>>Welcome back to the cubes coverage of dr khan 2021. I'm john for your host of the cube. We're here with Amanda Silver, corporate vice president, product developer division at Microsoft. Amanda, Great to see you you were on last year, Dr khan. Great to see you again a full year later were remote. Thanks for coming on. I know you're super busy with build happening this week as well. Thanks for making the time to come on the cube for Dr khan. >>Thank you so much for having me. Yeah, I'm joining you like many developers around the globe from my personal home office, >>developers really didn't skip a beat during the pandemic and again, it was not a good situation but developers, as you talked about last year on the front lines, first responders to creating value quite frankly, looking back you were pretty accurate in your prediction, developers did have an impact this year. They did create the kind of change that really changed the game for people's lives, whether it was developing solutions from a medical standpoint or even keeping systems running from call centres to making sure people got their their their goods or services and checks and and and kept sanity together. So. >>Yeah absolutely. I mean I think I think developers you know get the M. V. P. Award for this year because you know at the end of the day they are the digital first responders to the first responders and the pivot that we've had to make over the past year in terms of supporting remote telehealth, supporting you know online retail, curbside pickup. All of these things were done through developers being the ones pushing the way forward remote learning. You know my kids are learning at home right behind me right now so you might hear them during the interview that's happening because developers made that happen. >>I don't think mom please stop hogging the band with, they've got a gigabit. Stop it. Don't be streaming. My kids are all game anyway, Hey, great to have you on and you have to get the great keynote, exciting to see you guys continue the collaboration with Docker uh with GIT hub and Microsoft, A great combination, it's a 123 power punch of value. You guys are really kind of killing it. We heard from scott and dan has been on the cube. What's your thoughts on the partnership with the developer division team at Microsoft with Doctor, What's it all about this year? What's the next level? >>Well, I mean, I think, I think what's really awesome about this partnership is that we all have, we all are basically sharing a common mission. What we want to do is make sure that we're empowering developers, that we're focused on their productivity and that we're delivering value to them so they can do their job better so that they can help others. So that's really kind of what drives us day in and day out. So what we focus on is developer productivity. And I think that's a lot of what dana was talking about in her session, the developer division. Specifically, we really try to make sure that we're improving the state of the art from modern developers. So we want to make sure that every keystroke that they take, every mouse move that they make, it sounds like a song but every every one of those matter because we want to make sure that every developers writing the code that only they can write and in terms of the partnership and how that's going. You know my team and the darker team have been collaborating a ton on things like dr desktop and the Doctor Cli tool integrations. And one of the things that we do is we think about pain points and various workflows. We want to make sure that we're shaving off the edges of all of the user experience is the developers have to go through to piece all of these applications together. So one of the big pain points that we have heard from developers is that signing into the Azure cloud and especially our sovereign clouds was challenging. So we contributed back to uh back to doctor to actually make it easier to sign into these clouds. And so dr developers can now use dr desktop and the Doctor Cli to actually change the doctor context so that its Azure. So that makes it a lot easier to connect the other. Oh, sorry, go ahead. No, I was just >>going to say, I love the reference of the police song. Every breath you take, every >>mouth moving. Great, >>great line there. Uh, but I want to ask you while you're on this modern cloud um, discussion, what is I mean we have a lot of developers here at dr khan. As you know, you guys know developers in your ecosystem in core competency. From Microsoft, Kublai khan is a very operator like focus developed. This is a developer conference. You guys have build, what is the state of the art for a modern cloud developer? Could you just share your thoughts because this comes up a lot. You know, what's through the art? What's next jan new guard guard? It's his legacy. What is the state of the art for a modern cloud developer? >>Fantastic question. And extraordinarily relevant to this particular conference. You know what I think about often times it's really what is the inner loop and the outer loop look like in terms of cycle times? Because at the end of the day, what matters is the time that it takes for you to make that code change, to be able to see it in your test environment and to be able to deploy it to production and have the confidence that it's delivering the feature set that you need it to. And it's, you know, it's secure, it's reliable, it's performance, that's what a developer cares about at the end of the day. Um, at the same time, we also need to make sure that we're growing our team to meet our demand, which means we're constantly on boarding new developers. And so what I take inspiration from our, some of the tech elite who have been able to invest significant amounts in, in tuning their engineering systems, they've been able to make it so that a new developer can join a team in just a couple of minutes or less that they can actually make a code change, see that be reflected in their application in just a few seconds and deploy with confidence within hours. And so our goal is to actually be able to take that state of the art metric and democratize that actually bring it to as many of our customers as we possibly can. >>You mentioned supply chain earlier in securing that. What are you guys doing with Docker and how to make that partnership better with registries? Is there any update there in terms of the container registry on Azure? >>Yeah, I mean, you know, we, we we have definitely seen recent events and and it almost seems like a never ending attacks that that you know, increasingly are getting more and more focused on developer watering holes is how we think about it. Kind of developers being a primary target um for these malicious hackers. And so what it's more important than ever that every developer um and Microsoft especially uh really take security extraordinarily seriously. Our engineers are working around the clock to make sure that we are responding to every security incident that we hear about and partnering with our customers to make sure that we're supporting them as well. One of the things that we announced earlier this week at Microsoft build is that we've actually taken, get have actions and we've now integrated that into the Azure Security Center. And so what this means is that, you know, we can now do things like scan for vulnerabilities. Um look at things like who is logging in, where things like that and actually have that be tracked in the Azure security center so that not just your developers get that notification but also your I. T. Operations. Um In terms of the partnership with dR you know, this is actually an ongoing partnership to make sure that we can provide more guidance to developers to make sure that they are following best practices like pulling from a private registry like Docker hub or at your container registry. So I expect that as time goes on will continue to more in partnership in this space >>and that's going to give a lot of confidence. Actually, productivity wise is going to be a big help for developers. Great stuff is always good, good progress. They're moving the needle. >>Last time we >>spoke we talked about tools and setting Azure as the doctor context duty tooling updates here at dot com this year. That's notable. >>Yeah, I mean, I think, you know, there's one major thing that we've been working on which has a big dependency on docker is get help. Code space is now one of the biggest pain points that developers have is setting up a new DEV box, which they often have to do when they are on boarding a new employee or when they're starting a new project or even if they're just kicking the tires on a new technology that they want to be able to evaluate and sometimes creating a developer environment can actually take hours um and especially when you're trying to create a developer environment that matches somebody else's developer environment that can take like a half a day and you can spend all of your time just debugging the differences in environment variables, for example, um, containers actually makes that much easier. So what you can do with this, this services, you can actually create death environment spun up in the cloud and you can access it in seconds and you get from there are working coding environment and a runtime environment and this is repeatable via containers. So it means that there's no inadvertent differences introduced by each DEV. And you might be interested to know that underneath this is actually using Docker files and dr composed to orchestrate the debits and the runtime bits for a whole bunch of different stacks. And so this is something that we're actually working on in collaboration with the with the doctor team to have a common the animal format. And in fact this week we actually introduced a couple of app templates so that everybody can see this all in action. So if you check out a ca dot m s forward slash app template, you can see this in action yourself. >>You guys have always had such a strong developer community and one thing I love about cloud as it brings more agility, as we always talk about. But when you start to see the enterprise grow into, the direction is going now, it's almost like the developer communities are emerging, it's no longer about all the Lennox folks here and the dot net folks there, you've got windows, you've got cloud, >>it's almost >>the the the solidification of everyone kind of coming together. Um and visual studio, for instance, last year, I think you were talking about that to having to be interrogated dr composed, et cetera. >>How do you see >>this melting pot emerging? Because at the end of the day, you pick the language you love and you got devops, which is infrastructure as code doesn't matter. So give us your take on where we are with that whole progress of of making that happen. >>Well, I mean I definitely think that, you know, developer environments and and kind of, you know, our approach to them don't need to be as dogmatic as they've been in the past. I really think that, you know, you can pick the right tool and language and stand developer stack for your team, for your experience and you can be productive and that's really our goal. And Microsoft is to make sure that we have tools for every developer and every team so that they can build any app that they want to want to create. Even if that means that they're actually going to end up ultimately deploying that not to our cloud, they're going to end up deploying it to AWS or another another competitive cloud. And so, you know, there's a lot of things that we've been doing to make that really much easier. We have integrated container tools in visual studio and visual studio code and better cli integrations like with the doctor context that we had talked about a little bit earlier. We continue to try to make it easier to build applications that are targeting containers and then once you create those containers it's much easier to take it to another environment. One of the examples of this kind of work is now that we have WsL and the Windows subsystem for Lennox. This makes it a lot easier for developers who prefer a Windows operating system as their environment and maybe some tools like Visual Studio that run on Windows, but they can still target Lennox with as their production environment without any impedance mismatch. They can actually be as productive as they would be if they had a Linux box as their Os >>I noticed on this session, I got to call this out. I want to get your reaction to it interesting. Selection of Microsoft talks, the container based development. Visual studio code is one that's where you're going to show some some some container action going on with note and Visual Studio code. And then you get the machine learning with Azure uh containers in the V. S. Code. Interesting how you got, you know, containers with V. S. And now you've got machine learning. What does that tell the world about where Microsoft's at? Because in a way you got the cutting edge container management on one side with the doctor integration. Now you get the machine learning which everyone's talking about shifting, left more automation. Why are these sessions so important? Why should people attend? And what's the what's the bottom line? >>Well, like I said, like containers basically empower developer productivity. Um that's what creates the reputable environments, that's what allows us to make sure that, you know, we're productive as soon as we possibly can be with any text act that we want to be able to target. Um and so that's kind of almost the ecosystem play. Um it's how every developer can contribute to the success of others and we can amor ties the kinds of work that we do to set up an environment. So that's what I would say about the container based development that we're doing with both visual studio and visual studio code. Um in terms of the machine learning development, uh you know, the number of machine learning developers in the world is relatively small, but it's growing and it's obviously a very important set of developers because to train a machine learning uh to train an ml model, it actually requires a significant amount of compute resources, and so that's a perfect opportunity to bring in the research that are in a public cloud. Um What's actually really interesting about that particular develop developer stack is that it commonly runs on things like python. And for those of you who have developed in python, you know, just how difficult it is to actually set up a python environment with the right interpreter, with the right run time, with the right libraries that can actually get going super quickly, um and you can be productive as a developer. And so it's actually one of the hardest, most challenging developer stacks to actually set up. And so this allows you to become a machine learning developer without having to spend all of your time just setting up the python runtime environment. >>Yeah, it's a nice, nice little call out on python, it's a double edged sword. It's easier to sling code around on one hand, when you start getting working then you gotta it gets complicated can get well. Um Well the great, great call out there on the island, but good, good, good project. Let me get your thoughts on this other tool that you guys are talking about project tie. Uh This is interesting because this is a trend that we're seeing a lot of conversations here on the cube about around more too many control planes. Too many services. You know, I no longer have that monolithic application. I got micro micro applications with microservices. What the hell is going on with my services? >>Yeah, I mean, I think, you know, containers brought an incredible amount of productivity in terms of having repeatable environments, both for dev environments, which we talked about a lot on this interview already, but also obviously in production and test environments. Super important. Um and with that a lot of times comes the microservices architecture that we're also moving to and the way that I view it is the microservices architecture is actually accompanied by businesses being more focused on the value that they can actually deliver to customers. And so they're trying to kind of create separations of concerns in terms of the different services that they're offering, so they can actually version and and kind of, you know, actually improve each of these services independently. But what happens when you start to have many microservices working together in a SAS or in some kind of aggregate um service environment or kind of application environment is it starts to get unwieldy, it's really hard to make it so that one micro service can actually address another micro service. They can pass information back and forth. And you know what used to be maybe easy if you were just building a client server application because, you know, within the server tear all of your code was basically contained in the same runtime environment. That's no longer the case when every microservices actually running inside of its own container. So the question is, how can we improve program ability by making it easier for one micro service that's being used in an application environment, be to be able to access another another service and kind of all of that context. Um and so, you know, you want to be able to access the service is the the api endpoint, the containers, the ingress is everything, make everything work together as though it felt just as easy as as um you know, server application development. Um And so what this means as well is that you also oftentimes need to get all of these different containers running at the same time and that can actually be a challenge in the developer and test loop as well. So what project tie does is it improves the program ability and it actually allows you to just write a command like thai run so that you can actually in stan she ate all of these containers and get them up and running and basically deploy and run your application in that environment and ultimately make the dev testing or loop much faster >>than productivity gain. Right. They're making it simple to stand up. Great, great stuff. Let me ask you a question as we kind of wrap down here for the folks here at Dakar Con, are >>there any >>special things you'd like to talk about the development you think are important for the developers here within this space? It's very dynamic. A lot of change happening in a good way. Um, but >>sometimes it's hard to keep >>track of all the cool stuff happening. Could you take a minute to, to share your thoughts on what you think are the most important develops developments in this space? That that might be interesting to ducker con attendees. >>I think the most important things are to recognize that developer environments are moving to containerized uh, environments themselves so that they can be repeated, they can be shared, the work, configuring them can be amortized across many developers. That's important thing. Number one important thing. Number two is it doesn't matter as much what operating system you're running as your chrome, you know, desktop. What matters is ultimately the production environment that you're targeting. And so I think now we're in a world where all of those things can be mixed and matched together. Um and then I think the next thing is how can we actually improve microservices, uh programming development together um so that it's easier to be able to target multiple micro services that are working in aggregate uh to create a single service experience or a single application. And how do we improve the program ability for that? >>You know, you guys have been great supporters of DACA and the community and open source and software developers as they transform and become quite frankly the superheroes for the transformation, which is re factoring businesses. So this has been a big thing. I'd love to get your thoughts on how this is all coming together inside Microsoft, you've got your division, you get the developer division, you got GIT hub, got Azure. Um, and then just historically, and he put this up last year army of an ecosystem. People who have been contributing encoding with Microsoft and the partners for many, many decades. >>Yes. The >>heart Microsoft now, how's it all working? What's the news? I get Lincoln, Lincoln, but there's no yet developer model there yet, but probably is soon. >>Um Yeah, I mean, I think that's a pretty broad question, but in some ways I think it's interesting to put it in the context of Microsoft's history. You know, I think when I think back to the beginning of my career, it was kind of a one stack shop, you know, we was all about dot net and you know, of course we want to dot net to be the best developer environment that it can possibly be. We still actually want that. We still want that need to be the most productive developer environment. It could we could possibly build. Um but at the same time, I think we have to recognize that not all developers or dot net developers and we want to make sure that Azure is the most productive cloud for developers and so to do that, we have to make sure that we're building fantastic tools and platforms to host java applications, javascript applications, no Js applications, python applications, all of those things, you know, all of these developers in the world, we want to make sure it can be productive on our tools and our platforms and so, you know, I think that's really kind of the key of you know what you're speaking of because you know, when I think about the partnership that I have with the GIT hub team or with the Azure team or with the Azure Machine learning team or the Lincoln team, um A lot of it actually comes down to helping empower developers, improving their productivity, helping them find new developers to collaborate with, um making sure that they can do that securely and confidently and they can basically respond to their customers as quickly as they possibly can. Um and when, when we think about partnering inside of Microsoft with folks like linkedin or office as an example, a lot of our partnership with them actually comes down to improving their colleagues efficiency. We build the developer tools that office and lengthen are built on top of and so every once in a while we will make an improvement that has, you know, 5% here, 3% there and it turns into an incredible amount of impact in terms of operations, costs for running these services. >>It's interesting. You mentioned earlier, I think there's a time now we're living in a time where you don't have to be dogmatic anymore, you can pick what you like and go with it. Also that you also mentioned just now this idea of distributed applications, distributed computing. You know, distributed applications and microservices go really well together. Especially with doctor. >>Can you share >>your thoughts on the framework that you guys released called Dapper? >>Yeah, yeah. We recently released Dapper. It's called D A P R. You can look it up on GIT hub and it's a programming model for common microservices pattern, two common microservices patterns that make it really easy and automatic to create those kinds of microservices. So you can choose to work with your favorite state stores or databases or pub sub components and get things like cloud events for free. You can choose either http or g R B C so that you can get mesh capabilities like service discovery and re tries and you can bring your own secret store and easily be able to call it from any environment variable. It's also like I was talking about earlier, multi lingual. Um so you don't need to embrace dot net, for example, as you're programming language to be able to benefit from Dapper, it actually supports many programming languages and Dapper itself is actually written and go. Um and so, you know, all developers can benefit from something like Dapper to make it easier to create microservices applications. >>I mean, always great to have you on great update. Take a minute to give an update on what's going on with your division. I know you had to build conference this week. V. S has got the new preview title. We just talked about what are the things you want to get to plug in for? Take a minute to get to plug in for what you're working on, your goals, your objectives hiring, give us the update. >>Yeah, sure. I mean, you know, we we built integrated container tools in visual studio uh and the Doctor extension and Visual Studio code and cli extensions. Uh and you know, even in this most recent release of our Visual Studio product, Visual Studio 16 10, we added some features to make it easier to use DR composed better. So one of the examples of this is that you can actually have uh Oftentimes you need to be able to use multiple doctor composed files together so that you can actually configure various different container environments for a single single application. But it's hard sometimes to create the right Yeah. My file so that you can actually invoke it and invoke the the container and the micro services that you need. And so what this allows you to do is to actually have just a menu of the different doctor composed files so that you can select the runtime and test environment that you need for the subset of the portion of the application that you're working on at the end of the day. This is always about developer productivity. You know, like I said, every keystroke matters. Um and we want to make sure that you as a developer can focus on the code that only you can Right. >>Amanda Silver, corporate vice president product development division of Microsoft. Always great to see you and chat with you remotely soon. We'll be back in in real life with real events soon as we come out of the pandemic and thanks for sharing your insight and congratulations on your success this year and and congratulations on your announcement here at Dakar Gone. >>Thank you so much for having me. >>Okay Cube coverage for Dunkirk on 2021. I'm John for your host of the Cube. Thanks for watching. Mhm

>> Narrator: From around the globe, it's theCUBE with digital coverage of Dockercon Live 2020, brought to you by Docker and it's ecosystem partners. >> Everyone welcome back to Dockercon 2020, #Docker20. This is theCUBE and Docker's coverage of Dockercon 20. I'm John Furrier in the Palo Alto studios with our quarantine crew, we got a great interview segment here and big news around developer workflow code to cloud. We've got Amanda Silver, Corporate Vice President, product for developer tools at Microsoft and Scott Johnson, the CEO of Docker. Scott had a great Keynote talking about this relationship news has hit about the extension of the Microsoft partnership. So congratulations, Amanda, welcome to theCUBE. >> Thanks for having me. >> Amanda, tell us about what your role is at Microsoft. You guys are well known in the developer community. You had to develop a ecosystem even when I was in college going way back. Very modern now, the cloud is the key, code to cloud, that's the theme. Tell us about your role at Microsoft. >> Yeah, so I basically run the product, Product Design and User Research team that works on our developer tools at Microsoft. And so that includes the Visual Studio product as well as Visual Studio code that's become pretty popular in the last few years but it also includes things like the dotNET runtime and the TypeScript programming language, as well as all of our Azure tooling. >> What's your thoughts on the relationship with Docker? Obviously the news extension of an existing relationship, Microsoft's got a lot of tools, you got a lot of things you guys are doing, bringing the cloud to every business. Tell us about your thoughts on this relationship with Docker? >> Yeah well, we're very excited about the partnership for sure. Our goal is really to make sure that Azure is a fantastic place where all developers can kind of bring their code and they feel welcome. They feel natural. We really see a unique opportunity to make the experience really great for the Docker community by creating more integrated and seamless experience across Docker desktop, Windows and Visual Studio and we really appreciate how Docker has kind of, supported our Windows ecosystem to run in Docker as well. >> Scott, this relationship and an extension with Microsoft is really, I think, impressive and also notable because Microsoft's got so many tools out there and they have so successful with Azure. You guys have been so successful with your developer community but this also is a reflective of the new Docker. Can you share your thoughts on how this partnership with Microsoft, extending the way it is, with the growth of the cloud is a reflection of the new Docker? >> Yeah, absolutely John, it's a great question. One of the things that we've really been focused on since November is fully embracing the ecosystem and all the partnerships and all the possibilities of that ecosystem and part of that is just reality that we're a smaller company now and we can't do it all, nor should we do it all. Part of it's the reality that developers love choice and no one's going to change their minds on choice, and third is just acknowledging that there's so much creativity and so much energy outside the four walls of Docker that we'd be silly not to take advantage of that and welcome it and embrace it and provide that as a phenomenal experience for our developers. So this is a great example of that. The Snyk partnership we announced last week is a great example of that and you're going to see many more partnerships like this going forward that are reflective of exactly this point. >> You've been a visionary on the product side, interviewed before. Also deploying is more important than ever, that whole workflow simplifying, it's not getting complex, people want choice, building code, managing code, deploying code. This has been a big focus of yours. Can you just share your thoughts on where Microsoft comes in? Because they got stuff too, you've got stuff, it all works together. What's your thoughts? >> Right, so it needs to work together because developers want to focus on their app. They don't want to focus on duct taping and stringing together different siloed pools. So you can see in the demo and you'll see in demonstrations later throughout the conference, just the seamless experience that a developer gets in the Docker command line inner operating with Visual Studio Code, with the Docker command line and then deploying to Azure and what's wonderful about the partnership is that both parties put real engineering effort and design effort into making it a great experience. So a lot of the complexities around configuration, around default settings, around security, user management, all of that is abstracted out and taken away from the developers so they can focus on applications and getting those applications deployed to the cloud as quickly as possible. Getting their apps from code to cloud is the watchword or the call to action for this partnership and we think we've really hit it out of the park with the integration that you saw. >> Great validation in the critical part of the workflow you guys been part of. Amanda, we're living in a time we're doing these remote interviews. The COVID crisis has shown the productivity gains of working at home and working, sheltering in place but it also has highlighted the focus of developers, mainly who have also worked at home. They're been kind of used to this, you see the rigs. I saw at Microsoft build some amazing rigs from the studio, so these guys streaming their code demos. This is a Cambrian explosion of new kinds of productivity. You got the world's getting more complex at scale. This is what cloud does. What's your thoughts on this? 'Cause the tooling, there's more tools than ever, right? >> Yeah. >> I still got to deploy code. It's got to be more agile, it's got to be faster, it's got to be at scale. This is what you guys believe in. What's your thinking on all these tooling and abstraction layers? And the end of the day, developers still got to do their job. >> Yeah, well, absolutely. And now even more than ever, I think we've certainly seen over the past few months, a more rapid acceleration of digital transformation that has really happened in the past few years. Paper processes are now becoming digital processes all of a sudden. Everybody needs to work and learn from home and so there's just this rapid acceleration to kind of move everything to support our new remote first lifestyle. But even more so, we now have remote development teams actually working from home as well in a variety of different kinds of environments, whether they're using their own personal machine to connect to their infrastructure or they're using a work issued machine. It's more important than ever that developers are productive but they are productive as a team. Software is a team sport, we all need to be able to work together and to be able to collaborate. And one of the most important aspects of agility for developers is consistency. And what Docker really enables with containerization, is to make the infrastructure consistent and repeatable so that as developers are moving through the lifecycle from their local desktop and developing on their local desktop, to a test environment and to staging and to production, it's really, it's infrastructure for developers as well as operations. And so, that infrastructure, that's completely customizable for what the developers operating system of choice is, what their app stack is, all of those dependencies kind of running together. And so that's what really enables developers to be really agile and have a really fast iteration cycle but also to have that consistency across all of their development team. And we now need to think about things like, how are we actually going to bring on interns for the summer and make sure that they can actually set up their developer boxes in a consistent way that we can actually support them and things like Docker really help with that. >> As your container instances and Visual Studio cloud that you guys have has had great success. There's a mix and match formula here and the other day, developers want to ship the code. What's the message that you guys are sending here with this because I think productivity is one, simplification is the other but as developers, we're on the front lines and they're shipping in real time. This is a big part of the value proposition that you guys bringing to the table. >> Yeah, the core message is that any developer and their code is welcome (laughs) and that we really want to support them, empower them and increase their velocity and the impact that they can have. And so, having things like the fact that the Docker CLI is natively integrated into the Azure experience is a really important aspect of making sure that developers are feeling welcome and feeling comfortable. And now that the Docker CLI tools that are part of Docker desktop have access to native commands that work well with Azure container instances, Azure container instances, if anybody is unfamiliar with that, is the simplest and fastest way to kind of set up containers in Azure and so we believe that developers have really been looking for a really simple way to kind of get containers on Azure and now we have that really consistent experience across our servers, services and our tools. Visual Studio code and Visual Studio extensions make full use of Docker desktop and the Docker CLI so that they can get that combination of the productivity and the power that they're looking for. And in fact, we've integrated these as a design point since very early on in our partnership when we've been partnering with Docker for quite a while. >> Amanda, I want to ask you about the tool chain. We've heard about workflows, making it simpler. Bottom line from a developer standpoint, what's the bottom line for me? What does this mean to me, everyday developer out there? >> I really think it means, your productivity on your terms. And so, Microsoft has been a developer company since the very beginning with Bill Gates and GW Basic. And it's actually similar for Docker. They really have a developer first point of view, which certainly speaks to my heart and so one of the things that we're really trying to do with Docker is to make sure that we can create a workflow that's super productive at every stage of the developer experience, no matter which stack they're actually targeting, whether there's targeting Node or Python, or dotNET and C Sharp or Java, we really want to make sure that we have a super simple experience that you can actually initiate all of these commands, create Docker container images and use the Docker compose files. And then, just kind of do that consistently, as you're deploying it all the way up into your infrastructure in Azure. And the other thing that we really want to make sure is that that even post deployment, you can actually inspect and diagnose these containers and images without having to leave the tool. So we also think about the process of writing the code but also the process of kind of managing the code and remediating issues that might come up in production. And so we really want you to be able to look at containers up in the Azure, that are deployed into Azure and make sure that they're running and healthy and that if something's wrong, that you can actually open up a shell and be in an interactive mode and be able to look at the logs from those containers and even inspect one to see environment variables or other details. >> Yeah, that's awesome. Writing code, managing code and then you got to deploy, right? So what I've been loving about the past generation of Agile is deployment's been faster to play off all the time. Scott, this brings up that the ease of use but you'll want to actually leverage automation. This is the trend that you want to get into. You want to make it easy to write code, manage code but during the deployment phase, that's a big innovation. That's the last point, making that better and stronger. What's your thoughts on simplifying that? >> Well, as a big part of this partnership, John, that Docker and Microsoft embarked on, as you saw from the demo in the keynote, all within the Docker command line, the developer's able to do it in two simple commands, deploy an app, define and compose from their desktop to Azure. And there's a whole slew of automation and pre-configured smart defaults or sane defaults that have gone on behind the scenes and it a lot of hardcore engineering work on part of Docker-Microsoft together to simplify that and make that easy. And that goes exactly to your point, which is, the simpler you can make it, make an abstract way to kind of underline plumbing and infrastructure, the faster Devs can get their application from code to cloud. >> Scott, you've been a product CEO, you've been a product person now you're the CEO but you have a product back when you've been involved with a relationship with Microsoft for a long time. What's the state of the market right now? I see Microsoft has evolved because just the performance, corporate performance, the shift to the cloud has been phenomenal. Now developers getting more empowered, there's more demand for the pressure to put developers to do more and more creativity. So you've seen this evolve, this relationship, what does it mean? >> Yeah, it's honestly a wonderful question, John and I want to thank Amanda and the entire Microsoft team for being long standing partners with us on this journey. So it might not be known to everyone on today's day's event but Microsoft came to the very first Dockercon event way back in June 2014 and I had the privilege of greeting them and welcoming them and then they were full on, ready to see what all the excitement about Docker was about and really embraced it. And you mentioned kind of openness in Microsoft's growth over time in that dimension and we think Docker, together with Microsoft have really shown what an open developer community can do. That started back in 2014 and then we embarked on an open source collaboration around the Docker command line of the Docker engine, bringing that Docker engine from Linux and now moving it to Windows applications. And so all the sudden the promise of write once and use the same primitives, the same formats, the same command lines, as you can with Linux onto Windows applications, we brought that promise to the market. And it's been an ongoing journey together with Microsoft on open standards base, developer facing friendliness, ease of use, fast time to deploy and this partnership that we announced yesterday and we highlighted at the keynote is just another example of that ongoing relationship, laser-like focused on developer productivity and helping teams build great apps. >> Why do you like Azure in the cloud for Docker? Can you share why? >> Well, as Amanda has been sharing, it's super focused on, what are the needs of developers to help them continue to stay focused on their apps and not have their cognitive load burdened by other aspects of getting their apps to the cloud and Azure does a phenomenal job of simplifying and providing sane defaults out of the box. And as we've been talking about, it's also very open to partner integrations like the one we've announced yesterday and highlighted that make it just easy for development teams to choose their tools and build their apps and deploy them onto Azure as quickly as possible. So it's a phenomenal platform for developers and we're very excited and proud to partner with Microsoft on it. >> Amanda on your side, I see Docker's got millions of developers. you guys got millions of developers even more. How do you see the developers in Microsoft's side engaging with Docker desktop and Docker hub? Where does it all fit? I mentioned earlier how I see Docker context really improving the way that individuals and teams