. And a lot of the work that we would normally have to do manually is actually done for us through the data Clean rooms. >>Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos. And I mean, obviously you can relate to that having been in the data business for a while, but I wonder if you could elucidate on that. >>Sure, I mean data complexities air going to evolve over time in any traditional data architecture. Er, simply because you often have different teams at different periods in time trying thio, analyze and gather data across Ah, whole lot of different sources. And the complexity that just arises out of that is due to the different needs of specific stakeholders, their time constraints. And quite often, um, it's not always clear how much value they're gonna be able to extract from the data at the outset. So what we've tried to do to help break down the silos is allow individuals to see up front how much value they're going to get from the data by knowing that it's trustworthy right away. By knowing that it's something that they can use in their specific use case right away, and by ensuring that essentially, as they're continuing to kind of scale, the use cases that they're focused on their no longer required Thio make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world, >>for sure. I mean, copy creep, because it be the silent killer. Frank, I've followed you for a number of years. Your big thinker. You and I have had a lot of conversations about the near term midterm and long term. I wonder if you could talk about you know, when you're Kino. You talk about eliminating silos and connecting across data sources, which really powerful concept. But really only if people are willing and able to connect and collaborate. Where do you see that happening? Maybe What are some of the blockers there? >>Well, there's there's certainly, ah, natural friction there. I still remember when we first started to talk to to Salesforce, you know, they had discovered that we were top three destination off sales first data, and they were wondering why that was. And the reason is, of course, that people take salesforce data, push it to snowflake because they wanna overlay it with what data? Outside of Salesforce, you know, whether it's adobe or any other marketing data set and then they want to run very highly skilled processes, you know, on it. But the reflexes in the world of SAS is always like, No, we're an island were planning down to ourselves. Everybody needs to come with us as opposed to we We go, you know, to a different platform to run these type of processes. It's no different for the for the public club. Better day didn't mean they have, you know, massive moats around there. Uh, you know, their stories to, you know, really prevent data from from leaving their their orbit. Eso there is natural friction in, uh, in terms off for this to happen. But on the other hand, you know, there is an enormous need, you know, we can't deliver on on the power and potential of data unless we allow it to come together. Uh, snowflake is the platform that allows that to happen. Uh, you know, we were pleased with our relationship with Salesforce because they did appreciate you know why this was important and why this was necessary. And we think you know, other parts of the industry will gradually come around to it as well. So the the idea of a data cloud has really come, right? Uh, people are recognizing, you know, why does this matter now? It's not gonna happen overnight. There's a step global function of very big change in mentality and orientation. >>Yeah. It's almost as though the SAS ification of our industry sort of repeated some of the application silos and you build a hardened top around it. All the processes are hard around. OK, here we go. And you're really trying to break that, aren't you? Yeah, Exactly. Anita. Again, I wanna come back to this notion of governance. It's so it's so important. It's the first role in your title, and it really underscores the importance of this. Um, you know, Frank was just talking about some of the hurdles, and this is this is a big one. I mean, we saw this in the early days of big data. Where governance was this after thought it was like, bolted on kind of wild, Wild West. I'm interested in your governance journey. And maybe you could share a little bit about what role snowflake has played there in terms of supporting that agenda. Bond. Kind of What's next on that journey? >>Sure. Well, you know, I've I've led data teams in a numerous, uh, in numerous ways over my career. This is the first time that I've actually had the opportunity to focus on governance. And what it's done is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >>Well, I mean a big part of what you were talking about. At least my inference in your talk was really that the business folks didn't have to care about, you know, wonder about they cared about it. But they're not the wonder about and and about the privacy, the concerns, etcetera. You've taken care of all that. It's sort of transparent to them. Is that >>yeah, right. That's right. Absolutely So we focus on ensuring compliance across all of the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical to ensuring, you know, that were ableto do this. We don't we don't do it alone. But governance includes not just, you know, the compliance and the privacy. It's also about data access, and it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data. We focus on the infrastructure and making sure that we've architected for scale and all of these air really important components of our strategy. >>I got. So I have a question. Maybe each of you can answer. I I sort of see this our industry moving from, you know, products toe, then two platforms and platforms, even involving into ecosystems. And then there's this ecosystem of data. You guys both talked a lot about data sharing, But maybe Frank, you could start in Anita. You can add on to Frank's answer. You're obviously both both passionate about the use of data and trying to do so in a responsible way. That's critical, but it's also gonna have business impact. Frank, where's this passion come from? On your side. And how are you putting in tow action in your own organization? >>Well, you know, I'm really gonna date myself here, but, you know, uh, many, many years ago, uh, I saw the first glimpse off, uh, multidimensional databases that were used for reporting really on IBM mainframes on git was extraordinarily difficult. We didn't even have the words back then in terms of data, warehouses and all these terms didn't exist. People just knew that they wanted to have, um, or flexible way of reporting and being able Thio pivot data dimensionally and all these kinds of things. And I just whatever this predates, you know, Windows 3.1, which really set off the whole sort of graphical in a way of dealing with systems which there's not a whole generations of people that don't know any different, Right? So I I've lived the pain off. This problem on sort of had a front row seat to watching this this transpire over a very long period of time. And that's that's one of the reasons you know why I'm here. Because I finally seen a glimpse off. You know, I also as an industry fully fully just unleashing and unlocking the potential were not in a place where the technology is ahead of people's ability to harness it right, which we've never been there before, right? It was always like we wanted to do things that technology wouldn't let us. It's different now. I mean, people are just heads are spinning with what's now possible, which is why you see markets evolved very rapidly right now. We were talking earlier about how you can't take, you know, past definitions and concepts and apply them to what's going on the world. The world's changing right in front of your eyes right now. >>Sonita. Maybe you could add on to what Frank just said and share some of the business impacts and and outcomes that are notable since you're really applied your your love of data and maybe maybe touch on culture, data, culture, any words of wisdom for folks in the audience who might be thinking about embarking on a data cloud journey similar to what you've been on? >>Yeah, sure, I think for me. I fell in love with technology first, and then I fell in love with data, and I fell in love with data because of the impact the data can have on both the business and the technology strategy. And so it's sort of that nexus, you know, between all three and in terms of my career journey and some of the impacts that I've seen. I mean, I think with the advent of the cloud you know before. Well, how do I say that before the cloud actually became, you know, so prevalent and such a common part of the strategy that's required It was so difficult, you know, so painful. It took so many hours to actually be able to calculate, you know, the volumes of data that we had. Now we have that accessibility, and then on top of it with the snowflake data cloud, it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have toe have multiple copies of the data. And I think moving beyond some of the traditional mechanisms for for measuring business impact has has only been possible with the volumes of data that we have available to us today. And it's just it's phenomenal to see the speed at which we can operate and really, truly understand our customers, interests and their preferences, and then tailor the experiences that they really want and deserve for them. Um, it's it's been a great feeling. Thio, get to this point in time. >>That's fantastic. So, Frank, I gotta ask you if you're still in your spare time. You decided to write a book? I'm loving it. Um, I don't have a signed copy, so I'm gonna have to send it back and have you sign it. But your love, the inside baseball, it's just awesome. Eso really appreciate that. So but why did you decide to write a book? >>Well, there were a couple of reasons. Obviously, uh, we thought it was an interesting tale to tell for anybody who's interested in, you know what's going on. How did this come about, You know, where the characters behind the scenes and all this kind of stuff. But, you know, from a business standpoint, because this is such a step function, it's so non incremental. We felt like, you know, we really needed quite a bit of real estate to really lay out what the full narrative in context is on. Do you know, we thought books titled The Rise of the Data Cloud. That's exactly what it iss. And we're trying to make the case for that mindset, that mentality, that strategy. Uh, because all of us, you know, I think it's an industry were risk off, you know, persisting, perpetuating. Uh, you know, where we've been since the beginning off computing. So we're really trying to make a pretty forceful case for Look, there's an enormous opportunity out there. The different choices you have to make along the way. >>Guys, we got to leave it there. Frank. I know you and I are gonna talk again. Anita. I hope we have a chance to meet face to face and and talking the Cube live someday. You're phenomenal guests. And what a great story. Thank you both for coming on. Thank you. All right, you're welcome. And keep it right there, buddy. We'll be back for the next guest right after this short break and we're clear. All right. Not bad.

(upbeat music) >> Hello, everyone. Welcome to theCUBE's coverage of the "AWS Startup Showcase", season two, episode one. I'm Lisa Martin, and I'm excited to be joined by Snyk, next in this episode. Liran Tal joins me, the director of developer advocacy. Liran, welcome to the program. >> Lisa, thank you for having me. This is so cool. >> Isn't it cool? (Liran chuckles) All the things that we can do remotely. So I had the opportunity to speak with your CEO, Peter McKay, just about a month or so ago at AWS re:Invent. So much growth and momentum going on with Snyk, it's incredible. But I wanted to talk to you about specifically, let's start with your role from a developer advocate perspective, 'cause Snyk is saying modern development is changing, so traditional AppSec gatekeeping doesn't apply anymore. Talk to me about your role as a developer advocate. >> It is definitely. The landscape is changing, both developer and security, it's just not what it was before, and what we're seeing is developers need to be empowered. They need some help, just working through all of those security issues, security incidents happening, using open source, building cloud native applications. So my role is basically about making them successful, helping them any way we can. And so getting that security awareness out, or making sure people are having those best practices, making sure we understand what are the frustrations developers have, what are the things that we can help them with, to be successful day to day. And how they can be a really good part of the organization in terms of fixing security issues, not just knowing about it, but actually being proactively on it. >> And one of the things also that I was reading is, Shift Left is not a new concept. We've been talking about it for a long time. But Snyk's saying it was missing some things and proactivity is one of those things that it was missing. What else was it missing and how does Snyk help to fix that gap? >> So I think Shift Left is a good idea. In general, the idea is we want to fix security issues as soon as we can. We want to find them. Which I think that is a small nuance that what's kind of missing in the industry. And usually what we've seen with traditional security before was, 'cause notice that, the security department has like a silo that organizations once they find some findings they push it over to the development team, the R&D leader or things like that, but until it actually trickles down, it takes a lot of time. And what we needed to do is basically put those developer security tools, which is what Snyk is building, this whole security platform. Is putting that at the hands and at the scale of, and speed of modern development into developers. So, for example, instead of just finding security issues in your open source dependencies, what we actually do at Snyk is not just tell you about them, but you actually open a poll request to your source codes version and management system. And through that we are able to tell you, now you can actually merge it, you can actually review it, you can actually have it as part of your day-to-day workflows. And we're doing that through so many other ways that are really helpful and actually remediating the problem. So another example would be the IDE. So we are actually embedding an extension within your IDEs. So, once you actually type in your own codes, that is when we actually find the vulnerabilities that could exist within your own code, if that's like insecure code, and we can tell you about it as you hit Command + S and you will save the file. Which is totally different than what SaaS tools starting up application security testing was before because, when things started, you usually had SaaS tools running in the background and like CI jobs at the weekend and in deltas of code bases, because they were so slow to run, but developers really need to be at speed. They're developing really fast. They need to deploy. One development is deployed to production several times a day. So we need to really enable developers to find and fix those security issues as fast as we can. >> Yeah, that speed that you mentioned is absolutely critical to their workflow and what they're expecting. And one of the unique things about Snyk, you mentioned, the integration into how this works within development workflow with IDE, CIDC, they get environment enabling them to work at speed and not have to be security experts. I imagine are two important elements to the culture of the developer environment, right? >> Correct, yes. It says, a large part is we don't expect developers to be security experts. We want to help them, we want to, again, give them the tools, give them the knowledge. So we do it in several ways. For example, that IDE extension has a really cool thing that's like kind of unique to it that I really like, and that is, when we find, for example, you're writing code and maybe there's a batch traversal vulnerability in the function that you just wrote, what we'll actually do when we tell you about it, it will actually tell you, hey, look, these are some other commits made by other open source projects where we found the same vulnerability and those commits actually fixed it. So actually giving you example cases of what potentially good code looks like. So if you think about it, like who knows what patch reversal is, but prototype pollution like many types of vulnerabilities, but at the same time, we don't expect developers to actually know, the deep aspects of security. So they're left off with, having some findings, but not really, they want to fix them, but they don't really have the expertise to do it. So what we're doing is we're bridging that gap and we're being helpful. So I think this is what really proactive security is for developers, that says helping them remediate it. And I can give like more examples, like the security database, it's like a wonderful place where we also like provide examples and references of like, where does their vulnerability come from if there's like, what's fogging in open-source package? And we highlight that with a lot of references that provide you with things, the pull requests that fixed date, or the issue with where this was discussed. You have like an entire context of what is the... What made this vulnerability happen. So you have like a little bit more context than just specifically, emerging some stuff and updating, and there's a ton more. I'm happy to like dive more into this. >> Well, I can hear your enthusiasm for it, a developer advocate it seems like you are. But talking about the burdens of the gaps that you guys are filling it also seems like the developers and the security folks that this is also a bridge for those teams to work better together. >> Correct. I think that is not siloed anymore. I think the idea of having security champions or having threat modeling activities are really, really good, or like insightful both like developers and security, but more than just being insightful, useful practices that organizations should actually do actually bringing a discussion together to actually creating a more cohesive environment for both of those kind of like expertise, development and security to work together towards some of these aspects of like just mitigating security issues. And one of the things that actually Snyk is doing in that, in bringing their security into the developer mindset is also providing them with the ability to prioritize and understand what policies to put in place. So a lot of the times security organizations actually, the security org wants to do is put just, guardrails to make sure that developers have a good leeway to work around, but they're not like doing things that like, they definitely shouldn't do that, like prior to bringing a big risk into today organizations. And that's what I think we're doing also like great, which is the fact that we're providing the security folks to like put the policies in place and then developers who actually like, work really well within those understand how to prioritize vulnerabilities is an important part. And we kind of like quantify that, we put like an urgency score that says, hey, you should fix this vulnerability first. Why? Because it has, first of all, well, you can upgrade really quickly. It has a fix right there. Secondly, there's like an exploit in the wild. It means potentially an attacker can weaponize this vulnerability and like attack your organizations, in an automated fashion. So you definitely want to put that put like a lead on that, on that broken window, if so to say. So we ended up other kind of metrics that we can quantify and put this as like an urgency score, which we called a priority score that helps again, developers really know what to fix first, because like they could get a scan of like hundreds of vulnerabilities, but like, what do I start first with? So I find that like very useful for both the security and the developers working together. >> Right, and especially now, as we've seen such changes in the last couple of years to the threat landscape, the vulnerabilities, the security issues that are impacting every industry. The ability to empower developers to not only work at the speed with which they are accustomed and need to work, but also to be able to find those vulnerabilities faster prioritize which ones need to be fixed. I mean, I think of Log4Shell, for example, and when the challenge is going on with the supply chain, that this is really a critical capability from a developer empowerment perspective, but also from a overall business health and growth perspective. >> Definitely. I think, first of all, like if you want to step just a step back in terms of like, what has changed. Like what is the landscape? So I think we're seeing several things happening. First of all, there's this big, tremendous... I would call it a trend, but now it's like the default. Like of the growth of open source software. So first of all as developers are using more and more open source and that's like a growing trend of have like drafts of this. And it's like always increasing across, by the way, every ecosystem go, rust, .net, Java, JavaScript, whatever you're building, that's probably like on a growing trend, more open source. And that is, we will talk about it in a second what are the risks there. But that is one trend that we're saying. The other one is cloud native applications, which is also worth to like, I think dive deep into it in terms of the way that we're building applications today has completely shifted. And I think what AWS is doing in that sense is also creating a tremendous shift in the mindset of things. For example, out of the cloud infrastructure has basically democratized infrastructure. I do not need to, own my servers and own my monitoring and configure everything out. I can actually write codes that when I deploy it, when something parses this and runs this, it actually creates servers and monitoring, logging, different kinds of things for me. So it democratize the whole sense of building applications from what it was decades ago. And this whole thing is important and really, really fast. It makes things scalable. It also introduces some rates. For example, some of these configuration. So there's a lot that has been changed. And in that landscape of like what modern developer is and I think in that sense, we kind of can need a lead to a little bit more, be helpful to developers and help them like avoid all those cases. And I'm like happy to dive into like the open source and the