(upbeat music) >> Welcome to this CUBE Power Panel where we're going to talk about application modernization, also success templates, and take a look at some new survey data to see how CIOs are thinking about digital transformation, as we get deeper into the post isolation economy. And with me are three familiar VIP guests to CUBE audiences. Tony Bear, the principal at DB InSight, Doug Henschen, VP and principal analyst at Constellation Research and Sanjeev Mohan principal at SanjMo. Guys, good to see you again, welcome back. >> Thank you. >> Glad to be here. >> Thanks for having us. >> Glad to be here. >> All right, Doug. Let's get started with you. You know, this recent survey, which was commissioned by Couchbase, 650 CIOs and CTOs, and IT practitioners. So obviously very IT heavy. They responded to the following question, "In response to the pandemic, my organization accelerated our application modernization strategy and of course, an overwhelming majority, 94% agreed or strongly agreed." So I'm sure, Doug, that you're not shocked by that, but in the same survey, modernizing existing technologies was second only behind cyber security is the top investment priority this year. Doug, bring us into your world and tell us the trends that you're seeing with the clients and customers you work with in their modernization initiatives. >> Well, the survey, of course, is spot on. You know, any Constellation Research analyst, any systems integrator will tell you that we saw more transformation work in the last two years than in the prior six to eight years. A lot of it was forced, you know, a lot of movement to the cloud, a lot of process improvement, a lot of automation work, but transformational is aspirational and not every company can be a leader. You know, at Constellation, we focus our research on those market leaders and that's only, you know, the top 5% of companies that are really innovating, that are really disrupting their markets and we try to share that with companies that want to be fast followers, that these are the next 20 to 25% of companies that don't want to get left behind, but don't want to hit some of the same roadblocks and you know, pioneering pitfalls that the real leaders are encountering when they're harnessing new technologies. So the rest of the companies, you know, the cautious adopters, the laggards, many of them fall by the wayside, that's certainly what we saw during the pandemic. Who are these leaders? You know, the old saw examples that people saw at the Amazons, the Teslas, the Airbnbs, the Ubers and Lyfts, but new examples are emerging every year. And as a consumer, you immediately recognize these transformed experiences. One of my favorite examples from the pandemic is Rocket Mortgage. No disclaimer required, I don't own stock and you're not client, but when I wanted to take advantage of those record low mortgage interest rates, I called my current bank and some, you know, stall word, very established conventional banks, I'm talking to you Bank of America, City Bank, and they were taking days and weeks to get back to me. Rocket Mortgage had the locked in commitment that day, a very proactive, consistent communications across web, mobile, email, all customer touchpoints. I closed in a matter of weeks an entirely digital seamless process. This is back in the gloves and masks days and the loan officer came parked in our driveway, wiped down an iPad, handed us that iPad, we signed all those documents digitally, completely electronic workflow. The only wet signatures required were those demanded by the state. So it's easy to spot these transformed experiences. You know, Rocket had most of that in place before the pandemic, and that's why they captured 8% of the national mortgage market by 2020 and they're on track to hit 10% here in 2022. >> Yeah, those are great examples. I mean, I'm not a shareholder either, but I am a customer. I even went through the same thing in the pandemic. It was all done in digital it was a piece of cake and I happened to have to do another one with a different firm and stuck with that firm for a variety of reasons and it was night and day. So to your point, it was a forced merge to digital. If you were there beforehand, you had real advantage, it could accelerate your lead during the pandemic. Okay, now Tony bear. Mr. Bear, I understand you're skeptical about all this buzz around digital transformation. So in that same survey, the data shows that the majority of respondents said that their digital initiatives were largely reactive to outside forces, the pandemic compliance changes, et cetera. But at the same time, they indicated that the results while somewhat mixed were generally positive. So why are you skeptical? >> The reason being, and by the way, I have nothing against application modernization. The problem... I think the problem I ever said, it often gets conflated with digital transformation and digital transformation itself has become such a buzzword and so overused that it's really hard, if not impossible to pin down (coughs) what digital transformation actually means. And very often what you'll hear from, let's say a C level, you know, (mumbles) we want to run like Google regardless of whether or not that goal is realistic you know, for that organization (coughs). The thing is that we've been using, you know, businesses have been using digital data since the days of the mainframe, since the... Sorry that data has been digital. What really has changed though, is just the degree of how businesses interact with their customers, their partners, with the whole rest of the ecosystem and how their business... And how in many cases you take look at the auto industry that the nature of the business, you know, is changing. So there is real change of foot, the question is I think we need to get more specific in our goals. And when you look at it, if we can boil it down to a couple, maybe, you know, boil it down like really over simplistically, it's really all about connectedness. No, I'm not saying connectivity 'cause that's more of a physical thing, but connectedness. Being connected to your customer, being connected to your supplier, being connected to the, you know, to the whole landscape, that you operate in. And of course today we have many more channels with which we operate, you know, with customers. And in fact also if you take a look at what's happening in the automotive industry, for instance, I was just reading an interview with Bill Ford, you know, their... Ford is now rapidly ramping up their electric, you know, their electric vehicle strategy. And what they realize is it's not just a change of technology, you know, it is a change in their business, it's a change in terms of the relationship they have with their customer. Their customers have traditionally been automotive dealers who... And the automotive dealers have, you know, traditionally and in many cases by state law now have been the ones who own the relationship with the end customer. But when you go to an electric vehicle, the product becomes a lot more of a software product. And in turn, that means that Ford would have much more direct interaction with its end customers. So that's really what it's all about. It's about, you know, connectedness, it's also about the ability to act, you know, we can say agility, it's about ability not just to react, but to anticipate and act. And so... And of course with all the proliferation, you know, the explosion of data sources and connectivity out there and the cloud, which allows much more, you know, access to compute, it changes the whole nature of the ball game. The fact is that we have to avoid being overwhelmed by this and make our goals more, I guess, tangible, more strictly defined. >> Yeah, now... You know, great points there. And I want to just bring in some survey data, again, two thirds of the respondents said their digital strategies were set by IT and only 26% by the C-suite, 8% by the line of business. Now, this was largely a survey of CIOs and CTOs, but, wow, doesn't seem like the right mix. It's a Doug's point about, you know, leaders in lagers. My guess is that Rocket Mortgage, their digital strategy was led by the chief digital officer potentially. But at the same time, you would think, Tony, that application modernization is a prerequisite for digital transformation. But I want to go to Sanjeev in this war in the survey. And respondents said that on average, they want 58% of their IT spend to be in the public cloud three years down the road. Now, again, this is CIOs and CTOs, but (mumbles), but that's a big number. And there was no ambiguity because the question wasn't worded as cloud, it was worded as public cloud. So Sanjeev, what do you make of that? What's your feeling on cloud as flexible architecture? What does this all mean to you? >> Dave, 58% of IT spend in the cloud is a huge change from today. Today, most estimates, peg cloud IT spend to be somewhere around five to 15%. So what this number tells us is that the cloud journey is still in its early days, so we should buckle up. We ain't seen nothing yet, but let me add some color to this. CIOs and CTOs maybe ramping up their cloud deployment, but they still have a lot of problems to solve. I can tell you from my previous experience, for example, when I was in Gartner, I used to talk to a lot of customers who were in a rush to move into the cloud. So if we were to plot, let's say a maturity model, typically a maturity model in any discipline in IT would have something like crawl, walk, run. So what I was noticing was that these organizations were jumping straight to run because in the pandemic, they were under the gun to quickly deploy into the cloud. So now they're kind of coming back down to, you know, to crawl, walk, run. So basically they did what they had to do under the circumstances, but now they're starting to resolve some of the very, very important issues. For example, security, data privacy, governance, observability, these are all very big ticket items. Another huge problem that nav we are noticing more than we've ever seen, other rising costs. Cloud makes it so easy to onboard new use cases, but it leads to all kinds of unexpected increase in spikes in your operating expenses. So what we are seeing is that organizations are now getting smarter about where the workloads should be deployed. And sometimes it may be in more than one cloud. Multi-cloud is no longer an aspirational thing. So that is a huge trend that we are seeing and that's why you see there's so much increased planning to spend money in public cloud. We do have some issues that we still need to resolve. For example, multi-cloud sounds great, but we still need some sort of single pane of glass, control plane so we can have some fungibility and move workloads around. And some of this may also not be in public cloud, some workloads may actually be done in a more hybrid environment. >> Yeah, definitely. I call it Supercloud. People win sometimes-- >> Supercloud. >> At that term, but it's above multi-cloud, it floats, you know, on topic. But so you clearly identified some potholes. So I want to talk about the evolution of the application experience 'cause there's some potholes there too. 81% of their respondents in that survey said, "Our development teams are embracing the cloud and other technologies faster than the rest of the organization can adopt and manage them." And that was an interesting finding to me because you'd think that infrastructure is code and designing insecurity and containers and Kubernetes would be a great thing for organizations, and it is I'm sure in terms of developer productivity, but what do you make of this? Does the modernization path also have some potholes, Sanjeev? What are those? >> So, first of all, Dave, you mentioned in your previous question, there's no ambiguity, it's a public cloud. This one, I feel it has quite a bit of ambiguity because it talks about cloud and other technologies, that sort of opens up the kimono, it's like that's everything. Also, it says that the rest of the organization is not able to adopt and manage. Adoption is a business function, management is an IT function. So I feed this question is a bit loaded. We know that app modernization is here to stay, developing in the cloud removes a lot of traditional barriers or procuring instantiating infrastructure. In addition, developers today have so many more advanced tools. So they're able to develop the application faster because they have like low-code/no-code options, they have notebooks to write the machine learning code, they have the entire DevOps CI/CD tool chain that makes it easy to version control and push changes. But there are potholes. For example, are developers really interested in fixing data quality problems, all data, privacy, data, access, data governance? How about monitoring? I doubt developers want to get encumbered with all of these operationalization management pieces. Developers are very keen to deliver new functionality. So what we are now seeing is that it is left to the data team to figure out all of these operationalization productionization things that the developers have... You know, are not truly interested in that. So which actually takes me to this topic that, Dave, you've been quite actively covering and we've been talking about, see, the whole data mesh. >> Yeah, I was going to say, it's going to solve all those data quality problems, Sanjeev. You know, I'm a sucker for data mesh. (laughing) >> Yeah, I know, but see, what's going to happen with data mesh is that developers are now going to have more domain resident power to develop these applications. What happens to all of the data curation governance quality that, you know, a central team used to do. So there's a lot of open ended questions that still need to be answered. >> Yeah, That gets automated, Tony, right? With computational governance. So-- >> Of course. >> It's not trivial, it's not trivial, but I'm still an optimist by the end of the decade we'll start to get there. Doug, I want to go to you again and talk about the business case. We all remember, you know, the business case for modernization that is... We remember the Y2K, there was a big it spending binge and this was before the (mumbles) of