work with their environments in making sure that they're consistent but I think this really comes together as we work with Docker desktop and Docker Hub. When developers sign in to Docker Hub from Docker desktop, everything kind of lights up and so they can see all of the images in their repositories and they can also see the cloud environments that they're running them in. And so, once you sign into the Hub, you can see all the contexts that map to the logical environments they have access to, like Dev, NQA and maybe staging. And another use case that's really important is that we can access the same integration environment. So, I can have microservices that I've been working on but I can also see microservices that my teammates and their logs from the services that they've been working on, which I think is really great and certainly helps with team productivity. The other thing too, is that this also really helps with hybrid cloud deployments, where, you might have some on-premises hosted containers and you might have some that's hosted in a public cloud. And so you can see all of those things through your Docker Hub. >> Well, I got to say, I love the code to cloud tagline, I think that's very relevant and catchy. And I think, I guess to me what I'm seeing and I'd love to get your thoughts, Amanda on this is you oversee a key part of Microsoft's business that's important for developers, just the vibe and people are amped up right now. I know people are tensed, anxiety with the COVID-19 crisis but I think people are generally agreeing that this is going to be a massive inflection point for just more headroom needed for developers to accelerate their value on the front lines. What's your personal take on this? You've seen these waves before but now in this time, what are you most excited about? What are you optimistic about? What's your view on the opportunities? Can you share your thoughts, because people are going to get back to work. They're working now remotely but if we go back to hybrid world, they're going to be jamming on projects. >> Yeah, for sure but people are jamming on projects right now and I think that in a lot of ways, developers are first responders in that they are... Developers are always trying to support somebody else. We're trying to support somebody else's workflow and so we have examples of people who are creating new remote systems to be able to schedule meetings in hospitals for the doctors who are actually the first responders taking care of patients but at the end of the day, it's the developer who's actually creating that solution. And so we're being called to duty right now and so we need to make sure that we're actually there to support the needs of our users and that we're basically cranking on code as fast as we can. And to be able to do that, we have to make sure that every developer is empowered and they can move quickly but also that they can collaborate really quickly. And so I think that Docker Hub, Docker kind of helps you ensure that you have that consistency but you also have that connection to the infrastructure that's hosted by your your organization. >> I think you nailed, that's amazing insight. I think that's... The current situation in the community matters because there's a lot of frontline work being done to your point but then we got to rebuild, the modernization is happening as well coming out of this so there's going to be that. And there's a lot of camaraderie going on and massive community involvement I'm seeing more of. The empathy but also now there's going to be the building, the creation, the new creation. So, Scott, this is going to call for more simplicity and to abstract away the complexities. This is the core issue. >> Well, that's exactly right. And it is time to build and we're going to build our way out of this and it is the community that's responding. And so in some sense, Microsoft and Docker are there to support that moory energy and give them the tools to go and identify and have an impact as quickly as possible. I referenced in the keynote, completely bottoms up organic adoption of Docker desktop and Docker Hub in racing to provide solutions against the COVID-19 virus. It's a war against this pandemic that is heavily dependent on applications and data. And there's over 200 projects, community projects on Docker Hub today, where you've got tools and containers and data analysis all in service to the COVID-19 battle that's being fought. And then as you said, John, as we get through the other side, there's entire industries that are completely rethinking their approach that were largely offline before but now see the imperative and the importance of going online. And that tectonic shift, nearly overnight of offline to online behavior and commerce and social and going down the list, that requires new application development. And I'm very pleased about this partnership is that together, we're giving developers the tools to really take advantage of that opportunity and go and build our way out of it. >> Well, Scott, congratulations on a great extended partnership with Microsoft and the Docker brand. I'm a big fan from day one. I know you guys have pivoted on a new trajectory, which is phenomenal, very community oriented, very open source, very open. So congratulations on that. Amanda, thanks for spending the time to come on. I'll give you the final word. Take a minute to talk about what's new at Microsoft for the folks that know Microsoft, know they have a developer mindset from day one. Cloud is exploding, code to cloud. What's the update? What's the new narrative? What should people know about Microsoft with developer community? Can you share some data for the folks that aren't in the community or might want to join or the folks in the community who want to get an update? >> Yeah, it's a great kind of question. Right now, I think we are all really focused on making sure that we can empower developers throughout the world and that includes both those who are building solutions for their organizations today but also, I think we're going to end up with a ton of new developers over this next period, who are really entering the workforce and learning to create digital solutions. Overall, there's a massive developer shortage across the world. There's so much opportunity for developers to kind of address a lot of the needs that we're seeing out of organizations, again, across the world. And so I think it's just a really exciting time to be a developer and my only hope is that basically we're building tools that actually enable them to solve the problem. >> Awesome insight, and thank you so much for your time. Code to cloud developers are cranking away, they're the first responders, going to take care of business and then continue to build out the modern applications. And when you have a crisis like this, people cut right through the noise and get right to the tools that matter. So thanks for sharing the Microsoft-Docker partnership and the things that you guys are working on together. Thanks for your time. >> Thank you. >> Thank you. >> Okay, this is theCUBE's coverage. We are at Dockercon 2020 Digital. This is theCUBE Virtual. I'm John Furrier, bringing all the action, more coverage. Stay with us for more Dockercon Virtual after this short break. (gentle music)

>>From around the globe. It's the view with digital coverage of Docker con live 2020 brought to you by Docker and its ecosystem partners. >>LeBron. Welcome back to DockerCon 2020 hashtag Docker 20 this is the cube and Dockers coverage of Docker con 20 I'm Sean for you and the Palo Alto studios with our quarantine crew. We've got a great interview segment here in big news around developer workflow code to cloud. We've got Amanda silver corporate vice president, product for developer tools at Microsoft and Scott Johnson, the CEO of Docker. Scott had a great keynote talking about this relationship news has hit about the extension of the Microsoft partnership. So congratulations Amanda. Welcome to the cube. >>Thanks for having me. >>Amanda, tell us a bit about what your role is at Microsoft. You guys are well known in the developer community to develop an ecosystem when even when I was in college going way back, very modern. Now cloud is, is the key code to cloud. That's the theme. Tell us about your role at Microsoft. >>Yeah. So I basically run the product, uh, product design and user research team that works on our developer tools that Microsoft and so that includes the visual studio product as well as visual studio code. Um, that's become pretty popular in the last few years, but it also includes things like the.net runtime and the TypeScript programming language as well as all of our Azure tooling. >>What's your thoughts on the relationship with Docker? I'll show you the news extension of an existing relationship. Microsoft's got a lot of tools. You've got a lot of things you guys are doing, bringing the cloud to every business. Tell us about your thoughts on this relationship with Donker. >>Yeah, well we're very excited about the partnership for sure. Um, you know, our goal is really to make sure that Azure is a fantastic place where all developers can kind of bring their code and they feel welcome. They feel natural. Uh, we really see a unique opportunity to make the experience really great for Docker, for the Docker community by creating more integrated and seamless experience across Docker, desktop windows and visual studio. And we really appreciate how, how Docker is kind of, you know, supported our windows ecosystem to run in Docker as well. >>Scott, this relationship and an extension with Microsoft is really, uh, I think impressive and also notable because Microsoft's got so many, so many tools out there and they have so successful with Azure. You guys have been so successful with your developer community, but this also is reflective of the new Docker. Uh, could you share your thoughts on how this partnership with Microsoft extending the way it is with the growth of the cloud is a reflection of the new Docker? >>Yeah, absolutely. John's great question. One of the things that we've really been focused on since November is fully embracing the ecosystem and all the partnerships and all the possibilities of that ecosystem. And part of that is just reality. That we're a smaller company now and we can't do it all, nor should we do it all. Part of us. The reality that developers love voice and no one's gonna change their minds on choice. And third is just acknowledging that there's so much creativity and so much energy. The four walls of Docker that we'd be building, not the big advantage of that and welcome it and embrace it and provide that as a phenomenal experience part of Alfred's. So this is a great example of that. The sneak partnership we announced last week is a grant to have that and you're going to see many more of uh, partnerships like this going forward that are reflective of exactly this point. >>You've been a visionary on the product side of the interviewed before. Also deploying is more important than ever. That whole workflow, simplifying, it's not getting complex. People want choice, building code, managing code, deploying code. This has been a big focus of yours. Can you just share your thoughts on where Microsoft comes in because they got stuff too. You've got stuff, it all works together. What's your thoughts? >>Right? So it needs to work together, right? Because developers want to focus on their app. They don't want to focus on duct taping and springing together different siloed pools, right? So you can see in the demo and you'll see in, uh, demonstrations later throughout the conference. Just the seamless experience that a developer gets in the document man line inter-operating with visual studio code with the Docker command line and then deploying to Azure and what's what's wonderful about the partnership is that both parties put real engineering effort and design effort into making it a great experience. So a lot of the complexities around the figuration around default settings around uh, security, user management, all of that is abstracted out and taken away from the developer so they can focus on applications and getting those applications deployed to the proudest quickly as possible. Getting their app from code to cloud is the wok word or the or the call to action for this partnership. And we think we really hit it out of the park with the integration that you saw, >>Great validation and a critical part of the workflow. You guys have been part of Amanda, we're living in a time we're doing these remote interviews. The coven crisis has shown the productivity gains of working at home and working in sheltering in place, but also as highlighted, the focus of developers mainly who have also worked at home. They've kind of used to this. Do you see the rigs? I saw her at Microsoft build some amazing rigs from the studio. So these guys streaming their code demos. This is, um, a Cambrin explosion of new kinds of productivity. And yet the world's getting more complex at scale. This is what cloud does. What's your thoughts on this? Cause the tooling is more tools than ever, right? So I still gotta deploy code. It's gotta be more agile. It's gotta be faster. It's gotta be at scale. This is what you guys believe in. What's your thinking on all these tooling and abstraction layers and the end of the day, don't you still got to do their job? >>Yeah, well, absolutely. And now, even more than ever. I mean, I think we've, we've certainly seen over the past few months, uh, uh, a more rapid acceleration of digital transformation. And it's really happened in the past few years. Uh, you know, paper processes are now becoming digit digital processes. All of a sudden, you know, everybody needs to work and learn from home. And so there's just this rapid acceleration to kind of move everything to support our new remote lifestyle. Um, but even more so, you know, we now have remote development teams actually working from home as well in a variety of different kinds of, uh, environments. Whether they're using their own personal machine to connect to their infrastructure or they're using a work issued machine. You know, it's more important than ever that developers are productive, but they are productive as a team. Right? Software is a team sport. >>We all need to be able to work together and to be able to collaborate. And one of the most important aspects of agility for developers is consistency. And, uh, what Docker really enables is, uh, with, with containerization is to make the infrastructure consistent and repeatable so that as developers are moving through the life cycle from their local, local dev desktop and developing on their local desktop to a test environment and to staging and to production, it's really, it's infrastructure of or, or developers as well as operations. And so it's that, that infrastructure that's completely customizable for what the developer's operating system of choices, what their app stack is, all of those dependencies kind of running together. And so that's what really enables developers to be really agile and have a really, really fast iteration cycle but also to have that consistency across all of their development team. And you know, we, we now need to think about things like how are we actually going to bring on interns for the summer, uh, and make sure that they can actually set up their developer boxes in a consistent way that we can actually support them. And things like Docker really helped with that >>As your container instances and a visual studio cloud that you guys have has had great success. Um, there's a mix and match formula here. At the end of the day, developers want to ship the code. What's the message that you guys are sending here with this? Because I think productivity is one, simplification is the other, but as developers on the front lines and they're shipping in real time, this is a big part of the value proposition that you guys are bringing to the table. >>Yeah, I mean the, the core message is that any developer and their code is welcome, uh, and that we really want to support them and power them and increase their velocity and the impact that they can have. Um, and so, you know, having things like the fact that the Docker CLI is natively integrated into the Azure experience, uh, is a really important aspect of making sure that developers are feeling welcome and feeling comfortable. Um, and now that the Docker CLI tools are, that are part of Docker desktop, have access to native commands that work well with Azure container instances. Uh, Azure container instances, if anybody's on familiar with that, uh, is the simplest and fastest way to kind of set up containers and Azure. And, and so we believe that developers have really been looking for a really simple way to kind of get containers on Azure. And now we that really consistent experience across our service services and our tools and visual studio code and visual studio extensions make full use of Docker desktop and the Docker CLI so that they can get that combination of the productivity and the power that they're looking for. And in fact, we've, we've integrated these as a design point since very early on in our partnership when we've been partnering with, with Docker for quite a while. >>Amanda, I want to ask you about the, the, the, the tool chain. We've heard about workflows, making it simpler, bottom line, from a developer standpoint, what's the bottom line for me? What does this mean to me? Uh, every day developer out there? >>Um, I, I mean, I really think it means you know, your productivity on your terms. Um, and so, you know, Microsoft has been a developer company since the very, very beginning with, you know, bill Gates and, and, uh, GW basic. Um, and it's actually similar for Docker, right? They really have a developer first point of view, uh, which certainly speaks to my heart. And so one of the things that we're really trying to do with, with Docker is to make sure that we can create a workflow that's super productive at every stage of the developer experience, no matter which stack they're actually targeting, whether there's targeting node or Python or.net and C-sharp or Java. Uh, we really want to make sure that we have a super simple experience that you can actually initiate all of these commands, create, you know, Docker container images and use the compose Docker compose files. >>Um, and then, you know, just kind of do that consistently as you're deploying it all the way up into your infrastructure in Azure. And the other thing that we really want to make sure is that that even post deployment, you can actually inspect and diagnose these containers and images without having to leave the tool. Um, so we, we also think about the process of writing the code, but also the process of kind of managing the code and remediating issues that might come up in production. And so, you know, we really want you to be able to look at containers up in the Azure. Uh, up that are deployed into Azure and make sure that they're running and healthy and that if there, if something's wrong, that you can actually open up a shell and be in an interactive mode and be able to look at the logs from those containers and even inspect when to see environment variables or other details. >>Yeah, that's awesome. You know, writing code, managing code, and then you've got to deploy, right? So what I've been loving about the, the past generation of agile is deployment's been fast to deploy all the time. Scott, this brings up that the ease of use, but you want to actually leverage automation. This is the trend that you want to get in. You want, you don't want, you want to make it easy to write code, manage code. But during the deployment phase, that's a big innovation. That's the last point. Making that better and stronger. What's your thoughts on simplifying that? >>So that was a big part of this partnership, John, that the Docker in Microsoft embarked on and as you saw from the demo and the keynote, um, all within the man line, the developers able to do in two simple commands, deploy an app, uh, defining compose from the desktop to Azure and there's a whole slew of automation and pre-configured smart defaults or sane defaults that have gone on behind the scenes and that took a lot of hardcore engineering work on part of Docker and Microsoft together to simplify that and make that easy and that, that goes exactly to your point. We just like the simpler you can make it more, you can abstract a way to kind of underlying plumbing and infrastructure. The faster devs can get there. Their application from code to cloud. >>Scott, you've been a product CEO, you've been a product person, a CEO, but you have a product background. You've been involved with the relationship with Microsoft for a long time. What's the state of the market right now? I mean, obviously Microsoft has evolved. Look at just the performance corporate performance. The shift to the cloud has been phenomenal. Now developers getting more empowered, there's more demand for the pressure to put on developers to do more and more, more creativity. So you've seen this evolve, this relationship, what does it mean? >>Yeah, it's honestly a wonderful question, John. And I want to thank Amanda and the entire Microsoft team for being long standing partners with us on this journey. So it's might not be known to everyone on today's, uh, day's event. But Microsoft came to the very first Docker con event, uh, way back in June, 2014 and I had the privilege of, of reading them and welcoming them and they're, they were full on ready to see what all the excitement about Docker was about and really embrace it. And you mentioned kind of openness and Microsoft's growth over that, uh, over time in that dimension. And we think kind of Docker together with Microsoft have really shown what an open developer community can do. And that started back in 2014 and then we embarked on an open source collaboration around the Docker command line of the Docker engine, bringing that Docker engine from Linux and now moving it to windows applications. And so all of a sudden the promise of right ones and use the same primitives, the same formats, the same fan lines, uh, as you can with Linux onto windows applications. We brought that promise to the market and it's been an ongoing journey together with Microsoft of open standards based, developer facing friendliness, ease of use, fast time to deploy. And this, this partnership that we announced yesterday and we highlighted at the keynote is just another example of that ongoing relationship laser like focused on developer productivity and helping teams build great apps. >>Why do you like Azure in the cloud for Docker? Can you share why? >>Well, it's as Amanda has been sharing, it's super focused on what are the needs of developers to help them continue to stay focused on their apps and not have their cognitive load burdened by other aspects of getting their apps to the cloud. And Azure, phenomenal job of simplifying and providing sane defaults out of the box. And as we've been talking about, it's also very open to partner like the one we've announced >>Yesterday and highlighted, you know, but >>Uh, make it just easy for development teams to choose their tools and build their apps and deploy them onto Azure. It's possible. So, uh, it's, it's a phenomenal plan, one for developers and we're very excited and proud of partner with Microsoft on it. >>Amanda, on your side, I see DACA has got millions of developers. You guys got millions of developers even more. How do you see the developers in Microsoft side engaging with Docker desktop and Docker hub? Where does it all fit? >>I think it's a great question. I mean, I mentioned earlier how the Docker context can help individuals and teams kind of work in their environments work. Let me try that over. I mentioned earlier how I, how I see Docker context really improving the way that individuals and teams work with their environments and making sure that they're consistent. But I think this really comes together as we work with Docker desktop and Docker hub. Uh, when developers sign into Docker hub from Docker desktop, everything kind of lights up. And so they can see all of the images in their repositories and they can also see the cloud environments they're running them in. And so, you know, once you sign into the hub, you can see all the contexts that map to the logical environments that they have access to like dev and QA and maybe staging. And another use case that's really important is that, you know, we can access the same integration environment. >>So, so I could have, you know, microservices that I've been working on, but I can also see microservices that my, my teammates and their logs, uh, from the services that they've been working on, which I think is really, really great and certainly helps with, with team productivity. The other thing too is that this also really helps with hybrid cloud deployments, right? Where, you know, you might have some on premises, uh, hosted containers and you might have some that's hosted in a public cloud. And so you can see all of those things, uh, through your Docker hub. >>Well, I got to say I love the code to cloud tagline. I think that's very relevant and, and catchy. Um, and I think, I guess to me what I'm seeing, and I'd love to get your thoughts, Amanda, on this, as you oversee a key part of Microsoft's business that's important for developers, just the vibe and people are amped up right now. I know people are tense and anxiety with the covert 19 crisis, but I think people are generally agreeing that this is going to be a massive inflection point for just more headroom needed for developers to accelerate their value on the front lines. What's your personal take on this and you've seen these ways before, but now in this time, what are you most excited about? What are you optimist about? What's your view on the opportunities? Can you share your thoughts? Because people are going to get back to work or they're working now remotely, but when we go back to hybrid world, they're going to be jamming on projects. >>Yeah, for sure. But I mean, people are jamming on projects right now. And I think that, you know, in a lot of ways, uh, developers are our first responders in, you know, in that they are, developers are always trying to support somebody else, right? We're trying to somebody else's workflow and you know, so we have examples of people who are, uh, creating new remote systems to be able to, uh, schedule meetings in hospitals or the doctors who are actually the first, first responders taking care of patients. But at the end of the day, it's the developer who's actually creating that solution, right? And so we're being called the duty right now. Um, and so we need to make sure that we're actually there to support the needs of our users and that we're, we're basically cranking on code as fast as we can. Uh, and to be able to do that, we have to make sure that every developer is empowered and they can move quickly, but also that they can collaborate freely. And so, uh, I think that, you know, Docker hub Docker kind of helps you ensure that you have that consistency, but you also have that connection to the infrastructure that's hosted by your, your organization. >>I think you nailed that amazing insight. And I think that's, you know, the current situation in the community matters because there's a lot of um, frontline work being done to your point. But then we've got to rebuild. The modernization is happening as well coming out of this. So there's going to be that and there's a lot of comradery going on and massive community involvement. I'm seeing more of, you know, the empathy, but also now there's going to be the building, the creation, the new creation. So Scott, this is going to call for more simplicity and to abstract away the complexities. This is the core issue. >>Well that's exactly right and it is time to build, right? Um, and we're going to build our way out of this. Um, and it is the community that's responding. And so in some sense, Microsoft and Docker are there to support that, that community energy and give them the tools to go. And identify and have an impact as quickly as possible. We have referenced in the keynote, um, completely bottoms up organic adoption of Docker desktop and Docker hub in racing to provide solutions against the COBIT 19 virus. Right? It's a, it's a war against this pandemic that is heavily dependent on applications and data and there's over 200 projects, community projects on Docker hub today where you've got uh, cools and containers and data analysis all in service to the photo at 19 battle that's being fought. And then as you said, John, as we, as we get through this, the other side, there's entire industries that are completely rethinking their approach that were largely offline before that. Now see the imperative and the importance of going online and that tectonic shift nearly overnight of offline to online behavior and commerce and social and go on down the list that requires new application development. And I'm very pleased about this partnership is that together we're giving developers the tools to really take advantage of that opportunity and go and build our way out of it. >>Well, Scott, congratulations on a great extended partnership with Microsoft and the Docker brand. You know, I'm a big fan of from day one. I know you guys have pivoted on a new trajectory which is very community oriented, very open source, very open. So congratulations on that Amanda. Thanks for spending the time to come on. I'll give you the final word. Take a minute to talk about what's new at Microsoft. For the folks that know Microsoft, know they have a developer mindset from day one cloud is exploding code to cloud. What's the update? What's the new narrative? What should people know about Microsoft with developer community? Can you share from some, some, some uh, data for the folks that aren't in the community or might want to join with folks in the community who want to get an update? >>Yeah, it's a, it's a great, great kind of question. I mean, you know, right now I think we are all really focused on making sure that we can empower developers throughout the world and that includes both those who are building solutions for their organizations today. But also I think we're going to end up with a ton of new developers over this next period who are really entering the workforce and uh, and learning to create, you know, digital solutions overall. There's a massive developer shortage across the world. Um, there's so much opportunity for developers to kind of, you know, address a lot of the needs that we're seeing out of organizations again across the world. Um, and so I think it's just a really exciting time to be a developer. Uh, and you know, my, my uh, my only hope is that basically we're, we're building tools that actually enable them to solve problems. >>Awesome insight and thank you so much for your time code to cloud developers are cranking away that the first responders are going to take care of business and then continue to build out the modern applications. And when you have a crisis like this, people cut right through the noise and get right to the tools that matter. So thanks for sharing the Microsoft Docker partnership and the things that you guys are working on together. Thanks for your time. Okay. This is the cubes coverage. We are Docker con 2020 digital is the cube virtual. I'm Sean for bringing all the action. More coverage. 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>>Hi buddy, we're back. This is Dave Volante with the cube goes out to the shows. We extract the signal from the noise. Nate Silver's here. Nate, we've been saying that since 2010, rip you off. Hey Marcus feeder. Oh, you have that trademarks. Okay. So anyway, welcome to the cube. You man who needs no introduction, but in case you don't know Nate, uh, he's a very famous author, five 30 eight.com. Statistician influence, influential individual predictor of a lot of things including presidential elections. And uh, great to have you here. Great to be here. So we listened to your keynote this morning. We asked earlier if some of our audience, can you tweet it and you know, what would you ask Nate silver? So of course we got the predictable, how the red Sox going to do this year? Who's going to be in the world series? Are we going to attack Syria? >>Uh, will the fed E's or tightened? Of course we're down here. Who'd you vote for? Or they, you know, they all want to know. And of course, a lot of these questions you can't answer because it's too far out. But, uh, but anyway, again, welcome, welcome to the cube. Um, so I want to start by, uh, picking up on some of the themes in your keynote. Uh, you're here at the Tableau conference. Obviously it's all about about data. Uh, and you, your basic, one of your basic premises was that, um, people will misinterpret data, they'll just use data for their own own biases. You have been a controversial figure, right? A lot of people have accused you of, of bias. Um, how, what do you F how do you feel about that as a person who's, uh, you know, statistician, somebody who loves data? >>I think everyone has bias in the sense that we all have one relatively narrow perspective as compared to a big set of problems that we all are trying to analyze or solve or understand together. Um, you know, but I do think some of this actually comes down to, uh, not just bias, but kind of personal morality and ethics really. It seems weird to talk about it that way, but there are a lot of people involved in the political world who are operating to manipulate public opinion, um, and that don't really place a lot of value on the truth. Right. And I consider that kind of immoral. Um, but people like that I think don't really understand that someone else might act morally by actually just trying to discover the way the objective world is and trying to use science and research to, to uncover things. >>And so I think it's hard people to, because if they were in your shoes, they would try and manipulate the forecast and they would cheat and put their finger on their scale. They assume that anyone else would do the same thing cause they, they don't own any. Yeah. So will you, you've made some incredibly accurate predictions, uh, in the face of, of, of others that clearly had bias that, that, that, you know mispredicted um, so how did you feel when you got those, those attacks? Were you flabbergasted? Were you pissed? Were you hurt? I mean, all of the above having you move houses for, for you? I mean you get used to them with a lot of bullshit, right? You're not too surprised. Um, I guess it surprised me how, but how much the people who you know are pretty intelligent are willing to, to fool themselves and how specious arguments where meet and by the way, people are always constructing arguments for, for outcomes they happen to be rooting for. >>Right? It'd be one thing if you said, well I'm a Republican, but boy I think Obama's going to crush Romney electoral college or vice versa. But you should have an extra layer of scrutiny when you have a view that diverges from the consensus or what kind of the markets are saying. And by the way, you can go and they're betting Margaret's, you can go and you could have bet on the outcome of election bookies in the UK, other countries. Right. And they kind of had forecast similar to ours. We were actually putting their money where their mouth was. Agree that Obama was a. Not a lot, but a pretty heavy favorite route. Most of the last two months in the election. I wanted to ask you about prediction markets cause as you probably know, I mean the betting public are actually very efficient. Handicappers right over. >>So I'll throw a two to one shot is going to be to three to one is going to be a four to one, you know, more often than not. But what are your thoughts on, on prediction markets? I mean you just sort of betting markets, you'd just alluded it to them just recently or is that a, is that a good, well there a lot there then then I think the punditry right. I mean, you know, so with, with prediction markets you have a couple of issues. Number one is do you have enough, uh, liquidity, um, and my volume in the markets for them to be, uh, uh, optimal. Right. And I think the answer right now is maybe not exactly. And like these in trade type markets, knowing trade has been, has been shut down. In fact, it was pretty light trading volumes. It might've had people who stood to gain or lose, um, you know, thousands of dollars. >>Whereas in quote, unquote real markets, uh, the stakes are, are several orders of magnitude higher. If you look at what happened to, for example, just prices of common stocks a day after the election last year, um, oil and gas stocks lost billions of dollars of market capitalization after Romney lost. Uh, conversely, some, you know, green tech stocks or certain types of healthcare socks at benefit from Obamacare going into play gain hundreds of millions, billions of dollars in market capitalization. So real investors have to price in these political risks. Um, anyway, I would love to have see fully legal, uh, trading markets in the U S people can get bet kind of proper sums of money where you have, um, a lot of real capital going in and people can kind of hedge their economic risk a little bit more. But you know, they're, they're bigger and it's very hard to beat markets. They're not flawless. And there's a whole chapter in the book about how, you know, the minute you assume that markets are, are clairvoyant and perfect, then that's when they start to fail. >>Ironically enough. But they're very good. They're very tough to beat and they certainly provide a reality check in terms of providing people with, with real incentives to actually, you know, make a bet on, on their beliefs and people when they have financial incentives, uh, uh, to be accurate then a lot of bullshit. There's a tax on bullshit is one way. That's okay. I've got to ask him for anyway that you're still a baseball fan, right? Is that an in Detroit fan? Right. I'm a tiger. There's my bias. You remember the bird? It's too young to remember a little too. I, so I grew up, I was born in 78, so 84, the Kirk Gibson, Alan Trammell teams are kind of my, my earliest. So you definitely don't remember Mickey Lola cha. I used to be a big guy. That's right fan as well. But so, but Sony, right when Moneyball came out, we just were at the Vertica conference. >>We saw Billy being there and, and uh, when, when, when, when, when that book came out, I said Billy Bean's out of his mind for releasing all these secrets. And you alluded to in your talk today that other teams like the rays and like the red Sox have sort of started to adopt those techniques. At the same time, I feel like culturally when another one of your V and your Venn diagram, I don't want you vectors, uh, that, that Oakland's done a better job of that, that others may S they still culturally so pushing back, even the red Sox themselves, it can be argued, you know, went out and sort of violated the, the principles were of course Oakland A's can't cause they don't have a, have a, have a budget to do. So what's your take on Moneyball? Is the, is the strategy that he put forth sustainable or is it all going to be sort of level playing field eventually? >>I mean, you know, the strategy in terms of Oh fine guys that take a lot of walks, right? Um, I mean everyone realizes that now it's a fairly basic conclusion and it was kind of the sign of, of how far behind how many biases there were in the market for that, you know, use LBP instead of day. And I actually like, but that, that was arbitrage, you know, five or 10 years ago now, um, put butts in the seat, right? Man, if they win, I guess it does, but even the red Sox are winning and nobody goes to the games anymore. The red Sox, tons of empty seats, even for Yankees games. Well, it's, I mean they're also charging 200 bucks a ticket or something. you can get a ticket for 20, 30 bucks. But, but you know, but I, you know, I, I, I mean, first of all, the most emotional connection to baseball is that if your team is in pennant races, wins world series, right then that produces multimillion dollar increases in ticket sales and, and TV contracts down the road. >>So, um, in fact, you know, I think one thing is, is looking at the financial side, like modeling the martial impact of a win, but also kind of modeling. If you do kind of sign a free agent, then, uh, that signaling effect, how much does that matter for season ticket sales? So you could do some more kind of high finance stuff in baseball. But, but some of the low hanging fruit, I mean, you know, almost every team now has a Cisco analyst on their payroll or increasingly the distinctions aren't even as relevant anymore. Right? Where someone who's first in analytics is also listening to what the Scouts say. And you have organizations that you know, aren't making these kind of distinctions between stat heads and Scouts at all. They all kind of get along and it's all, you know, finding better ways, more responsible ways to, to analyze data. >>And basically you have the advantage of a very clear way of measure, measure success where, you know, do you win? That's the bottom line. Or do you make money or, or both. You can isolate guys Marshall contribution. I mean, you know, I am in the process now of hiring a bunch of uh, writers and editors and developers for five 38 right? So someone has a column and they do really well. How much of that is on the, the writer versus the ed or versus the brand of the site versus the guy at ESPN who promoted it or whatever else. Right. That's hard to say. But in baseball, everyone kind of takes their turn. It's very easy to measure each player's kind of marginal contribution to sort of balance and