cloud native. That was like follow-ups on this one. >> I want to get into a little bit more about your relationship with AWS. When I spoke with Peter McKay for re:Invent, he talked about the partnership being a couple of years old, but there's some kind of really interesting things that AWS is doing in terms of leveraging, Snyk. Talk to me about that. >> Indeed. So Snyky integrates with almost, I think probably a lot of services, but probably almost all of those that are unique and related to developers building on top of the AWS platform. And for example, that would be, if you actually are building your code, it connects like the source code editor. If you are pushing that code over, it integrates with code commits. As you build and CIS are running, maybe code build is something you're using that's in code pipeline. That is something that you have like native integrations. At the end of the day, like you have your container registry or Lambda. If you're using like functions as a service for your obligations, what we're doing is integrating with all of that. So at the end of the day, you really have all of that... It depends where you're integrating, but on all of those points of integration, you have like Snyk there to help you out and like make sure that if we find on any of those, any potential issues, anything from like licenses to vulnerabilities in your containers or just your code or your open source code in those, they actually find it at that point and mitigate the issue. So this kind of like if you're using Snyk, when you're a development machine, it kind of like accompanies you through this journey all over what a CIC kind of like landscape looks like as an architectural landscape for development, kind of like all the way there. And I think what you kind of might be I think more interested, I think to like put your on and an emphasis would be this recent integration with the Amazon Inspector. Which is as it's like very pivotal parts on the AWS platform to provide a lot of, integrate a lot of services and provide you with those insights on security. And I think the idea that now that is able to leverage vulnerability data from the Snyk's security intelligence database that says that's tremendous. And we can talk about that. We'd look for shell and recent issues. >> Yeah. Let's dig into that. We've have a few minutes left, but that was obviously a huge issue in November of 2021, when obviously we're in a very dynamic global situation period, but it's now not a matter of if an organization is going to be hit by vulnerabilities and security threats. It's a matter of when. Talk to me about really how impactful Snyk was in the Log4Shell vulnerability and how you help customers evade probably some serious threats, and that could have really impacted revenue growth, customer satisfaction, brand reputation. >> Definitely. The Log4Shell is, well, I mean was a vulnerability that was disclosed, but it's probably still a major part and going to be probably for the foreseeable future. An issue for organizations as they would need to deal with us. And we'll dive in a second and figure out like why, but in like a summary here, Log4Shell was the vulnerability that actually was found in Java library called Log4J. A logging library that is so popular today and used. And the thing is having the ability to react fast to those new vulnerabilities being disclosed is really a vital part of the organizations, because when it is asking factful, as we've seen Log4Shell being that is when, it determines where the security tool you're using is actually helping you, or is like just an added thing on like a checkbox to do. And that is what I think made Snyk's so unique in the sense. We have a team of those folks that are really boats, manually curating the ecosystem of CVEs and like finding by ourselves, but also there's like an entire, kind of like an intelligence platform beyond us. So we get a lot of notifications on chatter that happens. And so when someone opens an issue on an open source repository says, Hey, I found an issue here. Maybe that's an XSS or code injection or something like that. We find it really fast. And we at that point, before it goes to CVE requirement and stuff like that through like a miter and NVD, we find it really fast and can add it to the database. So this has been something that we've done with Log4Shell, where we found that as it was disclosed, not on the open source, but just on the open source system, but it was generally disclosed to everyone at that point. But not only that, because look for J as the library had several iterations of fixes they needed. So they fixed one version. Then that was the recommendation to upgrade to then that was actually found as vulnerable. So they needed to fix the another time and then another time and so on. So being able to react fast, which is, what I think helped a ton of customers and users of Snyk is that aspect. And what I really liked in the way that this has been received very well is we were very fast on creating those command line tools that allow developers to actually find cases of the Log4J library, embedded into (indistinct) but not true a package manifest. So sometimes you have those like legacy applications, deployed somewhere, probably not even legacy, just like the Log4J libraries, like bundled into a net or Java source code base. So you may not even know that you're using it in a sense. And so what we've done is we've like exposed with Snyk CLI tool and a command line argument that allows you to search for all of those cases. Like we can find them and help you, try and mitigate those issues. So that has been amazing. >> So you've talked in great length, Liran about, and detail about how Snyk is really enabling and empowering developers. One last question for you is when I spoke with Peter last month at re:Invent, he talked about the goal of reaching 28 million developers. Your passion as a director of developer advocacy is palpable. I can feel it through the screen here. Talk to me about where you guys are on that journey of reaching those 28 million developers and what personally excites you about what you're doing here. >> Oh, yeah. So many things. (laughs) Don't know where to start. We are constantly talking to developers on community days and things like that. So it's a couple of examples. We have like this dev site community, which is a growing and kicking community of developers and security people coming together and trying to work and understand, and like, just learn from each other. We have those events coming up. We actually have this, "The Big Fix". It's a big security event that we're launching on February 25th. And the idea is, want to help the ecosystem secure security obligations, open source or even if it's closed source. We like help you fix that though that yeah, it's like helping them. We've launched this Snyk ambassadors program, which is developers and security people, CSOs are even in there. And the idea is how can we help them also be helpful to the community? Because they are like known, they are passionate as we are, on application security and like helping developers code securely, build securely. So we launching all of those programs. We have like social impact related programs and the way that we like work with organizations, like maybe non-profit maybe they just need help, like getting, the security part of things kind of like figured out, students and things like that. Like, there's like a ton of those initiatives all over the boards, helping basically the world be a little bit more secure. >> Well, we could absolutely use Snyk's help in making the world more secure. Liran it's been great talking to you. Like I said, your passion for what you do and what Snyk is able to facilitate and enable is palpable. And it was a great conversation. I appreciate that. And we look forward to hearing what transpires during 2022 for Snyk so you got to come back. >> I will. Thank you. Thank you, Lisa. This has been fun. >> All right. Excellent. Liran Tal, I'm Lisa Martin. You're watching theCUBE's second season, season two of the "AWS Startup Showcase". This has been episode one. Stay tuned for more great episodes, full of fantastic content. We'll see you soon. (upbeat music)

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

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

>>Hello, buddy. And welcome back to the cubes. Coverage of Snowflake Data Cloud Summer 2020. We're tracking the rise of the data cloud and fresh off the keynotes. Hear Frank's Luqman, the chairman and CEO of Snowflake, and Anita Lynch, the vice president of data governance at Disney Streaming Services. Folks. Welcome E Need a Disney plus. Awesome. You know, we signed up early. Watched all the Marvel movies. Hamilton, the new Pixar movie Soul. I haven't gotten to the man DeLorean yet. Your favorite, but I really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the data cloud because I never liked the term Enterprise Data Warehouse. What you're doing is is so different from the sort of that legacy world that I've known all these years. But start with why the data cloud? What problems are you trying to solve? And maybe some of the harder challenges you're seeing? >>Yeah, I know. You know, we have We've come a long way in terms of workload execution, right? In terms of scale and performance and, you know, concurrent execution. We really taking the lid off sort of the physical constraints that that have existed on these types of operations. But there's one problem, uh, that were not yet, uh solving. And that is the silo ing and bunkering of data. Essentially, you know, data is locked in applications. It's locked in data centers that's locked in cloud cloud regions incredibly hard for for data science teams to really, you know, unlocked the true value of data. When you when you can address patterns that that exists across data set. So we're perpetuate, Ah, status we've had for for ever since the beginning off computing. If we don't start Thio, crack that problem now we have that opportunity. But the notion of a data cloud is like basically saying, Look, folks, you know, we we have to start inside, lowing and unlocking the data on bring it into a place where we can access it. Uh, you know, across all these parameters and boundaries that have historically existed, it's It's very much a step level function. Customers have always looked at things won't workload at that time. That mentality really has to go. You really have to have a data cloud mentality as well as a workload orientation towards towards managing data. Yeah, >>Anita is great here in your role at Disney, and you're in your keynote and the work. You're doing the governance work, and you're you're serving a great number of stakeholders, enabling things like data sharing. You got really laser focused on trust, compliance, privacy. This idea of a data clean room is really interesting. You know, maybe you can expand on some of these initiatives here and share what you you're seeing as some of the biggest challenges to success. And, of course, the opportunities that you're unlocking. >>Sure. I mean, in my role leading data to governance, it's really critical to make sure that all of our stakeholders not only know what data is available and accessible to them, they can also understand really easily and quickly whether or not the data that they're using is for the appropriate use case. And so that's a big part of how we scale data governance. And a lot of the work that we would normally have to do manually is actually done for us through the data. Clean rooms. >>Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos. And I mean, obviously you can relate to that having been in the data business for a while, but I wonder if you could elucidate on that. >>Sure, I mean data complexities air going to evolve over time in any traditional data architecture. Er, simply because you often have different teams at different periods in time trying thio, analyze and gather data across Ah, whole lot of different sources. And the complexity that just arises out of that is due to the different needs of specific stakeholders, their time constraints. And quite often, um, it's not always clear how much value they're going to be able to extract from the data at the outset. So what we've tried to do to help break down the silos is allow individuals to see up front how much value they're going to get from the data by knowing that it's trustworthy right away. By knowing that it's something that they can use in their specific use case right away, and by ensuring that essentially, as they're continuing to kind of scale the use cases that they're focused on. They're no longer required. Thio make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world, >>for sure. I mean, copy creep, because it be the silent killer. Frank, I followed you for a number of years. You know, your big thinker. You and I have had a lot of conversations about the near term midterm and long term. I wonder if you could talk about you know, when you're Kino. You talk about eliminating silos and connecting across data sources, which really powerful concept. But really only if people are willing and able to connect and collaborate. Where do you see that happening? Maybe What are some of the blockers there? >>Well, there's there's certainly, ah natural friction there. I still remember when we first started to talk to to Salesforce, you know, they had discovered that we were top three destination off sales first data, and they were wondering, you know why that was. And and the reason is, of course, that people take salesforce data, push it to snowflake because they wanna overlay it with what data outside of Salesforce. You know, whether it's adobe or any other marketing data set. And then they want to run very highly skilled processes, you know, on it. But the reflexes in the world of SAS is always like, no, we're an island were planning down to ourselves. Everybody needs to come with us as opposed to we We go, you know, to a different platform to run these type of processes. It's no different for the for the public club. Venter Day didn't mean they have, you know, massive moats around there. Uh, you know, their stories to, you know, really prevent data from from leaving their their orbit. Eso there is natural friction in in terms off for this to happen. But on the other hand, you know, there is an enormous need, you know, we can't deliver on on the power and potential of data unless we allow it to come together. Uh, snowflake is the platform that allows that to happen. You know, we were pleased with our relationship with Salesforce because they did appreciate you know why this was important and why this was necessary. And we think you know, other parts of the industry will gradually come around to it as well. So the the idea of a data cloud has really come, right? People are recognizing, you know, why does this matters now? It's not gonna happen overnight, And there's a step global function of very big change in mentality and orientation. You know, >>it's almost as though the SAS ification of our industries sort of repeated some of the application silos, and you build a hardened top around it. All the processes are hardened around it, and Okay, here we go. And you're really trying to break that, aren't you? Yeah, Exactly. Anita. Again, I wanna come back to this notion of governance. It's so it's so important. It's the first role in your title, and it really underscores the importance of this. Um, you know, Frank was just talking about some of the hurdles, and and this is this is a big one. I mean, we saw this in the early days of big data. Where governance was this after thought it was like, bolted on kind of wild, Wild West. I'm interested in your governance journey, and maybe you can share a little bit about what role Snowflake has played there in terms of supporting that agenda. Bond. Kind of What's next on that journey? >>Sure. Well, you know, I've I've led data teams in a numerous, uh, in numerous ways over my career. This is the first time that I've actually had the opportunity to focus on governance. And what it's done is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >>Well, I mean a big part of what you were talking about, at least my inference in your your talk was really that the business folks didn't have to care about, you know, wonder about they cared about it. But they're not the wonder about and and about the privacy, the concerns, etcetera. You've taken care of all that. It's sort of transparent to them. Is that >>yeah, right. That's right. Absolutely. So we focus on ensuring compliance across all the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical to ensuring, you know, that we're able Thio do this. We don't We don't do it alone. But governance includes not just, you know, the compliance and the privacy. It's also about data access, and it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data. We focus on the infrastructure and making sure that we've architected for scale and all of these air really important components of our strategy. >>I got. So I have a question. Maybe each of you can answer. I I sort of see this our industry moving from, you know, products. So then the platforms and platforms even involving into ecosystems. And then there's this ecosystem of of data. You guys both talked a lot about data sharing. But maybe Frank, you could start in Anita. You can add on to Frank's answer. You're obviously both both passionate about the use of of data and trying to do so in a responsible way. That's critical, but it's also gonna have business impact. Frank, where's this passion come from? On your side. And how are you putting in tow action in your own organization? >>Well, you know, I'm really gonna date myself here, but, you know, many, many years ago, you know, I saw the first glimpse off, uh, multidimensional databases that were used for reporting. Really, On IBM mainframes on debt was extraordinarily difficult. We didn't even have the words back then. In terms of data, warehouses and business. All these terms didn't exist. People just knew that they wanted to have, um, or flexible way of reporting and being able Thio pivot data dimensionally and all these kinds of things. And I just whatever this predates, you know, Windows 3.1, which, really, you know, set off the whole sort of graphical in a way of dealing with systems which there's not a whole generations of people that don't know any different. Right? So I I've lived the pain off this problem on sort of been had a front row seat to watching this This transpire over a very long period of time. And that's that's one of the reasons um, you know why I'm here? Because I finally seen, you know, a glimpse off, you know, also as an industry fully fully just unleashing and unlocking the potential were not in a place where the technology is ahead of people's ability to harness it right, which we've We've never been there before, right? It was always like we wanted to do things that technology wouldn't let us. It's different now. I mean, people are just heads are spinning with what's now possible, which is why you see markets evolved very rapidly right now. Way we were talking earlier about how you can't take, you know, past definitions and concepts and apply them to what's going on the world. The world's changing right in front of your eyes right now. >>Sonita. Maybe you could add on to what Frank just said and share some of the business impacts and and outcomes that air notable since you're really applied your your love of data and maybe maybe touch on culture, your data culture. You know any words of wisdom for folks in the audience who might be thinking about embarking on a data cloud journey similar to what you've been on? >>Yeah, sure, I think for me. I fell in love with technology first, and then I fell in love with data, and I fell in love with data because of the impact the data can have on both the business and the technology strategy. And so it's sort of that nexus, you know, between all three and in terms of my career journey and and some of the impacts that I've seen I mean, I think with the advent of the cloud, you know before, Well, how do I say that before the cloud actually became, you know, so prevalent in such a common part of the strategy that's required? It was so difficult, you know, so painful. It took so many hours to actually be able to calculate, you know, the volumes of data that we had. Now we have that accessibility, and then on top of it with the snowflake data cloud, it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have toe have multiple copies of the data. And I think moving beyond some of the traditional mechanisms for for measuring business impact has has only been possible with the volumes of data that we have available to us today. And it's just it's phenomenal to see the speed at which we can operate and really, truly understand our customers, interests and their preferences, and then tailor the experiences that they really want and deserve for them. Um, it's It's been a great feeling. Thio, get to this point in time. >>That's fantastic. So, Frank, I gotta ask you if you're still in your spare time, you decided to write a book? I'm loving it. Um, I don't have a signed copy, so I'm gonna have to send it back and have you sign it. But you're I love the inside baseball. It's just awesome. Eso really appreciate that. So But why did you decide to write a book? >>Well, there were a couple of reasons. Obviously, we thought it was an interesting tale to tell for anybody you know who is interested in, You know what's going on. How did this come about, You know, where the characters behind the scenes and all this kind of stuff. But, you know, from a business standpoint, because this is such a step function, it's so non incremental. We felt like, you know, we really needed quite a bit of real estate to really lay out what the full narrative and context is on. Do you know we thought books titled The Rise of the Data Cloud. That's exactly what it ISS and We're trying to make the case for that mindset, that mentality, that strategy. Because all of us, you know, I think is an industry or were risk off persisting, perpetuating, You know, where we've been since the beginning off computing. So we're really trying to make a pretty forceful case for Look, you know, there is an enormous opportunity out there, The different choices you have to make along the way. >>Guys, we got to leave it there. Frank. I know you and I are gonna talk again. Anita. I hope we have a chance to meet face to face and and talking the Cube live someday. You're phenomenal, guest. And what a great story. Thank you both for coming on. And thank you for watching. Keep it right there. You're watching the Snowflake Data Cloud Summit on the Cube.