the enterprise, right? CIOs, they'd be asked to develop new applications and the business maybe helps pay for it or offset the cost with the initial work and deployment then IT got stuck managing the sprawling portfolio for years. And a lot of the apps had limited adoption or only served a few users, so there were big pushes toward rationalizing the portfolio at that time, you know? So do I modernize, they had to make a decision, consolidate, do I sunset? You know, it was all based on value. So what's happening today and how are businesses making the case to modernize, are they going through a similar rationalization exercise, Doug? >> Well, the Y2K era experience that you talked about was back in the days of, you know, throw the requirements over the wall and then we had waterfall development that lasted months in some cases years. We see today's most successful companies building cross functional teams. You know, the C-suite the line of business, the operations, the data and analytics teams, the IT, everybody has a seat at the table to lead innovation and modernization initiatives and they don't start, the most successful companies don't start by talking about technology, they start by envisioning a business outcome by envisioning a transformed customer experience. You hear the example of Amazon writing the press release for the product or service it wants to deliver and then it works backwards to create it. You got to work backwards to determine the tech that will get you there. What's very clear though, is that you can't transform or modernize by lifting and shifting the legacy mess into the cloud. That doesn't give you the seamless processes, that doesn't give you data driven personalization, it doesn't give you a connected and consistent customer experience, whether it's online or mobile, you know, bots, chat, phone, everything that we have today that requires a modern, scalable cloud negative approach and agile deliver iterative experience where you're collaborating with this cross-functional team and course correct, again, making sure you're on track to what's needed. >> Yeah. Now, Tony, both Doug and Sanjeev have been, you know, talking about what I'm going to call this IT and business schism, and we've all done surveys. One of the things I'd love to see Couchbase do in future surveys is not only survey the it heavy, but also survey the business heavy and see what they say about who's leading the digital transformation and who's in charge of the customer experience. Do you have any thoughts on that, Tony? >> Well, there's no question... I mean, it's kind like, you know, the more things change. I mean, we've been talking about that IT and the business has to get together, we talked about this back during, and Doug, you probably remember this, back during the Y2K ERP days, is that you need these cross functional teams, we've been seeing this. I think what's happening today though, is that, you know, back in the Y2K era, we were basically going into like our bedrock systems and having to totally re-engineer them. And today what we're looking at is that, okay, those bedrock systems, the ones that basically are keeping the lights on, okay, those are there, we're not going to mess with that, but on top of that, that's where we're going to innovate. And that gives us a chance to be more, you know, more directed and therefore we can bring these related domains together. I mean, that's why just kind of, you know, talk... Where Sanjeev brought up the term of data mesh, I've been a bit of a cynic about data mesh, but I do think that work and work is where we bring a bunch of these connected teams together, teams that have some sort of shared context, though it's everybody that's... Every team that's working, let's say around the customer, for instance, which could be, you know, in marketing, it could be in sales, order processing in some cases, you know, in logistics and delivery. So I think that's where I think we... You know, there's some hope and the fact is that with all the advanced, you know, basically the low-code/no-code tools, they are ways to bring some of these other players, you know, into the process who previously had to... Were sort of, you know, more at the end of like a, you know, kind of a... Sort of like they throw it over the wall type process. So I do believe, but despite all my cynicism, I do believe there's some hope. >> Thank you. Okay, last question. And maybe all of you could answer this. Maybe, Sanjeev, you can start it off and then Doug and Tony can chime in. In the survey, about a half, nearly half of the 650 respondents said they could tangibly show their organizations improve customer experiences that were realized from digital projects in the last 12 months. Now, again, not surprising, but we've been talking about digital experiences, but there's a long way to go judging from our pandemic customer experiences. And we, again, you know, some were great, some were terrible. And so, you know, and some actually got worse, right? Will that improve? When and how will it improve? Where's 5G and things like that fit in in terms of improving customer outcomes? Maybe, Sanjeev, you could start us off here. And by the way, plug any research that you're working on in this sort of area, please do. >> Thank you, Dave. As a resident optimist on this call, I'll get us started and then I'm sure Doug and Tony will have interesting counterpoints. So I'm a technology fan boy, I have to admit, I am in all of all these new companies and how they have been able to rise up and handle extreme scale. In this time that we are speaking on this show, these food delivery companies would have probably handled tens of thousands of orders in minutes. So these concurrent orders, delivery, customer support, geospatial location intelligence, all of this has really become commonplace now. It used to be that, you know, large companies like Apple would be able to handle all of these supply chain issues, disruptions that we've been facing. But now in my opinion, I think we are seeing this in, Doug mentioned Rocket Mortgage. So we've seen it in FinTech and shopping apps. So we've seen the same scale and it's more than 5G. It includes things like... Even in the public cloud, we have much more efficient, better hardware, which can do like deep learning networks much more efficiently. So machine learning, a lot of natural language programming, being able to handle unstructured data. So in my opinion, it's quite phenomenal to see how technology has actually come to rescue and as, you know, billions of us have gone online over the last two years. >> Yeah, so, Doug, so Sanjeev's point, he's saying, basically, you ain't seen nothing yet. What are your thoughts here, your final thoughts. >> Well, yeah, I mean, there's some incredible technologies coming including 5G, but you know, it's only going to pave the cow path if the underlying app, if the underlying process is clunky. You have to modernize, take advantage of, you know, serverless scalability, autonomous optimization, advanced data science. There's lots of cutting edge capabilities out there today, but you know, lifting and shifting you got to get your hands dirty and actually modernize on that data front. I mentioned my research this year, I'm doing a lot of in depth looks at some of the analytical data platforms. You know, these lake houses we've had some conversations about that and helping companies to harness their data, to have a more personalized and predictive and proactive experience. So, you know, we're talking about the Snowflakes and Databricks and Googles and Teradata and Vertica and Yellowbrick and that's the research I'm focusing on this year. >> Yeah, your point about paving the cow path is right on, especially over the pandemic, a lot of the processes were unknown. But you saw this with RPA, paving the cow path only got you so far. And so, you know, great points there. Tony, you get the last word, bring us home. >> Well, I'll put it this way. I think there's a lot of hope in terms of that the new generation of developers that are coming in are a lot more savvy about things like data. And I think also the new generation of people in the business are realizing that we need to have data as a core competence. So I do have optimism there that the fact is, I think there is a much greater consciousness within both the business side and the technical. In the technology side, the organization of the importance of data and how to approach that. And so I'd like to just end on that note. >> Yeah, excellent. And I think you're right. Putting data at the core is critical data mesh I think very well describes the problem and (mumbles) credit lays out a solution, just the technology's not there yet, nor are the standards. Anyway, I want to thank the panelists here. Amazing. You guys are always so much fun to work with and love to have you back in the future. And thank you for joining today's broadcast brought to you by Couchbase. By the way, check out Couchbase on the road this summer at their application modernization summits, they're making up for two years of shut in and coming to you. So you got to go to couchbase.com/roadshow to find a city near you where you can meet face to face. In a moment. Ravi Mayuram, the chief technology officer of Couchbase will join me. You're watching theCUBE, the leader in high tech enterprise coverage. (bright music)

>> From around the globe, it's theCUBE, with coverage of KubeCon and CloudNativeCon Europe 2021 Virtual. Brought to you by Red Hat, the Cloud Native Computing Foundation and ecosystem partners. >> Hello, welcome back to theCUBE's coverage of KubeCon 2021 CloudNativeCon Europe Virtual, I'm John Furrier your host, preview with Brian Gracely from Red Hat Senior Director Product Strategy Cloud Business Unit Brian Gracely great to see you. Former CUBE host CUBE alumni, big time strategist at Red Hat, great to see you, always great. And also the founder of Cloudcast which is an amazing podcast on cloud, part of the cloud (indistinct), great to see you Brian. Hope's all well. >> Great to see you too, you know for years, theCUBE was always sort of the ESPN of tech, I feel like, you know ESPN has become nothing but highlights. This is where all the good conversation is. It's theCUBE has become sort of the the clubhouse of tech, if you will. I know that's that's an area you're focused on, so yeah I'm excited to be back on and good to talk to you. >> It's funny you know, with all the events going away loved going out extracting the signal from the noise, you know, game day kind of vibe. CUBE Virtual has really expanded, so it's been so much more fun because we can get more people easy to dial in. So we're going to keep that feature post COVID. You're going to hear more about theCUBE Virtual hybrid events are going to be a big part of it, which is great because as you know and we've talked about communities and ecosystems are huge advantage right now it's been a big part of the Red Hat story. Now part of IBM bringing that mojo to the table the role of ecosystems with hybrid cloud is so critical. Can you share your thoughts on this? Because I know you study it, you have podcasts you've had one for many years, you understand that democratization and this new direct to audience kind of concept. Share your thoughts on this new ecosystem. >> Yeah, I think so, you know, we're sort of putting this in the context of what we all sort of familiarly call KubeCon but you know, if we think about it, it started as KubeCon it was sort of about this one technology but it's always been CloudNativeCon and we've sort of downplayed the cloud native part of it. But even if we think about it now, you know Kubernetes to a certain extent has kind of, you know there's this feeling around the community that, that piece of the puzzle is kind of boring. You know, it's 21 releases in, and there's lots of different offerings that you can get access to. There's still, you know, a lot of innovation but the rest of the ecosystem has just exploded. So it's, you know, there are ecosystem partners and companies that are working on edge and miniaturization. You know, we're seeing things like Kubernetes now getting into outer space and it's in the space station. We're seeing, you know, Linux get on Mars. But we're also seeing, you know, stuff on the other side of the spectrum. We're sort of seeing, you know awesome people doing database work and streaming and AI and ML on top of Kubernetes. So, you know, the ecosystem is doing what you'd expect it to do once one part of it gets stable. The innovation sort of builds on top of it. And, you know, even though we're virtual, we're still seeing just tons and tons of contributions, different companies different people stepping up and leading. So it's been really cool to watch the last few years. >> Yes, interesting point about the CloudNativeCon. That's an interesting insight, and I totally agree with you. And I think it's worth double clicking on. Let me just ask you, because when you look at like, say Kubernetes, okay, it's enabled a lot. Okay, it's been called the dial tone of Cloud native. I think Pat Gelsinger of VMware used that term. We call it the kind of the interoperability layer it enables more large scale deployments. So you're seeing a lot more Kubernetes enablement on clusters. Which is causing more hybrid cloud which means more Cloud native. So it actually is creating a network effect in and of itself with more Cloud native components and it's changing the development cycle. So the question I want to ask you is one how does a customer deal with that? Because people are saying, I like hybrid. I agree, Multicloud is coming around the corner. And of course, Multicloud is just a subsystem of resource underneath hybrid. How do I connect it all? Now I have multiple vendors, I have multiple clusters. I'm cross-cloud, I'm connecting multiple clouds multiple services, Kubernetes clusters, some get stood up some gets to down, it's very dynamic. >> Yeah, it's very dynamic. It's actually, you know, just coincidentally, you know, our lead architect, a guy named Clayton Coleman, who was one of the Kubernetes founders, is going to give a talk on sort of Kubernetes is this hybrid control plane. So we're already starting