equilibrium and, and, and it's potentially achieved. But, and again, from your talk this morning modeling or volume of data doesn't Trump modeling, right? >>You need both. And you need culture. You need, you need, you know, you need volume of data, you need high quality data. You need, uh, a culture that actually has the right incentives align where you really do want to find a way to build a better product to make more money. Right? And again, they'll seem like, Oh, you know, how difficult should it be for a company to want to make more money and build better products. But, um, when you have large organizations, you have a lot of people who are, uh, who are thinking very short term or only about only about their P and L and not how the whole company as a whole is doing or have, you know, hangups or personality conflicts or, or whatever else. So, you know, a lot of success I think in business. Um, and certainly when it comes to use of analytics, it's just stripping away the things that, that get in the way from understanding and distract you. >>It's not some wave a magic wand and have some formula where you uncover all the secrets in the world. It's more like if you can strip away the noise there and you're going to have a much clearer understanding of, of what's really there. Uh, Nate, again, thanks so much for joining us. So kind of wanna expand on that a little bit. So when people think of Nate silver, sometimes they, you know, they think Nate silver analytics big data, but you're actually a S some of your positions are kind of, you take issue with some of the core notions of big data really around the, the, the importance of causality versus correlation. So, um, so we had Kenneth kookier on from, uh, the economist who wrote a book about big data a while back, the strata conference. And you know, he, in that book, they talk a lot about it really doesn't matter how valid anymore, if you know that your customers are gonna buy more products based on this dataset or this correlation that it doesn't really matter why. >>You just try to try to try to exploit that. Uh, but in your book you talk about, well and in the keynote today you talked about, well actually hypothesis testing coming in with some questions and actually looking for that causality is also important. Um, so, so what is your, what is your opinion of kind of, you know, all this hype around big data? Um, you know, you mentioned volume is important, but it's not the only thing. I mean, like, I mean, I'll tell you I'm, I'm kind of an empiricist about anything, right? So, you know, if it's true that merely finding a lot of correlations and kind of very high volume data sets will improve productivity. And how come we've had, you know, kind of such slow economic growth over the past 10 years, where is the tangible increase in patent growth or, or different measures of progress. >>And obviously there's a lot of noise in that data set as well. But you know, partly why both in the presentation today and in the book I kind of opened up with the, with the history is saying, you know, let's really look at the history of technology. It's a kind of fascinating, an understudied feel, the link between technology and progress and growth. But, um, it doesn't always go as planned. And I certainly don't think we've seen any kind of paradigm shift as far as, you know, technological, economic productivity in the world today. I mean, the thing to remember too is that, uh, uh, technology is always growing in and developing and that if you have roughly 3% economic growth per year exponential, that's a lot of growth, right? It's not even a straight line growth. It's like exponential growth. And to have 3% exponential growth compounding over how many years is a lot. >>So you're always going to have new technologies developing. Um, but what I, I'm suspicious that as people will say this one technology is, is a game changer relative to the whole history of civilization up until now. Um, and also, you know, again, a lot of technologies you look at kind of economic models where you have different factors or productivity. It's not usually an additive relationship. It's more a multiplicative relationships. So if you have a lot of data, but people who aren't very good at analyzing it, you have a lot of data but it's unstructured and unscrutinised you know, you're not going to get particularly good results by and large. Um, so I just want to talk a little bit about the, the kind of the, the cultural issue of adopting kind of analytics and, and becoming a data driven organization. And you talk a lot about, um, you know, really what you do is, is setting, um, you know, try to predict the probabilities of something happening, not really predicting what's going to happen necessarily. >>And you talked to New York, you know, today about, you know, knowledging where, you know, you're not, you're not 100% sure acknowledging that this is, you know, this is our best estimate based on the data. Um, but of course in business, you know, a lot of people, a lot of, um, importance is put on kind of, you know, putting on that front that you're, you know, what you're talking about. It's, you know, you be confident, you go in, this is gonna happen. And, and sometimes that can actually move markets and move decision-making. Um, how do you balance that in a, in a business environment where, you know, you want to keep, be realistic, but you want to, you know, put forth a confident, uh, persona. Well, you know, I mean, first of all, everyone, I think the answer is that you have to, uh, uh, kind of take a long time to build the narrative correctly and kind of get back to the first principles. >>And so at five 38, it's kind of a case where you have a dialogue with the readers of the site every day, right? But it's not that you can solve in one conversation. If you come in to a boss who you never talked to you before, you have to present some PowerPoint and you're like, actually this initiative has a, you know, 57% chance of succeeding and the baseline is 50% and it's really good cause the upside's high, right? Like you know, that's going to be tricky if you don't have a good and open dialogue. And it's another barrier by the way to success is that uh, you know, none of this big data stuff is going to be a solution for companies that have poor corporate cultures where you have trouble communicating ideas where you don't everyone on the same page. Um, you know, you need buy in from, from all throughout the organization, which means both you need senior level people who, uh, who understand the value of analytics. >>You also need analysts or junior level people who understand what business problems the company is trying to solve, what organizational goals are. Um, so I mean, how do you communicate? It's tricky, you know, maybe if you can't communicate it, then you find another firm or go, uh, go trade stocks and, and uh, and short that company if you're not violating like insider trading rules of, of various kinds. Um, you know, I mean, the one thing that seems to work better is if you can, uh, depict things visually. People intuitively grasp uncertainty. If you kind of portray it to them in a graphic environment, especially with interactive graphics, uh, more than they might've just kind of put numbers on a page. You know, one thing we're thinking about doing with the new 580 ESPN, we're hiring a lot of designers and developers is in case where there is uncertainty, then you can press a button, kind of like a slot, Michigan and simulate and outcome many times, then it'll make sense to people. Right? And they do that already for, you know, NCAA tournament stuff or NFL playoffs. Um, but that can help. >>So Nate, I asked you my, my partner John furry, who's often or normally the cohost of this show, uh, just just tweeted me asking about crowd spotting. So he's got this notion that there's all this exhaust out there, the social exhaustive social data. How do you, or do you, or do you see the potential to use that exhaust that's thrown off from the connected consumer to actually make predictions? Um, so I'm >>a, I guess probably mildly pessimistic about this for the reason being that, uh, a lot of this data is very new and so we don't really have a way to kind of calibrate a model based on it. So you can look and say, well, you know, let's say Twitter during the Republican primaries in 2016 that, Oh, Paul Ryan is getting five times as much favorable Twitter sentiment as Rick Santorum or whatever among Republicans. But, but what's that mean? You know, to put something into a model, you have to have enough history generally, um, where you can translate X into Y by means of some function or some formula. And a lot of data is so new where you don't have enough history to do that. And the other thing too is that, um, um, the demographics of who is using social media is changing a lot. Where we are right now you come to conference like this and everyone has you know, all their different accounts but, but we're not quite there yet in terms of the broader population. >>Um, you have a lot of kind of thought leaders now a lot of, you know, kind of young, smart urban tech geeks and they're not necessarily as representative of the population as a whole. That will over time the data will become more valuable. But if you're kind of calibrating expectations based on the way that at Twitter or Facebook were used in 2013 to expect that to be reliable when you want a high degree of precision three years from now, even six months from now is, is I think a little optimistic. Some sentiment though, we would agree with that. I mean sentiment is this concept of how many people are talking about a thumbs up, thumbs down. But to the extent that you can get metadata and make it more stable, longer term, you would see potential there is, I mean, there are environments where the terrain is shifting so fast that by the time you know, the forecast that you'd be interested in, right? >>Like things have already changed enough where like it's hard to do, to make good forecast. Right? And I think one of the kind of fundamental themes here, one of my critiques is some of the, uh, of, uh, the more optimistic interpretations of big data is that fundamentally people are, are, most people want a shortcut, right? Most people are, are fairly lazy like labor. What's the hot stock? Yeah. Right. Um, and so I'm worried whenever people talk about, you know, biased interpretations of, of the data or information, right? Whenever people say, Oh, this is going to solve my problems, I don't have to work very hard. You know, not usually true. Even if you look at sports, even steroids, performance enhancing drugs, the guys who really get the benefits of the steroids, they have to work their butts off, right? And then you have a synergy which hell. >>So they are very free free meal tickets in life when they are going to be gobbled up in competitive environments. So you know, uh, bigger datasets, faster data sets are going to be very powerful for people who have the right expertise and the right partners. But, but it's not going to make, uh, you know anyone to be able to kind of quit their job and go on the beach and sip my ties. So ne what are you working on these days as it relates to data? What's exciting you? Um, so with the, with the move to ESPN, I'm thinking more about, uh, you know, working with them on sports type projects, which is something having mostly cover politics. The past four or five years I've, I've kind of a lot of pent up ideas. So you know, looking at things in basketball for example, you have a team of five players and solving the problem of, of who takes the shot, when is the guy taking a good shot? >>Cause the shot clock's running out. When does a guy stealing a better opportunity from, from one of his teammates. Question. We want to look at, um, you know, we have the world cup the summer, so soccer is an interest of mine and we worked in 2010 with ESPN on something called the soccer power index. So continuing to improve that and roll that out. Um, you know, obviously baseball is very analytics rich as well, but you know, my near term focus might be on some of these sports projects. Yeah. So that the, I have to ask you a followup on the, on the soccer question. Is that an individual level? Is that a team level of both? So what we do is kind of uh, uh, one problem you have with the national teams, the Italian national team or Brazilian or the U S team is that they shift their personnel a lot. >>So they'll use certain guys for unimportant friendly matches for training matches that weren't actually playing in Brazil next year. So the system soccer power next we developed for ESPN actually it looks at the rosters and tries to make inferences about who is the a team so to speak and how much quality improvement do you have with them versus versus, uh, guys that are playing only in the marginal and important games. Okay. So you're able to mix and match teams and sort of predict on your flow state also from club league play to make inferences about how the national teams will come together. Um, but soccer is a case where, where we're going into here where we had a lot more data than we used to. Basically you had goals and bookings, I mean, and yellow cards and red cards and now you've collected a lot more data on how guys are moving throughout the field and how many passes there are, how much territory they're covering, uh, tackles and everything else. So that's becoming a lot smarter. Excellent. All right, Nate, I know you've got to go. I really appreciate the time. Thanks for coming on. The cube was a pleasure to meet you. Great. Thank you guys. All right. Keep it right there, everybody. We'll be back with our next guest. Dave Volante and Jeff Kelly. We're live at the Tableau user conference. This is the cube.

>>Now, this is siliconangle.com exclusive coverage of Sapphire. This is the cube, our flagship program where we go out to the events, extract the signal from the noise. This is our fourth year of the cube born four years ago at SAP and EMC world. And we uh, we call it the ESPN of tech back in the day. And uh, you know, we always joke that, you know, there wasn't any deep dive commentary, but we're pleased to actually have what ESPN would love to have on Adam silver, the deputy commissioner of the NBA. Um, Chris Berman and all the folks at the, at ESPN would eat their heart out to have, have you here. So welcome to the cube. Oh, thank you. It's great to be off. So SAP Owsley's how about the future of business and this modern era of, of a business with technology, real time NBA has been very progressive. >>You guys have a modernization going on now. The franchisors are changing, the fan base is changing. You're here at SAP. What are you talking about here at SAP with bill McDermott and the team and what does the NBA look at is as a sports franchise, a sports league, and with a lot of franchises it's moving and changing and the old contracts and the old rules aren't, aren't really relevant when you have Twitter and you have unlimited media, frictionless sharing. How do you, how do you view all that? Well, we're here looking for solutions. I mean frankly I that we know what we don't know. I mean just the specific example with HANA and SAP is that when David stern, I first met, sat down with bill McDermott a few years ago and said we have this issue with our stats database and we want to find a way to allow fans wherever they're located to engage deeply with those statistics, weather for predictive behavior and to analyze what they think is going to happen in a particular game or to make relative comparisons, you know, between players, among players in the league. >>And he explained to us what Hannah was and we ended up meeting with one of Bill's teams@sapwhoultimatelydesignedanewstatisticaldatabasesystemforuswhereyoucangoonnba.com and access, um, any of the kinds of permutations, statistics I've been talking about. And, and frankly, one of the reasons we got there, to your point about how the world is evolving around us is that there were lots of other sites that I won't name that were doing that, you know, they didn't necessarily have the official data. And I thought this isn't a question of sending out an army of lawyers to shut them down. This is a question of out competing them instead of how can anybody be doing a better job than the MBA. We own this data, we have the richest data, we have the deepest data, we haven't in real time, fans should want to come to NBC. Dot to get that information. The best defense is a good offense in this case. >>Right? Exactly, exactly. And so that's our relationship with SAP. And even just the several hours I've spent already here today, sort of in the green room behind the scenes talking to bill and his executive team. It's like, all right, here are some other business issues we have. We've talked about China earlier today. We've talked about how to connect with our fans and look, you know, that only a minuscule percentage of our fans actually experience our game in person. You know, that's just the nature of it. So it's really through technology, through innovation that we're going to connect with fans on a global basis. That's why I'm here. So what was your >>comment on the keynote about bill McDermott share with the folks here about his history >>with basketball and so, Oh, so anyways, with bill, isn't that interesting? Coincidentally, when we first sat down with bill, he mentioned that his grandfather was Bobby McDermott, Bobby McDermott I'd heard of. But honestly I hadn't thought a lot about, I went back to the office and we have an archivist named Paul Hirsch Heimer and our office say, Paul, tell me everything you can about Bobby McDorman. He goes, Bobby McDermott. And like he'd just off the top of his head, he goes, he goes, Bob McDermott, you might not realize this, but in 1946 he was named the greatest basketball player in the history of the then NBL, the predecessor league to the NBA. He was a five 11 guard. He averaged over 20 points a game. And back then this was like obviously more than 35 years before the three point shot. This was before anybody was averaging 20 points a game. And he was, I think the three time MVP. He won two championships. I could go on and on, but it's incredible. And you know, this morning I actually, my friend and colleague Paul Hirsch Heimer found an old Bobby McDermott trading card. I don't even have any boundaries, which I presented to bill. But you know, it's, it's like the coincidence that that's our relationship now. It's, it's this frankly a really cool >>kind of gesture to be fantastic. And bill McDermott, it's just a great guy. And the keynote up there, they had that little anchor desk, kind of like the cube format. >>And don't forget JBS basketball credentials. The Harvard basketball team, >>Twitter saying, I'm negotiating with JBS age. People thought I was really serious about coming on and anchoring the cubes a JV. If you're watching, we want you drafted by the Atlanta Hawks McDermott on his keynote, Dermot on his keynote, talked about an in business example. You know, talking about, always talks about, you know, if people using this and got the bartender, how they're instrumenting the tab and it's just a gut crest. Great example of instrumentation measurement data that they couldn't get before internet of things. Whatever industrial internet is GE calls it. I want to ask you something a little bit more about the NBA in this regard because the NBA has really done great strides of, I won't say cleaning up the game, but looking at the integrity of the players off the court and on the court. And that's been something that uh, you know, stern has done extremely well, but now you have, you have the ability to instrument the, the actual athletes. They're on Twitter, they're building their own direct fan basis. So, so the question is how do you guys look at that as an opportunity? And challenge, how do you guys, cause now you have more media there, they're self promoting, >>right? And look, I don't want to suggest by any means we can control, you know what they do, but we can monitor it. We can do it to a certain extent. And what we said to our players mean your employees. Just like I'm an employee of a company and I think, you know, and there are certain limits. I mean especially one thing we can do is say, you know, during 45 minutes before the game, through the game, certain period afterwards when we require media availability, we don't want them going off and tweeting in the corner. We want them talking to media and they recognize that's part of their job. We monitor it to a certain extent, but we also are realistic. You know, I think that we understand that it's an opportunity for them to connect directly with their fans or in certain cases people aren't their fans. But we, and we also understand that it's an expectation of fans in this day and age that they're going to have that direct access to our athletes. And I and, and I think it's synergistic. I mean it's, it's, it's effective. I mean recognizing that it's warts and all that. Players get themselves in trouble. League executives get themselves in trouble and owners get themselves in trouble increases. >>I know as well. I know you've got to run, so do you want to kind of get one last question and I'll see the TV contracts over the years have been pretty much territorial couple of networks and now you have cable, now you have unlimited, now you have NFL TV, MLB TV, NBA vertical and programming where you can control your own destiny, you have different inside looks, all that data. How you're looking at the future of media in that regard where now you have unlimited outlets potential. Can you talk about how you're looking at that and maybe some of the tech approaches that you did? >>Yeah, well I would just say it's, it's going to be a balance. We recognize that people still want aggregators or editors and that you mentioned ESPN at the top of the show. I mean people are still going to go to ESPN and expect to get the best highlights, you know, presumably the game of the week or the game of the night or whatever else. But in addition to that, there are some parents who only want to consume NBA, don't want to sit through sports center and get the hockey scores first or the baseball scores or whatever else. And for those people, there's NBA TV, there's nba.com and other outlets and like thousands of others that we didn't create. So I think for us it's a realization that you need to do both. And that some fans, you know are out there have want to consume us, you know, in an incredibly deep ways and get down to like the nitty gritty statistics and others just want to have the highlights and you've got to serve all those fans. >>Well, we get the hook from your handler. Adam silver, a deputy, >>how can my man, Jeff, I don't have a handler. This is fantastic. I appreciate, appreciate you taking the time. So my question, uh, I wanted to ask was, uh, so obviously SAP Sapphire, it's a, it's a technology event in some ways, but it's also a business event. And in this market we talked a lot about the technology but less maybe sometimes about the business value of all this technology. And you mentioned an example earlier about making data and predictive analytics available to um, all the fans out there who might not be my knife to go to an event. So can you translate that, how does that translate to business value for the NDA and more broadly when you're looking at areas where data might provide business value, how do you identify those areas where you want to focus on build new capabilities? Um, focusing on, you know, the data is, is the underlying kind of enabler and the technology is the enabler, but really how do you identify where the business value is? So, >>you know, I'll say one easy example again, just going back to the stats database that, that SAP HANA built for us. Um, we've already seen that we've doubled the amount of time that our fans spent on mba.com looking at statistics than we did before we had an Ohana database. So that's just a simple example and clear. There's all kinds of ways of monetizing that traffic when you dealt with there. But I think from a more general standpoint, it's about increasing engagement. Um, as I said in response to an earlier question, um, one of the fundamental things we look at in terms of television viewership is duration. And we found that from when the time I still got involved in the NBA a little over 20 years ago, let's say the average fan was watching two and a half hour game, 50 minutes. The average fan is now watching around 40 minutes just because it's the nature of the number of options they have. >>And if through deep data we can increase that engagement, we find other ways that the people remain interested in the game, frankly, that they may be a knick fan, but if their team is down 15 points and there's two minutes left, they're turning the channel. On the other hand, if they're engaged and they're thinking, all right, you know, what does Carmelo Anthony do typically with a minute left in the game, what are his fourth quarter statistics? How does he behave when a team is down? Ken, statistically a team overcome a deficit of 15 points in that many minutes. We find all those kinds of new approaches to the game help us monetize and help by increasing the level of passion, passion, and, and, and depth of fandom, you know, for our consumers. >>and in terms of actually making those decisions, how is the ability to, to do those kinds of analytics internally? How has that impacted how the NBA operates in terms of, you know, sometimes making data driven decisions and, and, and, and using data to, to kind of do business is that there's a cultural and a people issue? >>Well, yeah. Uh, I'll give you another example. And this is on the business side and that is ticket pricing. I mean, through analytics we've entered into a new world of flexible ticket pricing where it's dynamic pricing price. So now you know in the, in the old days, which weren't so long ago, you know, if you bought a season ticket package for the Orlando magic here in Orlando, of course you know it was the same price for every ticket. Now the teams that recognize that people that there's different values for different games, it's just a function of data and it's based on demand for those particular games. People may care more about seeing the Miami he played than they will another team that I won't mention at least in that particular season. And then secondly, teams are also realizing that people just like with the airlines, people will pay a certain price for the ability to lock in that seat two weeks before the game and they're going to pay a different price to get a seat an hour before the game. And so by mining all that data, we're in essence able to increase the yield from any particular game. >>Fantastic. Well Adam, I appreciate you taking the time to answer. It's only one question we didn't know we get the hook. So short of a Adam silver. Thanks for coming on because this is the NBA onside. The cue we call the ESPN of tech and we copied ESPN on Twitter. Thanks for coming. Thanks for coming on. The cube and NBA is transforming. I'll say digital media is, I was exploding and I'll see with technology like SAP, they're going to start doing new things. Thanks for coming on the really appreciate it. We'll be right back with our guests and deep dive into SAP and all the action here on the ground. This is exclusive coverage from siliconangle.com and Wiki bond. This is the Cuba right back after this short break.

>>The transformation of Dell into Dell emc. And now Dell Technologies has been one of the most remarkable stories in the history of the enterprise technology industry. The company has gone from a Wall Street darling rocket ship PC company to a Midling enterprise player, forced to go private to a debt laden powerhouse that controlled one of the most valuable assets in enterprise tech i e VMware, and now is a hundred billion dollar giant with a low margin business. A strong balance sheet in the broadest hardware portfolio in the industry and financial magic that Dell went through would make anyone's head spin. The last lever of Dell EMC of the Dell EMC deal was detailed in Michael Dell's book Play Nice But Win in a captivating chapter called Harry You and the Bolt from the Blue Michael Dell described how he and his colleagues came up with the final straw of how to finance the deal. >>If you haven't read it, you should. And of course, after years of successfully integrating EMC and becoming VMware's number one distribution channel, all of this culminated in the spin out of VMware from Dell and a massive wealth creation milestone pending, of course the Broadcom acquisition of VMware. So where's that leave Dell and what does the future look like for this technology powerhouse? Hello and welcome to the Cube's exclusive coverage of Dell Technology Summit 2022. My name is Dave Ante and I'll be hosting the program. Now today in conjunction with the Dell Tech Summit, we're gonna hear from four of Dell's senior executives, Tom Sweet, who's the CFO of Dell Technologies. He's gonna share his views on the company's position and opportunities going forward. He's gonna answer the question, why is Dell a good long-term investment? Then we'll hear from Jeff Boudreau, who's the president of Dell's ISG business. >>That unit is the largest profit driver of Dell. He's gonna talk about the product angle and specifically how Dell is thinking about solving the multi-cloud challenge. And then Sam Groot, who is the senior vice president of marketing, will come on the program and give us the update on Apex, which is Dell's as a service offering, and then the new Edge platform called Project Frontier. Now it's also cyber security Awareness month that we're gonna see if Sam has, you know, anything to say about that. Then finally, for a company that's nearly 40 years old, Dell actually has some pretty forward thinking philosophies when it comes to its culture and workforce. And we're gonna speak with Jen Vera, who's Dell's chief Human Resource Resource Officer about hybrid work and how Dell is thinking about the future of work. However, before we get into all this, I wanna share our independent perspectives on the company and some research that we'll introduce to frame the program. >>Now, as you know, we love data here at the cube and one of our partners, ETR has what we believe is the best spending intentions data for enterprise tech. So here's a graphic that shows ET R'S proprietary net score methodology in the vertical access. That's a measure of spending velocity. And on the X axis, his overlap of pervasiveness in the data sample, this is a cut for just the server, the storage, and the client sectors within the ETR taxonomy. So you can see Dell CSG products, laptops in particular are dominant on both the X and the Y dimensions. CSG is the client solutions group and accounts for nearly 60% of Dell's revenue and about half of its operating income. And then the arrow signifies that dot, that represents Dell's ISG business that we're gonna talk to Jeff Boudro about. That's the infrastructure solutions group. Now, ISG accounts for the bulk of of the remainder of Dell's business, and it is, it's, as I said, it's most profitable from a margin standpoint. >>It comprises the EMC storage business as well as the Dell server business and Dell's networking portfolio. And as a note, we didn't include networking in that cut had we done. So Cisco would've dominated the graphic. And frankly, Dell's networking business isn't industry leading in the same way that PCs, servers and storage are. And as you can see, the data confirms the leadership position Dell has in its client side, its server and its storage sectors. But the nuance is look at that red dotted line at 40% on the vertical axis that represents a highly elevated net score, and every company in the sector is below that line. Now we should mention that we also filtered the data for those companies with more than a hundred mentions in the survey, but the point remains the same. This is a mature business that generally is lower margin storage is the exception, but cloud has put pressure on margins even in that business in addition to the server space. >>The last point on this graphic is we put a box around VMware and it's prominently present on both the X and Y dimensions. VMware participates with purely software defined high margin offerings in this, in these spaces, and it gives you a sense of what might have been had Dell chosen to hold onto that asset or spin it into the company. But let's face it, the alternatives from Michael Dell were just too attractive and it's unlikely that a spin in would've unlocked the value in the way a spinout did, at least not in the near future. So let's take a look at the snapshot of Dell's financials. To give you a sense of where the company stands today, Dell is a company with over a hundred billion in revenue. Last quarter, it did more than 26 billion in revenue and grew at a quite amazing 9% rate for a company that size. >>But because it's a hardware company, primarily its margins are low with operating income, 10% of revenue, and at 21% gross margin with VMware on Dell's income statement before the spin, its gross margins. Were in the low thirties. Now, Dell only spends about 2% of revenue on r and d because because it's so big, it's still a lot of money. And you can see it is cash flow positive. Dell's free cash flow over the trailing 12 month period is 3.7 billion, but that's only 3.5% of trailing 12 month revenue. Dell's Apex, and of course it's hardware maintenance business is recurring revenue and that is only about 5 billion in revenue and it's growing at 8% annually. Now having said that, it's the equivalent of service now's total revenue. Of course, service now is 23% operating margin and 16% free cash flow margin and more than 5 billion in cash on the balance sheet and an 85 billion market cap. >>That's what software will do for you. Now Dell, like most companies, is staring at a challenging macro environment with FX headwinds, inflation, et cetera. You've heard the story and hence it's conservative and contracting revenue guidance. But the balance sheet transformation has been quite amazing. Thanks to VMware's cash flow, Michael Dell and his partners from Silver Lake at all, they put up around $4 billion of their own cash to buy EMC for 67 billion, and of course got VMware in the process. Most of that financing was debt that Dell put on its balance sheet to do the transaction to the tune of 46 billion. It added to the, to the balance sheet debt. Now Dell's debt, the core debt net of its financing operation is now down to 16 billion and it has 7 billion in cash in the balance sheet. So dramatic delta from just a few years ago. So pretty good picture. >>But Dell a hundred billion company is still only valued at 28 billion or around 26 cents on the revenue dollar H HP's revenue multiple is around 60 cents on the revenue dollar. HP Inc. Dell's, you know, laptop and PC competitor is around 45 cents. IBM's revenue multiple is almost two times. By the way, IBM has more than 50 billion in debt thanks to the Red Hat acquisition. And Cisco has a revenue multiple, it's over three x, about 3.3 x currently. So is Dell undervalued? Well, based on these comparisons with its peers, I'd say yes and no. Dell's performance relative to its peers in the market is very strong. It's winning and has an extremely adept go to market machine, but it's lack of software content and it's margin profile leads. One to believe that if it can continue to pull some valuation levers while entering new markets, it can get its valuation well above where it is today. >>So what are some of those levers and what might that look like going forward? Despite the fact that Dell doesn't have a huge software revenue component since spinning out VMware and it doesn't own a cloud, it plays in virtually every part of the hardware market and it can provide infrastructure for pr pretty much any application in any use case and pretty much any industry and pretty much any geography in the world and it can serve those customers. So its size is an advantage. However, the history for hardware heavy companies that try to get bigger has some notable failures, namely hp, which had to split into two businesses, HP Inc. And hp E and ibm, which has had in abysmal decade from a performance standpoint and has had to shrink to grow again and obviously do a massive 34 billion acquisition of Red Hat. So why will Dell do any better than these two? >>Well, it has a fantastic supply chain. It's a founder led company, which makes a cultural difference in our view, and it's actually comfortable with a low margin software, light business model. Most certainly, IBM wasn't comfortable with that and didn't have these characteristics, and HP was kind of just incomprehensible at the end. So Dell in my opinion, is a much better chance of doing well at a hundred billion or over, but we'll see how it navigates through the current headwinds as it's guiding down. Apex is essentially Dell's version of the cloud. Now remember, Dell got started late. HPE is further along from a model standpoint with GreenLake, but Dell has a larger portfolio, so they're gonna try to play on that advantage. But at the end of the day, these as a service offerings are simply ways to bring a utility model to existing customers and generate recurring revenue. >>And that's a good thing because customers will be loyal to an incumbent if it can deliver as a service and reduce risk for for customers. But the real opportunity lies ahead, specifically Dell is embracing the cloud model. It took a while, but they're on board as Matt Baker Dell's senior vice president of corporate strategy likes to say it's not a zero sum game. What it means by that is just because Dell doesn't own its own cloud, it doesn't mean Dell can't build value on top of hyperscale clouds, what we call super cloud. And that's Dell's strategy to take advantage of public cloud CapEx and connect on-prem to the cloud, create a unified experience across clouds and out to the edge that's ambitious and technically it's non-trivial. But listen to Dell's vice chairman and Coco, Jeff Clark, explain this vision, please play the clip. >>You said also technology and business models are tied together and enabler. That's if, if you believe that, then you have to believe that it's a business operating system that they want, They want to leverage whatever they can, and at the end of the day there's, they have to differentiate what they do. Well that, that's >>Exactly right. If I take that and what, what Dave was saying and and I, and I summarize it the following way, if we can take these cloud assets and capabilities, combine them in an orchestrated way to delivery a distributed platform, game over, >>Eh, pretty interesting, right? John Freer called it a business operating system. Essentially, I think of it sometimes as a cloud operating system or cloud operating environment to drive new business value on top of the hyperscale CapEx. Now, is it really game over? As Jeff Clark said, if Dell can do that, I'd say if it had that today, it might be game over for the competition, but this vision will take years to play out. And of course it's gotta be funded and now it's gonna take time. And in this industry it tends to move. Companies tend to move in lockstep. So as often as the case, it's gonna come down to execution and Dell's ability to enter new markets that are ideally, at least from my perspective, higher margin data management, extending data protection into cyber security as an adjacency and of course edge at telco slash 5G opportunities. >>All there for the taking. I mean, look, even if Dell doesn't go after more higher margin software content, it can thrive with a lower margin model just by penetrating new markets and throwing off cash from those markets. But by keeping close to customers and maybe through Tuck in acquisitions, it might be able to find the next nugget beyond today's cloud and on-prem models. And the last thing I'll call out is ecosystem. I say here ecosystem, ecosystem, ecosystem. Because a defining characteristic of a cloud player is ecosystem, and if Apex is Dell's cloud, it has the opportunity to expand that ecosystem dramatically. This is one of the company's biggest opportunities and challenges. At the same time, in my view, it's just scratching the surface on its partner ecosystem. And it's ecosystem today is is both reseller heavy and tech partner heavy. And that's not a bad thing, but in a, but it's starting to evolve more rapidly. >>The snowflake deal is an example of up to stack evolution, but I'd like to see much more out of that snowflake relationship and more relationships like that. Specifically I'd like to see more momentum with data and database. And if we live at a data heavy world, which we do, where the data and the database and data management offerings, you know, coexist and are super important to customers, like to see that inside of Apex, like to see that data play beyond storage, which is really where it is today and it's early days. The point is with Dell's go to market advantage, which which company wouldn't treat Dell like the on-prem hybrid edge super cloud player that I wanna partner with to drive more business. You'd be crazy not to, but Dell has a lot on its plate and we'd like to see some serious acceleration on the ecosystem front. In other words, Dell as both a selling partner and a business enabler with its platform, its programmable infrastructure as a service. And that is a moving target that will rapidly involve. And of course we'll be here watching and reporting. So thanks for watching this preview of Dell Technology Summit 2022. I'm Dave Vte. We hope you enjoy the rest of the program.