>>Hello. I miss Caribbean, the principal Off Lens Open Source project and senior director off Engineering at Mirandes. I'm excited to be here today at launch back 2020 Virtual conference. I will be your guide, helping you to navigate the rough waters off opportunities and containers and show you the way how to take full advantage off this great new technology with help off lens. The Coburn Edie's idea. It's happening all around us. Containers and Coburn ET is everywhere. Every day, hundreds of thousands off people create new clusters. Develop containerized application on they deploy those applications on top of Cuban Edie's. It has become the golden standard for container orchestration. How did we get here? The industry has been very creative and innovative in ways how to burn it is has been marketed with the help off develops movement, empowering individual development teams leveraging 12 factor model on infrastructure. As a code principles, we have created the need for a system that is able to obstruct everything. That's one a single system to rule them all. Cooper needs has become this system. It has become the operating system for cloud. But hey, people say Coburn Ages is difficult and complex. Absolutely many people on organizations are struggling to adopt kubernetes at scale terrorist complexity on complexity on top of complexity. On top off this, you might need to unlearn some of the things you have used to do in the past. Having had chance to speak with hundreds off, Cooper needs users on operators, from beginners to ninja level hackers. I feel Coburn Edie's is not too difficult or complex. People will get this perception on Lee when they are using primitive or were limited tools for job, or if they have failed to address the needs off all different stakeholders. By using proper quality tools and products, we can truly harness the power of communities on radically improved the speed of business To get there. In my mind, we have deserved at least two important stakeholders. First, mhm. We have hopes and idea means who want to use system for centralized kubernetes cluster creation operations and management in a listen take care a lot about underlying infrastructure, security and conformance. The industry has been serving teas people very well. He has an amazing products for this segment. Dr Enterprise Container Cloud. It's a great example off such a product. Secondly, we have developers who are, in fact the consumers off. The clusters provided by the ops and I T at means they are the people who actually access the clusters on daily basis. Take deploy, run, managed, debug, inspect on observed the workloads running on top of communities. The availability and quality off tools and products for this segment has been lacking. See, very luckily, that's not the case anymore. And that's the focus off my talk today to take away. I want you to have from this simple unless we have quality tools and products for both off these important stakeholders, we might not get all the benefits we were looking for. Docker Enterprise Container Cloud. We'll get you on top and when combined with the product, I'm about to talk. Next. We'll take you where you wanna be. I'm so excited about this lens. The Cooper needs I D. I. D stands for integrated development environment. We could call it in the credit operations environment as well, but let's stick with I D for a little bit longer. No, If you would be doing non virtual conference, I would be as asking how many off you have heard or actually tried using less >>before. It's okay, Let's make make it interactive. We can still do it all right. I'm probably I would see something to 20% of people raising their hands. To be honest, I'm amazed how many people have started using lens already. It's been out on Lee for just six months or so. Lens combines all a sense of tools and technologies >>required for streamlining cloud native applicants and development on Day two operations. It's all you need to take control off Coburn. Edie's clusters on workloads running on top, for example, you might have find hard time trying to understand what is really going on in your clusters with lens. You will have complete situational awareness off all your clusters on work clothes, and you will understand what's going on on quickly. Take actions if needed. Lenses designed for developers who need to work with Cooper needs on a daily basis. If you have somebody who is just getting started, lens will lower the barrier of entry because it will let you explore your clusters on workloads very easy. Take action to try out different things on diesel eyes, everything in a way that makes sense on provides full context. If you are very experienced ninja level heavy user, you will get things done fast. In essence, by using lens, you will become more productive on the quality off life is improved a lot lenses. A stand alone desktop application for Mac OS Windows and Linux operating systems. It's free and fully open. Source under Emmett license. If you want to get started, simply download the lens application from Lens website and start adding your clusters. Now you might wonder. How does lend play together with Mirandes >>offering sheep code faster at Mirandes, we want to convert open source innovation in the customer value. We want to be best in the world. At this. We want to increase developer velocity to continuously deliver code faster for public and private clouds. And in order to do that, we want to put capable person in the center. We want to invest in products and technologies that will improve the developer productivity that speed sheep gold faster. To have speed, we got to get right amount off simplicity. Choice on security simplicity does not mean less features. It means amazing usability on developer experience for using complex on feature rich systems Under the hood. Security means invisible security, something that is built into the system from >>beginning on its part of its DNA, something that is automatically applied to the underlying infrastructure and software running on top without need for developers to worry about too much choice. It's include chance. You should be able to choose the parts you want to use, for example, choice of the infrastructure, cloud providers or even host operating system running on your machines. Everything in here comes to life with talker in the price container cloud. Combined with lens, it's the end to end solution for harnessing the power of kubernetes and radically improving the speed of business. >>All right, I hope you got the idea how lens will play together with Mirandes offering on a highly law. Now I'd like to talk more about lens features in detail. Let's kick off with multi cluster management. Unlike multi cluster management systems designed for hopes and ideas, New people peace is the Monte Cluster management from the developers point of view, take a nap. Any number >>of cabernet, these clusters to provide quick and easy way to switch cluster context on access workloads Running on top thes clusters may be the ones provide provided by their hopes and ideas mean people, but they might be clusters running locally, used in some other projects or use for hobby purposes. As an example, the clusters are added simply simply by importing the cube conflict file and selecting the cluster context. Once added, it's fast and easy to switch between clusters. Since the requirement for acting a cluster is just a cube. Conflict file lens works with any any certified Cooper needs distributions where user might have obtained to keep conflict. Five. For example, Documented price Container Cloud. You see T e. K s G. K. A. K s rancher opens it. Minnick YouTube many, many other flavors off uber Nitties They all work straight out off the box. The creating above lens is that you will get one unified I e across all your clusters. >>No matter what's the flavor on. There is absolutely nothing that you need to install in. Cluster is in itself is great because most off the developers we're talking about in here do not have sufficient right to install anything like this in their clusters. Since we're now talking about access control, let's discuss how the role based access control is taken in account with lens. It's all about uber needs built in role based access control. As you know, clusters may be configured to use any supported identity providers, since lens will authenticate uses the Cooper needs with Cuba conflict file Cooper needs are back is automatically enforced. This is also reflected on the user interface user. Will Onley see those resources they are allowed to access? Lens do not need admin level privileges, service accounts or any other solution that would by bus. The Cooper needs are back. Next. We have a smart terminal less has a built in smart terminal. It comes with bundled common line tools such as cube cattle on help. It's different from your native terminal because the smart terminal will always have cube cattle command available on bond. It will automatically >>switch the version off cube cattle to match the currently selected Cooper Needs Cluster a P I. If FBI compatible version is not found, it will be downloaded automatically in the background. In addition to making sure you are always using the right version off cube kuttel the Smart Terminal will automatically assigned the Cube conflict context to match your currently selected co Bernie. This cluster as a summary. When you use lens with building Smart Terminal, you are always using the right version off cube cattle and context. I feel there is still something more I want to share with you. Visualizations lenses Very diesel on There is a lot of detail in the user experience. One of the great features in Lens is that building in the creation with Prometheus to visualize everything. As you might know, people working on the ups and i d at me inside of things have learned to write complex Primedia Square ease. Most likely, they have created beautiful death sport to look at data. Looking at the cluster's from the bird's eye perspective. If you are a developer, you are interested in your own stuff. Bird side perspective might be nice, but it doesn't help you to debug and trouble. Suit your own application. You don't necessarily have access to or want to learn Prometheus to write your own queries on out of context that sports. That is why lens will provide automatically civilization for all supportive resource types including the aggregated Use it, >>David Little person. Or, to be honest, ops on Idea Means to will get all the data they need, always in the right context. The basic metrics include CPU memory on disk with total capacity actual use. It requests on limits. The unrest metrics include bytes sent success, failure on request and response to race. Both statistics also include network bytes sent and received. Persistent pulling. Unclaimed metrics include disk usage and capacity. Wow, that was a lot on. To be honest, we are just barely scratching the surface off the available features. Let's move on and talk about lens from the community on open source project perspective. We'll start with statistic, not because I like statistics in particular, but because this project has some mind blowing stats to share. Let's remind ourselves that lens was made open source just a half a year ago. Since then, over 600,000 downloads over 50,000 users over >>8000 star gazers on get top. The users come from all around the world. It's one off the fastest training open source projects on git hub and definitely in Cuba needs ecosystem. It's the number one e or u I or whatever you wanna call it for Cuban, it is on. If you are not using it yet, you're probably missing out some something great. What's coming on next? We are working hard every day to make lens better. Our focus as a leader in this open source project is to remain vendor Notre Look Ways for collaboration with other vendors in the cloud Native technology ecosystem on focus on making features that directing most value for our users. Against this background, the near future roadmap includes exciting features like extensive a P I. While the building features off, lens might feel great. It's just the beginning. Lens extensive a p I that is going to be a new feature released as part off Lens 4.0, we'll let you at custom visualizations on functionality to support your preferred development. Work flaws. The Extensions AP I will provide options for extensive creators to but directly into the lens You I we are already working with the number off cloud Native technology ecosystem vendors to get their technology is deeply integrated on therefore more accessible true lens, for example, on extension for a container >>image scanning technology vendor, I might add a warning icon next to a port or a deployment where vulnerable image is detected in a decent. This extension might provide more details about this vulnerability when the port or deployment is clicked. This is just a simple example, but I hope you get the idea on Really, this is just beginning. We want to >>bring entire Coburn Edie's ecosystem together in a listen to extensions. A p I. We will work on features to enhance Cooper needs Developer were close, both locally on remote, enable teamwork and naturally improve the usability on fixed box reported by our users. There are so many great things coming. It's impossible to list everything in here. If you are interested, please take a look at the epics listed on our guitar free ball. Once again, if you're not using lens already, you're probably missing out on something great. Download and get started today. For the most amazing entrant experience, check out the Docker Enterprise Container Cloud as well. I wish you all a great time with Coburn. Edie's I'm looking forward to meet you all in person someday. Take care. Bye bye

>>from >>the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Hi, I'm Stew minimum with the Cube, and I've got Stephanie. Cheers. Who's the vice president and GM of the Red Hat Enterprise Linux business unit at Red Hat? Stefanie really excited for Red Hat Summit 2020 even though we have to be together, apart. But give our audience a little bit of a preview as what they can expect from this years edition. >>Thanks to you know, we're super excited. It will be different, but it will be a Red Hat summit, just the virtual experience edition. I think the key thing for us that we always look forward to at Summit is the collaboration. It's the ability to collaborate with folks that you haven't met before. The collaborate across teams to collaborate across customers and to share stories. And just like every other year, that's our focus this year. So it's very much about how do we do our best to create that in a virtual experience way, with chat rooms and with Q and A. But how do we engage at the end of the day. That's what some it's about. It's about engagement. Um, and hopefully what folks will see and feel is still our commitment to open source and collaboration and how that whole world of development is done, as well as see a collaboration across our portfolio and pulling together across all the product lines to bring the best of red hat together with some announcements. But again, we hope everyone comes in, comes with the intent to collaborate because that's really our goal about summit. >>Yeah, of course. Watch all the red hat experience. Red hat dot com has the website and the Cube will be there with our broadcast with Stephanie and lots of the team, including some of their customers. So definitely tune in and join us. Thanks so much. >>Yeah, Yeah, yeah, >>yeah, yeah, yeah

>> Narrator: Live from the Wynn Hotel in Las Vegas, it's theCUBE covering Magento Imagine 2018. Brought to you by Magento. >> Hey, welcome back to theCUBE. We are continuing our coverage live from the Wynn Las Vegas at Magento Imagine 2018. We've had a really exciting day talking about commerce and how it's limitless and changing dramatically. Joining me next is Sucharita Kodali, the vice president and principal analyst at Forrester. Sucharita, it's great to have you on theCUBE. >> Thanks for having me, Lisa. >> So commerce is limitless. We've been hearing this thematically all day. You primarily are working with retailers on their digital strategies. And you've been doing this for a long time. Let's talk about the evolution that you've seen in the retail space with everybody expecting to have access to whatever they want to buy in their pockets. >> Right, right, right. I would say, so I've been working in the retail industry for the last two decades. I've been an analyst for the last 10 plus years. I've really seen a number of changes. And if I had to just summarize the biggest changes, one is just the inventory across different retail channels. So, that's definitely been a huge huge one. It's like, how do you, how do you order online, but then fulfill the item from a physical store or fulfill the item from another store? So those are, that's basically the digital transformation of retailers. Those are investments that companies like WalMart and Target have really been doubling down on and focusing on. The second big change is Amazon. And they single-handedly have transformed the retail industry. They have increased consumer expectations. And what Amazon's also done is reinvented retail as a business model. Because it is no longer about just selling product and being profitable selling that product. Amazon actually is not profitable with a lot of the items that it sells. It makes money in other ways. And it is probably what I would describe as America's first retail conglomerate. And that becomes a really interesting question for other companies to compete, do you have to become a retail conglomerate? Then, the third big change is just brand selling direct to consumer. I remember when I started at Forrester, my very first project was with a large consumer electronics company that asked, Well, should we even sell directly to consumers? There's channel conflict and issues with our distributors. And now, that's not even a factor. It's sort of table stakes you have to sell direct to consumer. And that's probably where we'll continue to see a lot of retail sales in the future. >> So the Amazon model, we expect to be able to get whatever we want whenever we want it, have it shipped to us either at home or shipped to us so we can go pick it up at a store. It's really set the bar. In fact, they just announced the other day that a hundred million Amazon Prime members. I know people that won't buy something if it's not available through Prime. But I think this morning the gentleman that was on main stage from Amazon said at least 50% of their sales are not products they sell, they're through all of the other retailers that are using Amazon as a channel as part of their omni-channel strategy. If you think of a retailer from 20 years ago, how do they leverage your services and expertise and advice to become omni-channel? Because as today, you said essentially it's table stakes for companies to have to sell to consumers. >> Yeah, yeah. There are so many questions that really require, I call it destroying the retail orthodoxies. And retail has historically been about buyers and merchandisers buying goods. There's the old expression in retail, You stack 'em high and watch 'em fly. And that is just where buyers would, Take a company like Toys R Us, they would basically take what Mattel and Hasbro told them to buy. They would buy a ton of it, put it in stores. And because there was less competition back in the '80s, consumers actually would buy that merchandise. And unfortunately, the change for retailers is that consumers have so much more choice now. There's so such more innovation. There are small entrepreneurs who are creating fabulous products, consumer tastes have changed. And this old paradigm of Mattel and Hasbro, or kind of fill in the blank with whatever vendors and suppliers, pushing things is no longer relevant. So, there was just an article in the journal today about how Hasbro sales were down by double digits because Toys R Us is now going to go out of business. So those are the kinds of things that retailers who did not adjust to those changes, they are the ones that really suffer. They don't find ways to develop new inventory, they don't find new channels for growth, and they don't protect their own. They don't build a moat around their customers like Amazon has done, or they don't find ways to source inventory creatively. That's where the problems are. >> You think that's more of a function of a legacy organization; having so much technology that they don't know how to integrate it all together? What do you think are some of the forcing functions old orthodoxies that companies that don't do it well are missing? >> Yeah, it's a lot of it is just in the old ways of doing business. So, a lot of it