to see the tentacles come out of it. So you know how we do cross cloud networking how we do cross cloud provisioning of services. So like, how do I go discover what's in other clouds? You know and I think like you said, it took people a few years to figure out, like how do I use this new thing, this Kubernetes thing. How do I harness it. And, but the demand has since become "I have to do multi-cloud." And that means, you know, hey our company acquires companies, so you know, we don't necessarily know where that next company we acquire is going to run. Are they going to run on AWS? Are they going to, you know, run on Azure I've got to be able to run in multiple places. You know, we're seeing banking industries say, "hey, look cloud's now a viable target for you to put your applications, but you have to treat multiple clouds as if they're your backup domains." And so we're, you know, we're seeing both, you know the way business operates whether it's acquisitions or new things driving it. We're seeing regulations driving hybrid and multi-cloud and, even you know, even if the stalwart were to you know, set for a long time, well the world's only going to be public cloud and sort of you know, legacy data centers even those folks are now coming around to "I've got to bring hybrid to, to these places." So it's been more than just technology. It's been, you know, industries pushing it regulations pushing it, a lot of stuff. So, but like I said, we're going to be talking about kind of our future, our vision on that, our future on that. And, you know Red Hat everything we end up doing is a community activity. So we expect a lot of people will get on board with it >> You know, for all the old timers out there they can relate to this. But I remember in the 80's the OSI Open Systems Interconnect, and I was chatting with Paul Cormier about this because we were kind of grew up through that generation. That disrupted network protocols that were proprietary and that opened the door for massive, massive growth massive innovation around just getting that interoperability with TCP/IP, and then everything else happened. So Kubernetes does that, that's a phenomenal impact. So Cloud native to me is at that stage where it's totally next-gen and it's happening really fast. And a lot of people getting caught off guard, Brian. So you know, I got to to ask you as a product strategist, what's your, how would you give them the navigation of where that North star is? If I'm a customer, okay, I got to figure out where I got to navigate now. I know it's super volatile, changing super fast. What's your advice? >> I think it's a couple of pieces, you know we're seeing more and more that, you know, the technology decisions don't get driven out of sort of central IT as much anymore right? We sort of talk all the time that every business opportunity, every business project has a technology component to it. And I think what we're seeing is the companies that tend to be successful with it have built up the muscle, built up the skill set to say, okay, when this line of business says, I need to do something new and innovative I've got the capabilities to sort of stand behind that. They're not out trying to learn it new they're not chasing it. So that's a big piece of it, is letting the business drive your technology decisions as opposed to what happened for a long time which was we built out technology, we hope they would come. You know, the other piece of it is I think because we're seeing so much push from different directions. So we're seeing, you know people put technology out at the edge. We're able to do some, you know unique scalable things, you know in the cloud and so forth That, you know more and more companies are having to say, "hey, look, I'm not, I'm not in the pharmaceutical business. I'm not in the automotive business, I'm in software." And so, you know the companies that realize that faster, and then, you know once they sort of come to those realizations they realize, that's my new normal, those are the ones that are investing in software skills. And they're not afraid to say, look, you know even if my existing staff is, you know, 30 years of sort of history, I'm not afraid to bring in some folks that that'll break a few eggs and, you know, and use them as a lighthouse within their organization to retrain and sort of reset, you know, what's possible. So it's the business doesn't move. That's the the thing that drives all of them. And it's, if you embrace it, we see a lot of success. It's the ones that, that push back on it really hard. And, you know the market tends to sort of push back on them as well. >> Well we're previewing KubeCon CloudNativeCon. We'll amplify that it's CloudNativeCon as well. You guys bought StackRox, okay, so interesting company, not an open source company they have soon to be, I'm assuring, but Advanced Cluster Security, ACS, as it's known it's really been a key part of Red Hat. Can you give us the strategy behind that deal? What does that product, how does it fit in that's a lot of people are really talking about this acquisition. >> Yeah so here's the way we looked at it, is we've learned a couple of things over the last say five years that we've been really head down in Kubernetes, right? One is, we've always embedded a lot of security capabilities in the platform. So OpenShift being our core Kubernetes platform. And then what's happened over time is customers have said to us, "that's great, you've made the platform very secure" but the reality is, you know, our software supply chain. So the way that we build applications that, you know we need to secure that better. We need to deal with these more dynamic environments. And then once the applications are deployed they interact with various types of networks. I need to better secure those environments too. So we realized that we needed to expand our functionality beyond the core platform of OpenShift. And then the second thing that we've learned over the last number of years is to be successful in this space, it's really hard to take technology that wasn't designed for containers, or it wasn't designed for Kubernetes and kind of retrofit it back into that. And so when we were looking at potential acquisition targets, we really narrowed down to companies whose fundamental technologies were you know, Kubernetes-centric, you know having had to modify something to get to Kubernetes, and StackRox was really the leader in that space. They really, you know have been the leader in enterprise Kubernetes security. And the great thing about them was, you know not only did they have this Kubernetes expertise but on top of that, probably half of their customers were already OpenShift customers. And about 3/4 of their customers were using you know, native Kubernetes services and other clouds. So, you know, when we went and talked to them and said, "Hey we believe in Kubernetes, we believe in multi-cloud. We believe in open source," they said, "yeah, those are all the foundational things for us." And to your point about it, you know, maybe not being an open source company, they actually had a number of sort of ancillary projects that were open source. So they weren't unfamiliar to it. And then now that the acquisition's closed, we will do what we do with every piece of Red Hat technology. We'll make sure that within a reasonable period of time that it's made open source. And so you know, it's good for the community. It allows them to keep focusing on their innovation. >> Yeah you've got to get that code out there cool. Brian, I'm hearing about Platform Plus what is that about? Take us through that. >> Yeah, so you know, one of the things that our customers, you know, have come to us over time is it's you know, it's like, I've been saying kind of throughout this discussion, right? Kubernetes is foundational, but it's become pretty stable. The things that people are solving for now are like, you highlighted lots and lots of clusters, they're all over the place. That was something that our advanced cluster management capabilities were able to solve for people. Once you start getting into lots of places you've got to be able to secure things everywhere you go. And so OpenShift for us really allows us to bundle together, you know, sort of the complete set of the portfolio. So the platform, security management, and it also gives us the foundational pieces or it allows our customers to buy the foundational pieces that are going to help them do multi and hybrid cloud. And, you know, when we bundle that we can save them probably 25% in terms of sort of product acquisition. And then obviously the integration work we do you know, saves a ton on the operational side. So it's a new way for us to, to not only bundle the platform and the technologies but it gets customers in a mindset that says, "hey we've moved past sort of single environments to hybrid and multi-cloud environments. >> Awesome, well thanks for the update on that, appreciate it. One of the things going into KubeCon, and that we're watching closely is this Cloud native developer action. Certainly end users want to get that in a separate section with you but the end user contribution, which is like exploding. But on the developer side there's a real trend towards adding stronger consistency programmability support for more use cases okay. Where it's becoming more of a data platform as a requirement. >> Brian: Right. >> So how, so that's a trend so I'm kind of thinking, there's no disagreement on that. >> Brian: No, absolutely. >> What does that mean? Like I'm a customer, that sounds good. How do I make that happen? 'Cause that's the critical discussion right now in the DevOps, DevSecOps day, two operations. What you want to call it. This is the number one concern for developers and that solution architect, consistency, programmability more use cases with data as a platform. >> Yeah, I think, you know the way I kind of frame this up was you know, for any for any organization, the last thing you want to to do is sort of keep investing in lots of platforms, right? So platforms are great on their surface but once you're having to manage five and six and, you know 10 or however many you're managing, the economies of scale go away. And so what's been really interesting to watch with Kubernetes is, you know when we first got started everything was Cloud native application but that really was sort of, you know shorthand for stateless applications. We quickly saw a move to, you know, people that said, "Hey I can modernize something, you know, a Stateful application and we add that into Kubernetes, right? The community added the ability to do Stateful applications and that got people a certain amount of the way. And they sort of started saying, okay maybe Kubernetes can help me peel off some things of an existing platform. So I can peel off, you know Java workloads or I can peel off, what's been this explosion is the data community, if you will. So, you know, the TensorFlows the PItorches, you know, the Apache community with things like Couchbase and Kafka, TensorFlow, all these things that, you know maybe in the past didn't necessarily, had their own sort of underlying system are now defaulting to Kubernetes. And what we see because of that is, you know people now can say, okay, these data workloads these AI and ML workloads are so important to my business, right? Like I can directly point to cost savings. I can point to, you know, driving innovation and because Kubernetes is now their default sort of way of running, you know we're seeing just sort of what used to be, you know small islands of clusters become these enormous footprints whether they're in the cloud or in their data center. And that's almost become, you know, the most prevalent most widely used use case. And again, it makes total sense. It's exactly the trends that we've seen in our industry, even before Kubernetes. And now people are saying, okay, I can consolidate a lot of stuff on Kubernetes. I can get away from all those silos. So, you know, that's been a huge thing over the last probably year plus. And the cool thing is we've also seen, you know the hardware vendors. So whether it's Intel or Nvidia, especially around GPUs, really getting on board and trying to make that simpler. So it's not just the software ecosystem. It's also the hardware ecosystem, really getting on board. >> Awesome, Brian let me get your thoughts on the cloud versus the power dynamics between the cloud players and the open source software vendors. So what's the Red Hat relationship with the cloud players with the hybrid architecture, 'cause you want to set up the modern day developer environment, we get that right. And it's hybrid, what's the relationship with the cloud players? >> You know, I think so we we've always had two philosophies that haven't really changed. One is, we believe in open source and open licensing. So you haven't seen us look at the cloud as, a competitive threat, right? We didn't want to make our business, and the way we compete in business, you know change our philosophy in software. So we've always sort of maintained open licenses permissive licenses, but the second piece is you know, we've looked at the cloud providers as very much partners. And mostly because our customers look at them as partners. So, you know, if Delta Airlines or Deutsche Bank or somebody says, "hey that cloud provider is going to be our partner and we want you to be part of that journey, we need to be partners with that cloud as well." And you've seen that sort of manifest itself in terms of, you know, we haven't gone and set up new SaaS offerings that are Red Hat offerings. We've actually taken a different approach than a lot of the open source companies. And we've said we're going to embed our capabilities, especially, you know OpenShift into AWS, into Azure into IBM cloud working with Google cloud. So we'd look at them very much as a partner. I think it aligns to how Red Hat's done things in the past. And you know, we think, you know even though it maybe easy to sort of see a way of monetizing things you know, changing licensing, we've always found that, you've got to allow the ecosystem to compete. You've got to allow customers to go where they want to go. And we try and be there in