(upbeat music) >> The transformation of Dell into Dell EMC, and now Dell Technologies, has been one of the most remarkable stories in the history of the enterprise technology industry. The company has gone from a Wall Street darling rocketship PC company, to a middling enterprise player, forced to go private, to a debt-laden powerhouse that controlled one of the most valuable assets in enterprise tech, i.e., VMware. And now is a $100 billion dollar giant with a low-margin business, a strong balance sheet, and the broadest hardware portfolio in the industry. The financial magic that Dell went through would make anyone's head spin. The last lever of the Dell EMC deal was detailed in Michael Dell's book "Play Nice But Win," in a captivating chapter called "Harry You and the Bolt from the Blue." Michael Dell described how he and his colleagues came up with the final straw of how to finance the deal. If you haven't read it, you should. And of course, after years of successfully integrating EMC and becoming VMware's number-one distribution channel, all of this culminated in the spin-out of VMware from Dell, and a massive wealth-creation milestone, pending, of course, the Broadcom acquisition of VMware. So where's that leave Dell, and what does the future look like for this technology powerhouse? Hello, and welcome to theCUBE's exclusive coverage of Dell Technologies Summit 2022. My name is Dave Vellante, and I'll be hosting the program. Now, today in conjunction with the Dell Tech Summit, we're going to hear from four of Dell's senior executives. Tom Sweet, who's the CFO of Dell Technologies. He's going to share his views on the company's position and opportunities going forward. He's going to answer the question, why is Dell a good long-term investment? Then we'll hear from Jeff Boudreau, who's the President of Dell's ISG business. That unit is the largest profit driver of Dell. He's going to talk about the product angle, and specifically, how Dell is thinking about solving the multi-cloud challenge. And then Sam Grocott, who's the Senior Vice President of Marketing, will come on the program and give us the update on APEX, which is Dell's as-a-Service offering, and then the new edge platform called Project Frontier. Now, it's also Cybersecurity Awareness Month, that we're going to see if Sam has, you know, anything to say about that. Then finally, for a company that's nearly 40 years old, Dell actually has some pretty forward-thinking philosophies when it comes to its culture and workforce. And we're going to speak with Jenn Saavedra, who's Dell's Chief Human Resource Officer, about hybrid work, and how Dell is thinking about the future of work. However, before we get into all this, I want to share our independent perspectives on the company, and some research that we'll introduce to frame the program. Now, as you know, we love data here at theCUBE, and one of our partners, ETR, has what we believe is the best spending intentions data for enterprise tech. So here's a graphic that shows ETR's proprietary Net Score methodology on the vertical axis, that's a measure of spending velocity, and on the x-axis is overlap or pervasiveness in the data sample. This is a cut for just the server, the storage, and the client sectors within the ETR taxonomy. So you can see Dell's CSG products, laptops in particular, are dominant on both the x and the y dimensions. CSG is the Client Solutions Group, and accounts for nearly 60% of Dell's revenue, and about half of its operating income. And then the arrow signifies that dot that represents Dell's ISG business, that we're going to talk to Jeff Boudreau about. That's the Infrastructure Solutions Group. Now, ISG accounts for the bulk of the remainder of Dell's business, and it is its, as I said, its most profitable from a margin standpoint. It comprises the EMC storage business, as well as the Dell server business, and Dell's networking portfolio. And as a note, we didn't include networking in that cut. Had we done so, Cisco would've dominated the graphic. And frankly, Dell's networking business isn't industry leading in the same way that PCs, servers, and storage are. And as you can see, the data confirms the leadership position Dell has in its client side, its server, and its storage sectors. But the nuance is, look at that red dotted line at 40% on the vertical axis. That represents a highly elevated Net Score, and every company in the sector is below that line. Now, we should mention that we also filtered the data for those companies with more than a hundred mentions in the survey, but the point remains the same. This is a mature business that generally is lower margin. Storage is the exception, but cloud has put pressure on margins even in that business, in addition to the server space. The last point on this graphic is, we put a box around VMware, and it's prominently present on both the x and y dimensions. VMware participates with purely software-defined high-margin offerings in these spaces, and it gives you a sense of what might have been, had Dell chosen to hold onto that asset or spin it into the company. But let's face it, the alternatives for Michael Dell were just too attractive, and it's unlikely that a spin-in would've unlocked the value in the way a spin-out did, at least not in the near future. So let's take a look at the snapshot of Dell's financials, to give you a sense of where the company stands today. Dell is a company with over $100 billion dollars in revenue. Last quarter, it did more than 26 billion in revenue, and grew at a quite amazing 9% rate, for a company that size. But because it's a hardware company, primarily, its margins are low, with operating income 10% of revenue, and at 21% gross margin. With VMware on Dell's income statement before the spin, its gross margins were in the low 30s. Now, Dell only spends about 2% of revenue on R&D, but because it's so big, it's still a lot of money. And you can see it is cash-flow positive. Dell's free cash flow over the trailing 12-month period is 3.7 billion, but that's only 3.5% of trailing 12-month revenue. Dell's APEX, and of course its hardware maintenance business, is recurring revenue, and that is only about 5 billion in revenue, and it's growing at 8% annually. Now, having said that, it's the equivalent of ServiceNow's total revenue. Of course, ServiceNow has 23% operating margin and 16% free cash-flow margin, and more than $5 billion in cash on the balance sheet, and an $85 billion market cap. That's what software will do for you. Now Dell, like most companies, is staring at a challenging macro environment, with FX headwinds, inflation, et cetera. You've heard the story. And hence it's conservative, and contracting revenue guidance. But the balance sheet transformation has been quite amazing, thanks to VMware's cash flow. Michael Dell and his partners from Silver Lake et al., they put up around $4 billion of their own cash to buy EMC for 67 billion, and of course got VMware in the process. Most of that financing was debt that Dell put on its balance sheet to do the transaction, to the tune of $46 billion it added to the balance sheet debt. Now, Dell's debt, the core debt, net of its financing operation, is now down to 16 billion, and it has $7 billion in cash on the balance sheet. So a dramatic delta from just a few years ago. So, pretty good picture. But Dell, a $100 billion company, is still only valued at 28 billion, or around 26 cents on the revenue dollar. HPE's revenue multiple is around 60 cents on the revenue dollar. HP Inc., Dell's laptop and PC competitor, is around 45 cents. IBM's revenue multiple is almost two times. By the way, IBM has more than $50 billion in debt thanks to the Red Hat acquisition. And Cisco has a revenue multiple that's over 3x, about 3.3x currently. So is Dell undervalued? Well, based on these comparisons with its peers, I'd say yes, and no. Dell's performance, relative to its peers in the market, is very strong. It's winning, and has an extremely adept go-to-market machine, but its lack of software content and its margin profile leads one to believe that if it can continue to pull some valuation levers while entering new markets, it can get its valuation well above where it is today. So what are some of those levers, and what might that look like, going forward? Despite the fact that Dell doesn't have a huge software revenue component since spinning out VMware, and it doesn't own a cloud, it plays in virtually every part of the hardware market. And it can provide infrastructure for pretty much any application in any use case, in pretty much any industry, in pretty much any geography in the world. And it can serve those customers. So its size is an advantage. However, the history for hardware-heavy companies that try to get bigger has some notable failures, namely HP, which had to split into two businesses, HP Inc. and HPE, and IBM, which has had an abysmal decade from a performance standpoint, and has had to shrink to grow again, and obviously do a massive $34 billion acquisition of Red Hat. So why will Dell do any better than these two? Well, it has a fantastic supply chain. It's a founder-led company, which makes a cultural difference, in our view. And it's actually comfortable with a low-margin software-light business model. Most certainly, IBM wasn't comfortable with that, and didn't have these characteristics, and HP was kind of just incomprehensible at the end. So Dell in my opinion, has a much better chance of doing well at 100 billion or over, but we'll see how it navigates through the current headwinds as it's guiding down. APEX is essentially Dell's version of the cloud. Now, remember, Dell got started late. HPE is further along from a model standpoint with GreenLake, but Dell has a larger portfolio, so they're going to try to play on that advantage. But at the end of the day, these as-a-Service offerings are simply ways to bring a utility model to existing customers, and generate recurring revenue. And that's a good thing, because customers will be loyal to an incumbent if it can deliver as-a-Service and reduce risk for customers. But the real opportunity lies ahead. Specifically, Dell is embracing the cloud model. It took a while, but they're on board. As Matt Baker, Dell's Senior Vice President of Corporate Strategy, likes to say, it's not a zero-sum game. What he means by that is, just because Dell doesn't own its own cloud, it doesn't mean Dell can't build value on top of hyperscale clouds. What we call supercloud. And that's Dell's strategy, to take advantage of public cloud capex, and connect on-prem to the cloud, create a unified experience across clouds, and out to the edge. That's ambitious, and technically it's nontrivial. But listen to Dell's Vice Chairman and Co-COO, Jeff Clarke, explain this vision. Please play the clip. >> You said also, technology and business models are tied together, and an enabler. >> That's right. >> If you believe that, then you have to believe that it's a business operating system that they want. They want to leverage whatever they can, and at the end of the day, they have to differentiate what they do. >> Well, that's exactly right. If I take that and what Dave was saying, and I summarize it the following way: if we can take these cloud assets and capabilities, combine them in an orchestrated way to deliver a distributed platform, game over. >> Eh, pretty interesting, right? John Furrier called it a "business operating system." Essentially, I think of it sometimes as a cloud operating system, or cloud operating environment, to drive new business value on top of the hyperscale capex. Now, is it really game over, as Jeff Clarke said, if Dell can do that? Uh, (sucks in breath) I'd say if it had that today, it might be game over for the competition, but this vision will take years to play out. And of course, it's got to be funded. And that's going to take time, and in this industry, it tends to move, companies tend to move in lockstep. So, as often is the case, it's going to come down to execution and Dell's ability to enter new markets that are ideally, at least from my perspective, higher margin. Data management, extending data protection into cybersecurity as an adjacency, and of course, edge and telco/5G opportunities. All there for the taking. I mean, look, even if Dell doesn't go after more higher-margin software content, it can thrive with a lower-margin model just by penetrating new markets and throwing off cash from those markets. But by keeping close to customers, and maybe through tuck-in acquisitions, it might be able to find the next nugget beyond today's cloud and on-prem models. And the last thing I'll call out is ecosystem. I say here, "Ecosystem, ecosystem, ecosystem," because a defining characteristic of a cloud player is ecosystem, and if APEX is Dell's cloud, it has the opportunity to expand that ecosystem dramatically. This is one of the company's biggest opportunities and challenges at the same time, in my view. It's just scratching the surface on its partner ecosystem. And its ecosystem today is both reseller heavy and tech partner heavy. And that's not a bad thing, but it's starting to evolve more rapidly. The Snowflake deal is an example of up-the-stack evolution, but I'd like to see much more out of that Snowflake relationship, and more relationships like that. Specifically, I'd like to see more momentum with data and database. And if we live in a data-heavy world, which we do, where the data and the database and data management offerings, you know, coexist and are super important to customers, I'd like to see that inside of APEX. I'd like to see that data play beyond storage, which is really where it is today, in its early days. The point is, with Dell's go-to-market advantage, which company wouldn't treat Dell like the on-prem, hybrid, edge, supercloud player that I want to partner with to drive more business? You'd be crazy not to. But Dell has a lot on its plate, and we'd like to see some serious acceleration on the ecosystem front. In other words, Dell as both a selling partner and a business enabler with its platform, its programmable Infrastructure-as-a-Service. And that is a moving target that will rapidly evolve. And of course, we'll be here watching and reporting. So thanks for watching this preview of Dell Technologies Summit 2022. I'm Dave Vellante, we hope you enjoy the rest of the program. (upbeat music)

(upbeat music) >> The transformation of Dell into Dell EMC and now Dell Technologies has been one of the most remarkable stories in the history of the enterprise technology industry. The company has gone from a Wall Street darling rocket ship PC company, to a middling enterprise player forced to go private, to a debt-laden powerhouse that controlled one of the most valuable assets in enterprise tech i.e VMware. And now is a 100 billion dollar giant with a low margin business, a strong balance sheet, and the broadest hardware portfolio in the industry. Financial magic that Dell went through would make anyone's head spin. The last lever of Dell EMC, of the Dell EMC deal was detailed in Michael Dell's book, "Play Nice But Win." In a captivating chapter called Harry You and the Bolt from the Blue, Michael Dell described how he and his colleagues came up with the final straw of how to finance the deal. If you haven't read it, you should. And, of course, after years of successfully integrating EMC and becoming VMware's number one distribution channel, all of this culminated in the spin out of VMware from Dell in a massive wealth creation milestone. Pending, of course, the Broadcom acquisition of VMware. So where's that leave Dell and what does the future look like for this technology powerhouse? Hello, and welcome to theCUBE's exclusive coverage of Dell Technology Summit 2022. My name is Dave Vellante and I'll be hosting the program. Now, today in conjunction with the Dell Tech Summit, we're going to hear from four of Dell's senior executives Tom Sweet, who's the CFO of Dell Technologies. He's going to share his views on the company's position and opportunities going forward. He's going to answer the question, why is Dell a good long-term investment? Then we'll hear from Jeff Boudreau who's the president of Dell's ISG business. That unit is the largest profit driver of Dell. He's going to talk about the product angle and specifically, how Dell is thinking about solving the multi-cloud challenge. And then Sam Grocott who is the senior vice president of marketing will come on the program and give us the update on Apex, which is Dell's as-a-service offering, and then the new edge platform called Project Frontier. Now, it's also Cyber Security Awareness month that we're going to see if Sam has anything to say about that. Then finally, for a company that's nearly 40 years old, Dell actually has some pretty forward-thinking philosophies when it comes to its culture and workforce. And we're going to speak with Jennifer Saavedra who's Dell's chief human resource officer about hybrid work and how Dell is thinking about the future of work. However, before we get into all this, I want to share our independent perspectives on the company and some research that will introduce to frame the program. Now, as you know, we love data here at theCUBE and one of our partners, ETR has what we believe is the best spending intentions data for enterprise tech. So here's a graphic that shows ETR's proprietary net score methodology in the vertical axis. That's a measure of spending velocity. And on the x-axis is overlap of pervasiveness in the data sample. This is a cut for just the server, the storage, and the client sectors within the ETR taxonomy. So you can see Dell CSG products, laptops in particular are dominant on both the X and the Y dimensions. CSG is the client solutions group and accounts for nearly 60% of Dell's revenue and about half of its operating income. And then the arrow signifies that dot that represents Dell's ISG business that we're going to talk to Jeff Boudreau about. That's the infrastructure solutions group. Now, ISG accounts for the bulk of the remainder of Dell's business and it is, as I said, it's most profitable from a margin standpoint. It comprises the EMC storage business as well as the Dell server business and Dell's networking portfolio. And as a note, we didn't include networking in that cut. Had we done so, SISCO would've dominated the graphic. And frankly, Dell's networking business is an industry-leading in the same way that PCs, servers, and storage are. And as you can see, the data confirms the leadership position Dell has in its client side, its server and its storage sectors. But the nuance is look at that red dotted line at 40% on the vertical axis. That represents a highly elevated net score and every company in the sector is below that line. Now, we should mention that we also filtered the data for those companies with more than a 100 mentions in the survey, but the point remains the same. This is a mature business that generally is lower margin. Storage is the exception but cloud has put pressure on margins even in that business in addition to the server space. The last point on this graphic is we put a box around VMware and it's prominently present on both the X and Y dimensions. VMware participates with purely software-defined high margin offerings in these spaces, and it gives you a sense of what might have been had Dell chosen to hold onto that asset or spin it into the company. But let's face it, the alternatives from Michael Dell were just too attractive and it's unlikely that a spin in would've unlocked the value in the way a spin-out did, at least not in the near future. So let's take a look at the snapshot of Dell's financials to give you a sense of where the company stands today. Dell is a company with over a 100 billion dollars in revenue. Last quarter, it did more than 26 billion in revenue and grew at a quite amazing 9% rate for a company that size. But because it's a hardware company primarily, its margins are low with operating income 10% of revenue and at 21% gross margin. With VMware on Dell's income statement, before the spin its gross margins were in the low 30s. Now, Dell only spends about 2% of revenue on R&D because because it's so big, it's still a lot of money. And you can see it is cash flow positive, Dell's free cash flow over the trailing 12-month period is 3.7 billion but that's only 3.5% of trailing 12-month revenue. Dell's Apex and of course it's hardware maintenance business is recurring revenue and that is only about 5 billion in revenue and it's growing at 8% annually. Now having said that, it's the equivalent of Service now's total revenue. Of course, Service now has 23% operating margin and 16% free cash flow margin and more than $5 billion in cash on the balance sheet and an 85 billion dollar market cap. That's what software will do for you. Now, Dell, like most companies, is staring at a challenging macro environment with FX headwinds, inflation, et cetera. You've heard the story, and hence it's conservative and contracting revenue guidance. But the balance sheet transformation has been quite amazing thanks to VMware's cash flow. Michael Dell and his partners from Silver Lake et al, they put up around $4 billion of their own cash to buy EMC for $67 billion and of course got VMware in the process. Most of that financing was debt that Dell put on its balance sheet to do the transaction to the tune of $46 billion it added to the balance sheet debt. Now, Dell's debt, the core debt, net of its financing operation is now down to 16 billion and it has 7 billion in cash in the balance sheet. So dramatic delta from just a few years ago. So pretty good picture. But Dell, a 100 billion company, is still only valued at 28 billion or around 26 cents on the revenue dollar. HPE's revenue multiple is around 60 cents on the revenue dollar. HP Inc, Dell's laptop and PC competitor, is around 45 cents. IBM's revenue multiple is almost two times. By the way, IBM has more than $50 billion in debt thanks to the Red Hat acquisition. And Cisco has a revenue multiple, it's over 3X, about 3.3X currently. So is Dell undervalued? Well, based on these comparisons with its peers, I'd say yes and no. Dell's performance relative to its peers in the market is very strong. It's winning and has an extremely adept go to market machine. But it's lack of software content and it's margin profile leads one to believe that if it can continue to pull some valuation levers while entering new markets, it can get its valuation well above where it is today. So what are some of those levers and what might that look like going forward? Despite the fact that Dell doesn't have a huge software revenue component, since spinning out VMware, and it doesn't own a cloud, it plays in virtually every part of the hardware market. And it can provide infrastructure for pretty much any application, in any use case, in pretty much any industry, in pretty much any geography in the world and it can serve those customers. So its size is an advantage. However, the history for hardware-heavy companies that try to get bigger has some notable failures. Namely HP which had to split into two businesses, HP Inc and HPE, and IBM which has had in abysmal decade from a performance standpoint and has had to shrink to grow again and obviously do a massive $34 billion acquisition of Red Hat. So why will Dell do any better than these two? Well, it has a fantastic supply chain. It's a founder-led company which makes a cultural difference, in our view, and it's actually comfortable with a low margin software light business model. Most certainly, IBM wasn't comfortable with that and didn't have these characteristics and HP was kind of just incomprehensible at the end. So Dell in my opinion is a much better chance of doing well at a 100 billion or over, but we'll see how it navigates through the current headwinds as it's guiding down. Apex is essentially Dell's version of the cloud. Now remember, Dell got started late. HPE is further along from a model standpoint with GreenLake. But Dell has a larger portfolio so they're going to try to play on that advantage. But at the end of the day, these as-a-service offerings are simply ways to bring a utility model to existing customers and generate recurring revenue. And that's a good thing because customers will be loyal to an incumbent if it can deliver as-a-service and reduce risk for customers. But the real opportunity lies ahead, specifically Dell is embracing the cloud model. It took a while, but they're on board. As Matt Baker, Dell's senior vice president of corporate strategy likes to say, it's not a zero sum game. What he means by that is just because Dell doesn't own its own cloud, it doesn't mean Dell can't build value on top of hyperscale clouds, what we call super cloud. And that's Dell's strategy to take advantage of public cloud CapEx and connect on-prem to the cloud, create a unified experience across clouds and out to the edge. That's ambitious and technically it's non-trivial. But listen to Dell's vice chairman and co-COO Jeff Clarke explain this vision. Please play the clip. >> You said also technology and business models are tied together and enabler. If you believe that, then you have to believe that it's a business operating system that they want. They want to leverage whatever they can and at the end of the day, they have to differentiate what they do. >> No, that's exactly right. If I take that and what Dave was saying and I summarize it the following way. If we can take these cloud assets and capabilities, combine them in an orchestrated way to deliver a distributed platform, game over. >> Yeah, pretty interesting, right? John Freer called it a business operating system. Essentially, I think of it sometimes as a cloud operating system or cloud operating environment to drive new business value on top of the hyperscale CapEx. Now, is it really game over as Jeff Clarke said, if Dell can do that? I'd say if it had that today, it might be game over for the competition but this vision will take years to play out, and of course it's got to be funded. And now it's going to take time and in this industry, it tends to move, companies tend to move in lockstep. So as often as the case, it's going to come down to execution and Dell's ability to enter new markets that are ideally, at least from my perspective, higher margin. Data management, extending data protection into cyber security as an adjacency and, of course, edge at Telco slash 5G opportunities. All there for the taking. I mean, look, even if Dell doesn't go after more higher margin software content, it can thrive with a lower margin model just by penetrating new markets and throwing off cash from those markets. But by keeping close to customers and maybe through tuck in acquisitions, it might be able to find the next nugget beyond today's cloud and on-prem models. And the last thing I'll call out is ecosystem. I say here ecosystem, ecosystem, ecosystem. Because a defining characteristic of a cloud player is ecosystem and if Apex is Dell's cloud, it has the opportunity to expand that ecosystem dramatically. This is one of the company's biggest opportunities and challenges at the same time, in my view. It's just scratching the surface on its partner ecosystem. And it's ecosystem today is is both reseller heavy and tech partner heavy. And that's not a bad thing, but it's starting to evolve more rapidly. The snowflake deal is an example of up to stack evolution. But I'd like to see much more out of that Snowflake relationship and more relationships like that. Specifically, I'd like to see more momentum with data and database. And if we live at a data heavy world, which we do, where the data and the database and data management offerings coexist and are super important to customers, I'd like to see that inside of Apex. I'd like to see that data play beyond storage which is really where it is today and it's early days. The point is, with Dell's go to market advantage, which company wouldn't treat Dell like the on-prem, hybrid, edge, super cloud player, that I want to partner with to drive more business? You'd be crazy not to. But Dell has a lot on its plate and we'd like to see some serious acceleration on the ecosystem front. In other words, Dell as both a selling partner and a business enabler with its platform. Its programmable infrastructure as-a-service. And that is a moving target that will rapidly involve. And, of course, we'll be here watching and reporting. So thanks for watching this preview of Dell Technology Summit 2022. I'm Dave Vellante, we hope you enjoy the rest of the program. (upbeat music)

Okay. We're back at AWS reinforced 2022. My name is Dave Vellante, and this is the cube we're here in Boston, home of lobster and CDA. And we're here, the convention center where the cube got started in 2010, Shariq Qureshi is here the senior manager at Deloitte and two LL P and merit bear is back on the cube. Good to see >>You guys can't keep me away, >>Right? No. Well, we love having you on the cube shark set up your role at, at Deloitte and toosh what do you actually, what's your swim lane, if you will. >>Yeah, sure. You know, I spend, I wear a lot of hats. I spend a lot of time in the assurance, the controls advisory audit type of role. So I spend our time, a lot of time working with our clients to understand, you know, regulatory requirements, compliance requirements, and then controls that they need to have in place in order to address risks, technology risks, and ultimately business risks. >>So I like to put forth premise, you know, when I walk around a show like this and come up with some observations and then I like to share 'em and then people like me. Well, you know, maybe so help me course correct. My epiphany at this event is the cloud is becoming the first line of defense. The CISO at your customers is now the second line of defense. I think audit is maybe the th third line of defense. Do, do you buy that the sort of organizational layered approach? >>No, because in fact, what we're here to talk about today is audit manager, which is integrated, right? Like if you're doing so the whole notion of cloud is that we are taking those bottom layers of the stack, right? So the concrete floors up through layer for the hypervisor, the, the racks and stacks and HVAC and guards and gates up through the hypervisor, right? Our, our proprietary hardware nitro ecosystem, which has security inheritance is okay upon that. We are then virtualized. Right? And so what we're really talking about is the ways that audit looks different today, that you can reason about what you're doing. So you're doing infrastructure as code. You can do securities code, you can do compliances code, and that's the beauty of it. So like for better, or in your case for worse in your analogy, you know, these are integrated, these are woven together and they are an API call >>Seamless. >>It, it is like easy to describe, right? I mean, like you can command line knowledge about your resources. You can also reason about it. So like, this is something that's embedded, for example, an inspector you can do network reachability know whether you have an internet facing endpoint, which is a PCI, you know, requirement, but that'll be dashboarded in your security hub. So there's the cloud is all the stuff we take away that you don't have to deal with. And also all the stuff that we manage on top of it that then you can reason about and augment and, and take action on. >>Okay. So at the same time you can't automate the audit entirely. Right? So, but, but talk about the challenges of, of, of, of automating and auditing cloud environment. >>Yeah. I mean, when I look at cloud, you know, organizations move to take advantage of cloud characteristics and cloud capabilities, right? So elasticity, scalability is one of them. And, you know, for market conditions, business, business outcomes, you know, resources expand and contract. And one of the questions that we often get as an auditor is how do you maintain a control environment for resources that weren't there yesterday, but are there today, or that are, that are no longer there and that are there today. So how do you maintain controls and how do you maintain security consistently uniformly throughout an audit environment? It's not there. So that's a challenge auditors, you know, historically when you look at the on-prem environment, you have servers that are there, it's a physical, it's a physical box. You can touch it and see it. And if it goes down, then, you know, it's still there. You can hug >>It if you're some people >>It's still there. So, but you know, with, you know, with cloud things get torn down that you don't see. So how do you maintain controls? That's, you know, it, one challenges, it >>Sounds like you're describing a CMDB for audit. >>I mean, that's a, that's an outcome of having, you know, getting good controls of having a CMDB to keep track and have an inventory of your assets. >>But the problem with CMDB is they're out of date, like so, so quickly, is it different in the cloud world? >>Yeah, exactly. I mean, yes. And yes, they are outta date. Cuz like anything static will be manual and imprecise, like it's gonna be, did John go calculate, like go count how many servers we have. That's why I was joking about server huggers versus like virtualizing it. So you put out a call and you know, not just whether it exists, but whether it's been patched, whether it's, you know, like there are ways that we can reason about what we've done, permissioning pruning, you know, like, and these, by the way, correspond to audit and compliance requirements. And so yes, we are not like there, it's not a click of a, whatever, a snap of the fingers, right. It takes work to translate between auditors and us. And it also takes work to have customers understand how they can augment the way that they think about compliance. But a lot of this is just the good stuff that they already need to be doing, right? Knowing internet facing endpoints or whatever, you know, like pruning permissioning. And there's a lot of ways that, you know, access analyzer, for example, these are automated reasoning tools that come from our formal reasoning group, automated reason group that's in identity. Like they, computers can reason about things in ways that are more complex, as long as it can be resolved. It's like EEU utility in mathematics. You don't go out and try to count every prime number. We accept the infinitude of primes to be true. If you believe in math, then we can reason about it. >>Okay. So hearing that there's a changing landscape yeah. In compliance shift from a lot of manual work to one that's much more highly automated, maybe not completely integrated and seamless. Right. But, but working in that direction, right. Yeah. Is that right? And maybe you could describe that in a little bit more detail, how that, you know, journey has progressed. >>I mean, just the fact alone that you have, you know, a lot of services, a lot of companies that are out there that are trying to remove the manual component and to automate things, to make things more efficient. So then, you know, developers can develop and we can be more agile and to do the things that, you know, really what the core competencies are of the business to remove those manual, you know, components to take out the human element and there's a growing need for it. You know, like we always look at security as, you know, like a second class citizen, we don't take advantage of, you know, the, you know, the opportunities that we need to, to do to maintain controls. So, you know, there's an opportunity here for us to look at and, and automate compliance, to automate controls and, and to make things, you know, seamless >>As a fun side benefit, you will actually hopefully have improved your actual security and also retain your workforce because people don't wanna be doing manual processes. You know, they wanna be doing stuff that humans are designed for, which is creative thinking, innovation, you know, creating ways to make new pathways instead of just like re walking these roads that a computer can analyze, >>You mentioned audit manager, what is that? I mean, let's give a plug for the product or the service. What's that all about what problems does it solve? Let's get >>Into that. Yeah. I mean, audit manager is a first of its kind service. You're not gonna find this offered through any other hyperscaler it's specifically geared and tailored towards the second line, which is security and compliance and a third line function, which is internal audit. So what is it looking to do and what is it looking to address some of those challenges working in a cloud space working, and if you have a cloud footprint. So for example, you know, most organizations operate in a multi account strategy, right? You don't just have one account, but how do you maintain consistency of controls across all your accounts? Auto manager is a service that can give, you know, kind of that single pane of view that to see across your entire landscape, just like a cartographer has a map to see, you know, the entire view of what he's designing auto managers does the same thing only from a cloud perspective. So there's also other, you know, features and capabilities that auto managers trying to integrate, you know, that presents challenges for those in compliance those in the audit space. So, you know, most companies, organizations they have, you know, not just one framework like SOC two or GDPR, high trust, HIPAA PCI, you know, you can select an industry accepted framework and evaluate your cloud consumption against, you know, an industry accepted framework to see where you stand in terms of your control posture, your security hygiene, >>And that's exclusive to AWS. Is that what you're saying? You won't find that on any other hyper scale >>And you'll find similarities in other products, but you won't find something that's specifically geared towards the second line and third line. There's also other features and capabilities to collect evidence, which is, I don't see that in the marketplace. >>Well, the only reason I ask that is because, you know, you, everybody has multiple clouds and I would love, I would love a, you know, an audit manager that's, that's span that transcends, you know, one cloud, is that possible? Or is that something that is just not feasible because of the, the, the deltas between clouds? >>I mean, anything's possible with the APIs right now, the way that, you know, you have to ingrain in, right. There's, you know, a, a feature that was introduced recently for audit manager was the ability to pull in APIs from third party sources. So now you're not just looking, looking exclusively at one cloud provider, you're looking at your entire digital ecosystem of services, your tools, your SA solutions that you're consuming to get a full, comprehensive picture of your environment. >>So compliance, risk, audit security, they're like cousins that are all sort of hanging out on the same holiday, but, but they're different. Like what help us understand and squint through those different disciplines. >>Yeah. I mean, each of them have, you know, a different role and a hat to wear. So internal audit is more of your independent arm of management working or reporting directly towards, you know, to the audit committee or to the board to give an independent view on company control and posture security and compliance works with management to help design the, that there that are intended to prevent, detect, or even correct, you know, controls, breakdowns, you know, those action, those action verb items that you wanna prevent unauthorized access, or you wanna restrict changes from making its way into production unless it's approved and, and documented and tracked and so on and so forth. So each, you know, these roles they're very similar, but they're