is being heavily dependent, for instance, on buyers and merchandisers buying things. I mean, one of the biggest innovations that Amazon realized was that, look you can sell things without actually owning the inventory. And that is, their entire, what we call the third party marketplace, and that is just so simple. But if you were to ask a buyer at a major retailer a decade or two ago, "Why do you have to buy the inventory?" their response would be, Well, you have to buy the inventory, that's just the way it is. And it's like, well why? Why don't you try to find a new way to do business? And they never did. But it took Amazon to figure that out. And the great irony of why so many retailers continue to struggle is that Amazon has exposed the playbook on how to sell inventory without owning it. And so few retailers to this day have adopted that approach. And that's the great irony I think, is that that's the most profitable part of Amazon's business is that third party marketplace. And every retailer I've talked to is like, Oh, it's really hard. We can't do that. But, the part of Amazon's business that everyone is looking to imitate is their fast shipping. Which, is the most expensive part of their business. Amazon is only able to afford the fast free shipping because of the third party marketplace. Other retailers want to get the fast free shipping without the marketplace. And it just doesn't make any sense. And that's really the heart of the challenge is that they just don't think about alternative business models. They don't want to change the way that they've historically run their businesses. And some of this could mean that merchants are not as powerful in organizations. And maybe that's part of the pushback is that, there could be a lot of people who lose jobs. The future will be robo-buyers and financial services you have robo-advisors, why not robo-planners in retail? >> So one of the keys then, of eliminating some of the old orthodoxies for merchants is to be able to pivot and be flexible. But it has to start from where in an organization from a digital strategy perspective? Where do you help an organization not fall into the Toys R Us bucket? >> Yeah, I think a lot of it does have to start with merchandising and putting in some interesting digital tools to help merchants be more flexible. So, you want to flex to supply and demand. And some of that comes with integrating marketplaces into your own experience. Some of it can be investing in 3D printers that can make things that are plastic or metals based on demand. That's something that I always wondered why Toy R Us didn't, for instance, make Fidget Spinners on demand. Why did you have to get them with a six month leave time from China, it never made any sense. You can scale service, so use technology to match great store associates with a customer who may have a question. And you don't have to be in the same store. It can be a Facetime call with somebody who is far away. But very few retailers do that. And finally, the last bit is really to look at new alternative business models and finding new ways of making money beyond just selling inventory. >> That's really key because there are so many oppurtunities when companies go omni-channel of not just increasing sales and revenue, but also reducing attrition, making the buying process simple and seamless. Everybody wants one click, right? >> Right. >> Super seamless, super fast, and relevant. It's got to be something if you're going to attract my business, you need to be able to offer something where you know me to a degree. >> Absolutely. >> Or know what it is I might have a propensity to buy. >> Absolutely. And that's the entire area of personalization. And that personalization can be anything from a recommendation that I give you. It can be proactively pushing a recommendation. That's what companies like Stitch Fix do is I tell you what I want and then they send you a box in the mail of things I think you would like and oh, by the way are your size and within your budget. It can be customization. One of Nike's most successful parts of their business is their Nike ID program which allows you to customize shoes according to colors and different sort of embellishments that you may like. And that's exactly the kind of thing that more retailers need to be looking at. >> What are some of the trends maybe that a B2B organization might be able to love or some of the conveniences that we have as consumers and we expect in terms of-- Magento, I was looking on their website the other day and a study that they've done suggests 93 percent of B2B buyers want to be able to purchase online. So, new business models, new revenue streams, but it really is a major shift of sales in marketing to be able to deliver this high velocity low touch model. What are some of the things that a business like a Magento, could learn from say a Nike with how they have built this successful omni-channel experience? >> Well, interestingly I think one of the most important things to recognize is that every B2B buyer is also a B2C buyer. And their expectations are set by their experiences in B2C. So, if you have everything from all of the information at your fingertips, all of that information is optimized for mobile devices. You have different ways to view that information, you have all of your loaded costs, like shipping, or tax, or if there's cross-border. All of the information related to the time to ship, any customs and duties, all of that needs to be visible because in any experience that you have with say a site like Amazon, you're going to get that information. So, the expectation is absolutely there to have it in any situation whether it's B2B or whether it's buying components or kind of very long tail items. That's basically the cost of doing business at this point, is that you have to deliver all of the information that the customer wants and needs. And if you don't, the customer is just going to opt to go purchase that product at whatever destination offers it. >> Somewhere else. >> And somebody will. That's the challenge when you have 800 thousand Plus eCommerce sellers out there selling every product imaginable in the both B2B and B2C landscape. >> So, on the data side there's so much data out there that companies have any type of business to be able to take advantage of that. I know that there's, BI has so much potential. Are you hearing retailers start to embrace advanced analytics techniques, AI machine learning, Where are they with starting to do that? I know that some eyeglass companies have virtual reality augmented reality type of apps where you can kind of try on a pair of frames. Where are you seeing advanced analytics start to be successful and help retailers to be able to target buyers that might say, oh, I can't try that on? No, I want to go somewhere that I can touch and feel it. >> Yeah, well, it's emerging still. I mean, retailers have a lot of data. I think they're trying to figure out where is it most useful. And one of the places where it is incredibly useful is in the backend with fraud management. So, after retailers were forced to put in chip cards as a payment form, what you started to see was more of the fraud shifting to eCommerce. I just had two credit cards that had to be shut off because of E-commerce fraud. But that is where you see the fraudsters going to. And what you see as a result of that is some innovators in that space technology companies really leveraging machine learning, AI, other advanced data techniques to identify fraudulent transactions and to better help retailers eliminate or reduce the percent of transactions that have to then be charged back. So, that's probably one of the most promising areas. There are others that are emerging. We're seeing more visual recognition technologies. House for instance, is excellent at that and Pinterest too. If there's part of an image you like you can click on it or you can tap it and see other images like that. And that's incredibly difficult. And it was even more difficult 10-15 years ago, but it's becoming easier. There's the voice element, voice to text or text to voice. I think that the best applications they're often in customer service, there are so many interactions that happen anywhere in a consumer facing world. It doesn't even have to be within retail. You can think about the complaints to the airline industry or to a bank. And a lot of it falls into a black hole. You always hear that oh, This call may be recorded, but it is really difficult to go back and transcribe that. And to really synthesize that into major themes. And what ML in particular can do is to basically pull out those themes, it can automate all of that, and can give insights as to what you could be doing, what you should be doing, what are the opportunities that you may not have even known existed. So there are definitely emerging places. I mean even a visual recognition, so we talked about House and Pinterest. Another great example is the computer vision that you have in the Amazon Go stores. And there's a robot that the Wal Mart stores are now testing to go find if there are gaps in the inventory that need to be filled. Or if something is running low or out of stock. So there are definitely some interesting applications, but it's still early days for sure. >> So last question, we've got to wrap here, but, we're in April 2018, what are some of the, your top three recommendations for merchants, as they prepare for say Black Friday coming up in what, six or eight months. What are you top three recommendations for merchants to be successful and be able to facilitate a seamless online offline experience? >> Well, we always have kind of imbalances between supply and demand, and that's where I do think things like third party sellers, third party marketplaces are huge. So to be able to leverage that is certainly one opportunity. Another is to think creatively about promotions. In Japan they have these promotions called Fukubukuro promotions, and it's basically like grab bags of like all the left over inventory. But then they basically put it into mystery bags where you can buy it for half off. And consumers line up around the block at stores to go buy these grab bags. Because they also have also like a gamified approach where, you know, one of out 10 of the bags will have like an Ipad or some really high value item. So people really like these things, and they have trading parties. So just new ways of having promotions beyond just the typical door busters that retailers think about. And then kind of third I think is just try to pace out the demand. One of the big issues in E-commerce has been just the burst in demand that always happen in December. And that creates a lot of problems from the standpoint of actually shipping the orders. So the more that you can pull those transaction forward into November, the better off you are from a fulfillment and supply chain standpoint. >> Alright Sucharita thank you so much for stopping by theCUBE >> Thanks Lisa >> And sharing your insights on the trends and what's going on in the commerce and E-commerce space. Really enjoy talking with you. >> Nice to talk to you too. >> We want to thank you for watching. You're watching theCUBE live from Magento Imagine 2018, I'm Lisa Martin. Stick around, I'll be back with my next guest after a short break. (upbeat music)

(upbeat music) >> Welcome to this special CUBEConversation here in the Palo Alto CUBE studio. I'm John Furrier, the co-host, theCUBE co-founder of SiliconANGLE Media. In theCUBE we're here with Alan Cohen, CUBE alumni, joining us today for a special segment on the future of technology and the impact to society. Always good to get Alan's commentary, he's the Chief Commercial Officer for Illumio, industry veteran, has been through many waves of innovation and now more than ever, this next wave of technology and the democratization of the global world is upon us. We're seeing signals out there like cryptocurrency and blockchain and bitcoin to the disruption of industries from media and entertainment, biotech among others. Technology is not just a corner industry, it's now pervasive and it's having some significant impacts and you're seeing that in the news whether it's Facebook trying to figure out who they are from a data standpoint to across the board every company. Alan, great to see you. >> Always great to be here, I always feel like, I can't tell whether I'm at the big desk at ESPN or I've got the desk chair at CNBC, but that's what it's like being on theCUBE. >> Great to have you on extracting the signal noises, a ton of noise out there, but one of things of the most important stories that we're tracking is, that's becoming very obvious, and you're seeing it everywhere from Meed to all aspects of technology. Is the impact of technology to people in society, okay you're seeing the election, we all know what that is, that's now a front and center in the big global conversation, the Russian's role of hacking, the weaponizing of data, Facebook's taking huge brand hits on that, to emerging startups, and the startup game that we're used to in Silicon Valley is changing. Just the dynamics, I mean cryptocurrency raises billions of dollars but yet (laughs) something like 10, 20% of it's been hacked and stolen. It's a really wild west kind of environment. >> Well it's a very different environment. John, you and I have been in the technology industry certainly for a whole bunch of lines under our eyes over the years have gone there. My friend Tom Friedman has this phrase that he says, "Everybody's connected and nobody's in control," so the difference is that, as you just said, the tech industry is not a separate industry. The tech industry is in every product and service. Cryptocurrency is like, the concept of that money is just code. You know, our products and services are just code, it raises a couple of really core issues. Like for us on the security point of view, if I don't trust people with the products they're selling me, that I feel like they're going to be hacked, including my personal data, so your product now includes my personal information, that's a real problem because that could actually melt down commerce in a real way. Obviously the election is if I don't trust the social systems around it, so I think we're all at an, and I'd like to say world is still kind of like iRobot moment, and if you remember iRobot, it's like, people build all these robots to serve humankind and then one day the robots wake up and they go, "We have our own point of view on how things are going to work" and they take over, and I think whether it's the debate about AI, whether cryptocurrency's good or bad, or more importantly, the products and services I use, which are now all digitally connected to me, whether I trust them or not is an issue that I think everyone in our industry has to take a step back because without that trust, a lot of these systems are going to stop growing. >> Chaos is an opportunity, I think that's been quoted many times, a variety-- >> You sound like Jeff Goldblum in like Jurassic Park, yeah. (laughing) >> So chaos is upon us, but this is an opportunity. The winds are shifting, and that's an opportunity for entrepreneurs. The technology industry has to start working for us but we've got to be mindful of these blind spots and the blind spots are technology for good not necessarily just for profits, so that also is a big story right now. We see things like AI for good, Intel has been doing a lot of work on that area, and you see stars dedicated to societal impact, then young millennials, you see the demographic shift where they want to work on stuff that empowers people and changes society so a whole kind of new generation revolution and kind of hippie moment, if you look at the 60s, what the 60s were, right? >> Well there's people out in the street protesting, right? There were a couple of million women out in the street this weekend, so we are in that kind of moment again, people are not happy with things. >> And I believe this is a signal of a renaissance, a change, a sea change at enormous levels, so I want to get your thoughts on this. As technology goes out in mainstream, certainly from a security standpoint, your business Illumio is in that now where there's not a lot of control, just like you were mentioning before we came on that all the spends happening but no one has more than 4% market share. These are dynamics and this is not just within one vertical. What's your take on this, how do you view this sea change that's upon us, this tech revolution? >> Well, you know, think about it. You and I grew up in the era where clients server took over from main frame, right? So remember there was this big company called IBM and they owned a lot of the industry, and then it blew up for client server and then there were thousands of companies and it consolidated its way down, but when those thousands of new companies, like you didn't know what was going to be Apollo and what was going to be Oracle right? Like you didn't know how that was going to work out, there was a lot of change and a lot of uncertainty. I think now we're seeing this on a scale like that's 10x of this that there's so much innovation and there's so much connectedness going on very rapidly, but no one is in control. In the security market, you know, what's happening in our world is like, people said, okay I have to reestablish control over my data, I've lost that control, and I've lost it for good reasons, meaning I've evolved to the cloud, I've evolved to the app economy, I've done all of these things, and I've lost it for bad reasons because like am I, like I'm not really running my data center the way I should. We're in the beginning of a move in of people kind of reasserting that control, but it's very hard to put the genie back in the bottle because the world itself is so much more dynamic and more distributed. >> It's interesting, I've been studying communities and online communities for over a decade in terms of dynamics. You know, from the infrastructural level, how packets move to a human interaction. It's interesting, you mentioned that we're all connected and no one's in control, but you now see a ground swell of organic self-forming networks where communities are starting to work together. You kind of think about the analog world when we grew up without computers and networks, you kind of knew everyone, you knew your neighbor, you knew who the town loony was, you kind of knew things and people watch each other's kids and parents sat from the porch, let the kid play, that's the way that I grew up, but it was still chaotic but yet somewhat controlled by the group. So I got to ask you, when you see things like cryptocurrency, things like KYC, know your customer, anti money laundering, which is, you know these are policy based things, but we're in a world now where, you know, people don't know who their neighbors are. You're starting to see a dynamic where people are-- >> Put the phone down. >> Asserting themselves to know their neighbor, to know their customer, to have a connected tissue with context and so your trust and reputation become super important. >> Well I think people are really, so like every time there is a shift in technology, there's scary stuff. There's the fuddy-duddy moment where people are saying, "Oh we can't use that," or "I don't know that," and you know, clearly we're in this kind of new kam-ree and explosion of this cloud mobile blah blah blah type of computing thing and ... Blah blah blah is always a good intersection when you don't have a term. Then things form around it, and just as you said, so if you think about 25 years ago, right, people created The WELL and there was community writing first bulletin boards and like now we have Facebook and you go through a couple of generations and for a while, things feel out of control and then it reforms. I personally am an optimist. Ultimately I believe in the inherent goodness of people, but inherent goodness leaves you open and then, you know, could be manipulated, and people figure these things out. Whether it's cryptocurrency or AI, they are really exciting technologies that don't have any ground rules, right? What's going to happen I believe is that people are going to reestablish ground rules, they're going to figure out some of the core issues, and some of these things may make it, and some of these things may not make it. Like cryptocurrency, like I don't know whether it makes it or not, but certainly the blockchain as a technology we're going to be incorporating in what we do, and maybe the blockchain replaces VPNs and last generation's way of protecting zeros and ones. If AI is figuring out how to read an MRI in five minutes, it's a good thing, and if the AI is teaching you how to exclude old folks for me finding jobs, it's