the most consumable way possible. So that's worked out really well for us. >> So I got to bring up the end user participation component. That's a big theme here at KubeCon going into it and around the event is, and we've seen this trend happen. I mean, Envoy, Lyft the laying examples are out there. But they're more end-use enterprises coming in. So the enterprise class I call classic enterprise end user participation is at an all time high in opensource. You guys have the biggest portfolio of enterprises in the business. What's the trend that you're seeing because it used to be limited to the hyperscalers the Lyfts and the Facebooks and the big guys. Now you have, you know enterprises coming in the business model is working, can you just share your thoughts on CloudNativeCons participation for end users? >> Yeah, I think we're definitely seeing a blurring of lines between what used to be the Silicon Valley companies were the ones that would create innovation. So like you mentioned Lyft, or, you know LinkedIn doing Kafka or Twitter doing you know, whatever. But as we've seen more and more especially enterprises look at themselves as software companies right. So, you know if you talk about, you know, Ford or Volkswagen they think of themselves as a software company, almost more than they think about themselves as a car company, right. They're a sort of mobile transportation company you know, something like that. And so they look at themselves as I've got to I've got to have software as an expertise. I've got to compete for the best talent, no matter where that talent is, right? So it doesn't have to be in Detroit or in Germany or wherever I can go get that anywhere. And I think what they really, they look for us to do is you know, they've got great technology chops but they don't always understand kind of the the nuances and the dynamics of open-source right. They're used to having their own proprietary internal stuff. And so a lot of times they'll come to us, not you know, "Hey how do we work with the project?" But you know like here's new technology. But they'll come to us and they'll say "how do we be good, good stewards in this community? How do we make sure that we can set up our own internal open source office and have that group, work with communities?" And so the dynamics have really changed. I think a lot of them have, you know they've looked at Silicon Valley for years and now they're modeling it, but it's, you know, for us it's great because now we're talking the same language, you know we're able to share sort of experiences we're able to share best practices. So it is really, really interesting in terms of, you know, how far that whole sort of software is eating the world thing is materialized in sort of every industry. >> Yeah and it's the workloads of expanding Cloud native everywhere edge is blowing up big time. Brian, final question for you before we break. >> You bet. >> Thanks for coming on and always great to chat with you. It's always riffing and getting the data out too. What's your expectation for KubeCon CloudNativeCon this year? What are you expecting to see? What highlights do you expect will come out of CloudNativeCon KubeCon this year? >> Yeah, I think, you know like I said, I think it's going to be much more on the Cloud native side, you know we're seeing a ton of new communities come out. I think that's going to be the big headline is the number of new communities that are, you know have sort of built up a following. So whether it's Crossplane or whether it's, you know get-ops or whether it's, you know expanding around the work that's going on in operators we're going to see a whole bunch of projects around, you know, developer sort of frameworks and developer experience and so forth. So I think the big thing we're going to see is sort of this next stage of, you know a thousand flowers are blooming and we're going to see probably a half dozen or so new communities come out of this one really strong and you know the trends around those are going to accelerate. So I think that'll probably be the biggest takeaway. And then I think just the fact that the community is going to come out stronger after the pandemic than maybe it did before, because we're learning you know, new ways to work remotely, and that, that brings in a ton of new companies and contributors. So I think those two big things will be the headlines. And, you know, the state of the community is strong as they, as they like to say >> Yeah, love the ecosystem, I think the values are going to be network effect, ecosystems, integration standards evolving very quickly out in the open. Great to see Brian Gracely Senior Director Product Strategy at Red Hat for the cloud business unit, also podcasts are over a million episode downloads for the cloud cast podcast, thecloudcast.net. What's it Brian, what's the stats now. >> Yeah, I think we've, we've done over 500 shows. We're you know, about a million and a half listeners a year. So it's, you know again, it's great to have community followings and, you know, and meet people from around the world. So, you know, so many of these things intersect it's a real pleasure to work with everybody >> You're going to create a culture, well done. We're all been there, done that great job. >> Thank you >> Check out the cloud cast, of course, Red Hat's got the great OpenShift mojo going on into KubeCon. Brian, thanks for coming on. >> Thanks John. >> Okay so CUBE coverage of KubeCon, CloudNativeCon Europe 2021 Virtual, I'm John Furrier with theCUBE virtual. Thanks for watching. (upbeat music)

(upbeat music) >> Well, good day, everybody. And it's great to have you with us here on the theCUBE. As we continue our CUBE Conversations as a part of the AWS startup showcase. Pleased to welcome Jennifer Johnson in today. Jennifer is the Chief Marketing and Strategy Officer at Amplitude, which is a global leader in product intelligence. And she tells me her friends call her JJ. And so, today it's... Hello JJ, how are you doing? >> I'm doing great, John. How are you? >> I'm doing very well. Thanks for being with us, we appreciate the time. First off, tell us a little bit about Amplitude, about your work in general for those who might not be familiar, and also, I'd like to hear a little more about product intelligence and about that concept, if you will, and how that has certainly taken on probably a pretty different meaning in this digital world that we're in today. >> That's right. Well, so I've been at Amplitude, I joined in October of 2020. So not that long. And let me tell you, anyone who knows me knows that I am a CMO, but I am also a Category Designer. So, I look at companies, I look at opportunities as market creation opportunities. And we're going to talk about that 'cause that's a big reason why I joined Amplitude and why I'm so excited for the future of Amplitude. And so when we think about... Our website today says product intelligence. If you read between the lines and I tell you I'm a category designer, you might understand that maybe that will evolve over time. But what product intelligence actually means is, is that it really connects digital products to revenue. And what do I mean by that? And we all know that everything is digital. I don't need to tell you that everything is digital. The whole world just moved to digital. And it's interesting because, we think about digital and we think about the DoorDashs and the Pelotons of the world, but really it's every company in every industry. Some of our largest customers are 100-year old companies. And they have had to, not just because of the last year in the pandemic, but they've been really thinking about how do we disrupt ourselves, really. It's not even about disrupting the industry. It's actually about disrupting their own business around digital. So digital really, isn't a nice to have anymore. It's existential. And we all, I think we all know that at this point. But, if the whole world has moved to digital and I think I read something that IDC wrote, we're going to spend $6.8 trillion by 2023 on digital transformation. We're spending an enormous, I mean, I think enormous is even an understatement amount of money on digital. So what is the next thing that you have to do, once you've spent all this time and money and effort in probably millions of dollars, billions per company actually transforming, is you have to actually optimize it. And you have to figure out what digital products and digital investments you're making. You have to make sure that they actually connect to business outcomes. Things like, revenue, things like lifetime value, things like loyalty, things that drive your business forward. And that's really where product intelligence and the future where Amplitude is going is so critical. Because if you think about... Actually, one of our customers said it best. The customers of yesterday or the companies of yesterday, they put a website in front of their old way of doing things, their old products, their old way of doing things and called it digital. Like we just put a website in front of it so it's digital. That is no longer the case. Now it's about redesigning your business and transforming value through new digital products and services. So digital products are actually, the future of how businesses will operate in the new era. And so what happens is, companies say, "Okay, we need to go build all these new products "and services. "And we have these goals of growth and revenue "and we hope the revenue comes out the other end." But there's really no way for... Or no really effective way for companies to actually figure out how to manage and measure that in-between. You build a product, you put it out to market, revenue comes out the other end, but how do you actually know if you're building the right things in the first place? How do you know what features, what behaviors, what actions, what combinations of those, actually lead to things like engagement and revenue and loyalty. And then how do you actually go and double down on those? And what I mean by that is adapting the experience. If you know something works, and you know that every customer that looks like that person will do this, and you can predict an outcome, why wouldn't you serve that up to every single person that looks like that? And really that whole notion of prediction and understanding and prediction and adapting, that's really where Amplitude plays a role. And that's what got me really excited about joining Amplitude and really excited about the future is, every company is a digital company and really companies have to completely rethink how they manage digital because it isn't just putting website in front of it anymore. >> Yeah I mean, you've hit on something there. In fact, we've got a lot to unpack here, which is great. But you talk about that digital (mumbles) you got to have. It's existential now to doing your business which I think is absolutely correct. But because it's everybody, and it is everywhere and you've got a lot of categories, as a Chief Strategy Officer, I mean, you can't be all things to all people. You can't go off in every which way, so how are you focusing then in your efforts in terms of identifying maybe key categories or prime categories, as opposed to, looking at this huge landscape, and that can be overwhelming in some respects how are you focusing then? >> Yeah. I mean, there's two ways to look at it. And it is... Every company is a digital company, but really any company that has any kind of a digital product or a digital app, anything that's digital is a relevant target for Amplitude. Traditionally, we have focused with probably no surprise, we focused on the, probably what I'd say the digital native companies, the companies that are more mature, but really they grew up through digital native. Those are the DoorDashs, the Postmates, the Ubers, the Lyfts. And those companies were just built by design to think this way. "We're building products. "Our app is our business. Our product is our business." So we need to make sure that we deeply understand how the interactions with our customers through that experience actually translates, and how do we continue to tweak and test and optimize. And digitally native companies, tend to understand that inherently. So that's been a lot of the early adopters of Amplitude have been those digitally native companies. Now what we're seeing, and no surprise is, there's a really long tail of companies in more traditional industries. I mean, everything from, hospitality and restaurants. Obviously media is going through a huge digital disruption right now. Automotive. I mean, any company that's looking at how do we build new ways to engage and provide experiences to our customers through any kind of a digital means, a digital product, an app, those are relevant targets for Amplitude. So I think people think, "Oh, it's..." Every industry looks very different but the commonality is everyone needs to move to digital. And the great thing for Amplitude and for the market at large is a lot of our customers are these digitally native, what I would call the thought leaders around digital. And so if we can help bring that, bring those best practices and bring that approach to some of the more traditional companies, in traditional industries and help them become more like the Pelotons and the DoorDashs of the world, then that's great for everybody. >> You know, JJ, when you talk about, this transformation that's going on and the spaces in which is going on which is everywhere right now, I imagine there are still some folks who might be a little reluctant. And you talked about slapping a new website on the old material and they think they're done and they wash their hands and they go away. And it's not that simple. So what's that conversation like to people who maybe aren't willing to jump in, to take that "risk" as they see it, whereas you know, it's an essential to their business. >> Yeah. So, I do think that every disruption technologically speaking or other, is really change management. And digital's no different. It's not just about moving to digital, it's changing the way that you're organized. It's changing your business structure, your strategy, your priorities. So, I think that organizations