also different in terms of what their function is. >>How are customers dealing with regional differences? You mentioned GDPR, different regulations, data sovereignty, what are the global nuances and complexities that, that, that cloud brings. And how are you addressing those? >>Yeah. Merit, I don't know if you had any thoughts on that one. >>I mean, I think that a lot of what, and this will build off of your response to the sort of Venn diagrams of security and risk and compliance and audit. I think, you know, what we're seeing is that folks care about the same stuff. They care about privacy. They care about security. They care about incentivizing best practices. The form that that takes when it's a compliance framework is by definition a little bit static over time. Whereas security tends to be more quickly evolving with standards that are like industry standards. And so I think one of the things that, you know, all these compliance frameworks have in, in mind is to go after those best practices, the forms that they take may take different forms. You know what I mean? And so I, I see them as hopeful in the motivation sense that we are helping entities get the wherewithal, they need to grow up or mature or get even more security minded. I think there are times that they feel a little clunky, but you know, that's just Frank. Yeah. >>It, it, it can audit manager sort of help me solve that problem. Is that the intent? And I see what you're saying, merit, that there security is at a different pace than, than, you know, GDPR, a privacy, you know, person, >>Right. I mean, like security says, we want this outcome. We want to have, you know, data be protected. The compliance may say, it must be this particular encryption standard. You know what I mean? Like the form I see things taking over time will evolve and, and feels dynamic. Whereas I think that sometimes when we think about compliance and it's exactly why we need stuff like audit manager is to like help manage exactly what articulation of that are we getting in this place at this time for this regulated industry? And like almost every customer I have is regulated. If you're doing business, you're probably in PCI, right. >>And there's never just one silver bullet. So security is, is a number of things that you're gonna do, the number of tools that you're gonna have. And it's often the culture in, in what you develop in your people, your process and technology. So auto manager is one of the components of robust strategy on how to address security. >>But it's also one of those things where like, there are very few entities, maybe Deloitte is one that are like built to do compliance. They're built to do manufacturing, automotive hospitality. Yeah. You know, like they're doing some other industry as their industry. Right. And we wanna let them have less lag time as they make sure that they can do that core business. And the point is to enable them to move our, I mean like sure. I think that folks should move to the pod because of security, but you don't have to, you should move because it enables your business. And this is one of the ways in which it just like minimizes, you know, like whatever our tailwinds lagging or push it anyway, it pushes you. Right. I mean, like it minimizes the lag >>Definitely tailwind. So are you suggesting merit that you can inject that industry knowledge and specificity into things like audit manager and, and actually begin to automate that as, and of course Deloitte has, you know, industry expertise char, but, but, but how should we think about that? >>I mean, you're gonna, you're gonna look at your controls comprehensively a across the board. So if you operate in an industry, you're gonna look to see like, what's, what's important for you. What do you have to, you know, be mindful of? So if you have data residency concerns, you wanna make sure that you've tailored your controls based on the risks that you're addressing. So if there's a framework >>And remember that you can go in the console and choose what region you're, you know, like we never remove your data from your region that you have chosen, you know, like this is, there's an intentionality and an ability to do this with a click of a mouse or with an API call that's, you know, or with a cloud formation template. That's like, there is a deliberateness there. There's not just like best wishes. >>You know, >>ESG is in scope. I presume, you know, helping the CISO become more green, more diverse. Increasingly you're seeing ESG reports come out from major organizations. I presume that's part of the compliance, but maybe not, maybe it hasn't seeped in yet. Are you seeing >>For that? I think it's still a new service auto manager. It's still, you know, being developed, but, you know, continuous feedback to make sure that, you know, we're covering a, a broad range of services and, and, and those considerations are definitely in the scope. Yeah. >>I mean, are you hearing more of that from >>Clients? So, I mean, we have an internal commitment to sustainability, right. That has been very publicly announced and that I'm passionate about. We also have some other native tools that probably, you know, are worth mentioning here, like security hub that does, you know, CIS benchmarking and other things like that are traffic lighted in their dashboard. You know, like there are ways a lot of this is going to be the ways that we can take what might have been like an ugly ETL process and instead take the managed ness on top of it and, and consume that and allow your CISO to make high velocity decision, high velocity, high quality decisions. >>What's the relationship between your two firms? How do you work >>To I'm like we just met. >>Yeah. I sense that, so is it, is it, how do you integrate, I guess is >>A question. Yeah. I mean, I mean, from the audit perspective, our perspective, working with clients and understanding, you know, their requirements and then bringing the service audit manager from the technical aspect and how we can work together. So we have a few use cases, one we've working with the tech company who wanted to evaluate, you know, production workload that had content, you know, critical client information, client data. So they needed to create custom controls. We were working with them to create custom controls, which auto manager would evaluate their environment, which would, you know, there's a reporting aspect of it, which was used to, you know, to present to senior leadership. So we were working together with AWS and on helping craft what those custom controls were in implement at the customer. >>Yeah. I mean, among other things, delight can help augment workforce. It can help folks interpret their results when they get outputs and act upon them and understand industry standards for responsiveness there. I mean, mean like it's a way to augment your approach by, you know, bringing in someone who's done this before. >>Yeah. Cool, cool. Collaboration on a topic that's generally considered, sorry. Don't, don't hate me for saying this boring, but really important. And the fact that you're automating again makes it a lot more interesting guys. Excellent. Thanks for your sharp first time on the cube. Thank you. Absolutely on, appreciate it. Rapidly. Becoming a VIP. Thanks. Coming on. Hey, I'll take it. All right. Keep it right there. Thank you. This is Dave ante for the cube. You're watching our coverage of AWS reinforce 2022 from Boston. We'll be right back.

[Music] in the 2010s organizations became keenly aware that data would become the key ingredient in driving competitive advantage differentiation and growth but to this day putting data to work remains a difficult challenge for many if not most organizations now as the cloud matures it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible we've also seen better tooling in the form of data workflows streaming machine intelligence ai developer tools security observability automation new databases and the like these innovations they accelerate data proficiency but at the same time they had complexity for practitioners data lakes data hubs data warehouses data marts data fabrics data meshes data catalogs data oceans are forming they're evolving and exploding onto the scene so in an effort to bring perspective to the sea of optionality we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond hello everyone my name is dave vellante with the cube and i'd like to welcome you to a special cube presentation analyst predictions 2022 the future of data management we've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade let me introduce our six power panelists sanjeev mohan is former gartner analyst and principal at sanjamo tony bear is principal at db insight carl olufsen is well-known research vice president with idc dave meninger is senior vice president and research director at ventana research brad shimon chief analyst at ai platforms analytics and data management at omnia and doug henschen vice president and principal analyst at constellation research gentlemen welcome to the program and thanks for coming on thecube today great to be here thank you all right here's the format we're going to use i as moderator are going to call on each analyst separately who then will deliver their prediction or mega trend and then in the interest of time management and pace two analysts will have the opportunity to comment if we have more time we'll elongate it but let's get started right away sanjeev mohan please kick it off you want to talk about governance go ahead sir thank you dave i i believe that data governance which we've been talking about for many years is now not only going to be mainstream it's going to be table stakes and all the things that you mentioned you know with data oceans data lakes lake houses data fabric meshes the common glue is metadata if we don't understand what data we have and we are governing it there is no way we can manage it so we saw informatica when public last year after a hiatus of six years i've i'm predicting that this year we see some more companies go public uh my bet is on colibra most likely and maybe alation we'll see go public this year we we i'm also predicting that the scope of data governance is going to expand beyond just data it's not just data and reports we are going to see more transformations like spark jaws python even airflow we're going to see more of streaming data so from kafka schema registry for example we will see ai models become part of this whole governance suite so the governance suite is going to be very comprehensive very detailed lineage impact analysis and then even expand into data quality we already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management data catalogs also data access governance so these so what we are going to see is that once the data governance platforms become the key entry point into these modern architectures i'm predicting that the usage the number of users of a data catalog is going to exceed that of a bi tool that will take time and we already seen that that trajectory right now if you look at bi tools i would say there are 100 users to a bi tool to one data catalog and i i see that evening out over a period of time and at some point data catalogs will really become you know the main way for us to access data data catalog will help us visualize data but if we want to do more in-depth analysis it'll be the jumping-off point into the bi tool the data science tool and and that is that is the journey i see for the data governance products excellent thank you some comments maybe maybe doug a lot a lot of things to weigh in on there maybe you could comment yeah sanjeev i think you're spot on a lot of the trends uh the one disagreement i think it's it's really still far from mainstream as you say we've been talking about this for years it's like god motherhood apple pie everyone agrees it's important but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking i think one thing that deserves uh mention in this context is uh esg mandates and guidelines these are environmental social and governance regs and guidelines we've seen the environmental rags and guidelines imposed in industries particularly the carbon intensive industries we've seen the social mandates particularly diversity imposed on suppliers by companies that are leading on this topic we've seen governance guidelines now being imposed by banks and investors so these esgs are presenting new carrots and sticks and it's going to demand more solid data it's going to demand more detailed reporting and solid reporting tighter governance but we're still far from mainstream adoption we have a lot of uh you know best of breed niche players in the space i think the signs that it's going to be more mainstream are starting with things like azure purview google dataplex the big cloud platform uh players seem to be uh upping the ante and and addressing starting to address governance excellent thank you doug brad i wonder if you could chime in as well yeah i would love to be a believer in data catalogs um but uh to doug's point i think that it's going to take some more pressure for for that to happen i recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the 90s and that didn't happen quite the way we we anticipated and and uh to sanjeev's point it's because it is really complex and really difficult to do my hope is that you know we won't sort of uh how do we put this fade out into this nebulous nebula of uh domain catalogs that are specific to individual use cases like purview for getting data quality right or like data governance and cyber security and instead we have some tooling that can actually be adaptive to gather metadata to create something i know is important to you sanjeev and that is this idea of observability if you can get enough metadata without moving your data around but understanding the entirety of a system that's running on this data you can do a lot to help with with the governance that doug is talking about so so i just want to add that you know data governance like many other initiatives did not succeed even ai went into an ai window but that's a different topic but a lot of these things did not succeed because to your point the incentives were not there i i remember when starbucks oxley had come into the scene if if a bank did not do service obviously they were very happy to a million dollar fine that was like you know pocket change for them instead of doing the right thing but i think the stakes are much higher now with gdpr uh the floodgates open now you know california you know has ccpa but even ccpa is being outdated with cpra which is much more gdpr like so we are very rapidly entering a space where every pretty much every major country in the world is coming up with its own uh compliance regulatory requirements data residence is becoming really important and and i i think we are going to reach a stage where uh it won't be optional anymore so whether we like it or not and i think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption we were focused on features and these features were disconnected very hard for business to stop these are built by it people for it departments to to take a look at technical metadata not business metadata today the tables have turned cdo's are driving this uh initiative uh regulatory compliances are beating down hard so i think the time might be right yeah so guys we have to move on here and uh but there's some some real meat on the bone here sanjeev i like the fact that you late you called out calibra and alation so we can look back a year from now and say okay he made the call he stuck it and then the ratio of bi tools the data catalogs that's another sort of measurement that we can we can take even though some skepticism there that's something that we can watch and i wonder if someday if we'll have more metadata than data but i want to move to tony baer you want to talk about data mesh and speaking you know coming off of governance i mean wow you know the whole concept of data mesh is decentralized data and then governance becomes you know a nightmare there but take it away tony we'll put it this way um data mesh you know the the idea at least is proposed by thoughtworks um you know basically was unleashed a couple years ago and the press has been almost uniformly almost uncritical um a good reason for that is for all the problems that basically that sanjeev and doug and brad were just you know we're just speaking about which is that we have all this data out there and we don't know what to do about it um now that's not a new problem that was a problem we had enterprise data warehouses it was a problem when we had our hadoop data clusters it's even more of a problem now the data's out in the cloud where the data is not only your data like is not only s3 it's all over the place and it's also including streaming which i know we'll be talking about later so the data mesh was a response to that the idea of that we need to debate you know who are the folks that really know best about governance is the domain experts so it was basically data mesh was an architectural pattern and a process my prediction for this year is that data mesh is going to hit cold hard reality because if you if you do a google search um basically the the published work the articles and databases have been largely you know pretty uncritical um so far you know that you know basically learning is basically being a very revolutionary new idea i don't think it's that revolutionary because we've talked about ideas like this brad and i you and i met years ago when we were talking about so and decentralizing all of us was at the application level now we're talking about at the data level and now we have microservices so there's this thought of oh if we manage if we're apps in cloud native through microservices why don't we think of data in the same way um my sense this year is that you know this and this has been a very active search if you look at google search trends is that now companies are going to you know enterprises are going to look at this seriously and as they look at seriously it's going to attract its first real hard scrutiny it's going to attract its first backlash that's not necessarily a bad thing it means that it's being taken seriously um the reason why i think that that uh that it will you'll start to see basically the cold hard light of day shine on data mesh is that it's still a work in progress you know this idea is basically a couple years old and there's still some pretty major gaps um the biggest gap is in is in the area of federated governance now federated governance itself is not a new issue uh federated governance position we're trying to figure out like how can we basically strike the balance between getting let's say you know between basically consistent enterprise policy consistent enterprise governance but yet the groups that understand the data know how to basically you know that you know how do we basically sort of balance the two there's a huge there's a huge gap there in practice and knowledge um also to a lesser extent there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data you know basically through the full life cycle from developed from selecting the data from you know building the other pipelines from determining your access control determining looking at quality looking at basically whether data is fresh or whether or not it's trending of course so my predictions is that it will really receive the first harsh scrutiny this year you are going to see some organization enterprises declare premature victory when they've uh when they build some federated query implementations you're going to see vendors start to data mesh wash their products anybody in the data management space they're going to say that whether it's basically a pipelining tool whether it's basically elt whether it's a catalog um or confederated query tool they're all going to be like you know basically promoting the fact of how they support this hopefully nobody is going to call themselves a data mesh tool because data mesh is not a technology we're going to see one other thing come out of this and this harks back to the metadata that sanji was talking about and the catalogs that he was talking about which is that there's going to be a new focus on every renewed focus on metadata and i think that's going to spur interest in data fabrics now data fabrics are pretty vaguely defined but if we just take the most elemental definition which is a common metadata back plane i think that if anybody is going to get serious about data mesh they need to look at a data fabric because we all at the end of the day need to speak you know need to read from the same sheet of music so thank you tony dave dave meninger i mean one of the things that people like about data mesh is it pretty crisply articulates some of the flaws in today's organizational approaches to data what are your thoughts on this well i think we have to start by defining data mesh right the the term is already getting corrupted right tony said it's going to see the cold hard uh light of day and there's a problem right now that there are a number of overlapping terms that are similar but not identical so we've got data virtualization data fabric excuse me for a second sorry about that data virtualization data fabric uh uh data federation right uh so i i think that it's not really clear what each vendor means by these terms i see data mesh and data fabric becoming quite popular i've i've interpreted data mesh as referring primarily to the governance aspects as originally you know intended and specified but that's not the way i see vendors using i see vendors using it much more to mean data fabric and data virtualization so i'm going to comment on the group of those things i think the group of those things is going to happen they're going to happen they're going to become more robust our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access again whether you define it as mesh or fabric or virtualization isn't really the point here but this notion that there are different elements of data metadata and governance within an organization that all need to be managed collectively the interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not it's almost double 68 of organizations i'm i'm sorry um 79 of organizations that were using virtualized access express satisfaction with their access to the data lake only 39 expressed satisfaction if they weren't using virtualized access so thank you uh dave uh sanjeev we just got about a couple minutes on this topic but i know you're speaking or maybe you've spoken already on a panel with jamal dagani who sort of invented the concept governance obviously is a big sticking point but what are your thoughts on this you are mute so my message to your mark and uh and to the community is uh as opposed to what dave said let's not define it we spent the whole year defining it there are four principles domain product data infrastructure and governance let's take it to the next level i get a lot of questions on what is the difference between data fabric and data mesh and i'm like i can compare the two because data mesh is a business concept data fabric is a data integration pattern how do you define how do you compare the two you have to bring data mesh level down so to tony's point i'm on a warp path in 2022 to take it down to what does a data product look like how do we handle shared data across domains and govern it and i think we are going to see more of that in 2022 is operationalization of data mesh i think we could have a whole hour on this topic couldn't we uh maybe we should do that uh but let's go to let's move to carl said carl your database guy you've been around that that block for a while now you want to talk about graph databases bring it on oh yeah okay thanks so i regard graph database as basically the next truly revolutionary database management technology i'm looking forward to for the graph database market which of course we haven't defined yet so obviously i have a little wiggle room in what i'm about to say but that this market will grow by about 600 percent over the next 10 years now 10 years is a long time but over the next five years we expect to see gradual growth as people start to learn how to use it problem isn't that it's used the problem is not that it's not useful is that people don't know how to use it so let me explain before i go any further what a graph database is because some of the folks on the call may not may not know what it is a graph database organizes data according to a mathematical structure called a graph a graph has elements called nodes and edges so a data element drops into a node the nodes are connected by edges the edges connect one node to another node combinations of edges create structures that you can analyze to determine how things are related in some cases the nodes and edges can have properties attached to them which add additional informative material that makes it richer that's called a property graph okay there are two principal use cases for graph databases there's there's semantic proper graphs which are used to break down human language text uh into the semantic structures then you can search it organize it and and and answer complicated questions a lot of ai is aimed at semantic graphs another kind is the property graph that i just mentioned which has a dazzling number of use cases i want to just point out is as i talk about this people are probably wondering well we have relational databases isn't that good enough okay so a relational database defines it uses um it supports what i call definitional relationships that means you define the relationships in a fixed structure the database drops into that structure there's a value foreign key value that relates one table to another and that value is fixed you don't change it if you change it the database becomes unstable it's not clear what you're looking at in a graph database the system is designed to handle change so that it can reflect the true state of the things that it's being used to track so um let me just give you some examples of use cases for this um they include uh entity resolution data lineage uh um social media analysis customer 360 fraud prevention there's cyber security there's strong supply chain is a big one actually there's explainable ai and this is going to become important too because a lot of people are adopting ai but they want a system after the fact to say how did the ai system come to that conclusion how did it make that recommendation right now we don't have really good ways of tracking that okay machine machine learning in general um social network i already mentioned that and then we've got oh gosh we've got data governance data compliance risk management we've got recommendation we've got personalization anti-money money laundering that's another big one identity and access management network and i.t operations is already becoming a key one where you actually have mapped out your operation your your you know whatever it is your data center and you you can track what's going on as things happen there root cause analysis fraud detection is a huge one a number of major credit card companies use graph databases for fraud detection risk analysis tracking and tracing churn analysis next best action what-if analysis impact analysis entity resolution and i would add one other thing or just a few other things to this list metadata management so sanjay here you go this is your engine okay because i was in metadata management for quite a while in my past life and one of the things i found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it but grass can okay grafts can do things like say this term in this context means this but in that context it means that okay things like that and in fact uh logistics management supply chain it also because it handles recursive relationships by recursive relationships i mean objects that own other objects that are of the same type you can do things like bill materials you know so like parts explosion you can do an hr analysis who reports to whom how many levels up the chain and that kind of thing you can do that with relational databases but yes it takes a lot of programming in fact you can do almost any of these things with relational databases but the problem is you have to program it it's not it's not supported in the database and whenever you have to program something that means you can't trace it you can't define it you can't publish it in terms of its functionality and it's really really hard to maintain over time so carl thank you i wonder if we could bring brad in i mean brad i'm sitting there wondering okay is this incremental to the market is it disruptive and replaceable what are your thoughts on this space it's already disrupted the market i mean like carl said go to any bank and ask them are you using graph databases to do to get fraud detection under control and they'll say absolutely that's the only way to solve this problem and it is frankly um and it's the only way to solve a lot of the problems that carl mentioned and that is i think it's it's achilles heel in some ways because you know it's like finding the best way to cross the seven bridges of konigsberg you know it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique uh it it still unfortunately kind of stands apart from the rest of the community that's building let's say ai outcomes as the great great example here the graph databases and ai as carl mentioned are like chocolate and peanut butter but technologically they don't know how to talk to one another they're completely different um and you know it's you can't just stand up sql and query them you've got to to learn um yeah what is that carlos specter or uh special uh uh yeah thank you uh to actually get to the data in there and if you're gonna scale that data that graph database especially a property graph if you're gonna do something really complex like try to understand uh you know all of the metadata in your organization you might just end up with you know a graph database winter like we had the ai winter simply because you run out of performance to make the thing happen so i i think it's already disrupted but we we need to like treat it like a first-class citizen in in the data analytics and ai community we need to bring it into the fold we need to equip it with the tools it needs to do that the magic it does and to do it not just for specialized use cases but for everything because i i'm with carl i i think it's absolutely revolutionary so i had also identified the principal achilles heel of the technology which is scaling now when these when these things get large and complex enough that they spill over what a single server can handle you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down so that's still a problem to be solved sanjeev any quick thoughts on this i mean i think metadata on the on the on the word cloud is going to be the the largest font uh but what are your thoughts here i want to like step away so people don't you know associate me with only meta data so i want to talk about something a little bit slightly different uh dbengines.com has done an amazing job i think almost everyone knows that they chronicle all the major databases that are in use today in january of 2022 there are 381 databases on its list of ranked list of databases the largest category is rdbms the second largest category is actually divided into two property graphs and rdf graphs these two together make up the second largest number of data databases so talking about accolades here this is a problem the problem is that there's so many graph databases to choose from they come in different shapes and forms uh to bright's point there's so many query languages in rdbms is sql end of the story here we've got sci-fi we've got gremlin we've got gql and then your proprietary languages so i think there's a lot of disparity in this space but excellent all excellent points sanji i must say and that is a problem the languages need to be sorted and standardized and it needs people need to have a road map as to what they can do with it because as you say you can do so many things and so many of those things are unrelated that you sort of say well what do we use this for i'm reminded of the saying i learned a bunch of years ago when somebody said that the digital computer is the only tool man has ever devised that has no particular purpose all right guys we gotta we gotta move on to dave uh meninger uh we've heard about streaming uh your prediction is in that realm so please take it away sure so i like to say that historical databases are to become a thing of the past but i don't mean that they're going to go away that's not my point i mean we need historical databases but streaming data is going to become the default way in which we operate with data so in the next say three to five years i would expect the data platforms and and we're using the term data platforms to represent the evolution of databases and data lakes that the data platforms will incorporate these streaming capabilities we're going to process data as it streams into an organization and then it's going to roll off into historical databases so historical databases don't go away but they become a thing of the past they store the data that occurred previously and as data is occurring we're going to be processing it we're going to be analyzing we're going to be acting on it i mean we we only ever ended up with historical databases because we were limited by the technology that was available to us data doesn't occur in batches but we processed it in batches because that was the best we could do and it wasn't bad and we've continued to improve and we've improved and we've improved but streaming data today is still the exception it's not the rule right there's there are projects within organizations that deal with streaming data but it's not the default way in which we deal with data yet and so that that's my prediction is that this is going to change we're going to have um streaming data be the default way in which we deal with data and and how you label it what you call it you know maybe these databases and data platforms just evolve to be able to handle it but we're going to deal with data in a different way and our research shows that already about half of the participants in our analytics and data benchmark research are using streaming data you know another third are planning to use streaming technologies so that gets us to about eight out of ten organizations need to use this technology that doesn't mean they have to use it throughout the whole organization but but it's pretty widespread in its use today and has continued to grow if you think about the consumerization of i.t we've all been conditioned to expect immediate access to information immediate responsiveness you know we want to know if an uh item is on the shelf at our local retail store and we can go in and pick it up right now you know that's the world we live in and that's spilling over into the enterprise i.t world where we have to provide those same types of capabilities um so that's my prediction historical database has become a thing of the past streaming data becomes the default way in which we we operate with data all right thank you david well so what what say you uh carl a guy who's followed historical databases for a long time well one thing actually every database is historical because as soon as you put data in it it's now history it's no longer it no longer reflects the present state of things but even if that history is only a millisecond old it's still history but um i would say i mean i know you're trying to be a little bit provocative in saying this dave because you know as well as i do that people still need to do their taxes they still need to do accounting they still need to run general ledger programs and things like that that all involves historical data that's not going to go away unless you want to go to jail so you're going to have to deal with that but as far as the leading edge functionality i'm totally with you on that and i'm just you know i'm just kind of wondering um if this chain if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way m applications work um saying that uh an application should respond instantly as soon as the state of things changes what do you say about that i i think that's true i think we do have to think about things differently that's you know it's not the way we design systems in the past uh we're seeing more and more systems designed that way but again it's not the default and and agree 100 with you that we do need historical databases you know that that's clear and even some of those historical databases will be used in conjunction with the streaming data right so absolutely i mean you know let's take the data warehouse example where you're using the data warehouse as context and the streaming data as the present you're saying here's a sequence of things that's happening right now have we seen that sequence before and where what what does that pattern look like in past situations and can we learn from that so tony bear i wonder if you could comment i mean if you when you think about you know real-time inferencing at the edge for instance which is something that a lot of people talk about um a lot of what we're discussing here in this segment looks like it's got great potential what are your thoughts yeah well i mean i think you nailed it right you know you hit it right on the head there which is that i think a key what i'm seeing is that essentially and basically i'm going to split this one down the middle is i don't see that basically streaming is the default what i see is streaming and basically and transaction databases um and analytics data you know data warehouses data lakes whatever are converging and what allows us technically to converge is cloud native architecture where you can basically distribute things so you could have you can have a note here that's doing the real-time processing that's also doing it and this is what your leads in we're maybe doing some of that real-time predictive analytics to take a look at well look we're looking at this customer journey what's happening with you know you know with with what the customer is doing right now and this is correlated with what other customers are doing so what i so the thing is that in the cloud you can basically partition this and because of basically you know the speed of the infrastructure um that you can basically bring these together and or and so and kind of orchestrate them sort of loosely coupled manner the other part is that the use cases are demanding and this is part that goes back to what dave is saying is that you know when you look at customer 360 when you look at let's say smart you know smart utility grids when you look at any type of operational problem it has a real-time component and it has a historical component and having predictives and so like you know you know my sense here is that there that technically we can bring this together through the cloud and i think the use case is that is that we we can apply some some real-time sort of you know predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction we have this real time you know input sanjeev did you have a comment yeah i was just going to say that to this point you know we have to think of streaming very different because in the historical databases we used to bring the data and store the data and then we used to run rules on top uh aggregations and all but in case of streaming the mindset changes because the rules normally the inference all of that is fixed but the data is constantly changing so it's a completely reverse way of thinking of uh and building applications on top of that so dave menninger there