a bad thing. I think as technology forms, there's always Spectre and 007, right? There's always good and bad sides and you know, I think if you believe-- >> I'm with you on that. I think value shifts and I think ultimately it's like however you want to look at it will shift to something, value activity will be somewhere else. Behind me in the bookshelf is a book called The World is Flat and you're quoted in it a lot as a futurist because you have inherently that kind of view, well that's not what you do for a living, but you're kind of in an opt-- >> Alan: Marketing, futurist, kind of same thing. >> Thomas Friedman, the book, that was a great book and at that time, it was game changing. If you take that premise into today where we are living in a flat world and look at cryptocurrency, and then over with the geo political landscape, I mean I just can't see why the Federal Reserve wouldn't reign in this cryptocurrency because if Japan's going to control a bunch of, or China, it's going to be some interesting conversations. I mean I would be like all over that if I was in the Federal Reserve. >> I think people-- Look, cryptocurrency's really interesting and I think people a little over-rotated. If you look at the amount of GDP that's invested in cryptocurrency, it's like, I don't know, there might've been, you know 20 years ago the same amount involved invested in Beanie Babies, right? I mean things show up for a while and the question is is it sustainable over time? Now I'm trained as an economist, you and I have had this conversation, so I don't know how you have a series of monetary without kind of governmental backing, I just don't understand. But I do understand that people find all kinds of interesting ways to trade, and if it's an exchange, like I mean what's the difference between gold and cryptocurrency? Somebody has ascribed a value to something that really has no efficacy outside of its usage. Yeah I mean you can make a filling or bracelets out of gold but it doesn't really mean anything except people agree to a unit of value. If people do that with cryptocurrency, it does have the ability to become a real currency. >> I want to pick your perspective on this being an economist, this is is the hottest area of cryptocurrency, it's also known as token economics, is a concept. >> Alan: Token economics. >> You know that's an area that theCUBE, with CUBE coins, experimenting with tokens. Tokens technically are used for things in mobile and whatnot but having a token as a utility in a network is kind of the whole concept, so the big trend that we're seeing and no one's really talking about this yet is instead of having a CTO, Chief Technology Officer, they're looking for a CEO, a Chief Economist Officer, because what you're seeing with the MVP economy we're living in and this gamification which became growth hack which didn't really help users, the notion of decentralized applications and token economics can open the door for some innovation around value and it's an economic problem, how you have a fiscal policy of your token, there's a monetary policy, what's it tied to? A product and a technology, so you now have a now a new, twisted, intertwined mechanism. >> Well you have it as part of this explosion, right? We're at a period of time, it feels like there's a great amount of uncertainly because everything's, you know, there's a lot of different forces and not everybody's in control of them, and you know, it's interesting. Google has this architecture, they call it BeyondCorp, where the concept is like networks are not trusted so I will just put my trust in this device, Duo Security's a great example of a company that's built a technology, a security technology around it which is completely antithetical to everything we know about networks and security. They're saying everything's the internet, I'll just protect the device that it's on. It's a kind of perfect architecture for a world like where nobody is in charge, so just isolate those, buy this, what is a device? It's a token too, it's a person, your iPhone's your personal token. Then over time, systems will form around it. I think we just have to, we always have to learn how to function in a different type of economy. I mean democracy was a new economy 250 years ago that kind of screwed around with most of the world, and a lot of people didn't think it would make it, in fact we went through two World War wars that it was a little on the edge whether democracy was going to make it and it seems to have done okay, like it was pretty good IPO to buy into. You know, in 1776. But it's always got risks and struggles with it. I think if, ultimately it comes together, it's whether a large group of people can find a way to function socially, economically, and with their personal safety in these systems. >> You bring up a great point, so I want to go to the next level in this conversation which is around-- >> Alan: You've got the wrong guy if you're going to the next level because I just tapped out. >> No, no, no we'll get you there. It's my job to get you there. The question is that everyone always wants to look at, whether it's someone looking at the industry or actors inside the industries across the board, mainly the tech and we'll talk about tech, is the question of are we innovating? You brought up some interesting nuances that we talk about with token economics. I mean Steve Jobs had the classic presentation where he had street signs, technology meets liberal arts. That's a mental image that people who know Steve Jobs, know Apple, was a key positioning point for Apple at that time which was let's make computers and technology connect with society, liberal arts. But we were just talking about is the business impact of technology, the economics, and that's just not like just some hand waving, making technology integrate with business. You're in the security business, There are some gamification technology, gamification that's business built into the products. So the question is, if we have the integration of business, technology, economics, policy, society rolling into the product definitions of innovation, does that change the lens and the aperture of what innovation is? >> I think it does, right? The IT industry's somewhere between three and four trillion dollars depends on how it counts in. It grows pretty slowly, it grows by a low single digit. That tells me as composite, like is that, that slow growth is a structural signal about how consumers of technology think in a macro sense. On a micro sense, things shift very rapidly, right? New platforms show up, new applications show up, all kinds of things show up. What I don't think we have done yet, to your point, is in this new integrated world, the role of technology is not just technology anymore. I don't think, you know you said you need Chief Economical Officer, what about Chief Political Officer? What about a Chief Social Officer? How many heads of HR make decisions about the insertion of systems into their business? And that's what this kind of iRobot concept is in my mind which is that you know, we are exceeding control of things that used to be done by human beings to systems and when you see control, the social mores, the political mores, the cultural mores, and the human emotional mores have to move with it. We don't tend to think about things like that. We're like, "I win and my competitors lose." Like technology used to be much more of a zero sum, my tech's better than yours. But the question is not just is my tech better than yours, is my customer better off in their industry for the consumption of my technology of inserting it into their offering or their service? You know what, that is probably going to be the next area of study. The other thing that's very important in whether, any of you have read Peter Thiel's book Zero to One, the nature of competition technology used to feel like a flat playing field and now the other thing that's rising is do you have super winners? And then what is the power of the super winners? So you mentioned whether it's Facebook or Google or Amazon or you know, or Microsoft, the FANG companies right? Their roles are so much more significant now than the Four Horsemen of the Nasdaq were in 2000 when you had Intel and Cisco and Oracle and Saht-in it's a different game. >> You're seeing that now. That's a good point, so you're reinforcing kind of this notion that the super players if you will are having an impact, you're mentioning the confluence of these new sectors, you know, government, policy, social are new areas. The question is, this sounds like a strategic imperative for the industry, and we're early so it's not like there's a silver bullet or is there, it doesn't sound like there, so to me that's not really in place yet, I mean. >> Oh no. We're not even in alpha. We have demo code for the new economy and we're trying to get the new model funded. >> John: That's the demo version, not the real version. It's the classic joke. >> Yeah this not the alpha or the beta version that like you're going to go launch it. If people think they're launching it, I think it's a little preliminary and you know, it's not just financial investment, it's like do I buy in? I'll tell you something that's really interesting. I've been visiting a bunch of our customers lately and the biggest change I'd say in the last two years is they now have to prove to their customers they're going to be good custodians of their data. Think about that, like you could go to any digital commerce you do, any website you use and you give them basically the ticket to the Furrier family privacy, you do, but you don't spend a lot of time questioning whether they're really going to protect your data. That has changed. And it's really changing in B2B and in government organizations. >> The role of data to us is regulation, GDPR in Europe, but this is a whole new dynamic. >> It's not just my data because I'm worried about my credit card getting hacked, I'm worried about my identity. Like am I going to show up as a meme in some social media feed that's substituted for the news? I don't want to use the FN word, but you know what I mean? It is a really brave new world. It's like a hyper-democracy and a hyper-risky state at the same time. >> We're living in an area of massive pioneering, new grounds, this is new territory so there's a lot of strategic imperatives that are yet not defined. So now let's take it to how people compete. We were talking before we came on camera, you mentioned the word we're in an MVP economy, minimum viable product concept, and you're seeing that being a standard operating procedure for essentially de-risking this challenge. The old way of you know, build it, ship it, will it work? We're seeing the impact from Hollywood to big tech companies to every industry. >> Well you've got a coffee mug for a company that does both. Amazon does MVP in entertainment, like we'll create one pilot and see if it goes as opposed to ordering a season for 17 million dollars to hey, let's try this feature and put it out on AWS. What's interesting is I don't think we've completely tilted but the question is will buyers of technology, of entertainment products, of any product start to say, "I'll try it." You know like, look, I've done four startups and I always know there's somebody I can go to get and try my early product. There are people that just have an appetite, right? The Jeffrey Moores, early adapter, all the way to the left of the-- >> They'll buy anything new. >> They'll try it, they're interested, they have the time and the resources, or they're just intellectually curious. But it was always a very small group of people in the IT industry. What I think that the MVP economy is starting to do is look, I Kickstarted my wallet. I don't know if I'm the only person who bought that skinny little wallet on Kickstarter, it doesn't matter to me, it had appeal. >> What's the impact of the MVP economy? Is it going to change to the competitive landscape like Peter Thiel was suggesting? Does it change the economics? Does it change the makeup of the team? All of the above? What's your thoughts on how this is going to impact? Certainly the encumbrance will seem to be impacted or not. >> I think two things happen. One, it attacks the structural way markets work. If you go back to classical economics, land, labor, and capital, and people who own those assets, now you add information as a fourth. If those guys were around now they would say that would be the fourth core asset, production, I'm sorry, means of production is the term. The people who can dominate that would dominate a market. Now that that's flattened out, you know, I think it pushes against the traditional structures and it allows new giants to kind of show up overnight. I mean the e-commerce market is rife with companies that have, like look at Stich Fix. A company driven by AI, fashions, tries to figure out what you like, sends it to you every month, just had a monster IPO. We invented, by the way the Spiegal Catalog, except like with a personal assistant and you know, it's changed that in just a short number of years. I think two things happen. One is you'll get new potential giants but certainly new players in the market quickly. Two, it'll force a change in the business model of every company. If you're in a cab in any city in the world, I'm not saying whether the app works there or not, Uber and Lyft has forced every cab company to show you here's the app to call the cab. They haven't quite caught up to the rest of the experience. What I think happens is ultimately, the larger players in an industry have to accommodate that model. For people like me, people who build companies or large technology companies, we may have to start thinking about MVPing of features early on, working with a small group, which is a little what the beta process is but now think about it as a commercial process. Nobody does it, but I bet sure a lot of people will be doing it in five years. >> I want to get your take on that approach because you're talking about really disrupting, re-imagining industry, the Spiegal catalog now becomes digital with technology, so the role of technology in business, we kind of talked about the intertwine of that and its nuance, it's going to get better in my opinion. But specifically the IT, the information technology industry is being disrupted. Used to be like a department, and the IT department will give you your phone on your desk, your PC on your desk or whatever, now that's being shattered and everyone that's participating in that IT industry is evolving. What's your take on the IT industry's disruption? >> Well look, it started 20 years ago when Marc Benioff and Salesforce decided to sell the sales forces instead of IT people, right? They went around to the end buyer. I don't think it's a new trend, I think a lot of technology leaders now figure out how to go to the business buyer directly and make their pitch and interestingly enough, the business buyer, if the IT team doesn't get on board, will do that. >> John: Because of cloud computing and ... >> Because of everything. The modern analog I think in our world is that the developers are increasingly in control. Like my friend Martin Casado up in Andreessen talks about this a lot. The traditional model on our industry is you build a product, you launch it, you launch your company, you work with the traditional analyst firms, you try to get a little bit of halo, you get customer references, those are the things you do and there was a very wall structured, for example, enterprise buying cycle. >> And playbook. >> Playbook, and there's the challenger sale and there's Jeffrey Moore and there's like seeing God. You've got your textbooks on how it's been done. As everything turns into code, the people who work with code for a living increasingly become the front end of your cycle and if you can get to them, that changes. Like I mean think about like, you know, Tom wrote about this actually in The World is Flat, like Linux started as a patchy. It didn't start with the IT department, it started with developers and there was the Linux foundation and now Linux is everything. >> There's a big enemy called the big mini computer, and not operating systems and work stations. >> Wiped out whole parts of Boston and other parts of the world, right? >> Exactly, that's why I moved out here. >> You filed client's server out here. >> I filed a smell of innovation. No but this is interesting because this location of industries is happening, so with that, so they also on the analog, so Martin's at Andreessen, so we'll do a little VC poke there at the VCs because we love them of course, they're being dislocated-- >> I don't (mumbles) my investors. >> Well no, their playbook is being challenged. Here's an example, go big or go home investment thesis seems not to be working. Where if you get too much cash on the front end, with the MVP economy we were just riffing on and with the big super powers, the Amazons and the Googles, you can't just go big or go home, you're going to be going home more than going big. >> I think they know that. I mean Dee-nuh Suss-man who's I think Chief Investment Officer at Nasdaq has a very well known talking line that there are half as many public companies as there were 10 years ago, so the exit scenario for our industry is a little bit different. We now have things like acqui-hires, right we have other models for monetization, but I think what the flip side of it is, we're in the-- >> Adapt or die because the value will shift. Liquidity's changing, which acqui-hires-- >> I think the investment community gets it completely and they spend a lot more time with the developer mindset. In fact I think there's been a doubling down focus on technical founders versus business founders for companies for just that reason because as everything turns to code, you got to hang out with the code community. I think there are actually-- >> You think there'll be more doubling down on technical founders? You do, okay. >> Yeah I think because that is ultimately the shift. There are business model shifts, but it's, you know, I mean like Uber was a business model shift, I mean the technology was the iPhone and GPS and they wrote an app for it, but it was a business model shift, so it can be a business model shift. >> And then scale. >> And then scale and then all of those other things. But I think if you don't think about developers when you're in our, and it's like we built Illumio because a developer could take the product and get started. I mean you can, developers actually can write security policy with our product because there's a class of customers, where as not everyone where that matters. There's other people where the security team is in charge or the infrastructure team is in charge but I think everything is based on zeros and ones and everything is based on code and if you're not sensitive to how code gets bought, consumed, I mean there's a GitHub economy which is I don't even have to write the code, I'll go look at your code and maybe use pieces of it, which has always been around. >> Software disruption is clear. Cloud computing is scale. Agile is fast, and with de-risking capabilities, but the craft is coming back and some will argue, we've talked about on theCUBE before is that, you know, the craftsmanship of software is moving to up the stack in every industry, so-- >> I think it's more like a sports league. I love the NBA, right? In the old days, your professional team, you'd scout people in college. Now they used to scout them in high school, now they're scouting kids in middle school. >> (laughs) That's sad. >> Well what it says is that you have to-- >> How can you tell? >> You know but they can, right? I think you know, your point about it craft, you're going to start tracking developers as they go through their career and invest and bet on them. >> Don't reveal our secrets to theCUBE. We have scouts everywhere, be careful out there. (laughs) >> But think about that, imagine it's like there's such a core focus on hiring from college, but we had an intern from high school two years ago. We hire freshman. >> Okay so let's go, I want to do a whole segment on this but I want to just get this point because we're both sports fans and we can riff on sports all day long. >> I'm just not getting the chance >> And the greatness of Tom Brady >> to talk about the Patriots. >> And Tom Brady's gotten his sixth finger attached to his hands for his sixth ring coming up. No but this is interesting. Sports is highly data driven. >> Alan: Yep. >> Okay and so what you're getting at here, with an MVP economy, token economics is more of a signal, not yet mainstream, but you can almost go there and think okay data driven gives you more accuracy so if you can bring data driven to the tech world, that's