know they have to go there now. And even the ones that are reluctant, I'd say if they're reluctant they're probably going to get disrupted. So I think everyone understands they need to go there. Our role is really to help organizations get there, without... I mean, digital, the word that usually follows digital is transformation. And I think a lot of people think that digital transformation needs to be this, three to five year strategic journey, and cost millions of dollars with armies of consultants. And really what we're helping to do is, help organizations just answer the question, "how is our product tied to our revenue?" And we do that by bringing the data to the teams that actually need it. And it was really surprising to me to understand the process in some of these really large enterprises, around how product and marketing teams get data. And a lot of times if you have a question about something, if you're a product manager obviously you want to understand how is our product doing? What features are resonating? What features are leading to things like engagement or revenue or subscriptions or loyalty or whatever it is. As a marketer you also want to know that. As a marketer you also want to know, what campaigns are we driving that are actually creating value. Are there things that we should be doing? Are there areas we should double down on? And so the process is if you have a question about something or a hypothesis that you want to answer, a lot of times you have to send this request to some centralized data team or a data science team. Organizations have, large B2C organizations. Most of them have armies of data scientists and business intelligence platforms. And you send a request and you might get an answer back in a few weeks, maybe a month and maybe it's the right answer or usually what happens, and I think we can all relate to this. Is you ask a question and you get data back and then it sparks five more questions. And so that whole process is the cyclical thing that I always say, by the time you actually figure out the answer to your question, it's enough time to get Amazoned in the new digital era. And so what we're actually doing is helping to bring that data which we all know is the crown jewel of any organization. We're bringing that data and we're democratizing it and bringing it to all the teams that actually need it. Unlock it from data scientists and BI, and bring it to the teams that need it, whether it's product, whether it's marketing, whether it's sales, whether it's customer success. And the greatest thing is it's not a tool for everyone. And then all of a sudden you have these siloed tools, marketing has their tool, product has their tool, CS has their tool. Is you actually have one platform, one system, and one source of data that all those teams use. So marketing doesn't say, "Well yeah, my mind says this "and it looks at it from this lens." And product says, "Well, my data says this, "but it looks at it from this lens." All of a sudden you've removed that entire conversation or that entire debate. And that changes everything. It changes the way that companies get insights into customer behavior. It changes the way that they build products. It changes the way that the teams work together. Product and marketing can now work off of a common set of data. And so really Amplitude is helping to drive that change. And you don't have to do it through a three-year implementation with an army of consultants that come in. It's something that can be done very easily. And so, I know everyone wants an easy button. It is quite easy though. It's not the three-year or even the one-year transformation. It's actually a way to bring that data to the teams that need it quickly. The other thing I'd say to it is, it's bringing the right data to them. I was reading something from Gartner that said, 85% of marketing analytics tools, now these are tools that usually track things like ad attribution, website visits, and how that relates to revenue. Well in a customer acquisition scenario, well, you just want to know what ads actually lead to a cart. Put someone going to a cart, someone purchasing that was probably sufficient, but in the new world, that's just not answering the same question. Like if you need to answer a question of what features, what behaviors, what actions within the product actually drive business outcomes, knowing what ads people clicked on and what web visits that people had, that's not going to answer... It's just answering a totally different question. And 85% of companies are using marketing analytics tools to actually answer questions like what features, do we need to build? So that's another key point here is, companies need to answer this question. They know they do. They just don't have the tools to do it and the data to do it. So they're using tools that were designed for a completely different purpose. And so really that's another great thing about Amplitude, is we're actually giving them the actual, the right data to answer the questions. >> So, if you're somebody's headlights, for down the road, then in terms of, you're looking for behavior, straights and patterns. You're looking for increased customer engagements, and you have all these wonderful tools now, not that you're missing anything, but where do you think that you could even sharpen the pencil a little bit more so that down the road here, what do you think technologically you are capable or that you would like to be able to deliver, because of making that an even richer engagement, even a bigger, a deeper dig. >> Yeah. Well, I mean, so, we have this immense deep, fast, smart database of customer behavior. So if you think of it, it's almost like the possibilities are endless. Anything that you need to be able to know or any question you could ask of your data to know what combinations of features, what combinations of behaviors actually lead to things like retention or churn or revenue. And then you can actually start to model those into cohorts. If I know that a customer does these five things in this order, and they're five times more likely to churn, well then, any customer that actually, doesn't just look like that based on your demographics, who you are, where you live, et cetera, but based on actually what you do in the product. We can start to cohort them and say, "this person actually looks like this other person "based on their behavior." And therefore we might actually personalize an experience for them. We might send them an offer if we think they're going to churn because we know they're likely to churn base 'cause other people that look like them do. Or we're not going to send them anything because we already know they're loyal. So they're already likely to buy. So it's answering more questions, but then it's also, how do you actually use that to, really personalize experiences? And that word is so overused, but in this way, I mean, it's not about I'm going to serve you a piece of content because I know what industry you work in, or I know where you live. I'm actually going to personalize your experience because I know that you, John, as an individual, do these things and therefore I know that you are either, a loyal customer, or you've got a high likelihood to churn, et cetera. And then I'm going to personalize an experience, that's a good experience for you but also a good experience for the business. So, I think there's more types of analytics. There's more ways to personalize and build experiences. I think in the modern way, not the old demographic way. But also, even every organization around the company, like everyone touches the customer. So, customer experience as we know is, I hate to call it the buzzword. Of course, everybody wants a great customer experience but everybody talks about customer experience. Anyone who touches the customer is part of customer experience, which is basically the whole company. And so if you think about, today, there's obviously product teams, marketing teams, are heavy users of Amplitude. But going forward, I mean, imagine a world where, anytime you have a touch point with a customer, you can use this insight into what they're actually doing in the product to get some level of intelligence that you didn't have before, and use it to proactively give them a better experience. Whether it's, at renewal time, or you know that they're likely to do something so you offer something that gives them a better experience or you're in customer service. And wouldn't it be great to actually know if someone's logging a support ticket. What they're actually doing in the product is going to help you give them a better support experience, et cetera, et cetera. I mean, the options here I think are, because of the data that we have and the way that we can, like you said, build these patterns and pattern match what features and actions lead to outcomes, I think the options are limitless. And I think this is the new way. Like companies that understand this is the Holy grail of the new way of digital and understanding your customers and having this intelligence into the product is the new way to engage, the customers that get that are going to be the customers that win. >> Well, it is a new game, you're right. I think limitless is a really good word too because the capabilities that you're developing and the product and services you're providing, really are limitless. So thanks for sharing the time and the insight, a pleasure to have you on theCUBE. Thanks for being here. >> Thank you. It's been great. Thank you, John. >> You've got John Walls here on theCUBE, CUBE Conversation on the AWS startup showcase. I'm talking with Jennifer Johnson from Amplitude. (soft music)

>> Live from Seattle, Washington it's theCUBE covering KubeCon and CloudNativeCom North America 2018. Brought to you by Red Hat the cloud-native computing foundation and its ecosystem partner. >> Welcome back everyone, it's theCUBE's live coverage here in Seattle of KubeCon and CloudNativeCon 2018. I'm John Furrier, with Stu Miniman, host of theCUBE. Three days of live coverage. Wall to wall, 8000 people here. Doubled from the previous event in North America, expanding globally, we are here with Liz Rice, technology analyst, evangelist at Aqua Security and program co-chair here at KubeCon, CloudNativeCon. Liz, thanks for joining us. >> Thank you for having me. >> I know you had a busy day, keynotes and all. A lot of activity, a lot of hand shaking, walking around, very crowded. >> It is, we're packed. We're absolutely at capacity here and the event sold out and it's busy. >> A lot of energy, real quick, I know you guys did a lot of work, you guys always do a great job, exceptional performance again. >> Thank you. >> CNCF does a great job on the content programming. It's about the open source communities. That's fundamental, a lot of end users, both participating and consuming. Vendor list is expanding. Putting the program together gets challenging when you have these kind of numbers. What were the themes? How did you put it all together? What was resonating? What's the focus? >> Yeah, it was so hard, we had so many applications that we could only accept 13%, which makes it almost impossible some of the decisions you have to make. And some of the things that were coming out, were like Knative, a lot of submissions around Knative. Serverless in general obviously being quite a hot topic, I would say across our industry. Really great talks from end users and we've seen a few on the keynote stage. Where some brands that we're all aware of, people like Airbnb, sharing their stories of what they've done to make their deployments, their cloud-native deployments, their use of kubernetes successful. So it's not just working from the ties, and doing some experiments, they are telling us how they've done this for real. >> You had a very successful KubeCon in Copenhagen. And so how did you integrate from Copenhagen to here. What were some of the inefficiencies? Obviously, the bigger numbers here. You recently had China the success where, we've reported on SiliconANGLE, the open source consumption and contribution is off the charts. It's huge, it's growing and it's a new dynamic. So between China, and Copenhagen, here, interesting things happening. >> China was phenomenal for me. It was my first trip to China, so it was eye-opening in all sorts of respects. And one of the really interesting things there was the use of machine learning. The uses of kube flow, real life examples. Again I think there is something about how much data they've been able to collect in China. But we heard some really great stories of, for example, electricity companies using machine learning on kubernetes to predict demand. It was fascinating. >> It's a lot of adoption. >> Yes. >> They are at the front end, they are a mobile culture. IOT is booming over there, it's just massive. >> Absolutely. >> Alright here in Seattle, obviously Seattle home of AWS, and I was just talking to some folks here locally in Seattle, just this morning, they said they think this is the biggest conference of the year here in Seattle. Which is really telling where you guys have come from. Interesting dynamic. A lot of new ecosystem partners. What's happening? It seems to be energy, the buzz. There's a subtext here that's buzzing around the hallways. What's the most important thing that people should be taking away from this event this year? >> I think the scale of it is coming from real adoption and businesses that are moving their applications into the cloud. Public cloud and hybrid cloud and finding success through doing that with cloud native components. You mentioned the end users who want to be part of the community, and they actually wanted to contribute to the community. You can look around the hall and see booths from, like Uber's over there. They're really contributing to this community. It's not just a bunch of enthusiasts, it's for real. >> Problems being solved, real company end users. >> So Liz, one of the things we've been looking at this is not a monolith here. You've actually got a whole lot of communities. As I've been wandering the floor, if I'm talking to people. We had Matt come on to talk about Envoy and they had their own conference at the beginning of