seemed to be some disagreement about the default or now what kind of time frame are you are you thinking about is this end of decade it becomes the default what would you pin i i think around you know between between five to ten years i think this becomes the reality um i think you know it'll be more and more common between now and then but it becomes the default and i also want sanjeev at some point maybe in one of our subsequent conversations we need to talk about governing streaming data because that's a whole other set of challenges we've also talked about it rather in a two dimensions historical and streaming and there's lots of low latency micro batch sub second that's not quite streaming but in many cases it's fast enough and we're seeing a lot of adoption of near real time not quite real time as uh good enough for most for many applications because nobody's really taking the hardware dimension of this information like how do we that'll just happen carl so near real time maybe before you lose the customer however you define that right okay um let's move on to brad brad you want to talk about automation ai uh the the the pipeline people feel like hey we can just automate everything what's your prediction yeah uh i'm i'm an ai fiction auto so apologies in advance for that but uh you know um i i think that um we've been seeing automation at play within ai for some time now and it's helped us do do a lot of things for especially for practitioners that are building ai outcomes in the enterprise uh it's it's helped them to fill skills gaps it's helped them to speed development and it's helped them to to actually make ai better uh because it you know in some ways provides some swim lanes and and for example with technologies like ottawa milk and can auto document and create that sort of transparency that that we talked about a little bit earlier um but i i think it's there's an interesting kind of conversion happening with this idea of automation um and and that is that uh we've had the automation that started happening for practitioners it's it's trying to move outside of the traditional bounds of things like i'm just trying to get my features i'm just trying to pick the right algorithm i'm just trying to build the right model uh and it's expanding across that full life cycle of building an ai outcome to start at the very beginning of data and to then continue on to the end which is this continuous delivery and continuous uh automation of of that outcome to make sure it's right and it hasn't drifted and stuff like that and because of that because it's become kind of powerful we're starting to to actually see this weird thing happen where the practitioners are starting to converge with the users and that is to say that okay if i'm in tableau right now i can stand up salesforce einstein discovery and it will automatically create a nice predictive algorithm for me um given the data that i that i pull in um but what's starting to happen and we're seeing this from the the the companies that create business software so salesforce oracle sap and others is that they're starting to actually use these same ideals and a lot of deep learning to to basically stand up these out of the box flip a switch and you've got an ai outcome at the ready for business users and um i i'm very much you know i think that that's that's the way that it's going to go and what it means is that ai is is slowly disappearing uh and i don't think that's a bad thing i think if anything what we're going to see in 2022 and maybe into 2023 is this sort of rush to to put this idea of disappearing ai into practice and have as many of these solutions in the enterprise as possible you can see like for example sap is going to roll out this quarter this thing called adaptive recommendation services which which basically is a cold start ai outcome that can work across a whole bunch of different vertical markets and use cases it's just a recommendation engine for whatever you need it to do in the line of business so basically you're you're an sap user you look up to turn on your software one day and you're a sales professional let's say and suddenly you have a recommendation for customer churn it's going that's great well i i don't know i i think that's terrifying in some ways i think it is the future that ai is going to disappear like that but i am absolutely terrified of it because um i i think that what it what it really does is it calls attention to a lot of the issues that we already see around ai um specific to this idea of what what we like to call it omdia responsible ai which is you know how do you build an ai outcome that is free of bias that is inclusive that is fair that is safe that is secure that it's audible etc etc etc etc that takes some a lot of work to do and so if you imagine a customer that that's just a sales force customer let's say and they're turning on einstein discovery within their sales software you need some guidance to make sure that when you flip that switch that the outcome you're going to get is correct and that's that's going to take some work and so i think we're going to see this let's roll this out and suddenly there's going to be a lot of a lot of problems a lot of pushback uh that we're going to see and some of that's going to come from gdpr and others that sam jeeve was mentioning earlier a lot of it's going to come from internal csr requirements within companies that are saying hey hey whoa hold up we can't do this all at once let's take the slow route let's make ai automated in a smart way and that's going to take time yeah so a couple predictions there that i heard i mean ai essentially you disappear it becomes invisible maybe if i can restate that and then if if i understand it correctly brad you're saying there's a backlash in the near term people can say oh slow down let's automate what we can those attributes that you talked about are non trivial to achieve is that why you're a bit of a skeptic yeah i think that we don't have any sort of standards that companies can look to and understand and we certainly within these companies especially those that haven't already stood up in internal data science team they don't have the knowledge to understand what that when they flip that switch for an automated ai outcome that it's it's gonna do what they think it's gonna do and so we need some sort of standard standard methodology and practice best practices that every company that's going to consume this invisible ai can make use of and one of the things that you know is sort of started that google kicked off a few years back that's picking up some momentum and the companies i just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing you know so like for the sap example we know for example that it's convolutional neural network with a long short-term memory model that it's using we know that it only works on roman english uh and therefore me as a consumer can say oh well i know that i need to do this internationally so i should not just turn this on today great thank you carl can you add anything any context here yeah we've talked about some of the things brad mentioned here at idc in the our future of intelligence group regarding in particular the moral and legal implications of having a fully automated you know ai uh driven system uh because we already know and we've seen that ai systems are biased by the data that they get right so if if they get data that pushes them in a certain direction i think there was a story last week about an hr system that was uh that was recommending promotions for white people over black people because in the past um you know white people were promoted and and more productive than black people but not it had no context as to why which is you know because they were being historically discriminated black people being historically discriminated against but the system doesn't know that so you know you have to be aware of that and i think that at the very least there should be controls when a decision has either a moral or a legal implication when when you want when you really need a human judgment it could lay out the options for you but a person actually needs to authorize that that action and i also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases and to some extent they always will so we'll always be chasing after them that's that's absolutely carl yeah i think that what you have to bear in mind as a as a consumer of ai is that it is a reflection of us and we are a very flawed species uh and so if you look at all the really fantastic magical looking supermodels we see like gpt three and four that's coming out z they're xenophobic and hateful uh because the people the data that's built upon them and the algorithms and the people that build them are us so ai is a reflection of us we need to keep that in mind yeah we're the ai's by us because humans are biased all right great okay let's move on doug henson you know a lot of people that said that data lake that term's not not going to not going to live on but it appears to be have some legs here uh you want to talk about lake house bring it on yes i do my prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering i say offering that doesn't mean it's going to be the dominant thing that organizations have out there but it's going to be the predominant vendor offering in 2022. now heading into 2021 we already had cloudera data bricks microsoft snowflake as proponents in 2021 sap oracle and several of these fabric virtualization mesh vendors join the bandwagon the promise is that you have one platform that manages your structured unstructured and semi-structured information and it addresses both the beyond analytics needs and the data science needs the real promise there is simplicity and lower cost but i think end users have to answer a few questions the first is does your organization really have a center of data gravity or is it is the data highly distributed multiple data warehouses multiple data lakes on-premises cloud if it if it's very distributed and you you know you have difficulty consolidating and that's not really a goal for you then maybe that single platform is unrealistic and not likely to add value to you um you know also the fabric and virtualization vendors the the mesh idea that's where if you have this highly distributed situation that might be a better path forward the second question if you are looking at one of these lake house offerings you are looking at consolidating simplifying bringing together to a single platform you have to make sure that it meets both the warehouse need and the data lake need so you have vendors like data bricks microsoft with azure synapse new really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements can meet the user and query concurrency requirements meet those tight slas and then on the other hand you have the or the oracle sap snowflake the data warehouse uh folks coming into the data science world and they have to prove that they can manage the unstructured information and meet the needs of the data scientists i'm seeing a lot of the lake house offerings from the warehouse crowd managing that unstructured information in columns and rows and some of these vendors snowflake in particular is really relying on partners for the data science needs so you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement well thank you doug well tony if those two worlds are going to come together as doug was saying the analytics and the data science world does it need to be some kind of semantic layer in between i don't know weigh in on this topic if you would oh didn't we talk about data fabrics before common metadata layer um actually i'm almost tempted to say let's declare victory and go home in that this is actually been going on for a while i actually agree with uh you know much what doug is saying there which is that i mean we i remembered as far back as i think it was like 2014 i was doing a a study you know it was still at ovum predecessor omnia um looking at all these specialized databases that were coming up and seeing that you know there's overlap with the edges but yet there was still going to be a reason at the time that you would have let's say a document database for json you'd have a relational database for tran you know for transactions and for data warehouse and you had you know and you had basically something at that time that that resembles to do for what we're considering a day of life fast fo and the thing is what i was saying at the time is that you're seeing basically blur you know sort of blending at the edges that i was saying like about five or six years ago um that's all and the the lake house is essentially you know the amount of the the current manifestation of that idea there is a dichotomy in terms of you know it's the old argument do we centralize this all you know you know in in in in in a single place or do we or do we virtualize and i think it's always going to be a yin and yang there's never going to be a single single silver silver bullet i do see um that they're also going to be questions and these are things that points that doug raised they're you know what your what do you need of of of your of you know for your performance there or for your you know pre-performance characteristics do you need for instance hiking currency you need the ability to do some very sophisticated joins or is your requirement more to be able to distribute and you know distribute our processing is you know as far as possible to get you know to essentially do a kind of brute force approach all these approaches are valid based on you know based on the used case um i just see that essentially that the lake house is the culmination of it's nothing it's just it's a relatively new term introduced by databricks a couple years ago this is the culmination of basically what's been a long time trend and what we see in the cloud is that as we start seeing data warehouses as a checkbox item say hey we can basically source data in cloud and cloud storage and s3 azure blob store you know whatever um as long as it's in certain formats like you know like you know parquet or csv or something like that you know i see that as becoming kind of you know a check box item so to that extent i think that the lake house depending on how you define it is already reality um and in some in some cases maybe new terminology but not a whole heck of a lot new under the sun yeah and dave menger i mean a lot of this thank you tony but a lot of this is going to come down to you know vendor marketing right some people try to co-opt the term we talked about data mesh washing what are your thoughts on this yeah so um i used the term data platform earlier and and part of the reason i use that term is that it's more vendor neutral uh we've we've tried to uh sort of stay out of the the vendor uh terminology patenting world right whether whether the term lake house is what sticks or not the concept is certainly going to stick and we have some data to back it up about a quarter of organizations that are using data lakes today already incorporate data warehouse functionality into it so they consider their data lake house and data warehouse one in the same about a quarter of organizations a little less but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake so it's pretty obvious that three quarters of organizations need to bring this stuff together right the need is there the need is apparent the technology is going to continue to verge converge i i like to talk about you know you've got data lakes over here at one end and i'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a in a server and you ignore it right that's not what a data lake is so you've got data lake people over here and you've got database people over here data warehouse people over here database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities so it's obvious that they're going to meet in the middle i mean i think it's like tony says i think we should there declare victory and go home and so so i it's just a follow-up on that so are you saying these the specialized lake and the specialized warehouse do they go away i mean johnny tony data mesh practitioners would say or or advocates would say well they could all live as just a node on the on the mesh but based on what dave just said are we going to see those all morph together well number one as i was saying before there's always going to be this sort of you know kind of you know centrifugal force or this tug of war between do we centralize the data do we do it virtualize and the fact is i don't think that work there's ever going to be any single answer i think in terms of data mesh data mesh has nothing to do with how you physically implement the data you could have a data mesh on a basically uh on a data warehouse it's just that you know the difference being is that if we use the same you know physical data store but everybody's logically manual basically governing it differently you know um a data mission is basically it's not a technology it's a process it's a governance process um so essentially um you know you know i basically see that you know as as i was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring but there are going to be cases where for instance if i need let's say like observe i need like high concurrency or something like that there are certain things that i'm not going to be able to get efficiently get out of a data lake um and you know we're basically i'm doing a system where i'm just doing really brute forcing very fast file scanning and that type of thing so i think there always will be some delineations but i would agree with dave and with doug that we are seeing basically a a confluence of requirements that we need to essentially have basically the element you know the ability of a data lake and a data laid out their warehouse we these need to come together so i think what we're likely to see is organizations look for a converged platform that can handle both sides for their center of data gravity the mesh and the fabric vendors the the fabric virtualization vendors they're all on board with the idea of this converged platform and they're saying hey we'll handle all the edge cases of the stuff that isn't in that center of data gradient that is off distributed in a cloud or at a remote location so you can have that single platform for the center of of your your data and then bring in virtualization mesh what have you for reaching out to the distributed data bingo as they basically said people are happy when they virtualize data i i think yes at this point but to this uh dave meningas point you know they have convert they are converging snowflake has introduced support for unstructured data so now we are literally splitting here now what uh databricks is saying is that aha but it's easy to go from data lake to data warehouse than it is from data warehouse to data lake so i think we're getting into semantics but we've already seen these two converge so is that so it takes something like aws who's got what 15 data stores are they're going to have 15 converged data stores that's going to be interesting to watch all right guys i'm going to go down the list and do like a one i'm going to one word each and you guys each of the analysts if you wouldn't just add a very brief sort of course correction for me so sanjeev i mean governance is going to be the maybe it's the dog that wags the tail now i mean it's coming to the fore all this ransomware stuff which really didn't talk much about security but but but what's the one word in your prediction that you would leave us with on governance it's uh it's going to be mainstream mainstream okay tony bear mesh washing is what i wrote down that's that's what we're going to see in uh in in 2022 a little reality check you you want to add to that reality check is i hope that no vendor you know jumps the shark and calls their offering a data mesh project yeah yeah let's hope that doesn't happen if they do we're going to call them out uh carl i mean graph databases thank you for sharing some some you know high growth metrics i know it's early days but magic is what i took away from that it's the magic database yeah i would actually i've said this to people too i i kind of look at it as a swiss army knife of data because you can pretty much do anything you want with it it doesn't mean you should i mean that's definitely the case that if you're you know managing things that are in a fixed schematic relationship probably a relational database is a better choice there are you know times when the document database is a better choice it can handle those things but maybe not it may not be the best choice for that use case but for a great many especially the new emerging use cases i listed it's the best choice thank you and dave meninger thank you by the way for bringing the data in i like how you supported all your comments with with some some data points but streaming data becomes the sort of default uh paradigm if you will what would you add yeah um i would say think fast right that's the world we live in you got to think fast fast love it uh and brad shimon uh i love it i mean on the one hand i was saying okay great i'm afraid i might get disrupted by one of these internet giants who are ai experts so i'm gonna be able to buy instead of build ai but then again you know i've got some real issues there's a potential backlash there so give us the there's your bumper sticker yeah i i would say um going with dave think fast and also think slow uh to to talk about the book that everyone talks about i would say really that this is all about trust trust in the idea of automation and of a transparent invisible ai across the enterprise but verify verify before you do anything and then doug henson i mean i i look i think the the trend is your friend here on this prediction with lake house is uh really becoming dominant i liked the way you set up that notion of you know the the the data warehouse folks coming at it from the analytics perspective but then you got the data science worlds coming together i still feel as though there's this piece in the middle that we're missing but your your final thoughts we'll give you the last well i think the idea of consolidation and simplification uh always prevails that's why the appeal of a single platform is going to be there um we've already seen that with uh you know hadoop platforms moving toward cloud moving toward object storage and object storage becoming really the common storage point for whether it's a lake or a warehouse uh and that second point uh i think esg mandates are uh are gonna come in alongside uh gdpr and things like that to uh up the ante for uh good governance yeah thank you for calling that out okay folks hey that's all the time that that we have here your your experience and depth of understanding on these key issues and in data and data management really on point and they were on display today i want to thank you for your your contributions really appreciate your time enjoyed it thank you now in addition to this video we're going to be making available transcripts of the discussion we're going to do clips of this as well we're going to put them out on social media i'll write this up and publish the discussion on wikibon.com and siliconangle.com no doubt several of the analysts on the panel will take the opportunity to publish written content social commentary or both i want to thank the power panelist and thanks for watching this special cube presentation this is dave vellante be well and we'll see you next time [Music] you

(bright music) >> In the 2010s, organizations became keenly aware that data would become the key ingredient to driving competitive advantage, differentiation, and growth. But to this day, putting data to work remains a difficult challenge for many, if not most organizations. Now, as the cloud matures, it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible. We've also seen better tooling in the form of data workflows, streaming, machine intelligence, AI, developer tools, security, observability, automation, new databases and the like. These innovations they accelerate data proficiency, but at the same time, they add complexity for practitioners. Data lakes, data hubs, data warehouses, data marts, data fabrics, data meshes, data catalogs, data oceans are forming, they're evolving and exploding onto the scene. So in an effort to bring perspective to the sea of optionality, we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond. Hello everyone, my name is Dave Velannte with theCUBE, and I'd like to welcome you to a special Cube presentation, analysts predictions 2022: the future of data management. We've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade. Let me introduce our six power panelists. Sanjeev Mohan is former Gartner Analyst and Principal at SanjMo. Tony Baer, principal at dbInsight, Carl Olofson is well-known Research Vice President with IDC, Dave Menninger is Senior Vice President and Research Director at Ventana Research, Brad Shimmin, Chief Analyst, AI Platforms, Analytics and Data Management at Omdia and Doug Henschen, Vice President and Principal Analyst at Constellation Research. Gentlemen, welcome to the program and thanks for coming on theCUBE today. >> Great to be here. >> Thank you. >> All right, here's the format we're going to use. I as moderator, I'm going to call on each analyst separately who then will deliver their prediction or mega trend, and then in the interest of time management and pace, two analysts will have the opportunity to comment. If we have more time, we'll elongate it, but let's get started right away. Sanjeev Mohan, please kick it off. You want to talk about governance, go ahead sir. >> Thank you Dave. I believe that data governance which we've been talking about for many years is now not only going to be mainstream, it's going to be table stakes. And all the things that you mentioned, you know, the data, ocean data lake, lake houses, data fabric, meshes, the common glue is metadata. If we don't understand what data we have and we are governing it, there is no way we can manage it. So we saw Informatica went public last year after a hiatus of six. I'm predicting that this year we see some more companies go public. My bet is on Culebra, most likely and maybe Alation we'll see go public this year. I'm also predicting that the scope of data governance is going to expand beyond just data. It's not just data and reports. We are going to see more transformations like spark jawsxxxxx, Python even Air Flow. We're going to see more of a streaming data. So from Kafka Schema Registry, for example. We will see AI models become part of this whole governance suite. So the governance suite is going to be very comprehensive, very detailed lineage, impact analysis, and then even expand into data quality. We already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management, data catalogs, also data access governance. So what we are going to see is that once the data governance platforms become the key entry point into these modern architectures, I'm predicting that the usage, the number of users of a data catalog is going to exceed that of a BI tool. That will take time and we already seen that trajectory. Right now if you look at BI tools, I would say there a hundred users to BI tool to one data catalog. And I see that evening out over a period of time and at some point data catalogs will really become the main way for us to access data. Data catalog will help us visualize data, but if we want to do more in-depth analysis, it'll be the jumping off point into the BI tool, the data science tool and that is the journey I see for the data governance products. >> Excellent, thank you. Some comments. Maybe Doug, a lot of things to weigh in on there, maybe you can comment. >> Yeah, Sanjeev I think you're spot on, a lot of the trends the one disagreement, I think it's really still far from mainstream. As you say, we've been talking about this for years, it's like God, motherhood, apple pie, everyone agrees it's important, but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking. I think one thing that deserves mention in this context is ESG mandates and guidelines, these are environmental, social and governance, regs and guidelines. We've seen the environmental regs and guidelines and posts in industries, particularly the carbon-intensive industries. We've seen the social mandates, particularly diversity imposed on suppliers by companies that are leading on this topic. We've seen governance guidelines now being imposed by banks on investors. So these ESGs are presenting new carrots and sticks, and it's going to demand more solid data. It's going to demand more detailed reporting and solid reporting, tighter governance. But we're still far from mainstream adoption. We have a lot of, you know, best of breed niche players in the space. I think the signs that it's going to be more mainstream are starting with things like Azure Purview, Google Dataplex, the big cloud platform players seem to be upping the ante and starting to address governance. >> Excellent, thank you Doug. Brad, I wonder if you could chime in as well. >> Yeah, I would love to be a believer in data catalogs. But to Doug's point, I think that it's going to take some more pressure for that to happen. I recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the nineties and that didn't happen quite the way we anticipated. And so to Sanjeev's point it's because it is really complex and really difficult to do. My hope is that, you know, we won't sort of, how do I put this? Fade out into this nebula of domain catalogs that are specific to individual use cases like Purview for getting data quality right or like data governance and cybersecurity. And instead we have some tooling that can actually be adaptive to gather metadata to create something. And I know its important to you, Sanjeev and that is this idea of observability. If you can get enough metadata without moving your data around, but understanding the entirety of a system that's running on this data, you can do a lot. So to help with the governance that Doug is talking about. >> So I just want to add that, data governance, like any other initiatives did not succeed even AI went into an AI window, but that's a different topic. But a lot of these things did not succeed because to your point, the incentives were not there. I remember when Sarbanes Oxley had come into the scene, if a bank did not do Sarbanes Oxley, they were very happy to a million dollar fine. That was like, you know, pocket change for them instead of doing the right thing. But I think the stakes are much higher now. With GDPR, the flood gates opened. Now, you know, California, you know, has CCPA but even CCPA is being outdated with CPRA, which is much more GDPR like. So we are very rapidly entering a space where pretty much every major country in the world is coming up with its own compliance regulatory requirements, data residents is becoming really important. And I think we are going to reach a stage where it won't be optional anymore. So whether we like it or not, and I think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption. We were focused on features and these features were disconnected, very hard for business to adopt. These are built by IT people for IT departments to take a look at technical metadata, not business metadata. Today the tables have turned. CDOs are driving this initiative, regulatory compliances are beating down hard, so I think the time might be right. >> Yeah so guys, we have to move on here. But there's some real meat on the bone here, Sanjeev. I like the fact that you called out Culebra and Alation, so we can look back a year from now and say, okay, he made the call, he stuck it. And then the ratio of BI tools to data catalogs that's another sort of measurement that we can take even though with some skepticism there, that's something that we can watch. And I wonder if someday, if we'll have more metadata than data. But I want to move to Tony Baer, you want to talk about data mesh and speaking, you know, coming off of governance. I mean, wow, you know the whole concept of data mesh is, decentralized data, and then governance becomes, you know, a nightmare there, but take it away, Tony. >> We'll put this way, data mesh, you know, the idea at least as proposed by ThoughtWorks. You know, basically it was at least a couple of years ago and the press has been almost uniformly almost uncritical. A good reason for that is for all the problems that basically Sanjeev and Doug and Brad we're just speaking about, which is that we have all this data out there and we don't know what to do about it. Now, that's not a new problem. That was a problem we had in enterprise data warehouses, it was a problem when we had over DoOP data clusters, it's even more of a problem now that data is out in the cloud where the data is not only your data lake, is not only us three, it's all over the place. And it's also including streaming, which I know we'll be talking about later. So the data mesh was a response to that, the idea of that we need to bait, you know, who are the folks that really know best about governance? It's the domain experts. So it was basically data mesh was an architectural pattern and a process. My prediction for this year is that data mesh is going to hit cold heart reality. Because if you do a Google search, basically the published work, the articles on data mesh have been largely, you know, pretty uncritical so far. Basically loading and is basically being a very revolutionary new idea. I don't think it's that revolutionary because we've talked about ideas like this. Brad now you and I met years ago when we were talking about so and decentralizing all of us, but it was at the application level. Now we're talking about it at the data level. And now we have microservices. So there's this thought of have we managed if we're deconstructing apps in cloud native to microservices, why don't we think of data in the same way? My sense this year is that, you know, this has been a very active search if you look at Google search trends, is that now companies, like enterprise are going to look at this seriously. And as they look at it seriously, it's going to attract its first real hard scrutiny, it's going to attract its first backlash. That's not necessarily a bad thing. It means that it's being taken seriously. The reason why I think that you'll start to see basically the cold hearted light of day shine on data mesh is that it's still a work in progress. You know, this idea is basically a couple of years old and there's still some pretty major gaps. The biggest gap is in the area of federated governance. Now federated governance itself is not a new issue. Federated governance decision, we started figuring out like, how can we basically strike the balance between getting let's say between basically consistent enterprise policy, consistent enterprise governance, but yet the groups that understand the data and know how to basically, you know, that, you know, how do we basically sort of balance the two? There's a huge gap there in practice and knowledge. Also to a lesser extent, there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data. You know, basically through the full life cycle, from develop, from selecting the data from, you know, building the pipelines from, you know, determining your access control, looking at quality, looking at basically whether the data is fresh or whether it's trending off course. So my prediction is that it will receive the first harsh scrutiny this year. You are going to see some organization and enterprises declare premature victory when they build some federated query implementations. You going to see vendors start with data mesh wash their products anybody in the data management space that they are going to say that where this basically a pipelining tool, whether it's basically ELT, whether it's a catalog or federated query tool, they will all going to get like, you know, basically promoting the fact of how they support this. Hopefully nobody's going to call themselves a data mesh tool because data mesh is not a technology. We're going to see one other thing come out of this. And this harks back to the metadata that Sanjeev was talking about and of the catalog just as he was talking about. Which is that there's going to be a new focus, every renewed focus on metadata. And I think that's going to spur interest in data fabrics. Now data fabrics are pretty vaguely defined, but if we just take the most elemental definition, which is a common metadata back plane, I think that if anybody is going to get serious about data mesh, they need to look at the data fabric because we all at the end of the day, need to speak, you know, need to read from the same sheet of music. >> So thank you Tony. Dave Menninger, I mean, one of the things that people like about data mesh is it pretty crisply articulate some of the flaws in today's organizational approaches to data. What are your thoughts on this? >> Well, I think we have to start by defining data mesh, right? The term is already getting corrupted, right? Tony said it's going to see the cold hard light of day. And there's a problem right now that there are a number of overlapping terms that are similar but not identical. So we've got data virtualization, data fabric, excuse me for a second. (clears throat) Sorry about that. Data virtualization, data fabric, data federation, right? So I think that it's not really clear what each vendor means by these terms. I see data mesh and data fabric becoming quite popular. I've interpreted data mesh as referring primarily to the governance aspects as originally intended and specified. But that's not the way I see vendors using it. I see vendors using it much more to mean data fabric and data virtualization. So I'm going to comment on the group of those things. I think the group of those things is going to happen. They're going to happen, they're going to become more robust. Our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half, so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access. Again, whether you define it as mesh or fabric or virtualization isn't really the point here. But this notion that there are different elements of data, metadata and governance within an organization that all need to be managed collectively. The interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not, it's almost double, 68% of organizations, I'm sorry, 79% of organizations that were using virtualized access express satisfaction with their access to the data lake. Only 39% express satisfaction if they weren't using virtualized access. >> Oh thank you Dave. Sanjeev we just got about a couple of minutes on this topic, but I know you're speaking or maybe you've always spoken already on a panel with (indistinct) who sort of invented the concept. Governance obviously is a big sticking point, but what are your thoughts on this? You're on mute. (panelist chuckling) >> So my message to (indistinct) and to the community is as opposed to what they said, let's not define it. We spent a whole year defining it, there are four principles, domain, product, data infrastructure, and governance. Let's take it to the next level. I get a lot of questions on what is the difference between data fabric and data mesh? And I'm like I can't compare the two because data mesh is a business concept, data fabric is a data integration pattern. How do you compare the two? You have to bring data mesh a level down. So to Tony's point, I'm on a warpath in 2022 to take it down to what does a data product look like? How do we handle shared data across domains and governance? And I think we are going to see more of that in 2022, or is "operationalization" of data mesh. >> I think we could have