kind of an interesting point. What's your thoughts on that? >> Yeah I mean look, I think you have to track everything. You have to follow things, and by the way, we have great tools now, you can track people through LinkedIn. There's all kinds of vehicles to tracking individuals, you track products, you track everything, and you know look, we were talking about this before we went on the show right, people make decisions based on analytics increasingly. Now the craft part is what's interesting and I'm not the complete expert, I'm on the business side, I'm not an engineer by training, but look a lot of people understand a great developer is better than five bad developers. >> Well Mark Andris' 10x is a classic example of that. >> There's clearly a star system involved, so if I think in middle school or in high school, you're going to be a good developer, and I'm going to track your career through college and I'm going to try to figure out how to attach. That's why we started hiring freshmen. >> Well my good friend Dave Girouard started a company that does that, will fund the college education for people that they want to bet on. >> Sure, they're just taking an option in them. >> Yeah, option on their earnings. Exactly. >> They are. >> It sounds like token economics to me. (laughs) >> You know you can sell anything. We are in that economy, you can sell those pieces. The good news is I think it can be a great flattener, meaning that it can move things back more to a meritocracy because if I'm tracking people in high school, I'm not worrying whether they're going to go to Stanford or Harvard or Northwestern, right? I'm going to track their abilities in an era and it's interesting, speaking about craft, you know, what are internships? They're apprenticeships. I mean it is a little bit like a craft, right? Because you're basically apprenticing somebody for a future payout for them coming to work for you and being skilled because they don't know anything when they come and work, I shouldn't say that, they actually know a lot of things. >> Alan, great to have you on theCUBE as always, great to come in and get the update. We'll certainly do more but I'd like to do a segment on you on the startup scene and sort of the venture capital dynamics, we were tracking that as well, we've been putting a lot of content out there. We believe Silicon Valley's a great place. This mission's out there, we've been addressing them, but we really want to point the camera this year at some of the great stuff, so we're looking forward to having you come back in. My final question for you is a personal one. I love having these conversations because we can look back and also look forward. You do a lot of mentoring and you're also helping a lot of folks in the industry within just your realm but also startups and peers. What's your advice these days? Because there's a lot of things, we just kind of talked a lot of it. When people come to you for advice and say, "Alan, I got a career change," or "I'm looking at this new opportunity," or "Hey, I want to start a company," or "I started a company," how is your mentoring and your advisory roles going on these days? Can you share things that you're advising? Key points that people should be aware of. >> Well look, ultimately ... I never really thought about it, you just asked the question so, ultimately, I think to me it comes down to own your own fate. What it means is like do something that you're really passionate about, do something that's going to be unique. Don't be the 15th in any category. Jack Welch taught us a long time ago that the number one player in a market gets 70% of the economic value, so you don't want to play for sixth place. It's like Ricky Bobby said, if you're not first, you're last. (John chuckles) I mean you can't always be first, but you should play for that. I think for a lot of companies now, I think they have to make sure that, and people participating, make sure that you're not playing the old playbook, you're not fighting yesterday's battle. Rhett Butler in Gone With the Wind said, "There's a lot of money in building up an empire, "and there's even more money in tearing it down." There are people who enter markets to basically punish encumbrance, take share because of innovation, but I think the really inspirational is you know, look forward five years and find a practical but aggressive path to being part of that side of history. >> So are we building up or are we taking down? I mean it seems to me, if I'm not-- >> You're always doing both. The ocean is always fighting the mountains, right? That is the course of, right? And then new mountains come up and the water goes someplace else. We are taking down parts of the client server industry, the stack that you and I built a lot of our personal career of it, but we're building this new cloud and mobile stack at the same time. And you're point is we're building a new currency stack and we're going to have to build a new privacy stack. It's never, the greatest thing about our industry is there's always something to do. >> How has the environment of social media, things out there, we're theCUBE, we do our thing with events, and just in general, change the growth plans for individuals if you were, could speak to your 23 year old self right now, knowing what you know-- >> Oh I have one piece of advice I give everybody. Take as much risk as humanly possible in your career earlier on. There's a lot of people that have worked with me or worked for me over the years, you know people when they get into their 40s and they go, "I'm thinking about doing a startup," I go, "You know when you got two kids in college "and you're trying to fund your 401K, "working for less cash and more equity may not be "the most comfortable conversation in your household." It didn't work well in my household. I mean I'm like Benjamin Button. I started in big companies, I'm going to smaller companies. Some day it's just going to be me and a dog and one other guy. >> You went the wrong way. >> Yeah I went the wrong way and I took all the risk later. Now I was lucky in part that the transition worked. When I see younger folks, it's always like, do the riskiest thing humanly possible because the penalty is really small. You have to find a job in a year, right? But you know, you don't have the mortgage, and you don't have the kids to support. I think people have to build an arc around their careers that's suitable with their risk profile. Like maybe you don't buy into bitcoin at 19,000. Could be wrong, could be 50,000 sometime, but you know it's kind of 11 now and it's like-- >> Yeah don't go all in on 19, maybe take a little bit in. It's the play and run-- >> Dollar cost averaging over the years, that's my best fidelity advice. I think that's what's really important for people. >> What about the 45 year old executive out there, male or female obviously, the challenges of ageism? We're in economy, a gig economy, whatever you want to call, MVP economics, token economics, this is a new thing. Your advice to someone who's 45 who just says "Hey you're too old for our little hot startup." What should they do? >> Well being on the other side of that history I understand it firsthand. I think that you have an incumbent role in your career to constantly re-educate yourself. If you show up, whether you're a 25, 35, 45, 55, or 65, I hope I'm not working when I'm 75, but you never know right? (mumbles) >> You'll never stop working, that's my prediction. >> But you know have you mastered the new skills? Have you reinvented yourself along the way? I feel like I have a responsibility to feed the common household. My favorite part of my LinkedIn profile, it says, "Obedient worker bee at the Cohen household," because when I go home, I'm not in charge. I've always felt that it's up to me to make sure I'm not going to be irrelevant. That to me is, you know, that to me, I don't worry about ageism, I worry about did I-- >> John: Relevance. >> Yeah did I make myself self-obsolescent? I think if you're going to look at your career and you haven't looked at your career in 15 years and you're trying to do something, you may be starting from a deficit. So the question, what can I do? Before I make that jump, can I get involved, can I advise some small companies? Could I work part time and on the weekends and do some things so that when you finally make that transition, you have something to offer and you're relevant in the dialogue. I think that's, you know, nobody trains you, right? We're not good as an industry-- >> Having a good community, self-learning, growth mindset, always be relevant is not a bad strategy. >> Yeah, I mean because I find increasingly, I see people of all ages in companies. There is ageism, there is no doubt. There's financial ageism and then there's kind of psychological bias ageism, but if you keep yourself relevant and you are the up to speed in your thing, people will beat a path to want to work for you because there's still a skill gap in our industry-- >> And that's the key. >> Yeah, make sure that you're on the right side of that skill gap, and you will always have something to offer to people. >> Alan, great to have you come in the studio, great to see you, thanks for the commentary. It's a special CUBEConversation, we're talking about the future of technology impact the society and a range of topics that are emerging, we're on a pioneering, new generational shift and theCUBE is obviously covering the most important stories in Silicon Valley from figuring out what fake news is to impact to the humans around the world and again, we're doing our part to cover it. Alan Cohen, CUBEConversation, I'm John Furrier, thanks for watching. (upbeat music)

>> Hi, Lisa Martin, on the ground, with the Cube at Google for the sixth annual CloudNOW Top Women in Cloud awards. And we are very excited to be joined by our next guest, Bridgit Kromhout, the principal Cloud developer advocate from Microsoft. Welcome to the Cube! >> Welcome, to me, wait. You know what? I feel like, it's so funny. I spend so much time hosting podcasts that I'm primed to start welcoming guests. (laughter) So. >> Well, thank you. I feel very welcomed. >> Hi. Thank you so much for having me. >> And we love your Microsoft-reflected hair extensions. That's so fantastic (laughter) So, Bridgit, you are a computer scientist by training, what was your education like? Were you a STEM kid from grade school all the way through graduating college? >> Yeah, it's kind of funny. I actually wasn't and I think that there's maybe a take away there for people who think, oh, it would be too hard to switch into computers. There's too much to learn. I mean, yes, there is a lot to learn, but I didn't have a computer until I was 16, so, and, I didn't know I was going to major in Computer Science until I took a programming class and realized I loved it and dropped all my other classes and completely switched my major. And I think that there's probably a lot of opportunities today that there weren't back when I did this in the 90's. You know, all sorts of boot camps and that sort of thing, but I think probably just that you can choose to go into tech from any starting point. ' Cause, like, not having a computer as a kid, I would go over to friend's houses and play Oregon Trail and, you know, Dive Dysentery, but I wouldn't have that at home and I turned out fine. >> Well, I love that you took a class and you tried it and that was transformative. I think that's one of the great lessons that even your experience can share is, try it. >> Absolutely. >> And it probably opened up your world too. Did it, well yeah, let's talk about that. >> Yeah. >> Did it open up your world to expose more of what computer science is than what you may have thought? >> You know, I had gone to some summer math camps as a teenager and you know, played around with fractals and you know, programs to generate fractals, like on the, I think it was probably SJI workstations, that the college we were at had and it was interesting to me but maybe not necessarily something I could take action on until I got to college and got access to unit systems and it's like the little kid in Jurassic Park, this is a unit system. I know this. (laughter) You know, I think that getting the opportunity to try things, whether it's in an academic setting or just with all of the free resources that are available today, it's super important. >> So, you went to the University of Minnesota, what surprised your or delighted you through your curriculum in computer science, when you were there? >> You know, it's kind of funny. I feel like there was a lot of emphasis on algorithms and data structures and probably, because I was working for the CS department as a Student Systems Administrator at the same time, I kept thinking like, well bigger notation, this is great, but let's talk about troubleshooting things on this, you know, Solaris system, because that's what I would actually do and I think that there is, I've come to realize over time that there's a lot of benefit to both. Like, you could spend a lot of time going down a rabbit hole if you don't have a firm theoretical background of what's actually possible, and how you can speed up a system. So, it's good to have that theoretical background, but I think it's also really important to focus on the like, the observability and the usability of systems and your detailed troubleshooting steps. I think of it like, you spent a lot of time in college taking classes where they emphasize the Scientific Method and you learning to prove that gravity works was never the point. >> Right. >> Because, obviously, we all know that but you learning how to isolate variables and observe accurately, helps a lot in terms of solving problems in production systems later. >> Good insight. So, you're very involved in the community. You are, you mentioned, podcasts. You go to conferences. You blog. What inspires you to share your knowledge, your experiences, and be involved in the community? >> I mean, I think that I had a manager some years ago who encouraged me to speak at a local UN conference and I brought a co-worker and spoke with him and it was a very new experience for me and I was nervous and what I realized is, that the room was full of people who, they weren't there to stare at me or judge me, they were there because they really hoped to get some insights for things they were trying to do and I think realizing that, whatever it is that you're putting out there in the world, people aren't looking at it to judge you, they're looking at it 'cause they need something and realizing that makes it so much more interesting and also, less scary to share. >> I imagine rewarding, as well. >> I think so. Like, especially because people are often looking for ways that they can drive change inside their organization, how they can convince somebody to use the exciting new framework or the exciting new, you know, container orchestration or whatever, that they're trying to use. Like, a lot of times, people who are paying attention to the wider world of tech really want to use exciting new things, but, hey, spoiler alert, if you work in a company with more than two people, there will probably be at least two opinions. >> Yeah. (laughter) >> So, you have to. >> You can basically go and do that, right? >> Yeah. Right? >> Yeah. >> So, you have to have not just all the technical background. I like to joke that, you know, I majored in Computer Science 'cause I didn't want to talk to people and, oops, turns out, tech is full of humans. Software is made of people. >> Yep, right. >> Like sort of an ingredient, right? >> Yeah. >> And, it's like, you can't, you can't avoid that and I say, just embrace it. >> I love that. Do you have any themes to your podcasts or to your blogs? >> Yeah, I think there's a talk I gave a number of times in the last year called, Containers Will Not Fix Your Broken Culture And Other Hard Truths. >> Interesting. >> And, then I gave, I decided a few months after I gave that one enough times that I was bored of hearing myself talk, I started giving one called Computers Are Easy, People Are Hard, because I think that the tech stuff that we're all excited about has a lot of socio-technical components, in terms of the interactions. >> Yeah. >> Like, every single technical choice you want to make has a certain weight and gravity to it of the way the other people feel about how you maybe made their job harder or easier or maybe that they now feel displaced. Maybe they're not sure what their place is in the exciting new world where you changed everything out from under them and they were just hoping to hold on a couple years more, until they retired and I think, as a mid-career professional, shall we put it that way? I, of course, I see all the kids these days TM, but I also see and sympathize with all the people who, who really prefer the industry not have another giant C change right this second. >> Right. >> 'Cause they kind of just want to vest and get out and it's like, I think we have to be empathetic and understanding of everyone's perspective along that entire spectrum, 'cause there's a lot of benefit to exciting change and there's also a lot of benefit to contextual knowledge of your local environment. >> Right. >> And, it's like, people at different ends of, you know, their career trajectory have you know, a varying degree of either of those, and I think it's really important and positive to listen to everyone. >> I love that because culture is something that we talk about a lot with technology executives that we're talking to in the Cube, whether it's a C level or a line of business manager or a product person and cultural change is hard. >> Really hard. >> To impact but, you bring up a great point about where you are on the career trajectory. You're opinions or experience is going to influence that. >> It totally will. I mean, especially because, so, I just started a couple months ago, working at Microsoft. I spent the two years before that working at Pivotal, talking to a lot of our customers in large enterprises and governments and you know, banks and that sort of thing and you have a lot of resistance to and fear of change when it feels like the stakes are really high and there's a lot of uncertainty and so, anywhere that, from a technical point of view, you can help with that uncertainty. Whether it's by, instead of the artisanally, hand-whittled servers in your data center, maybe looking at public Cloud, anything that can make steps more reproducible, so that you don't have to cling so much to what you were doing before and can, hopefully, extend past that. Like, there's a lot of places where that the exciting wave of IT improvement that a lot of orgs are doing intersects with people's desire to maybe have challenges but also, still feel valued. Like, there's a lot of places where, considering those human factors, when making exciting organizational change happen, which everybody needs to for their profit motives or you know, their organizational mission, in general. I think it's really beneficial. >> Speaking of feeling valued, who do you value? Who are some of your mentors that inspire you today? >> You know, it's funny you should ask that because I feel like mentorship is one of those things where I have a giant question mark. I'm not sure if I've had it done right or have ever done it right or whatever. I would say I'm definitely inspired by a lot of the women I know in technology. In particular, like, for example, Jessie Frazelle. I happen to work on the same team with her now at Microsoft, which we did not, either of us, know that the other one was going there when I had her keynote, Dev Up Stays Minneapolis, last summer and then, in just a couple months later, it was like, oh, you're going to Microsoft? What team? We're going to the same team. This is fantastic! >> Wow, that's great. >> But, I bring her up as an example because I think that if you, no matter how long you've been in tech and she's younger than I am and has been in tech a shorter amount of time, and yet, like, she both contributes, you know, solid technical content. She has commits in the Linux kernel, but she also makes sure to put information out there to help other people. I think that, that's a really, it's what I look up to and what I try to emulate is it's great to be technical, but we also have to be human. >> I love that. Well, Bridget, thank you so much for stopping by the Cube and sharing your story and congrats on the award. >> Thank you so much. >> We thank you for watching again. Lisa Martin, on the ground, with the Cube at Google for the CloudNow Top Women in Cloud Awards. Thanks for watching. (upbeat music)