the week and they had 250 people. As I'm wandering around, you talk to a number and it's like oh, I'm here all about Helm. You know there's different service meshes all over the place that everybody is talking about. >> Yeah another big theme. >> You're heavily focused on the security aspects there. I believe you've got a project that Aqua has been involved in. It was kube-hunter if I've got it. Maybe before you talk about kube-hunter, maybe just talk about balancing, this isn't one community, it's gotten really big. Do we need to break this into a micro-services space show? We'll have the core, but lots of other things and spread it out all over the world. >> Sure, it's a real challenge as this community is growing so fast and trying to keep the community feel. Balancing what the contributors want to do and making sure they're getting value and having the conversations they want, but also enabling the vendors, and the end users, and every constituent part to get something good out of this conference. It's a challenge as this gets bigger. There's no kind of, if this doubles again, will it feel the same? That's hard to imagine. So we got to think carefully about how-- >> We've seen that happen and it would not, even from last year to this year was a big change for a lot of people. >> For sure. >> So kube-hunter tell us about that. >> Yeah, kube-hunter, yes, kube-hunter is one of our open source projects at Aqua. It's basically penetration testing for kubernetes clusters, so it's written in Python. It attempts to make network requests looking for things like the open ports. It will tell you if you got some misconfigurations, 'cause a lot of the security issues with kubernetes can come about through poor configuration. And the other thing you can do, you can run it from externally to your cluster. You can also run it inside a pod inside your cluster and then that's simulating what might happen if an attacker got into your cluster, what could they do from there. They compromised a pod which could happen to a software vulnerability. Once they're in the pod, how vulnerable are you? What's the blast radius of that attack? And kube-hunter can help you see whether it's a complete disaster or actually fairly contained. >> Alright, Liz how are we doing from a security standpoint? We've watched the rise of containers over the last few years. And it's like okay wait do I need to put in some kind of lightweight VM? Do I do something there? What can I trust? What do I do? At AWS Reinvent a couple of weeks ago, there's the whole container marketplace. Feels like we are making progress but still plenty of work to do. >> Right, right, container security has lots of parts to it as you go through the life cycle of a container. Actually at AWS Reinvent, Aqua was recognized as having, I think they called it competency. Which I think it's a bit better than competency in container security. >> That's a complement I believe. >> Yeah, really complement, really competent. I think as community on the open source level, there are lots of good things happening. For example, the defaults in kubernetes have been getting better and better. If you are an enterprise, and particularly if you're a financial user, or a media company, or a government organization, you have much stronger requirements from a security perspective and that's where the open source tooling on its own may not be sufficient, and you may need to plug in commercial solutions like Aqua to really beef that up. And also to provide that end to end security right from when you're building your image through to the run time protection which is really powerful. >> Security has got to be built in from the beginning. Let me get your thoughts on end user traction and the huge demand for what end users are doing. I know you guys are seeing on the program side, the Linux foundation, CNC was talking about trying to get more case studies. We're seeing the end users prominent here. You mentioned Uber, Apple's here. A bunch of other companies, they're here. So end users are not only just contributing, they are also consuming. How are the new enterprises that are coming in consuming and interacting and engaging with kubernetes? Where are they on the IQ, if you will, level and what are they engaging on? Kubernetes has matured a bit and ready. It's been deployed, people using it. People gathering around it, but now people are starting to consume and deploy it at different scales. What's the end user uptake? What's the hot areas? What do you see the most people digging in? >> Great question, so I think we are seeing a lot of, particularly, I want to say like mature start-ups, so the Ubers and the Airbnbs and the Lyfts. They've got these massive scaled technology problems, and kubernetes is giving them, and the whole cloud-native community around it, it's giving them the ability to do these kind of custom things that they need to do. The kind of weird and wonderful things. They can add whatever adaptations they need, that maybe they wouldn't get if they were in a traditional architecture. So they're kind of the prominent voices that we are hearing right now. But at Aqua we are seeing some of these, maybe what you might call more traditional businesses like banks. They want to replicate that. They want to shape functionality really quickly. They are seeing challenges from upstart and they want to compete. So they know they've got to shift functionality quickly. They've got to do continuous deployment. Containers enable that. The whole cloud-native world enables that and that's where the adoption's from. >> They can take the blueprints from the people who built it from the ground up, the large scale startups, cloud-native in the beginning, and kind of apply the traditional IT kind of approach with the same tooling and the same platform. >> And we are seeing some interesting things around making that easier. So things like the CNAB, the cloud-native application bundling, that is coming out at Microsoft and Docker are involved in that. I think that's all to do with making it easier for enterprises to just go, yeah, this is the application I want to run it in the cloud. >> So let me ask you a question around the customer end users that we see coming onboard, because you have the upstream kind of community, the downstream benefits are impacting certainly IT and then developers, right? The classic developers, IT is starting to reimagine their infrastructure. All the goodness with cloud, and machine learning, and application is being redefined. It's changing the investment. So in 2019, what's your view on how companies are shaping their investment strategy to IT investment or technology investment strategies with cloud-native? Because this is a real trend that you just pointed out. Okay I'm a big company and I've used the old way and now I want the new way. So there's a lot of okay, instant start. Turn the key, does it run? There's a lot of managed services here, so the new persona of customer. How does that impact their investment, IT investments in your mind? What are you seeing please share any color commentary around that? >> I'm sure we're all aware that we're seeing shifts away from the traditional data center into public cloud which has implications around opex rather than capex. And I guess following on from that people worrying about whether vendor lock-in is a thing. Should they be just adopting in one public cloud or perhaps putting their eggs across different baskets? Should they be using these managed platforms? We have all these different distributions, we have these different managed solutions for kubernetes, there's a lot of choice out there. I think it's going to be interesting to see how that shapes out over the next few years. Are all these different distributions going to find a niche or how's that going to work? >> Matt Klein had a great observation. He was on earlier today from Lyft. He says look to solve a problem, use the tech to solve a problem, and then iterate, build on that. It's iteration mull of dev, ops. I think that's a good starting point. There's no magic silver bullet here. There's no magic answer, I think it's more of just get in there and get it going. The other question I have for you is 2019 prediction for kubernetes. What's going to happen this coming year? We're seeing this picture now, 8000 people, diverse audience. >> Yeah. >> What's the prediction 2019 for kubernetes? >> Oh, great question. I think maybe broader than just kubernetes, but the kind of cloud-native. Because kubernetes is like Janet said in her keynote this morning it's essentially boring. It kind of does what it's supposed to do now. I think what's going to be interesting is seeing those other pieces around it and above it, the improved developer experiences making it easier for companies to adopt. Maybe some of these choices around things like what service mesh you're going to use. How you're going to implement your observability. How you're going to deploy all this stuff without needing to hire 20 super detailed experts. We've got all the experts in this stuff. They're kind of here. The early adopters, great. Maybe that next wave, how are they going to be able to take advantage of this cloud-native? >> I think the programmability is key. Well great to have-- >> I think a big part of that is actually is going to be serverless. The ease of using serverless rather than the flexibility you get out of-- >> The millisecond latency around compute, yeah it's great. Well thanks for coming on, really appreciate it. Final question for you, what surprised you this year? Is there one thing that jumped out at you that you didn't expect? Good, bad or ugly? Great show here, it was packed. The waiting list was like 1500. What was the surprise this year from a program standpoint? >> I think actually the nicest surprise was the contribution of Phippy and all those lovely characters from Phippy Goes to the Zoo and those characters being donated by Microsoft, Matt Butcher and Karen Chu's work, was terrific. And it's just beautiful, just lovely. >> That's awesome, thanks so much Liz. Appreciate Liz right here. Program co-chair at KubeCon, CloudNativeCon, also technology evangelist at Aqua Security. That's her day job and her other job, she's running the content programming which is very huge here. Congratulations, I know it's tough work, a great job. >> Thank you very much. >> It's theCUBE coverage, breaking down all the action here at KubeCon and CloudNativeCon. I'm John Furrier and Stu Miniman, stay with us. Three days of wall-to-wall coverage. We're only on day two, we've got a whole nother day. A lot of great stories coming out of here and great content. Stay with us for more after this short break. (upbeat music)

>> Announcer: Live from San Jose in the heart of Silicon Valley, It's theCUBE covering DataWorks Summit 2017. Brought to you by Hortonworks. >> Welcome back to theCUBE. We are live at the DataWorks Summit on day two. We have had a great day and a half learning a lot about the next generation of big data, machine learning, artificial intelligence, I'm Lisa Martin, and my co-host is George Gilbert. We are next joined by a CUBE alumni, Ron Bodkin, the VP and General Manager of Artificial Intelligence for Teradata. Welcome back to theCUBE! >> Well thank you Lisa, it's nice to be here. >> Yeah, so talk to us about what you're doing right now. Your keynote is tomorrow. >> Ron: Yeah. >> What are you doing, what is Teradata doing in helping customers to be able to leverage artificial intelligence? >> Sure, yeah so as you may know, I ha`ve been involved in this conference and the big data space for a long time as the founding CEO of Think Big Analytics. We were involved in really helping customers in the beginning of big data in the enterprise. And so, we are seeing a very similar trend in the space of artificial intelligence, right? The rapid advances in recent years in deep learning have opened up a lot of opportunity to really create value from all the data the customers have in their data ecosystems, right? So Teradata has a big role to play in having high quality product, Teradata database, analytic ecosystem products such as Hadoop, such as QueryGrid for connecting these systems together, right? So what we're seeing is our customers are very excited by artificial intelligence, but what we're really focused on is how do they get to the value, right? What can they do that's really going to get results, right? And we bring this perspective of having this strong solutions approach inside of Teradata, and so we have Think Big Analytics consulting for data science, we now have been building up experts in deep learning in that organization, working with customers, right? We've brought product functionality so we're innovating around how do we keep pushing the Teradata product family forward with functionality around streaming with listeners. Functionality like the ability to, how do you take GPU and start to think about how can we add that and make that deploy efficiently inside our customer's data center. How can you take advantage of innovation in open source with projects like TensorFlow and Keras becoming important for our customers. So we're seeing is a lot of customers are excited about use cases for artificial intelligence. And tomorrow in the keynote I'm going to touch on a few of them, ranging from applications like preventative maintenance, anti-fraud in banking, to e-commerce recommendations and we're seeing those are some of the examples of use cases where customers are saying hey, there's a lot of value in combining traditional machine learning, wide learning, with deep learning using neural nets to generalize. >> Help us understand if there's an arc where there's the mix of what's repeatable and what's packagable, or what's custom, how that changes over time, or whether it's just by solution. >> Yeah, it's a great question. Right, I mean I think there's a lot of infrastructure that any of these systems need to rest on. So having data infrastructure, having quality data that you can rely on is foundational, and so you need to get that installed and working well as a beginning point. Obviously having repeatable products that manage data with high SLAs and supporting not use production