a whole hour on this topic, couldn't we? Maybe we should do that. But let's corner. Let's move to Carl. So Carl, you're a database guy, you've been around that block for a while now, you want to talk about graph databases, bring it on. >> Oh yeah. Okay thanks. So I regard graph database as basically the next truly revolutionary database management technology. I'm looking forward for the graph database market, which of course we haven't defined yet. So obviously I have a little wiggle room in what I'm about to say. But this market will grow by about 600% over the next 10 years. Now, 10 years is a long time. But over the next five years, we expect to see gradual growth as people start to learn how to use it. The problem is not that it's not useful, its that people don't know how to use it. So let me explain before I go any further what a graph database is because some of the folks on the call may not know what it is. A graph database organizes data according to a mathematical structure called a graph. The graph has elements called nodes and edges. So a data element drops into a node, the nodes are connected by edges, the edges connect one node to another node. Combinations of edges create structures that you can analyze to determine how things are related. In some cases, the nodes and edges can have properties attached to them which add additional informative material that makes it richer, that's called a property graph. There are two principle use cases for graph databases. There's semantic property graphs, which are use to break down human language texts into the semantic structures. Then you can search it, organize it and answer complicated questions. A lot of AI is aimed at semantic graphs. Another kind is the property graph that I just mentioned, which has a dazzling number of use cases. I want to just point out as I talk about this, people are probably wondering, well, we have relation databases, isn't that good enough? So a relational database defines... It supports what I call definitional relationships. That means you define the relationships in a fixed structure. The database drops into that structure, there's a value, foreign key value, that relates one table to another and that value is fixed. You don't change it. If you change it, the database becomes unstable, it's not clear what you're looking at. In a graph database, the system is designed to handle change so that it can reflect the true state of the things that it's being used to track. So let me just give you some examples of use cases for this. They include entity resolution, data lineage, social media analysis, Customer 360, fraud prevention. There's cybersecurity, there's strong supply chain is a big one actually. There is explainable AI and this is going to become important too because a lot of people are adopting AI. But they want a system after the fact to say, how do the AI system come to that conclusion? How did it make that recommendation? Right now we don't have really good ways of tracking that. Machine learning in general, social network, I already mentioned that. And then we've got, oh gosh, we've got data governance, data compliance, risk management. We've got recommendation, we've got personalization, anti money laundering, that's another big one, identity and access management, network and IT operations is already becoming a key one where you actually have mapped out your operation, you know, whatever it is, your data center and you can track what's going on as things happen there, root cause analysis, fraud detection is a huge one. A number of major credit card companies use graph databases for fraud detection, risk analysis, tracking and tracing turn analysis, next best action, what if analysis, impact analysis, entity resolution and I would add one other thing or just a few other things to this list, metadata management. So Sanjeev, here you go, this is your engine. Because I was in metadata management for quite a while in my past life. And one of the things I found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it, but graphs can, okay? Graphs can do things like say, this term in this context means this, but in that context, it means that, okay? Things like that. And in fact, logistics management, supply chain. And also because it handles recursive relationships, by recursive relationships I mean objects that own other objects that are of the same type. You can do things like build materials, you know, so like parts explosion. Or you can do an HR analysis, who reports to whom, how many levels up the chain and that kind of thing. You can do that with relational databases, but yet it takes a lot of programming. In fact, you can do almost any of these things with relational databases, but the problem is, you have to program it. It's not supported in the database. And whenever you have to program something, that means you can't trace it, you can't define it. You can't publish it in terms of its functionality and it's really, really hard to maintain over time. >> Carl, thank you. I wonder if we could bring Brad in, I mean. Brad, I'm sitting here wondering, okay, is this incremental to the market? Is it disruptive and replacement? What are your thoughts on this phase? >> It's already disrupted the market. I mean, like Carl said, go to any bank and ask them are you using graph databases to get fraud detection under control? And they'll say, absolutely, that's the only way to solve this problem. And it is frankly. And it's the only way to solve a lot of the problems that Carl mentioned. And that is, I think it's Achilles heel in some ways. Because, you know, it's like finding the best way to cross the seven bridges of Koenigsberg. You know, it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique, it's still unfortunately kind of stands apart from the rest of the community that's building, let's say AI outcomes, as a great example here. Graph databases and AI, as Carl mentioned, are like chocolate and peanut butter. But technologically, you think don't know how to talk to one another, they're completely different. And you know, you can't just stand up SQL and query them. You've got to learn, know what is the Carl? Specter special. Yeah, thank you to, to actually get to the data in there. And if you're going to scale that data, that graph database, especially a property graph, if you're going to do something really complex, like try to understand you know, all of the metadata in your organization, you might just end up with, you know, a graph database winter like we had the AI winter simply because you run out of performance to make the thing happen. So, I think it's already disrupted, but we need to like treat it like a first-class citizen in the data analytics and AI community. We need to bring it into the fold. We need to equip it with the tools it needs to do the magic it does and to do it not just for specialized use cases, but for everything. 'Cause I'm with Carl. I think it's absolutely revolutionary. >> Brad identified the principal, Achilles' heel of the technology which is scaling. When these things get large and complex enough that they spill over what a single server can handle, you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down. So that's still a problem to be solved. >> Sanjeev, any quick thoughts on this? I mean, I think metadata on the word cloud is going to be the largest font, but what are your thoughts here? >> I want to (indistinct) So people don't associate me with only metadata, so I want to talk about something slightly different. dbengines.com has done an amazing job. I think almost everyone knows that they chronicle all the major databases that are in use today. In January of 2022, there are 381 databases on a ranked list of databases. The largest category is RDBMS. The second largest category is actually divided into two property graphs and IDF graphs. These two together make up the second largest number databases. So talking about Achilles heel, this is a problem. The problem is that there's so many graph databases to choose from. They come in different shapes and forms. To Brad's point, there's so many query languages in RDBMS, in SQL. I know the story, but here We've got cipher, we've got gremlin, we've got GQL and then we're proprietary languages. So I think there's a lot of disparity in this space. >> Well, excellent. All excellent points, Sanjeev, if I must say. And that is a problem that the languages need to be sorted and standardized. People need to have a roadmap as to what they can do with it. Because as you say, you can do so many things. And so many of those things are unrelated that you sort of say, well, what do we use this for? And I'm reminded of the saying I learned a bunch of years ago. And somebody said that the digital computer is the only tool man has ever device that has no particular purpose. (panelists chuckle) >> All right guys, we got to move on to Dave Menninger. We've heard about streaming. Your prediction is in that realm, so please take it away. >> Sure. So I like to say that historical databases are going to become a thing of the past. By that I don't mean that they're going to go away, that's not my point. I mean, we need historical databases, but streaming data is going to become the default way in which we operate with data. So in the next say three to five years, I would expect that data platforms and we're using the term data platforms to represent the evolution of databases and data lakes, that the data platforms will incorporate these streaming capabilities. We're going to process data as it streams into an organization and then it's going to roll off into historical database. So historical databases don't go away, but they become a thing of the past. They store the data that occurred previously. And as data is occurring, we're going to be processing it, we're going to be analyzing it, we're going to be acting on it. I mean we only ever ended up with historical databases because we were limited by the technology that was available to us. Data doesn't occur in patches. But we processed it in patches because that was the best we could do. And it wasn't bad and we've continued to improve and we've improved and we've improved. But streaming data today is still the exception. It's not the rule, right? There are projects within organizations that deal with streaming data. But it's not the default way in which we deal with data yet. And so that's my prediction is that this is going to change, we're going to have streaming data be the default way in which we deal with data and how you label it and what you call it. You know, maybe these databases and data platforms just evolved to be able to handle it. But we're going to deal with data in a different way. And our research shows that already, about half of the participants in our analytics and data benchmark research, are using streaming data. You know, another third are planning to use streaming technologies. So that gets us to about eight out of 10 organizations need to use this technology. And that doesn't mean they have to use it throughout the whole organization, but it's pretty widespread in its use today and has continued to grow. If you think about the consumerization of IT, we've all been conditioned to expect immediate access to information, immediate responsiveness. You know, we want to know if an item is on the shelf at our local retail store and we can go in and pick it up right now. You know, that's the world we live in and that's spilling over into the enterprise IT world We have to provide those same types of capabilities. So that's my prediction, historical databases become a thing of the past, streaming data becomes the default way in which we operate with data. >> All right thank you David. Well, so what say you, Carl, the guy who has followed historical databases for a long time? >> Well, one thing actually, every database is historical because as soon as you put data in it, it's now history. They'll no longer reflect the present state of things. But even if that history is only a millisecond old, it's still history. But I would say, I mean, I know you're trying to be a little bit provocative in saying this Dave 'cause you know, as well as I do that people still need to do their taxes, they still need to do accounting, they still need to run general ledger programs and things like that. That all involves historical data. That's not going to go away unless you want to go to jail. So you're going to have to deal with that. But as far as the leading edge functionality, I'm totally with you on that. And I'm just, you know, I'm just kind of wondering if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way applications work. Saying that an application should respond instantly, as soon as the state of things changes. What do you say about that? >> I think that's true. I think we do have to think about things differently. It's not the way we designed systems in the past. We're seeing more and more systems designed that way. But again, it's not the default. And I agree 100% with you that we do need historical databases you know, that's clear. And even some of those historical databases will be used in conjunction with the streaming data, right? >> Absolutely. I mean, you know, let's take the data warehouse example where you're using the data warehouse as its context and the streaming data as the present and you're saying, here's the sequence of things that's happening right now. Have we seen that sequence before? And where? What does that pattern look like in past situations? And can we learn from that? >> So Tony Baer, I wonder if you could comment? I mean, when you think about, you know, real time inferencing at the edge, for instance, which is something that a lot of people talk about, a lot of what we're discussing here in this segment, it looks like it's got a great potential. What are your thoughts? >> Yeah, I mean, I think you nailed it right. You know, you hit it right on the head there. Which is that, what I'm seeing is that essentially. Then based on I'm going to split this one down the middle is that I don't see that basically streaming is the default. What I see is streaming and basically and transaction databases and analytics data, you know, data warehouses, data lakes whatever are converging. And what allows us technically to converge is cloud native architecture, where you can basically distribute things. So you can have a node here that's doing the real-time processing, that's also doing... And this is where it leads in or maybe doing some of that real time predictive analytics to take a look at, well look, we're looking at this customer journey what's happening with what the customer is doing right now and this is correlated with what other customers are doing. So the thing is that in the cloud, you can basically partition this and because of basically the speed of the infrastructure then you can basically bring these together and kind of orchestrate them sort of a loosely coupled manner. The other parts that the use cases are demanding, and this is part of it goes back to what Dave is saying. Is that, you know, when you look at Customer 360, when you look at let's say Smart Utility products, when you look at any type of operational problem, it has a real time component and it has an historical component. And having predictive and so like, you know, my sense here is that technically we can bring this together through the cloud. And I think the use case is that we can apply some real time sort of predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction, we have this real-time input. >> Sanjeev, did you have a comment? >> Yeah, I was just going to say that to Dave's point, you know, we have to think of streaming very different because in the historical databases, we used to bring the data and store the data and then we used to run rules on top, aggregations and all. But in case of streaming, the mindset changes because the rules are normally the inference, all of that is fixed, but the data is constantly changing. So it's a completely reversed way of thinking and building applications on top of that. >> So Dave Menninger, there seem to be some disagreement about the default. What kind of timeframe are you thinking about? Is this end of decade it becomes the default? What would you pin? >> I think around, you know, between five to 10 years, I think this becomes the reality. >> I think its... >> It'll be more and more common between now and then, but it becomes the default. And I also want Sanjeev at some point, maybe in one of our subsequent conversations, we need to talk about governing streaming data. 'Cause that's a whole nother set of challenges. >> We've also talked about it rather in two dimensions, historical and streaming, and there's lots of low latency, micro batch, sub-second, that's not quite streaming, but in many cases its fast enough and we're seeing a lot of adoption of near real time, not quite real-time as good enough for many applications. (indistinct cross talk from panelists) >> Because nobody's really taking the hardware dimension (mumbles). >> That'll just happened, Carl. (panelists laughing) >> So near real time. But maybe before you lose the customer, however we define that, right? Okay, let's move on to Brad. Brad, you want to talk about automation, AI, the pipeline people feel like, hey, we can just automate everything. What's your prediction? >> Yeah I'm an AI aficionados so apologies in advance for that. But, you know, I think that we've been seeing automation play within AI for some time now. And it's helped us do a lot of things especially for practitioners that are building AI outcomes in the enterprise. It's helped them to fill skills gaps, it's helped them to speed development and it's helped them to actually make AI better. 'Cause it, you know, in some ways provide some swim lanes and for example, with technologies like AutoML can auto document and create that sort of transparency that we talked about a little bit earlier. But I think there's an interesting kind of conversion happening with this idea of automation. And that is that we've had the automation that started happening for practitioners, it's trying to move out side of the traditional bounds of things like I'm just trying to get my features, I'm just trying to pick the right algorithm, I'm just trying to build the right model and it's expanding across that full life cycle, building an AI outcome, to start at the very beginning of data and to then continue on to the end, which is this continuous delivery and continuous automation of that outcome to make sure it's right and it hasn't drifted and stuff like that. And because of that, because it's become kind of powerful, we're starting to actually see this weird thing happen where the practitioners are starting to converge with the users. And that is to say that, okay, if I'm in Tableau right now, I can stand up Salesforce Einstein Discovery, and it will automatically create a nice predictive algorithm for me given the data that I pull in. But what's starting to happen and we're seeing this from the companies that create business software, so Salesforce, Oracle, SAP, and others is that they're starting to actually use these same ideals and a lot of deep learning (chuckles) to basically stand up these out of the box flip-a-switch, and you've got an AI outcome at the ready for business users. And I am very much, you know, I think that's the way that it's going to go and what it means is that AI is slowly disappearing. And I don't think that's a bad thing. I think if anything, what we're going to see in 2022 and maybe into 2023 is this sort of rush to put this idea of disappearing AI into practice and have as many of these solutions in the enterprise as possible. You can see, like for example, SAP is going to roll out this quarter, this thing called adaptive recommendation services, which basically is a cold start AI outcome that can work across a whole bunch of different vertical markets and use cases. It's just a recommendation engine for whatever you needed to do in the line of business. So basically, you're an SAP user, you look up to turn on your software one day, you're a sales professional let's say, and suddenly you have a recommendation for customer churn. Boom! It's going, that's great. Well, I don't know, I think that's terrifying. In some ways I think it is the future that AI is going to disappear like that, but I'm absolutely terrified of it because I think that what it really does is it calls attention to a lot of the issues that we already see around AI, specific to this idea of what we like to call at Omdia, responsible AI. Which is, you know, how do you build an AI outcome that is free of bias, that is inclusive, that is fair, that is safe, that is secure, that its audible, et cetera, et cetera, et cetera, et cetera. I'd take a lot of work to do. And so if you imagine a customer that's just a Salesforce customer let's say, and they're turning on Einstein Discovery within their sales software, you need some guidance to make sure that when you flip that switch, that the outcome you're going to get is correct. And that's going to take some work. And so, I think we're going to see this move, let's roll this out and suddenly there's going to be a lot of problems, a lot of pushback that we're going to see. And some of that's going to come from GDPR and others that Sanjeev was mentioning earlier. A lot of it is going to come from internal CSR requirements within companies that are saying, "Hey, hey, whoa, hold up, we can't do this all at once. "Let's take the slow route, "let's make AI automated in a smart way." And that's going to take time. >> Yeah, so a couple of predictions there that I heard. AI simply disappear, it becomes invisible. Maybe if I can restate that. And then if I understand it correctly, Brad you're saying there's a backlash in the near term. You'd be able to say, oh, slow down. Let's automate what we can. Those attributes that you talked about are non trivial to achieve, is that why you're a bit of a skeptic? >> Yeah. I think that we don't have any sort of standards that companies can look to and understand. And we certainly, within these companies, especially those that haven't already stood up an internal data science team, they don't have the knowledge to understand when they flip that switch for an automated AI outcome that it's going to do what they think it's going to do. And so we need some sort of standard methodology and practice, best practices that every company that's going to consume this invisible AI can make use of them. And one of the things that you know, is sort of started that Google kicked off a few years back that's picking up some momentum and the companies I just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing. You know, so like for the SAP example, we know, for example, if it's convolutional neural network with a long, short term memory model that it's using, we know that it only works on Roman English and therefore me as a consumer can say, "Oh, well I know that I need to do this internationally. "So I should not just turn this on today." >> Thank you. Carl could you add anything, any context here? >> Yeah, we've talked about some of the things Brad mentioned here at IDC and our future of intelligence group regarding in particular, the moral and legal implications of having a fully automated, you know, AI driven system. Because we already know, and we've seen that AI systems are biased by the data that they get, right? So if they get data that pushes them in a certain direction, I think there was a story last week about an HR system that was recommending promotions for White people over Black people, because in the past, you know, White people were promoted and more productive than Black people, but it had no context as to why which is, you know, because they were being historically discriminated, Black people were being historically discriminated against, but the system doesn't know that. So, you know, you have to be aware of that. And I think that at the very least, there should be controls when a decision has either a moral or legal implication. When you really need a human judgment, it could lay out the options for you. But a person actually needs to authorize that action. And I also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases. In some extent, they always will. So we'll always be chasing after them. But that's (indistinct). >> Absolutely Carl, yeah. I think that what you have to bear in mind as a consumer of AI is that it is a reflection of us and we are a very flawed species. And so if you look at all of the really fantastic, magical looking supermodels we see like GPT-3 and four, that's coming out, they're xenophobic and hateful because the people that the data that's built upon them and the algorithms and the people that build them are us. So AI is a reflection of us. We need to keep that in mind. >> Yeah, where the AI is biased 'cause humans are biased. All right, great. All right let's move on. Doug you mentioned mentioned, you know, lot of people that said that data lake, that term is not going to live on but here's to be, have some lakes here. You want to talk about lake house, bring it on. >> Yes, I do. My prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering. I say offering that doesn't mean it's going to be the dominant thing that organizations have out there, but it's going to be the pro dominant vendor offering in 2022. Now heading into 2021, we already had Cloudera, Databricks, Microsoft, Snowflake as proponents, in 2021, SAP, Oracle, and several of all of these fabric virtualization/mesh vendors joined the bandwagon. The promise is that you have one platform that manages your structured, unstructured and semi-structured information. And it addresses both the BI analytics needs and the data science needs. The real promise there is simplicity and lower cost. But I think end users have to answer a few questions. The first is, does your organization really have a center of data gravity or is the data highly distributed? Multiple data warehouses, multiple data lakes, on premises, cloud. If it's very distributed and you'd have difficulty consolidating and that's not really a goal for you, then maybe that single platform is unrealistic and not likely to add value to you. You know, also the fabric and virtualization vendors, the mesh idea, that's where if you have this highly distributed situation, that might be a better path forward. The second question, if you are looking at one of these lake house offerings, you are looking at consolidating, simplifying, bringing together to a single platform. You have to make sure that it meets both the warehouse need and the data lake need. So you have vendors like Databricks, Microsoft with Azure Synapse. New really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements, can meet the user and query concurrency requirements. Meet those tight SLS. And then on the other hand, you have the Oracle, SAP, Snowflake, the data warehouse folks coming into the data science world, and they have to prove that they can manage the unstructured information and meet the needs of the data scientists. I'm seeing a lot of the lake house offerings from the warehouse crowd, managing that unstructured information in columns and rows. And some of these vendors, Snowflake a particular is really relying on partners for the data science needs. So you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement. >> Thank you Doug. Well Tony, if those two worlds are going to come together, as Doug was saying, the analytics and the data science world, does it need to be some kind of semantic layer in between? I don't know. Where are you in on this topic? >> (chuckles) Oh, didn't we talk about data fabrics before? Common metadata layer (chuckles). Actually, I'm almost tempted to say let's declare victory and go home. And that this has actually been going on for a while. I actually agree with, you know, much of what Doug is saying there. Which is that, I mean I remember as far back as I think it was like 2014, I was doing a study. I was still at Ovum, (indistinct) Omdia, looking at all these specialized databases that were coming up and seeing that, you know, there's overlap at the edges. But yet, there was still going to be a reason at the time that you would have, let's say a document database for JSON, you'd have a relational database for transactions and for data warehouse and you had basically something at that time that resembles a dupe for what we consider your data life. Fast forward and the thing is what I was seeing at the time is that you were saying they sort of blending at the edges. That was saying like about five to six years ago. And the lake house is essentially on the current manifestation of that idea. There is a dichotomy in terms of, you know, it's the old argument, do we centralize this all you know in a single place or do we virtualize? And I think it's always going to be a union yeah and there's never going to be a single silver bullet. I do see that there are also going to be questions and these are points that Doug raised. That you know, what do you need for your performance there, or for your free performance characteristics? Do you need for instance high concurrency? You need the ability to do some very sophisticated joins, or is your requirement more to be able to distribute and distribute our processing is, you know, as far as possible to get, you know, to essentially do a kind of a brute force approach. All these approaches are valid based on the use case. I just see that essentially that the lake house is the culmination of it's nothing. It's a relatively new term introduced by Databricks a couple of years ago. This is the culmination of basically what's been a long time trend. And what we see in the cloud is that as we start seeing data warehouses as a check box items say, "Hey, we can basically source data in cloud storage, in S3, "Azure Blob Store, you know, whatever, "as long as it's in certain formats, "like, you know parquet or CSP or something like that." I see that as becoming kind of a checkbox item. So to that extent, I think that the lake house, depending on how you define is already reality. And in some cases, maybe new terminology, but not a whole heck of a lot new under the sun. >> Yeah. And Dave Menninger, I mean a lot of these, thank you Tony, but a lot of this is going to come down to, you know, vendor marketing, right? Some people just kind of co-op the term, we talked about you know, data mesh washing, what are your thoughts on this? (laughing) >> Yeah, so I used the term data platform earlier. And part of the reason I use that term is that it's more vendor neutral. We've tried to sort of stay out of the vendor terminology patenting world, right? Whether the term lake houses, what sticks or not, the concept is certainly going to stick. And we have some data to back it up. About a quarter of organizations that are using data lakes today, already incorporate data warehouse functionality into it. So they consider their data lake house and data warehouse one in the same, about a quarter of organizations, a little less, but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake. So it's pretty obvious that three quarters of organizations need to bring this stuff together, right? The need is there, the need is apparent. The technology is going to continue to converge. I like to talk about it, you know, you've got data lakes over here at one end, and I'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a server and you ignore it, right? That's not what a data lake is. So you've got data lake people over here and you've got database people over here, data warehouse people over here, database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities. So it's obvious that they're going to meet in the middle. I mean, I think it's like Tony says, I think we should declare victory and go home. >> As hell. So just a follow-up on that, so are you saying the specialized lake and the specialized warehouse, do they go away? I mean, Tony data mesh practitioners would say or advocates would say, well, they could all live. It's just a node on the mesh. But based on what Dave just said, are we gona see those all morphed together? >> Well, number one, as I was saying before, there's always going to be this sort of, you know, centrifugal force or this tug of war between do we centralize the data, do we virtualize? And the fact is I don't think that there's ever going to be any single answer. I think in terms of data mesh, data mesh has nothing to do with how you're physically implement the data. You could have a data mesh basically on a data warehouse. It's just that, you know, the difference being is that if we use the same physical data store, but everybody's logically you know, basically governing it differently, you know? Data mesh in space, it's not a technology, it's processes, it's governance process. So essentially, you know, I basically see that, you know, as I was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring, but there are going to be cases where, for instance, if I need, let's say like, Upserve, I need like high concurrency or something like that. There are certain things that I'm not going to be able to get efficiently get out of a data lake. And, you know, I'm doing a system where I'm just doing really brute forcing very fast file scanning and that type of thing. So I think there always will be some delineations, but I would agree with Dave and with Doug, that we are seeing basically a confluence of requirements that we need to essentially have basically either the element, you know, the ability of a data lake and the data warehouse, these need to come together, so I think. >> I think what we're likely to see is organizations look for a converge platform that can handle both sides for their center of data gravity, the mesh and the fabric virtualization vendors, they're all on board with the idea of this converged platform and they're saying, "Hey, we'll handle all the edge cases "of the stuff that isn't in that center of data gravity "but that is off distributed in a cloud "or at a remote location." So you can have that single platform for the center of your data and then bring in virtualization, mesh, what have you, for reaching out to the distributed data. >> As Dave basically said, people are happy when they virtualized data. >> I think we have at this point, but to Dave Menninger's point, they are converging, Snowflake has introduced support for unstructured data. So obviously literally splitting here. Now what Databricks is saying is that "aha, but it's easy to go from data lake to data warehouse "than it is from databases to data lake." So I think we're getting into semantics, but we're already seeing these two converge. >> So take somebody like AWS has got what? 15 data stores. Are they're going to 15 converge data stores? This is going to be interesting to watch. All right, guys, I'm going to go down and list do like a one, I'm going to one word each and you guys, each of the analyst, if you would just add a very brief sort of course correction for me. So Sanjeev, I mean, governance is going to to be... Maybe it's the dog that wags the tail now. I mean, it's coming to the fore, all this ransomware stuff, which you really didn't talk much about security, but what's the one word in your prediction that you would leave us with on governance? >> It's going to be mainstream. >> Mainstream. Okay. Tony Baer, mesh washing is what I wrote down. That's what we're going to see in 2022, a little reality check, you want to add to that? >> Reality check, 'cause I hope that no vendor jumps the shark and close they're offering a data niche product. >> Yeah, let's hope that doesn't happen. If they do, we're going to call them out. Carl, I mean, graph databases, thank you for sharing some high growth metrics. I know it's early days, but magic is what I took away from that, so magic database. >> Yeah, I would actually, I've said this to people too. I kind of look at it as a Swiss Army knife of data because you can pretty much do anything you want with it. That doesn't mean you should. I mean, there's definitely the case that if you're managing things that are in fixed schematic relationship, probably a relation database is a better choice. There are times when the document database is a better choice. It can handle those things, but maybe not. It may not be the best choice for that use case. But for a great many, especially with the new emerging use cases I listed, it's the best choice. >> Thank you. And Dave Menninger, thank you by the way, for bringing the data in, I like how you supported all your comments with some data points. But streaming data becomes the sort of default paradigm, if you will, what would you add? >> Yeah, I would say think fast, right? That's the world we live in, you got to think fast. >> Think fast, love it. And Brad Shimmin, love it. I mean, on the one hand I was saying, okay, great. I'm afraid I might get disrupted by one of these internet giants who are AI experts. I'm going to be able to buy instead of build AI. But then again, you know, I've got some real issues. There's a potential backlash there. So give us your bumper sticker. >> I'm would say, going with Dave, think fast and also think slow to talk about the book that everyone talks about. I would say really that this is all about trust, trust in the idea of automation and a transparent and visible AI across the enterprise. And verify, verify before you do anything. >> And then Doug Henschen, I mean, I think the trend is your friend here on this prediction with lake house is really becoming dominant. I liked the way you set up that notion of, you know, the data warehouse folks coming at it from the analytics perspective and then you get the data science worlds coming together. I still feel as though there's this piece in the middle that we're missing, but your, your final thoughts will give you the (indistinct). >> I think the idea of consolidation and simplification always prevails. That's why the appeal of a single platform is going to be there. We've already seen that with, you know, DoOP platforms and moving toward cloud, moving toward object storage and object storage, becoming really the common storage point for whether it's a lake or a warehouse. And that second point, I think ESG mandates are going to come in alongside GDPR and things like that to up the ante for good governance. >> Yeah, thank you for calling that out. Okay folks, hey that's all the time that we have here, your experience and depth of understanding on these key issues on data and data management really on point and they were on display today. I want to thank you for your contributions. Really appreciate your time. >> Enjoyed it. >> Thank you. >> Thanks for having me. >> In addition to this video, we're going to be making available transcripts of the discussion. We're going to do clips of this as well we're going to put them out on social media. I'll write this up and publish the discussion on wikibon.com and siliconangle.com. No doubt, several of the analysts on the panel will take the opportunity to publish written content, social commentary or both. I want to thank the power panelists and thanks for watching this special CUBE presentation. This is Dave Vellante, be well and we'll see you next time. (bright music)