>> We just started reselling Veeam We now have a combination of a very strong technology portfolio, deep integration, and a commitment to good market partnership. The combination, we think, will be very exciting for HP, Nimble, and Veeam customers in the years to come. (relaxed electronic music) >> Announcer: Live from New Orleans it's theCUBE covering Veeam On 2017. Brought to you by Veeam. >> Welcome back to New Orleans, everybody. This is theCUBE, the leader in live tech coverage. We go out to the events and we extract the signal from the noise. I'm Dave Vellante with Stu Miniman. Dave Russell is here. He's a vice-president and distinguished analyst at Gartner. David, good to see you. Thanks for coming on. >> Hey, good to see you guys. Nice to see you again. >> So, we were talking off camera. I mean, you are probably the number one known backup, data protection analyst in the business and have been for quite some time. You've seen it all. Give us the state of backup, recovery, data protection, availability, whatever you want to call it. But where are we today? >> You know, in some regards, I don't know if we're any different than we were 28 years ago when I got into the business. The interesting thing is, my wife actually got into this before I did. We were both mainframe developers of backup at IBM and I didn't really want to get a real job. Maybe you could argue I still don't have a real job but what I wanted to do is to stay in grad school forever and I started doing backup there in grad school for undergraduate computing lab. And about six years ago, I showed my wife some of the polls that we do at Gartner events. We can do realtime feedback, what's your greatest challenge, what are your issues with backup? And then she said that was kind of interesting. Two years ago, she came to an event we did in Las Vegas and afterwards she came up and I was hoping she was going to say, "Hey, you did a good job." She said, "What in the heck have you been doing? "These are the same problems when I left the industry 20 years ago to be a mom." Everybody still has too much data, too little backup window, the cost is too high, the complexity is too great. So a lot of infrastructure changes but not a lot of the same pain points have shifted dramatically. What has shifted, though, is cost is even more important than it ever was. Obviously, we could talk about volume of data but now we maybe want to have multiple copies of even our backup data. We want faster access to that backup data. 'Cause we want, now, backup to be a high-availability replication solution not just the tape in the vault somewhere. So there's now speed requirements on our backup. So, I could keep going forever but I'll just let it out to say that as an industry, we still have many of the same challenges that we've always had, arguably for decades and decades. Now, the challenge is the cat's out of the bag, meaning the rest of the business sometimes is aware of just how costly this is. Just how difficult this is from an op-ecs perspective. We can't go hire five, 10 smart people to do this. >> And the backup window, is it correct to say it's essentially disappeared? >> Yeah, there's some organizations that really feel like we don't have a backup window 'cause if we just take a step back, what is really backup, nevermind how you could use it for other use cases like DevOps. Backup, if you state it in the most unappealing terms, it's how much data are you willing to lose, how much time are you willing to take to go get that aged copy of data. And, of course, the rhetorical answer would be well, I don't want any of those bad things to happen, right. But at the end of the day, that's really our frustration. >> David: And I want it back instantly. >> Yeah. >> Okay, so that's obviously putting great pressure on the businesses. So when you look at Veeam's ascendancy, I've been saying it all day and I'd like to test this with you, it sort of coincided, obviously, with VMware and when people had to sort of rethink their VMware backups. You just did a webinar entitled "Backup: Fix it or Ditch it." I feel like a lot of people went through that, answering that question in early VMware days. So, give us, what was the conclusion of that webinar? >> Yeah, well, the number one thing is frustration. And we've done a lot of drill down on what are you frustrated on. Number one is cost, number two is complexity and we could even break this up by large enterprise, mid-size, and smaller enterprise but there's a lot of similarities. So now, where do you come out on fix it or ditch it? The answer for many organizations, is a little bit of both. And what I mean by that, this is kind of mind-boggling, I think, is that backup space used to be sweep the floor. If you were in an incumbent vendor, you wanted to kick out any other solution, if you were an organization, you wanted to collapse from three, five backup products to one backup product, and if you were an emerging vendor, what do you want to do? Go kick out the incumbent vendor. But now, an organization says, "You know, maybe we'd like "to completely change, but we can't. "So we're going to try and fix what we've got." And that's usually what I recommend, at least try and get the value out of what you've already bought and deployed But we're going to implement something else, too. So, there's probably 15 years or more of trying to collapse the number of solutions. Now an organization says, not 'cause I want five solutions but because through pain, basically, not getting my needs met, I'm going to continue running two solutions or expand to two solutions. And you could argue Veeam invented that. They came in on the virtual end, exactly to your point, and then it was a land and expand. We see this happening, though, in the industry overall. >> Dave, I have to think that just the current state of cloud is compounding what you're talking about. Customers have their own data centers, they have virtualized environments. I think Veeam said this morning the average customer they have is only 75% virtualized so they've got 25 physical. Everybody's got SASS, everybody's using some public cloud, at least for some test data. Veeam says that they can now go everywhere but most customers are probably doing piecemeal deployments. Everything in IT is additive. What do you see, how does cloud impact that space in general? >> Well, my biggest fear on the cloud aspect, whether it's software as a service or public cloud, someone's going to rent you infrastructure, is that we're going to learn some lessons the hard way. Again, meaning that most organizations typically think well, if we went to software as a service, they'll take care of it. We have no responsibility anymore or didn't we "get rid of that problem" meaning backup or DR. And the answer is no. You're still the owner of the data. And where it gets shades of gray is that SASS provider's going to give you some level of protection, some level of backup. Chances are they're not going to give you everything you had when you had that email system on premise. So my fear is that organizations are going to suffer an outage and realize there is still a need for additional protection. Right now, many organizations, they're running a bit exposed or don't even realize that they're running a bit exposed. >> Yeah, what is the state of those SASS providers and public cloud providers? Is Veeam still best of breed to go in those environments or are we starting to see them all offer their own native pieces? >> Well, I think we're in a transition period because there's a number of third party solutions that can be good at handling this and you'd have to believe that ... So, take Microsoft for example. They're in the unique position of having had on premise applications and now having public cloud and so eventually, someone's going to say well, here's all the things we did for exchange on premise. Why can't we get all that availability beyond 60, 90 day retention if we go to SharePoint Online or exchange in Azure. There's a tension that's taking place right now. Right now, at this point in time, though, I think if an organization really wants to protect their data like they have and they're used to having been doing on prem, they're going to need a third party solution, whether it's Veeam or someone else. >> David, I want to ask you about your magic corner on data center backup and recovery software. It struck me that ... I don't want to overdo it. I know you guys are very sensitive about each quadrant and how customers should interpret that but we all do the same thing. We go right to the leader. People fight to be in the upper right. And it struck me that Veeam was the only relatively smaller company that sort of knows their way in there. And they're known for SMB but in the magic quadrant you were saying this is really the upper end of M and larger organizations. So what is it that sort of sets leadership apart and how is it that Veeam was able to get in there with those established, much larger players? >> Yeah, that's a great question because exactly what you said, the competitive response would have been isn't Veeam just deployed in small environments? And collectively, we take about two and a half thousand end user inquiry calls a year in backup. So we started seeing a number of trends a couple of years earlier that hey, Fortune 500 companies are deploying Veeam and it's not in the plant in Mexico City or in a small, little area. It's in the Detroit Motor City in the data center and we're seeing a bid for six figures or higher, in some cases. So that's when we started realizing, hey wait a minute. The point of being cast an enterprise supplier is to actually be in the enterprise. They're already in the enterprise. So that's what we started to notice and finally we said another issue we have with putting some of the leaders in quadrants, are they really leading the market or pushing the market? And we really felt that Veeam had kind of crossed over the point last year when we issued the quadrant in June that they were causing the market to shift, whether it was having better virtualization capability, changing to socket-level pricing, addressing ease of use. They were doing things and give sort of "extra credit" for a provider that can not only sense what the market is looking for but kind of push the market. >> Can you explain the socket-based pricing a little bit and how that affected the market? 'Cause I know a number of vendors have made some pricing changes. IBM in particular sort of said everybody can buy anything and use credits there and that was, I felt, a move to keep the install base where it is. Veeama interpreting was different with the socket-based pricing. What was that, did it have an effect on the market in any other way? >> Yeah, the short answer is it absolutely effected the market because you look at the number of heterogeneous backup vendors that have come out and now offer socket-based pricing. So they're doing this in response to Veeam. And what we see now is the organization, depending on who the buyer is, they have no idea what terabytes are. I know what server deployment we have, meaning how much socket we've got so it was just speaking to that constituency in a buying motion that they understood. >> Stu: Something they could quantify. >> Exactly. >> Veeam made a number of announcements this morning and some prior to the show. Anything jump out at you? CDP's one of the ones we've been talking the most. Maybe you could give us your quick competitive analysis of how that looks. >> Well, CDP was near and dear to my heart. In 2005, it was September 2005, almost the same day Microsoft came out with their data protection manager for CDP, Backup Exec came out with CDP. >> Stu: I was trying to remember when Kosha came out because I was at the company that acquired Kosha. >> Yeah, sure. So Kosha, Topio, you know, it can go on. And CDP, around 2005 and 6 was really a lot of buzz, going to change everything. The problem was it was difficult to do because thee infrastructure didn't facilitate it. So, back then you had to split the volume manager and have multiple rights. Now, today's announcement on CDP where you don't have to have a lot of extra infrastructure but it's the hypervisor that's splintering this off for you. IL filtering that's making this easier, making this actually achievable. I think that's going to be really compelling. Most people here I've been talking to say this is going to be great for critical applications. There were some shops I spoke with in the mid-2000s, you know, five, six, seven years, that said we use CDP even on general file systems and why? It's because if I keep making a delete and I call up the help desk and it's like, oh, Dave hit confirm to delete again. He called up to say can you get me my file back and it's the fifth time I've called this week. Well, data protection would allow us to go let him self-service perhaps, but definitely use less data. >> So, for Veeam to get that CDP granularity, if I could talk about that for a second. It's got to obviously rely on VMware APIs. Are you, I'm sure you're tracking this, but are you concerned about Dell EMC gaming the system? Historically, what have you seen there? Difficulty getting hands on SDKs? Trying to put the incumbent in an advantage. What are your thoughts on that? >> Well, you're right. Historically, especially at the storage rate perspective, proprietary APIs or sort of supporting SMIS but having quote "extensions" which are basically proprietary off to the side, were an issue. Here is a case where I think it's in the hypervisor's best interest, and soon it'll be in Microsoft's best interest with Hyper-V and you could go on and on about the other platforms to offer the capability as well. So there is a danger but I don't see how the sort of storage oligarchs are going to be able to fence that off in this case. >> Yeah, I call them the cartel. Is Veeam now, because of its ascendancy, part of that oligarchy? >> Well, I think you have to say approaching half a billion dollars in revenue, it's sort of like the enterprise question. How many enterprises do you have to get in before you enterprise? Well, how many hundreds of millions of dollars do you have to make before you're one of the big ones? >> What do you make of this messaging of Veeam, companies like Veeam, don't want to talk about backup anymore. Backups kind of past ... You see some start-ups like Datos the other day said no, no, we're not a backup company. Okay, and then there's shifting to this notion of availability. Does that resonate with customers? Is that the way customers are thinking about this or is it just sort of good marketing? >> It resonates with some customers. Now, personally, I like it 'cause to me availability is an umbrella. We can put backup and we can put disaster recovery and high availability under there. And maybe you can sort of find a way that DevOps and copy data kind of plays under availability. It doesn't actually work in all geographies. So, I was in Tokyo at a Gartner data center conference three weeks ago, I guess, almost. And they don't really, availability doesn't sound good and disaster recovery sounds worse because that meant you had disaster. So how much disaster recovery do you want to buy? Well, none because I don't want any disasters. So availability is a little regionalized. There are definitely some shops that just say look, I have a backup budget and that's what I need to go and do better. I have a backup pain point, etc. I think, though, whether it's replication and instant VM mounting and the notion of DevOps, we're seeing more and more organizations get their head around ... Whether they want to call it availability or something else but it's beyond backup. >> Well, what's come through loud and clear, however, is your point about cost. I mean, it seems like customers are still insanely focused on cost and that's because backup generally is insurance. So cost and complexity have to be minimized and a lot of the backup platforms that are out there are expensive and they're anything but simple. >> Yeah, and you look at the economics. We've seen negative pricing pressure on dollars per terabyte of backup software now for three years running. Now, list price and obviously, no one really pays list, but list price starting with just a small number of terabytes, some vendors were 10,000 dollars, some vendors were 14 and a half thousand dollars a terabyte and you and I go down to whatever shop and we go buy a terabyte drive, if you can find a one terabyte drive, for a couple hundred dollars. >> David: Four terabytes now. >> And obviously, the data written on it is where the real value is but you see the mismatch of I'm spending list price 14,000 dollars terabytes to protect 140 dollars worth of equipment. There's a problem here. So, whether you're the VP of infrastructure, the purchasing department, or just the backup admin that says I have a problem because I can't go buy now the agent for the database that I'm trying to buy 'cause we've already spent all this money on just the base backup platform. >> Yeah, there's really this 10 year pressure on all infrastructure pricing. Cloud, open source, is really putting pressure on that. So, David, thanks very much for coming on theCUBE. We really appreciate your insights and keep up the great work. >> It was great to see you guys. Thanks for having me. >> You're welcome. Alright, keep it right there everybody. We'll be back with our next guest. It's theCUBE, we're live from New Orleans, Veeam On 2017. (relaxed electronic music)

>> Voiceover: Live from Orlando, Florida, it's theCube, covering Sapphire Now. Headline sponsored by SAP HANA Cloud, the leader in platform-as-a-service, with support from Consul, Inc, the Cloud internet company. Now here are your hosts, John Furrier and Peter Burris. >> Everyone, we are live in Orlando, Florida for a special presentation of theCube at SAP Sapphire Now's theCube SiliconANGLE's flagship program. We go out to the events and extract the signal from noise. I'm John Furrier, with my co-host Peter Burris Want to give a shout out to our sponsors. Without them, we would not be here. SAP HANA Cloud Platform Console Inc, Capgemini and EMC, thanks for your support, really excited to be here. Wall-to-wall coverage, three days. Over forty videos going to be hitting YouTube: SiliconANGLE.com/youtube. Our next guest is Dan Lahl, VP of SAP HANA Cloud Platform Product Marketing, welcome to theCube, thanks for having us. >> Thank you, John. You got all that out without a stumble. That was fantastic. >> I memorize it. >> That's great. >> Without our sponsors, we wouldn't be here, thank you very much. Thanks to you, and it's a been great support from you and your team. Really appreciate it, welcome to theCube. >> Love being here. You guys have something very unique in how you bring a play-by-play but from an analyst's perspective, very, very unique. >> Someone called me Pat Summerall, and Peter, John Madden yesterday, which was a great compliment because our lives are ESPN of tech. >> And I like it because it means I'm the better looking one. >> Exactly. >> NFL Gameday, but the game is on. >> Peter: Who's a guy? >> John: Boom! (laughs) >> Boom the Cloud is here! >> It's the whiteboard. But all seriously, great conversation. One of the things that's emerging out of the whole HANA Cloud Platform Ecosystem play is that it's really buzzing, and it's not like sizzle, but it's steak on the grill as well. So, just a lot of meat on the bone and the thing that we're seeing is that SAP has been putting themselves out there with tech. And not trying to do the land grab, not saying, hey, we're SAP and this is all a marketing program to get more SAP share for our other stuff. There's clear separation between SAP stuff, whether it's, whatever the customers are buying, and then an open way for developers; both SAP developers and, now, mainstream developers, iOS and Apple so, huge shift. And the Ecosystem's super excited, so I got to ask you, how do you guys separate out the market? Explain to the folks out there how this all fits in because the HANA Cloud platform is more open, it's really non-SAP, in a way. And there's other clouds out there, and let's face it, you guys weren't getting the buzz. A little bit late to the party, and you've got the product in good position right now. But you got Amazon out there, as your Microsoft was here, you know, doing relationship with you, your partnering with Apple, IBM was on, Cisco, all the big guys are here working with you. Separate out what it means. >> So let me back up, let me back up and give you all the HANA buzzwords, we've been very confusing to the market on how we brand it to different HANA products. There's the HANA database, data managing platform, we came out with that in 2011; very similar to Oracle from SQL Interface standpoint, very different from a technology standpoint. All in memory, and everybody knows that by now. Then, we