use, but also how do you let data scientists analyze data in a lab and make that work well. So there's that foundational data layer. Then there's the whole integration of the data science into applications, which is critical, analytics, ops, agile ways of making it possible to take the data and build repeatable processes, and those are very horizontal, right? There's some variation, but those work the same in a lot of use cases. At this stage, I'd say, in deep learning, just like in machine learning generally, you still have a lot of horizontal infrastructure. You've got Spark, you've got TensorFlow, those are support use case across many industries. But then you get to the next level, you get specific problems, and there's a lot of nuance. What modeling techniques are going to work, what data sets matter? Okay, you've got time series data and a problem like fraud. What techniques are going to make that work well? And recommendations, you may have a long tail of items to think about recommending. How do you generalize across the long tail where you can't learn. People who use some relatively small thing or go to an obscure website, or buy an obscure product, there's not enough data to say are they likely to buy something else or do something else, but how do you categorize them so you get statistical power to make useful recommendations, right? Those are things that are very specific that there's a lot of repeatability and a specific solution area of. >> This is, when you talk about the data assets that might be specific to a customer and then I guess some third party or syndicated sources. If you have an outcome in mind, but not every customer has the same inventory of data, so how do you square that circle? >> That's a great question. And I really think that's a lot of the opportunity in the enterprise of applying analytics, so this whole summit DataWorks is about hey, the power of your data. What you can get by collecting your data in a well-managed ecosystem and creating value. So, there's always a nuance. It's like what's happening in your customers, what's your business process, what's special about how you interact, what's the core of your business? So I guess my view is that the way anybody that wants to be a winner in this new digital era and have processes that take advantage of artificial intelligence is going to have to use data as a competitive advantage and build on their unique data. So because we see a lot of times enterprises struggle with this. There's a tendency to say hey, can we just buy a package off the shelf SaaS solution and do that? And for context, for things that are the same for everybody in an industry, that's a great choice. But if you're doing that for your core differentiation of your business, you're in deep trouble in this digital era. >> And that's a great place, sorry George, really quickly. That this day and age, every company is a technology company. You mentioned a use case in banking, fraud detection, which is huge. There's tremendous value that can be gleaned from artificial intelligence, and there's also tremendous risk to them. I'm curious, maybe just kind of a generalization. Where are your customers on this journey in terms of have they, are you going out to customers that have already embraced Hadoop and have a significant amount of data that they say, all right, we've got a lot of data here, we need to understand the context. Where are customers in that maturity evolution? >> Sure, so I'd say that we're really fast-approaching the slope of enlightenment for Hadoop, which is to say the enthusiasm of three years ago when people thought Hadoop was going to do everything have kind of waned and there's now more of an appreciation, like there's a lot of value in having a data warehouse for high value curated data for large-scale use. There's a lot of value in having a data lake of fairly raw data that can be used for exploration in the data science arena. So there's emerging, like what is the best architecture for streaming and how do you drive realtime decisions, and that's still very much up in the air. So I'd say that most of our customers are somewhere on that journey, I think that a lot of them have backed off from their initial ambitions that they bought a little too much of the hype of all that Hadoop might do and they're realizing what it is good for, and how they really need to build a complementary ecosystem. The other thing I think is exciting though is I see the conversation is moving from the technology to the use cases. People are a lot more excited about how can we drive value and analytics, and let's work backwards from the analytics value to the data that's going to support it. >> Absolutely. >> So building on that, we talk about sort of what's core and if you can't have something completely repeatable that's going to be core to your sustainable advantage, but if everyone is learning from data, how does a customer achieve a competitive advantage or even sustain a competitive advantage? Is it orchestrating learning that feeds, that informs processes all across the business, or is it just sort of a perpetual Red Queen effect? >> Well, that's a great question. I mean, I think there's a few things, right? There's operational excellence in every discipline, so having good data scientists, having the right data, collecting data, thinking about how do you get network effects, those are all elements. So I would say there's a table-stakes aspect that if you're not doing this, you're in trouble, but then if you are it's like how do you optimize and lift your game and get better at it? So that's an important fact that you see companies that say how do we acquire data? Like one of the things that you see digital disruptors, like a Tesla, doing is changing the game by saying we're changing the way we work with our customers to get access to the data. Think of the difference between every time you buy a Tesla you sign over the rights for them to collect and use all your data, when the traditional auto OEMs are struggling to get access to a lot of the data because they have intermediaries that control the relationship and aren't willing to share. And a similar thing in other industries, you see in consumer packaged goods. You see a lot of manufacturers there are saying how do we get partnerships, how do we get more accurate data? The old models of going out to the Nielsens of the world and saying give us aggregates, and we'll pay you a lot to give us a summary report, that's not working. How do we learn directly in a digital world about our consumers so we can be more relevant? So one of the things is definitely that control of data and access to data, as well as we see a lot of companies saying what are the acquisitions we can make? What are start ups and capabilities that we can plug in, and complement to get data, to get analytic capability that we can then tailor for our needs? >> It's funny that you mention Tesla having more cars on the road, collecting more data than pretty much anyone else at this point. But then there's like Stanford's sort of luminary for AI, Fei-Fei Li. She signed on I think with Toyota, because she said they sell 10 million cars a year, I'm going to be swimming in data compared to anyone else, possible exception of GM or maybe some Chinese manufacturer. So where does, how can you get around scale when using data at scale to inform your models? How would someone like a Tesla be able to get an end run around that? So that's the battle, the disruptor comes in, they're not at scale, but they maybe change the game in some way. Like having different terms that give them access to different kinds of data, more complete data. So that's sort of part of the answer, is to disrupt an industry you need a strategy what's different, right, like in Tesla's case an electric vehicle. And they've been investing in autonomous vehicles with AI, of course everybody in the industry is seeing that and is racing. I mean, Google really started that whole wave going a long time ago as another potential disruptor coming in with their own unique data asset. So, I think it's all about the combination of capabilities that you need. Disruptors often bring a commitment to a different business process, and that's a big challenge is a lot of times the hardest things are the business processes that are entrenched in existing organizations and disruptors can say we're rethinking the way this gets done. I mean, the example of that in ride sharing, the Ubers and Lyfts of the world, deities where they are re-conceiving what does it mean to consume automobile services. Maybe you don't want to own a car at all if you're a millennial, maybe you just want to have access to a car when you need to go somewhere. That's a good example of a disruptive business model change. >> What are some things that are on the intermediate-term horizon that might affect how you go about trying to create a sustainable advantage? And here I mean things like where deep learning might help data scientists with feature engineering so there's less need for, you can make data scientists less of a scarce resource. Or where there's new types of training for models where you need less data? Those sorts of things might disrupt the practice of achieving an advantage with current AI technology. >> You know, that's a great question. So near-term, the ability to be more efficient in data science is a big deal. There's no surprise that there's a big talent gap, big shortage of qualified data scientists in the enterprise and one of the things that's exciting is that deep learning lets you get more information out of the data, so it learns more so that you'd have to do less future engineering. It's not like a magic box you just pour in raw data to deep learning and out comes the answers, so you still need qualified data scientists, but it's a force multiplier. There's less work to do in future engineering, and therefore you get better results. So that's a factor, you're starting to see things like a hyperparameter search where people will create neural networks that search for the best machine learning model, and again get another level of leverage. Now, today doing that is very expensive. The amount of hardware to do that, very few organizations are going to spend millions of dollars to sort of automate the discovery of models, but things are moving so fast. I mean, even just in the last six weeks to have Nvidia and Google both announce significant breakthroughs in hardware. And I just had a colleague forward me a paper for recent research that says hey this technique could produce a hundred times faster results in deep learning convergence. So you've got rapid advances in investment in the hardware and the software. Historically software improvements have outstripped hardware improvements throughout the history of computing, so it's quite reasonable to expect you'll have 10 thousand times the price performance for deep learning in five years. So things that today might cost a hundred million dollars and no one would do, could cost 10 thousand dollars in five years, and suddenly it's a no-brainer to apply a technique like that to automate something instead of hiring more scarce data scientists that are hard to find, and make the data scientists more productive so they're spending more time thinking about what's going on and less time trying out different variations of how do I configure this thing, does this work, does this, right? >> Oh gosh, Ron, we could keep chatting away. Thank you so much for stopping by theCUBE again, we wish you the best of luck in your keynote tomorrow. I think people are going to be very inspired by your passion, your energy, and also the tremendous opportunity that is really sitting right in front of us. >> Thank you, Lisa, it's a very exciting time to be in the data industry, and the emergence of AI and the enterprise, I couldn't be more excited by it. >> Oh, excellent, well your excitement is palpable. We want to thank you for watching. We are live on theCUBE at the DataWorks Summit day 2, #dws17. For my cohost George Gilbert, I'm Lisa Martin, stick around. We'll be right back. (upbeat electronic melody)