have another initiative called S/4HANA. That's taking all of the applications, putting them onto the HANA data management platform. So that's the app stack. So business suite is now S/4HANA. So data management was HANA, S/4HANA, app stack. Then we have something called the HANA Enterprise Cloud, and that's just basically a managed service. You want to take your landscape, give it to our data center, let us manage for you. >> For SAP stuff? >> SAP stuff. Yeah, not any of the red stuff or anybody else's apps but >> But some of the partner extensions? >> But some of the partner extensions, yes. And that has to be certified, but basically it's a managed service. So you want to give your data center over to SAP? Guarantee that it will run, we'll upgrade all of the apps and enhancement packs and that kind of thing. So that's HANA Enterprise Cloud. And then finally, HANA Cloud Platform is something different altogether. It really is our offer, open platform as a service. So, any of the applications that SAP is shipping today, whether that be business suite, S/4HANA, Success Factors, Ariba, Concur, Cloud for Customer, you name it, can be extended or integrated using HANA Cloud Platform. Okay, so HANA data management, HEC, the managed service, S/4HANA, the new app stack, HCP, really the extension platform for that SAP Ecosystem. Okay? Now I say that, it's an open platform. It's Java-based, can you believe it? It's not ABAP-based, it's Java-based. Node.js, all open systems. We announced at the show that we're shipping Cloud Foundry with Node.js runtimes scripting languages like Ruby and Python and PHP and Go. Databases like Mongo and Postgres and Redis, it's open systems, baby, right? >> All the tools that they are offering. >> Exactly, they can do that. Yeah. So, any programmer under 30, we can now approach and have a conversation with. They don't have to learn a German programming language, right? Now, whether it's good or bad, it doesn't make any difference, it's open systems, right? And so that's kind of the framework of what we announced. >> What's that mean to developers? Let's take that forward, okay, open cloud platform, okay, great, under 30, or, just open source is so good now all the Q&A, all the questions are on Stack Overflow and all these Node.js and technology out to be used, so that's what people want. Okay, what's the impact to me? I'm the developer. What does it mean? What's in it for me? Do I have access to all the SAP stuff? I'm used to dealing with all these different tools to put systems together. >> That's the beauty, John, is all of those tools that you use, as an open systems developer, you can now, through HANA Cloud Platform, get to the back end systems that we didn't expose before, expect through an ABAP stack. Right, you don't have to learn BAPIs, you don't have to learn ABAP. You can use your Java capabilities, using Eclipse if you want, if you want to do it on your desktop device, or use a web IDE that's Java-based, right? >> But you're exposing these through API? >> Exactly, exactly, through either APIs or through integration services, through a direct connect back to the back ends. And we not only expose data, but also processes as well, so you can take advantage of a process. One of the things we announced this week was the API Business Hub. So now, we're going to deliver a catalog of APIs, where we'll publish into and an open system developer can say Oh, what's with that management accounting services? That hooks back into S/4HANA, I just need to call the API and take advantage of those management accounting services. Very cool. >> So on the Apple relationship, which is an iOS-based thing, the developer can then go to the Enterprise customer, so this is the Ecosystem now, okay I'm a developer. I have a whitespace, I see some unique thing, a problem that my customer has, that I can solve, or I'm an entrepreneur and say Hey, you know, I have a unique idea, I want to solve that problem. I code it but I might rely on SAP data, say an ERP, I could tap that-- >> You can now tap it. >> John: And integrate it in seamlessly? >> Yes, and show it natively on an iOS device. That's what we're delivering through the ACP software development kit SDK. So you're an Apple developer today. Well, you could develop the next SnapChat or some consumer-to-consumer app. But interesting, the bulk of Apple devices or the bulk of devices in the Enterprise, are Apple devices. They're not Android devices. Apple's done some work on that, upwards of 75% are actually Apple devices. So now, you're a developer, you want to get access to all of those different applications that SAP has, delivered in beautiful 1990s master detail today. >> Let's face it, I mean, we had this comment on theCube which we concur with, the user experience of Enterprise software is dated, and old, and people are bringing their phones to work. >> That's really kind of you to say dated and old, okay? I would have said old and crappy, okay? >> No one wakes up and says, hey I can't wait to download my Enterprise app and use it on the weekend. It's like root canal, don't love it, but you need it. >> Part number 000743xp, okay so now they can get into all of those processes without having to know the back end process. Through the SDK, we're going to expose all of those. >> Share some data on some of the onboard. I know you had a lot of early adopters and now the program's ramping up. We've talked over the past year and you guys are tweaking the product. You want to make sure the product was solid, that was key. Might have been delayed a little bit, but the timing of the Apple announcement, perfect. But I can imagine that the developers are excited because certainly in the Ecosystem out there, in Silicon Valley and beyond, there's a softening, it's kind of a bubble bursting, if you will, on the consumer stuff, so there might not be a couple more unicorns. The few unicorns that come along at every cycle of innovation. But the Enterprise is hot, so the buzz on the street is the Enterprise is hot, that's where you make money. As everyone works for a revenue model, you got to break even, so, there's a big focus on that in the entrepreneurial ecosystem. So, is there an uptake that you can share or any stats on the kinds of new onboarding that you guys are doing. >> Yeah, so just this week, we also announced that IBM is taking all of their MobileFirsts for iOS applications. They're going to participate in the SDK and they're going to move all of their applications onto the HANA cloud platform. They had a beautiful UI that they built for a hundred little mobile apps that were enterprise ready, but not enterprise connected. So now they're going to connect all those hundred little apps like Find&Fix, and Parts Manager and that kind of thing. >> I can see the slogan now. Enterprise: Ready to Connect. >> Exactly. >> Connecting. >> It's pretty decent validation of some of the things we're talking about here. >> Exactly, and the HCP play in it, for SAP is that's the gearbox to get them back to all of the SAP apps. Whether they be On Premise business suite, On Premise S/4HANA, Workforce Management, with Success Factors and Fieldglass. It's the gearbox to get them back to all of those. >> So let me ask the question, you're in a private market so you've got your eye on the prize in the market, you're forward-facing, but also you've got to work with the product teams and deal with that. Do you see a window of opportunity right now? Because the timing of having the product ready with HANA Cloud Platform plus the Apple relationship and the IBM stuff, which is more validation, a window of opportunity, the wind is at your back. This window, you've got a short window to kind of go out and win. Are you worried about that? Are you guys investing heavily now, do you see now a time to throttle it up and pedal to medal, straight and narrow, 90 miles an hour? >> You know, I actually see it as the wave is forming. Okay, I don't think our customer base knows that much about HANA Cloud Platform, it really has its coming out party at TechWave, last October. It's now exposed to the business group. We had the techie outage, now its the business outing. I see the wave starting to form, okay? And we've got to catch the wave and we got to ride the crap out of it. And there's a lot of stuff on the product side we have to deliver. There's a lot more that we have to do for integrating into our existing systems. We have to provide more direct, not direct connections, we've already got that piece, but more integration with the processes. We're not all the way there yet. So we have to push our product, our product management and engineering teams to do that. And that's not always easy at a big company like SAP that has all these different divisions building processes. And then the other hard part is, you got to make sure our sales reps are introducing us into every single customer account as a gearbox, as the agility platform. So that's starting to happen. So I wouldn't even say we're on the wave yet. We're starting to catch the wave. >> So let me build on that. I have two questions. I don't want to say they're quick. But here's the first one, here's what our CIO clients are telling us. One of the advantages of everything you said, platform, a lot of entry points, means that their business can pick their own road map for how they go to S/4HANA, as opposed to having single one-way, and that's the only way in, that'll extend the adoption cycle. Do you see that being a positive thing ultimately for not only SAP, in getting this message, and getting this product out, but also all the partners and the Ecosystem to drive this whole thing forward? >> Let me answer the customer part of that first. The way we have set up S/4 and HCP, is S/4 is the core that you really don't want to touch that much, you don't want to customize that much, you don't want to extend, you do that in HCP. Why would you want to do that? Well, as we deliver new enhancement packs, and we're delivering every couple of quarters, on the S/4 platform. Every time you do a customization inside the app, when you have to upgrade, you have to do regression tests, you got to check to customizations against the new rev. It becomes, in technical terms, a hairball. It becomes a huge hairball. Take that off the plate, just do it on HANA Cloud Platform. And so that's the customer angle to it, the partner angle to it is very simple, and it's a win-win for partners and for us. They can, and for customers as well, they can build a little app on the platform, snap it into S/4, Success Factor, and make it look like an app that's part of our SAS application, okay? The customer doesn't have to provision anything. The customer takes a tile and puts it on their Success Factor application. We win, because they're consuming it on HCP, so we're monetizing that too. So the partner has an easy path, the customer gets something easy, we help monetize on that. >> It's a great story and a lot of folks are looking forward, so for example, some of our clients are telling us, We are looking at the S/4platform, the S/4HANA platform, we came to it through analytics. So here's an interesting question Dan, you've got a lot of background in database. So the old way of thinking about building a database application is you didn't want to write an application required more than 80, 90, 100 disk I/Os. >> Yeah. Now we're talking about in-memory databases, calmative organization, provide any number of different straight-forward, common interfaces from a few standpoints back to the application. We're talkin' about what used to be or the equivalent of tens of thousands, maybe even hundreds of thousands of I/Os. What does that mean to the types of applications that we're going to be able to build in the Ecosystem over the course of the next few years. >> So you're right in that all data's immediately available in-memory ready to go. But here's the cool thing that I think you were getting at. You can build a structure one time, you build a table structure one time. On top of that, you just build views, logical views. And then your queries or your application looks at the logical view. Now logical views aren't somethin' new. It was just horrible to do it on a disk-based databse. >> Yep, very digital. >> You have to do tons of optimizations. In a memory database, it doesn't matter. It's all there. You just look at the logical view. So we're going to see people stacking up more and more and more logical views. Specifically in the analytics case, we see that all the time. From a partner standpoint, they're going to build their table structure, and then mix and match different application types using logical views. And you know, in HANA, we provide calc views and attribute views. So even better ways to do that. >> But the bottom line is the way you get to that ability to take a tile and drop it into a system and add that functionality, is because that underlying platform can support that view in an almost unlimited way. >> Exactly, whether the data is in HANA in the Cloud, or whether the data is still on premise through a direct connection back in the existing HANA system on premise. >> Of course unstructured data complicates the database equation, but also they have to coexist with the schemas and the structured databases out there. Has that thrown a curve ball at you guys at all? Or not a problem at all with HANA? >> So you know we've got an answer for that with Vora. I don't know if you've talked to any of the Vora folks, but you know what Vora brings to the party is it brings in-memory capabilities. It's an in-memory indexer for dup data. So instead of pointing your sequel query or building a MapReduce or using Hive or one of those technologies-- >> Or data lakes-- >> Or whatever, you just point it at Vora, and it's already indexed in memory. So our plan and our hope is that soon Vora will be on the HANA Cloud Platform. So that's just another piece of technology-- >> Peter: Way of generating a view. >> It's another service that we provide for generating a view on top of the dup data. >> Yeah, that's key. So talk about the Ecosystem innovation. Because one of the things I loved in McDermott's opening keynote, and I love the term, business model innovation. 'Cause that just really speaks to a whole new level of innovation. Usually it's tech innovation. >> Yeah. >> You get destructive enablers, platforms. At the end of the day, the application of the tools and platforms, however they're developed, by whomever, impact something. That's the business. That's the revenue. These new processes that are emerging. IoT is a great example. It's kind of an unknown process. It's hard to automate that workflow because it's evolving in real time. What innovations can you point to that you see, and that SAP sees as key mile markers, if you will, that shows that these things are being innovated on the business model side with the Ecosystem? >> Yeah, I'll give you two examples, one that's kind of just a speed up. And then I'll give you one that's a business model. So Hamburg Port Authority is the Port Authority for Hamburg, the second largest port in Europe. For them to keep up with the competition, they're going to have to double and triple in the next 15 years, the amount of goods going through their port. They have nowhere to build out. They cannot make their port bigger. It's surrounded by a city. There's nowhere for them to go. So they're using HANA Cloud Platform to basically create a grid. They're creating a utility or a cell network grid of all the containers that are sensorized, all of the trucks that have telematics information in the trucks. And they're also bringing in traffic information so that when the container comes in, they can bring the exact truck in that needs to get it in the right path into the port. If you think about that, that's a cellular network. And that's what they built using HANA Cloud Platform. So it's a semi-change in business model for the technology-- >> So minutes matter to them. >> Seconds matter to them, literally. The faster they can match up the container with the truck that's going to move that container, the better off they are. >> They got to clear the inventory. Sounds like a business problem. >> Exactly, exactly right? And think about it, they're probably going to sensorize the ships as well. They're going to stage those guys coming in over time. >> John: What's the other example? >> The other example is really interesting. This small company in Germany that builds forklifts, There can be nothing more pedantic than a forklift. It picks up a pallet, it moves the pallet, it puts it down. So here's what this company's done. It's called Still Forklifts. They are using HANA Cloud Platform to match up their order system, which is an SAP with the forklifts that are sensorized on HANA Cloud Platform so that the order system will send the order to get picked by the forklift. And the forklift and the order system have the maps of where everything is in the warehouse. >> The client's order system. >> The client's order system. And they've also now, they haven't done it yet, but they're working on a forklift to forklift integration so that if this guy's over in this part of the warehouse he has to pick something up over here. This forklift is over here. They meet in the middle. Trade some product, get it out to the docking station. >> So the forklift is an IoT device to the order system. And it opens up the possibility of greater automation within the warehouse floor. >> And they've changed their business model. They're no longer selling forklifts. They're selling pounds of goods moved within the warehouse. From in the warehouse to shipped. And they're billing on a monthly basis based on pounds of goods shipped. They're not selling forklifts anymore. That is pretty cool. >> So that's a complete shift. >> That's a business model shift. >> It's an outcome shift. >> Yeah, absolutely. >> They're selling the outcome. >> Exactly, exactly. And they had to think differently about their business. They had to think, we are not a forklift operator. We're a goods mover operator. >> Or to your business model, we were a forklift operator. Now we're a goods mover, an in-warehouse goods mover. >> Exactly, exactly. >> That's a great example and also a huge innovation. Because now, as the keynotes were saying, people are afraid to go out of business. And so the opportunity for the Ecosystem is, put one of those guys at check. They'll get the check. If they don't move, you take their territory. >> Exactly. >> So it's a nice cycle, SAP wins on both sides. >> On both sides, yeah, very cool. >> All right Dan, I got to ask you the question. Plans for this year, you got the Apple. You got the Cloud Platform. You have all this goodness goin' on. What's the plans for the year. Give us a taste of some of the things that you want to achieve this year, out in the market. And what KPIs are you looking at-- >> Yeah, what are we going to be talking about this time next year? >> I think we're going to be talking about what did you guys do in the area of Cloud Foundry. Have you guys really delivered on your Cloud Foundry promise of going opensource and moving toward portability? So next year, if we're fortunate enough to speak again, That's what I want you to ask me. Where are you guys on delivering Cloud Foundry? Pushing opensource, open development for developers even further as we talked at the outset of the interview. And then secondly, where are we on the API business hub? What is SAP doing to expose the thousands of business services that we have to our customers? To be able to use the HANA Cloud Platform with a catalog of business services that we're exposing to help them extend or modify or build that new application. >> And new onboarding numbers, having numbers showing both. >> That's right. Now what that means from a revenue standpoint, it means, you know we got to double or triple our business next year. We're not talkin' a 10%, 15% growth. We're talking an order of magnitude growth for our part of the business. >> And so you'll be investing more in marketing, training, tools. >> All of the above, all of the above. >> Hey, companies want to get into the enterprise, and the existing enterprise suppliers want to stay in the enterprise. >> Exactly, exactly. >> John: So it's a good time to be an arms dealer. >> Exactly, and we'll supply it with the HANA Cloud Platform. >> John: Dan, thanks so much for sharing your insight here on theCube. Really appreciate it, and great to meet your team. >> As well. >> And everyone here has been fantastic. We are live, here in Orlando. The theme is live, here at SAP this year. And of course we got the live coverage from theCube. This is theCube, I'm John Furrier, with Peter Burris. We'll be right back. You're watchin' theCube. (soft electronic music)