>> Hey welcome back everybody, Jeff Frick here with theCUBE we're at the data privacy day at Twitter's world headquarters in downtown San Fransciso and we're really excited to get into it with our next guest Dr. Andreas Weigend, he is now at the Social Data Lab, used to be at Amazon, recently published author. Welcome. >> Good to be here, morning. >> Absolutely, so give us a little about what is Social Data Lab for people who aren't that familiar with it and what are you doing over at Berkeley? >> Alright, so let's start with what is social data? Social data is a data people create and share whether they know it or not and what that means is Twitter is explicit but also a geo location or maybe even just having photos about you. I was in Russia all day during the election day in the United States with Putin, and I have to say that people now share on Facebook what the KGB wouldn't have gotten out of them under torture. >> So did you ever see the Saturday Night Live sketch where they had a congressional hearing and the guy the CIA guy says, Facebook is the most successful project that we've ever launched, people tell us where they are who they're with and what they're going to do, share pictures, location, it's a pretty interesting sketch. >> Only be taught by Black Mirror, some of these episodes are absolutely amazing. >> People can't even watch is it what I have not seen I have to see but they're like that's just too crazy. Too real, too close to home. >> Yeah, so what was the question? >> So let's talk about your new book. >> Oh that was social data. >> Yeah social data >> Yeah, and so I call it actually social data revolution. Because if you think back, 10, 20 years ago we absolutely we doesn't mean just you and me, it means a billion people. They think about who they are, differently from 20 years ago, think Facebook as you mentioned. How we buy things, we buy things based on social data we buy things based on what other people say. Not on what some marketing department says. And even you know, the way we think about information I mean could you do a day without Google? >> No >> No. >> Could you go an hour without Google? >> An hour, yes, when I sleep. But some people actually they Google in their sleep. >> Well and they have their health tracker turned on while they sleep to tell them if they slept well. >> I actually find this super interesting. How dependent I am to know in the morning when I wake up before I can push a smiley face or the okay face or the frowny face, to first see how did I sleep? And if the cycles were nice up and down, then it must have been a good night. >> So it's interesting because the concept from all of these kind of biometric feedback loops is if you have the data, you can change your behavior based on the data, but on the other hand there is so much data and do we really change our behaivor based on the data? >> I think the question is a different one. The question is alright, we have all this data but how can we make sure that this data is used for us, not against us. Within a few hundred meters of here there's a company where employees were asked to wear a fit bit or tracking devices which retain more generally. And then one morning one employee came in after you know not having had an exactly solid night of sleep shall we say and his boss said I'm sorry but I just looked at your fit bit you know this is an important meeting, we can't have you at that meeting. Sorry about that. >> True story? >> Yeah >> Now that's interesting. So I think the fit bit angle is interesting when that is a requirement to have company issued health insurance and they see you've been sitting on your couch too much. Now how does that then run into the HIPPA regulations. >> You know, they have dog walkers here. I'm not sure where you live in San Francisco. But in the area many people have dogs. And I know that a couple of my neighbors they give when the dog walker comes to take the dog, they also give their phone to the dog walker so now it looks like they are taking regular walks and they're waiting for the discount from health insurance. >> Yeah, it's interesting. Works great for the person that does walk or gives their phone to the dog walker. But what about the person that doesn't, what about the person that doesn't stop at stop signs. What happens in a world on business models based on aggregated risk pooling when you can segment the individual? >> That is a very very very biased question. It's a question of fairness. So if we know everything about everybody what would it mean to be fair? As you said, insurance is built on pooling risk and that means by nature that there are things that we don't know about people. So maybe, we should propose lbotomy data lobotomy. So people actually have some part chopped off out of the data chopped off. So now we can pool again. >> Interesting >> Of course not, the answer is that we as society should come up with ways of coming up with objective functions, how do we weigh the person you know taking a walk and then it's easy to agree on the function then get the data and rank whatever insurance premium whatever you're talking about here rank that accordingly. So I really think it's a really important concept which actually goes back to my time at Amazon. Where we came up with fitness functions as we call it. And it takes a lot of work to have probably spent 50 hours on that with me going through groups and groups and groups figuring out, what do we want the fitness function to be like? You have to have the buy in of the groups you know it they just think you know that is some random management thing imposed on us, it's not going to happen. But if they understand that's the output they're managing for, then not bad. >> So I want to follow up on the Amazon piece because we're big fans of Jeff Hamilton and Jeff Bezzos who we go to AWS and it's interesting excuse me, James Hamilton when he talks about the resources that EWS can bring to bear around privacy and security and networking and all this massive infrastructure being built in terms of being able to protect privacy once you're in the quote un-quote public cloud versus people trying to execute that at the individual company level and you know RSA is in a couple of weeks the amount of crazy scary stuff that is coming in for people that want interviews around some of this crazy security stuff. When you look at kind of public cloud versus private cloud and privacy you know supported by a big heavy infrastructure like what EWS has versus a Joe Blow company you know trying to implement them themselves, how do you see that challenge. I mean I don't know how the person can compete with having the resourses again the aggregated resources pool that James Hamilton has to bring to barrel this problem. >> So I think we really need to distinguish two things. Which is security versus privacy. So for security there's no question in my mind that Joe Blow, with this little PC has not a chance against our Chinese or Russian friends. Is no question for me that Amazon or Google have way better security teams than anybody else can afford. Because it is really their bread and butter. And if there's a breach on that level then I think it is terrible for them. Just think about the Sony breach on a much smaller scale. That's a very different point from the point of privacy. And from the point about companies deliberately giving the data about you for targeting purposes for instance. And targeting purposes to other companies So I think for the cloud there I trust, I trust Google, I trust Amazon that they are doing hopefully a better job than the Russian hackers. I am more interested in the discussion on the value of data. Over the privacy discussion after all this is the world privacy day and there the question is what do people understand as the trade off they have, what they give in order to get something. People have talked about Google having this impossible irresistible value proposition that for all of those little data you get for instance I took Google Maps to get here, of course Google needs to know where I am to tell me to turn left at the intersection. And of course Google has to know where I want to be going. And Google knows that a bunch of other people are going there today, and you probably figure out that something interesting is happening here. >> Right >> And so those are the interesting questions from me. What do we do with data? What is the value of data? >> But A I don't really think people understand the amount of data that they're giving over and B I really don't think that they understand I mean now maybe they're starting to understand the value because of the value of companies like Google and Facebook that have the data. But do you see a shifting in A the awareness, and I think it's even worse with younger kids who just have lived on their mobile phones since the day they were conscious practically these days. Or will there be a value to >> Or will they even mobile before they were born? Children now come pre-loaded, because the parents take pictures of their children before they are born >> That's true. And you're right and the sonogram et cetera. But and then how has mobile changed this whole conversation because when I was on Facebook on my PC at home very different set of information than when it's connected to all the sensors in my mobile phone when Facebook is on my mobile phone really changes where I am how fast I'm moving, who I'm in proximity to it completely changed the privacy game. >> Yes so geo location and the ACLU here in Northern California chapter has a very good quote on that. "Geo location is really extremely powerful variable" Now what was the question? >> How has this whole privacy thing changed now with the proliferation of the mobile, and the other thing I would say, when you have kids that grew up with mobile and sharing on the young ones don't use Facebook anymore, Instagram, Snap Chat just kind of the notion of sharing and privacy relative to folks that you know wouldn't even give their credit card over the telephone not that long ago, much less type it into a keyboard, um do they really know the value do they really understand the value do they really get the implications when that's the world in which they've lived in. Most of them, you know they're just starting to enter the work force and haven't really felt the implications of that. >> So for me the value of data is how much the data impacts a decision. So for the side of the individual, if I have data about the restaurant, and that makes me decide whether to go there or to not go there. That is having an impact on my decision thus the data is valuable. For a company a decision whether to show me this offer or that offer that is how data is valued from the company. So that kind of should be quantified The value of the picture of my dog when I was a child. That is you know so valuable, I'm not talking about this. I'm very sort of rational here in terms of value of data as the impact is has on decisions. >> Do you see companies giving back more of that value to the providers of that data? Instead of you know just simple access to useful applications but obviously the value exceeds the value of the application they're giving you. >> So you use the term giving back and before you talked about kids giving up data. So I don't think that it is quite the right metaphor. So I know that metaphor come from the physical world. That sometimes has been data is in your oil and that indeed is a good metaphor when it comes to it needs to be refined to have value. But there are other elements where data is very different from oil and that is that I don't really give up data when I share and the company doesn't really give something back to me but it is much interesting exchange like a refinery that I put things in and now I get something not necessarily back I typically get something which is very different from what I gave because it has been combined with the data of a billion other people. And that is where the value lies, that my data gets combined with other peoples data in some cases it's impossible to actually take it out it's like a drop of ink, a drop in the ocean and it spreads out and you cannot say, oh I want my ink back. No, it's too late for that. But it's now spread out and that is a metaphor I think I have for data. So people say, you know I want to be in control of my data. I often think they don't have deep enough thought of what they mean by that. I want to change the conversation of people saying You what can I get by giving you the data? How can you help me make better decisions? How can I be empowered by the data which you are grabbing or which you are listening to that I produce. That is a conversation which I want to ask here at the Privacy Day. >> And that's happening with like Google Maps obviously you're exchanging the information, you're walking down the street, you're headed here they're telling you that there's a Starbucks on the corner if you want to pick up a coffee on the way. So that is already kind of happening right and that's why obviously Google has been so successful. Because they're giving you enough and you're giving them more and you get in this kind of virtuous cycle in terms of the information flow but clearly they're getting a lot more value than you are in terms of their you know based on their market capitalization you know, it's a very valuable thing in the aggregation. So it's almost like a one plus one makes three >> Yes. >> On their side. >> Yes, but it's a one trick pony ultimately. All of the money we make is rats. >> Right, right that's true. But in-- >> It's a good one to point out-- >> But then it begs the question too when we no longer ask but are just delivered that information. >> Yes, I have a friend Gam Dias and he runs a company called First Retail, and he makes the point that there will be no search anymore in a couple of years from now. What are you talking about? I search every day, but is it. Yes. But You know, you will get the things before you even think about it and with Google now a few years ago when other things, I think he is quite right. >> We're starting to see that, right where the cards come to you with a guess as to-- >> And it's not so complicated If let's see you go to the symphony you know, my phone knows that I'm at the symphony even if I turn it off, it know where I turned it off. And it knows when the symphony ends because there are like a thousand other people, so why not get Ubers, Lyfts closer there and amaze people by wow, your car is there already. You know that is always a joke what we have in Germany. In Germany we have a joke that says, Hey go for vacation in Poland your car is there already. But maybe I shouldn't tell those jokes. >> Let's talk about your book. So you've got a new book that came out >> Yeah >> Just recently released, it's called Data for the People. What's in it what should people expect, what motivated you to write the book? >> Well, I'm actually excited yesterday I got my first free copies not from the publisher and not from Amazon. Because they are going by the embargo by which is out next week. But Barnes and Noble-- >> They broke the embargo-- Barnes and Noble. Breaking news >> But three years of work and basically it is about trying to get people to embrace the data they create and to be empowered by the data they create. Lots of stories from companies I've worked with Lots of stories also from China, I have a house in China I spend a month or two months there every year for the last 15 years and the Chinese ecosystem is quite different from the US ecosystem and you of course know that the EU regulations are quite different from the US regulations. So, I wrote on what I think is interesting and I'm looking forward to actually rereading it because they told me I should reread it before I talk about it. >> Because when did you submit it? You probably submitted it-- >> Half a year >> Half a year ago, so yeah. Yeah. So it's available at Barnes and Noble and now Amazon >> It is available. I mean if you order it now, you'll get it by Monday. >> Alright, well Dr. Andreas Weigin thanks for taking a few minutes, we could go forever and ever but I think we've got to let you go back to the rest of the sessions. >> Thank you for having me. >> Alright, pleasure Jeff Frick, you're watching theCUBE see you next time.