hey welcome back to los angeles thecube is live here at kubecon cloud native con 2021 we're so excited to be here in person lisa martin with dave nicholson and we are here with priyanka sharma the executive director of cnc at prayanka welcome to the program thank you so much for having me first of all congratulations on doing an event in person in such a safe clean way i was really impressed when i walked in this morning was asked for my vaccination record my temperature was scanned you're proving you can do these events safely this isn't rocket science so agreed and i'm so glad you appreciate all the measures we've put in place because this is how we can do it in-person interaction is essential for us as human beings for us as professionals and so we owe it to each other to just do the right thing you know have a vaccine requirement wear your masks have these what i call the traffic light uh system where if you have a green a green band it means people can come a little closer it's okay red means please at least six feet of distance and these things go a long way in making an event successful in times like this they do i love that when i saw that mine keeps falling off i'm cold so it keeps falling i'm green just so you know i know you're green what about you i'm green here you can can i have yours that's my favorite and you know you fell off again you had um the three folks that came up who were uh like uh co-chairs co-chairs yeah yeah and uh and they did almost a little almost a little skit yes that on the surface people could say well that's ridiculous and it's like no it's not it's giving everybody the guidelines so that everyone can be comfortable because when i see your green wristband i understand that you are comfortable because i don't want to accidentally reach out to give you a fist bump when you might be particularly of course yeah yeah so no visual cues make it easy yes yeah yeah very very easy very comfortable talk about the energy at the event this is the second full day tina was standing room only yesterday give us an overview of the energy and some of the things that are happening since you can't replicate those hallway networking conversations on video conference i know exactly what you mean man it is so lovely to be in person to meet people and you know for those who are comfortable there's like the fist bumps and the hugs and the big smiles and that energy i haven't seen it in almost two years um and even you know just standing on stage as i was telling you folks uh off camera i've been in this role for over a year and a close to a year and a half i've done three cube cons already but this was my first in person and being on that stage experiencing the energy of the people in that room like when i asked everyone during my keynote i was like are you all proud to be team cloud native and i got a resounding yes back from the audience that's what i'm talking about yeah you know it was amazing what's some of the news that's breaking lots of stuff going on obviously some first one in person in almost two years but talk to me about some of the the news that's breaking here at the event yes so so much new stuff to share um from our side on cncf our journey has been very much about being celebrating our culture and welcoming more and more people into it so that we can have more folks in team cloud native to take various jobs to find fulfillment and all those great things right and all of our announcements are around that theme of people finding a place here people paying it forward in this community and building the culture the first one i would like to share is the announcement of the kubernetes and cloud native associate certification so this is an exam that is going to go live end of the year so people sign up apparently the beta signups went away like this after i announced it so it was really cool wow popular by demanding yeah very very popular and it's it's an exam for folks who are brand new to cloud native and it the studying for it you'll go through you know the fun fundamentals of kubernetes what is the cncf landscape what are the key projects and ultimately you will actually deploy an application using coop cuddle commands and it's such a great primer so so how brand new can someone be when you when you say brand new are you talking about someone who already has a phd in computer science but hasn't done anything in the kubernetes space tell me how brand new can you be uh-huh that's a very good question and it is literally you can come with zero knowledge you would of course have to study for the exam and like go through that journey but the idea is that it is the gateway and so it is possible you're a phd in computer science but you've studied some esoteric part of computer science that's very unconnected to what we do sure go ahead and take it but maybe most likely you would like the more advanced certifications better but if you're let's say a marketer looking to break into the cloud native industry this is the move take this exam and suddenly all these employers you speak their language they'll be impressed that you took it and it's it's an opportunity to advance your career the oh community is huge i was looking at the website the other day 138 000 contributors yes from more than 177 countries 186 is the latest number 186 awesome 289 plus million lines of code written this community is really so productive and so prolific and it's great that you're offering more folks that don't have the background like you were saying to be able to get in and get started absolutely it's our whole thing of bring in more people because as you all probably know there's so much demand for cloud native skill sets across job functions so that's why we're here to help with yeah i you know i i want to double click on this as we say because you hear the word inclusive associated with this whole community so much um you're talking about something that is a certification yeah a marketer okay fine but we're really talking about anyone who has the drive to potentially completely transform their lives yes and in this age where things can be done remotely you don't necessarily have to live in silicon valley or cambridge massachusetts to do this or in one of the other global centers of technology anywhere yeah so that's the that's the kind of energy that's part of this that isn't a part of any large industry focused conference because you really are making opportunities for people of all backgrounds to change their lives so i don't know i don't am i extending a a virtual thank you from all of those people whose lives have been changed and will be changed in the future maybe i am but so but talk about inclusiveness in in you know from from other perspectives yes i think that you know talent drive skills none of these are exclusive to a certain zip code you know people everywhere have great qualities and deserve chances and why shouldn't they be part of a community that as you said is especially inclusive feels especially nice to be a part of and that's what i exhorted the community to do in my keynote yesterday which is that our ranks will grow and we should go out of our way to make sure our ranks grow and we do that by shining a light on our culture telling people to join in lending a hand and you know letting people's personalities shine even when they'll be different from who we are whether in terms of job function or skill set whatever and i think that's the top level um paradigm that we want to have right where we are always welcoming people when we think of inclusiveness it is you know there is certifications like kcna did do a great job there are also efforts that we must always be doing so something that we work on constantly consistently is contributor strategy where we're working on creating ladders and pathways for folks to become open source contributors it is known now that open source contributions lead to job advancement in your career right and so the whole goal is bring people in not just to hang out not just to talk but to actually grow and actually kubecon cloudnativecon is a great example of another little thing we do which is uh we uh award uh underrepresented minorities and people who are who need need-based funds scholarships to attend nice yeah and it's changed one thousand 1518 lives already and we actually uh in uh in this event have announced that we are renaming the scholarship to the dan khan scholarship fund um i i do you folks know dan yes did yeah so dan he breathed life into team cloud native right he built this organization to have the impact that it does today and all the while he was relentlessly focused on diversity equity inclusion so it was it was just like the idea came from within the team and the minute someone said it it just struck a chord with all of us yeah we're like we're doing this no question and it was one of the fastest decisions we've ever made i saw uh some results of a dei micro survey on the website where 75 percent of respondents say this community is becoming more inclusive there's obviously work to go but as a female in technology you feel that you see that as well yes i think i'm very proud of that survey that we did by the way because it's our way we're going to keep doing it it's our way to keep a pulse on the ecosystem because you can keep doing initiatives right but if people are not feeling great then who cares and so um but yes i think dei is a journey if there is no destination right always we have to be thinking harder trying harder to you know i think for example something cncf's done a great job is identifying particularly gender diverse folks who are in the community and maybe could deserve a role of high responsibility so i'm really proud that our technical oversight committee which is our really the top technical people in the ecosystem who desi decide project stuff they are led by a woman there's many women on that and it's they're all very exemplary awesome technologists and so i think um the diversity survey gives us like a hint into like the things people do like and i mean the fact remains we need to do more to source more people to come into the ecosystem we need to always be changing and evolving with the needs of the community right as i mentioned the community is 138 000 strong 6.8 million plus contributions so far you can imagine by opening that dei door just the thought diversity that comes in alone and the number of projects that will come from folks that just come in with a different mindset oh 100 we are already seeing that um we started off as folks who had you know lots of projects from the great big tech companies people who had web scale problems as i call it and that was great but in recent years the end users who are initially just consuming this technology and that too slowly are now hook line and sinker in and we have like argo cd came from intuit which is an end user uh backstage came from spotify which is an end user so this trend is growing and the diversity as you said is continuing yeah i i'm particularly interested in the dynamic where you have people who have their day job if you will where their employer is absolutely 100 encouraging them to participate in the community to develop things that will not only help the employer and that mission but also building uh solutions for everyone and providing enrichment for the for the person and and i i'm i'm going to make a little bit of a prediction i want to get your thoughts on this i think that um one of the silver linings of what we've been through in the pandemic having a lot of people at home having that relationship with your primary employer be just a little bit different and just a little bit more removed i think everyone is realizing that you know what um we all need a passion play to be a part of in addition to whatever we're doing to put bread on the table in the immediate future and so i i think that i want to hear your thoughts there's going to be an explosion in contributions from people and hopefully a lot more openness on the part of employers to let people dedicate their time to this do you do you see that do you think that yes i think i think you're really on to something here um something i mentioned in my keynote right was this conversation i've had with so many that we in this community our identity is cloud native first so we're folks who are in team cloud native before we are working at insert company name you know um google at t spotify whatever it's not a dig on the company it's actually a celebration of those companies because they are liking the developments that happen in open source they are appreciating the value these people are creating and they're employing them so absolutely there is this ongoing trend of folks seeing great value in folks who understand this cloud native projects in particular and of course right because we have been such a great place for industry collaboration lots of vendors have great products make lots of money on these projects and that's as it should be and so the value of the people contributing to these projects is very high and it will only continue to grow i imagine so so here we are in los angeles at kubecon cloud native con 21 what's what's next well uh the good news is this was the first of many to come hybrid events in person plus virtual and the next one is happening in end of may in valencia for europe 22. valencia spain and i have heard beautiful weather very nice people amazing food so just for that that alone is worth registration yes i know right it's going to be amazing i'm so excited and i hope i will see you folks there sign me up i've never been to spain i'm there me too let's do it excited let's do it for our spanish-speaking uh viewers i will say claroque he you can't you do you can do it all you can speak spanish on the queue we can have something honestly i'm impressed i'm impressed i can't i can't do that any and you donated your green card so thank you so much so nice congratulations on the event thank you uh for growing the community for and growing the diversity of it and for the the projects that are going on now and we're sure many more to come we look forward to seeing you in valencia in may thank you so much see you in valencia all right we'll see you there for dave nicholson i'm lisa martin we are live in los angeles the cube is covering kubecon and cloudnativecon at 21. stick around we'll be back after a short break with our next guest

(music) >> Hi, welcome to today's session of the CUBE's presentation of the AWS STARTUP SHOWCASE. New breakthroughs in DevOps, Data Analytics and Cloud Management Tools. This segment features HackerOne for DevOps. I'm Lisa Martin, and I am joined by Alex Rice, the founder and CTO of HackerOne. Alex, welcome to the program. >> Thank you for having me. >> Alex and I are going to spend the next 20 minutes or so talking about strengthening cloud application security with HackerOne. I want to go ahead Alex, and start you founded HackerOne back in 2012. Talk to me about, why you founded it? What were the glaring obvious gaps in the market? >> So I, I started out with the software development engineering background before moving into security about halfway through my career. And one of the things that's always bothered me about the security industry is how unreliable our feedback loops are. We only ever really get quality software by having as many, many points of feedback as possible in there from customer surveys and analytics and monitoring. And the security industry has just been really spotty about that. So when I was running the product security team for, for Facebook for a number of years, one of the surprising things that we did, that ended up being one of the best feedback loops we had, we just said to the, to the, the world hackers out there, if you find a vulnerability, find a security flaw, find something that we missed, we'll reward you for it. And we were really blown away with what very creative folks all across the world came back with. And so this concept of inviting outside friendly hackers to point out your flaws in exchange for compensation, ends up being a very valuable tool for any engineering team and any, any security team, particularly those that are adapting to more modern, faster agile environments. >> Right? Like DevOps. So you've amassed a community of over 1.2 million good actors, ethical hackers as you say. How do you vet those folks since there's so many nefarious actors out there? >> It's a great question what we start with. The bulk of the programs that we run on HackerOne are public. They're open to the world. There are organizations like Facebook and GM and the department of defense that say to anybody out there, if you find something that we've missed, we want to know about it. So it doesn't, you're not giving the hackers any special permissions or access that they wouldn't normally have. You're, you're inviting them to collaborate with you. From there we learned a lot about the hackers skillsets and demeanor and their track record to then vet them for more private or targeted programs. So while there are these public programs, that is where those million hackers originate from that list is, is vetted and filtered down for more private engagements. Because most folks building technology, they don't need a million hackers to help them out. They need 10 of the right hackers on their team at the right time. And vetting them and matching those hackers to the right challenges is, is a core part of what we try to do here at HackerOne. >> One of the things that we talk a lot about on this program is, you know, the last five years, this shortage, the cybersecurity skills gap. Is, is HackerOne's answer to that? These 1.2 million ethical hackers who can find those vulnerabilities that are open vectors for criminals to exploit. >> It's part of it. It's very much a part of it. My personal hypothesis about this on a big part of why we have such a glaring skills gap is because we've tried to separate it out from core engineering and DevOps principles. The most secure products out there, the ones that hopefully you trust and we all use regularly. Security is a core part of their engineering practices. It's a core part of their DevOps practices and the skillset overlaps dramatically there. And so we've had a lot more success in involving the core DevOps and engineering teams in security practices and really doing it as, as any other component of, of quality software development. And the challenge of that is that you're not going to find everything that you need in a single job description. If you're building a modern application or deploying modern infrastructure, the diversity of skill sets that you need is just staggering. And if you try to apply the old employment model of, okay, I need a security expert on this application. I need an expert in AWS and Kubernetes and RDS, and queuing systems and encryption for my and database security and account takeover. You quickly realize that it's just impossible for every organization that needs all that expertise to hire somebody with all that expertise. So our, our approach and what we try to do is to make sure that the core teams own responsibility for that security, but they're able to tap experts when they need them at, at, in a model that is really much more acclimated to how modern software is built. >> Got it. Okay. Interesting. Talk to me about the HackerOne security platform. Let's kind of dissect that. >> Absolutely. So there's a, there's a few different types of programs that we run for customers. At our, at its hard. There are public programs that we refer to as, as vulnerability disclosure programs. This is usually a security ad, it could be as simple as a security ad for a email address report vulnerabilities. That's really just an invitation to the world out there that says. Hey, we, our application is available to the public and you as a member of the public, if you find a security issue that we should be aware of, we'd like to hear about it. And it's incredible the amount of value that software teams receive just from asking, this putting that invitation out there. Then in parallel with those, for the organizations that are looking for more talented, a deeper dive we've run bug bounty programs, which is a very similar flavor, but the, our engineering and software teams will post bounties for the specific types of issues that they care about. Meaning if you can find a way to compromise user data, or if you can get access to our infrastructure, we'll reward $5,000 or $10,000. And you're specifically asking people to help you find things that will align with your goals and protect your customers. And then the, the third model that we do are our security assessments. These are a very targeted point in time assessments. They're not ongoing commitments. There are when a DevOps team is deploying a new application or releasing a new architecture or running new infrastructure, when they need a very targeted set of expertise for a constrained timeline to fit into their release processes, we can run assessments of matching just a small number of factors to what you care about and tie all that into your to release process. >> Okay. Let's talk about now, we know, one of the things that we've seen in the last 18 months as this massive acceleration to digital, we've seen a much more cloud adoption and really lifelines. Zoom, Netflix, for example, being these lifelines. As more organizations are moving to the cloud, we think, well, maybe risks are getting higher. With respect to customers that are moving to AWS. How does hacker one security platform help? >> The potential of technology. If it wasn't clear before the pandemic started, it should be clear to everybody now, like it is, it's unbelievable the positive impact it's able to have on our lives. And at the same time, most people don't trust technology. We as a technology industry have done a poor job of earning the public's trust that the technology that many of their lives are starting to depend upon is as trustworthy as they needed to be. And that's not a new challenge. Like as long as we've been developing software, there have been bugs, there have been security problems, but it's really amplified it both with the pace of development and just how accessible that's becoming to that to the world. And so in, in prior development models where we were releasing software, much more infrequently, where it was deployed in very controlled environments and accessible only to specific people who happen to be in a physical location or had a particular corporate account, that's all starting to change. Software is being released so much faster at a, at a pace that their traditional security models were already struggling to keep up with. And now are just completely, completely outclass. That's the trend number one that's changed. It's just the speed at which we have to apply. Security is, is unprecedented in this new world. And then at the same time, the access has just gone through the roof, the way of operating a modern business and surfing modern customers dictates that we have to meet them where they are wherever they are in the world, which means the adversaries have the same level of access that we're now affording to our, to our customers. So for our financial services customers that have gone completely remote access in the, in the last year, that's a whole range of attack surface. It wasn't accessible for many of them are using cloud systems to do that. Our healthcare customers that previously a tech service, it was only accessible when you were actually in the hospital is now open in large parts of the public and has many many more private conversations than it did before. And it's more than anything else that realization that we need this technology to be always on accessible anywhere in the world and trusted because people need to trust it. Like their lives depend on it. Literally has, has really changed how we need to look at this challenge. >> Yeah. That speed at which the attack surface is just spreading. And I was looking at some cybersecurity data in the last week or so, and there's really no signs of it slowing down. We saw this, the rapid shift to remote work a year and a half ago, remote learning. And we've got obviously we're in this hybrid world now where, you know, companies are in hybrid cloud, we're in this hybrid workforce of some remote, some homes, some doing both back and forth with that attack surface spreading. Give me an idea of some of the customers that you guys are working with to help them with HackerOne secure their AWS environments. >> Yeah. Our customer base really follows technology adoption trends. All of our early customers were, were tech companies that are kind of the ones that pioneered this model. Facebook, Google, Microsoft, Twitter, Uber were the, the early tech companies that quickly over the first ones to realize that the traditional approach to security model was just insufficient for a new cloud forward environment. Behind them you'll find technological, technology leaders in every industry. It's hard to just talk about the tech industry today. When you look at any industry out there, you can find one or two examples of very technology forward companies. On the finance side, customers like Goldman Sachs and Capital One. They really view themselves as technology companies these days. They're not finished service organizations or banking organizations, they're first and foremost technology companies. They were the first, some of the first to adopt this, this model. On the military side, the department of defense was one of the first organizations to do this cause they've long had, they're both one of the most traditional organizations out there. They've always had innovation arms to adapting practices like this. The automobile industry was a little bit early on the technology adoption trend. As consumers started relying on and demanding more technology in their vehicles. They were one of the early adopters of, of a practice here. And in the more recent years, the line has just completely gone away. We don't really use what we were engaging with a customer you don't really even ask. Are you, what's your, what's your digital strategy? or do you have a technology team? or are you developing first party applications? Do you use any cloud services? The answer to it is just is it's yes. So much more often than it's not. I think there's the safe assumption in 2021 is if you're, if you're doing business, you are probably have a software engineering team, you are probably deploying on the cloud. And if you're not, you're probably not going to be doing business in the, in the next decade. >> Right. That's, that's going to be a big differentiator, but you bring up a good point that every you can, you can almost say every company these days is a tech company or needs to become a tech powered company, a data-driven company. That is critical to especially organizations in this climate being able to pivot continuously as our world is changing. I want you to walk us through Alex, some of the HackerOne assessments that folks can do specifically in the AWS environment. >> For specifically for AWS, what we found is there's a category of AWS and we're really a cloud customers that want the always on security feedback loops that come from bounty programs. And so we, we've had that offering for quite a while of folks that want a feedback, no matter when it happens, because they're continuously received releasing applications. But then increasingly one of the use cases that we discovered was folks were in the midst of moving new applications to AWS, almost on a, on a weekly or monthly cadence. And they need needed a security testing cycle that would keep pace with that. Particularly folks that are ongoing any type of cloud migration or lifted shift of their, of their applications. And so we, we rolled out at AWS tailored specific version of our security assessment product. You can get it in the AWS marketplace as well, that lets you spin up a targeted security assessment on demand through the, through your native AWS tooling, whenever you need it. And the most common use case being this, we plan to open up access to this application next week. We'd love to have some hackers kicking the tires on it this week before the whole world has the opportunity to do that. All of those findings are then integrated back into Rietta U.S security hub, and tailored in a way that is meant for the DevOps teams and engineering teams that are deploying to, to be able to tell us what's going on. We're not asking folks to, to break out into specific security workflows. We really fundamentally believe that security accessible to DevOps teams is, is what's needed to keep us all moving fast and ship trustworthy now applications in the cloud. >> Is that at all a facilitator, you know, when we talk about DevOps folks, security folks, Devsecops. We talk about sort of the, the cultural shift and developers needing the DevOps folks need to be focusing on getting applications out at speed, security folks, developers, you know, we don't want to have to have security responsibilities. Are you helping to facilitate some of those? >> Yeah. We are, and it is more of a personal opinion here, but as someone who's worked on on many engineering teams and built multiple application and product security teams, the strongest ones in the industry, the lines between the product team and the product security team or the DevOps team or the security team are non-existent, those experts exist on to. I hate terms like Devsecops. We, it's necessary to, to approach things, but like if you're going to have a term like DevSecOps, you need to expand it to like DevQaSec in for ops. And it's just, you can't possibly capture every skillset and the critical aspect of quality software development in, in a short little acronym like that. And to me, DevSecOps just feels like a, an attempt by the industry to get invited to a party that nobody wants them at. And I really think we have to rewire our thinking. And if you have a, a development and an operations team, which are the two core functions there that doesn't take hands-on responsibility for the security of what they're developing and operating you're in trouble. Right? The more you try to outsource that to another team, another set of expertise, the worst you're going to be. There's a, there's a analogy that I draw to this that is a little bit of a poor analogy, but it, if it works well for me. For those of us that have been around in software engineering for, for long enough, there was a huge push in the early two thousands to build quality assurance processes across the board. Like everyone was investing in QA and building our QA teams. And every study across the board showed quality just tank after people invested millions in QA and quality assurance. And when, when you dig into it, it's intuitive, right? Like as soon as you can say. Oh, thank goodness quality is now somebody else's job. I've got, there's a dedicated team that can think about quality and deal with quality. Quality goes away. And security follows the exact same paradigm. Modern software is too complex, too interconnected, to be able to expect somebody else to completely do it for you. And so we really try to consult our customers on you should be thinking about organizational structures and responsibility, major SIGs that ensure developers and operations have the seat at the table in the security of the product. And then the challenge is how do we get the right people onto those teams? How do we get the right experience to them versus bolting it on with another acronym in the middle? >> I love your opinion there. In terms of facilitating that the latter part of what you just spoke, how are you finding those conversations within customers going? Is this now, I mean, think about it from a security perspective, it's going up to the board level imperative. Are you finding, especially in the last 18 months that your conversations with organizations are changing as that escalates up the chain? >> They are, but we also take a very pragmatic approach to this. I give you a very, a, a fairly, a personal opinion there on how to do it. The reality is most organizations aren't structured that way. They have a DevOps team, they have a security team, and the two are often in somewhat of an adversarial relationship. And, and we, we certainly work within those environments. You certainly can have a mature security program in an environment like that. It's not like there's one silver bullet to solve it, but we do work closely with our customers to try to bring down those walls. And increasingly technology leaders are engaged and hands-on, and are looking for ways to make this better. Five years ago, the CSO, The Chief Information Security Officer was almost always our main buyer, and our main point of contact. Is much, much more common now to see VPs of engineering, CIO's, CTOs have direct line responsibility for, security teams. And I think we're starting to see the early shifts of work structures that reflect that. If you have a DevOps team and you have a security team, that's responsible for the security of what the DevOps team is doing, and they are reporting to the same executive where there are major points of bureaucracy and politics between them. Every executive we talked to feels that, they lived through an experience like that, and they're motivated to start bringing those balls down. >> They've been through that pain and know the imperative give up getting alignment. So we've talked a lot in the last minute here. So I'm curious, we talked a lot about what HackerOne is doing, what you're doing for the AWS community, what's in it for your customers, but I'd love to understand just really quickly what's in it for the hackers? I do understand that you guys have more ethical hackers than black hats out there are out there, they're new assistants, which is good to know. But, what's in it? You know, from a bounty perspective for the hackers that work with you. >> We believe we're creating meaningful economic opportunity for, for hackers out there. We've had over a dozen hackers that have made a million dollars on the platform helping customers. But more importantly, it maps to how you want to develop your skillset. As hackers, a big part of the cyber security workforce challenge is these unrealistic job expectations that require every security engineer to be a Jack of all trades and work across 10 different product teams and master all of these skills. Whereas this model allows hackers to specialize. You can be a specialist in a very particular piece of technology and apply that specialization across everyone that depends upon it, and focus on what you can do best without dealing with the office politics or the unrealistic job expectations of what's needed in a modern school professional. It's one of the most painful things about the security community is you'll, you'll look at junior entry-level job descriptions for security engineers that already require five years of experience and expertise in 10 different technologies, which is just it's unrealistic. You're you're not going to find it. You don't want to, to be that individual. But it's also, it's back to what we were talking about earlier. It's trying to ask to find unicorns for roles that are just not in line with how modern software is built. And so I think for that, for the hacker community, what we hope we're doing is we hope we're creating meaningful economic opportunity. We're also hope we're enabling folks to develop and contribute to society with their skills in a way that they would like to. >> Awesome. Alex, thank you so much for joining me today, giving me kind of a background on what HackerOne's doing, what you're doing for AWS, the opportunities what's in it for me as a customer, what's in it for me as an ethical hacker. It's been great having you on the program. >> Thank you very much. Take care. >> This has been our coverage of the AWS startup showcase new breakthroughs in DevOps, data analytics and cloud management tools for Alex Rice. I'm Lisa Martin. Thanks for watching. (music)

>>mhm >>mm >>Hello and welcome back to HPD discovered 2021. My name is Dave Volonte and we're going to dig into H P E. Green Lake, we've heard a lot about this, we want to find out how real it is and test a little bit of how how can help solve your business problems. We also want to understand Green Lake relative to the competition. HPV was the first, as you probably know to declare it all in with an as a service model and virtually every major infrastructure player has now followed suit. So we want to hear from HP directly how it's different from the competition, where it's innovating and that means we're gonna poke a little bit of customer examples and how the partner ecosystem is adopting and responding to Green Lake and with me is the right person to do this is keith White, who is the senior Vice President General Manager of the Green Lake cloud services business unit at HP, keith, great to see you, thanks for coming back to the cube. >>Okay, fantastic to see you as always. So thanks so much for having me. >>Yeah, it's our pleasure. So look, we're hearing a lot leading up to discover and at this event about Green Lake you got momentum now, everybody's excited about it. What's driving demand? Where's the excitement coming from? >>No, it's a great question. And you know, the reality is customers are expecting this cloud experience, right? So they they've been using the public cloud, they've been engaging on that front and this cloud experience is really driven, a pretty high amount of customer expectations, make itself served, make it automated, make it easy to consume, only want to pay for what I'm using and then manage it all for me on the back end. But 60 to 70% of apps and data will stay on prem per Gardner and I D. C. And so give me that experience on prem. And so that's why I think Green Lake has gotten so much interest, so much positive growth and momentum is because we're bringing that cloud experience to our customers in their data center, in their Coehlo or at the edge and that's where they want to see it just as much. And so since the world is now hybrid, we have a fantastic solution for folks. >>So you, you were first in this game and so you took some arrows and I'm interested in how Green Lake has evolved, Take us through the journey maybe what were some of the bumps in the road that you had to overcome? Maybe how it compares with the competition. Maybe some of the things that they're going to have to go through as well to get to the point where you are. >>No, it's true. And you know, the great thing is HP as a company is really moving to be much more of a cloud services and software company. And you know, we're seeing this from our competition, as you mentioned, have followed suit. But in essence, you know, you have to move from just sort of providing lease type financing type scenarios for our customers into truly delivering that cloud experience. And that's what's been so exciting over this last year is we've gone from just the basic cloud services, compute storage, networking and VMS to really providing containers as a service, bare metal as a service. Uh, machine learning ops, S. A P V. D I. You know, we've now created a set of workloads and as you heard it discover we're now delivering industry solutions, so electronic medical records for hospitals or high delivery payment transaction processing for, for financial, so that the challenge of moving from just sort of leasing basic capabilities to a true cloud experience that again pay as I go, fully automated self serve, all managed for me has really been a challenge and it's exciting, it's exciting to see customers jump on and really sort of lean in and see the business value that comes from having that level of solution >>keith, am I correct in that pretty much every large tech company has a services arm and they could, they could sort of brute force, some kind of cloud like experience and that's kind of what people have done historically the layer in a financial like leasing financial as you said and and but every situation was unique, it was kind of a snowflake if you will and you guys are probably there a few years ago as well and so I'm interested in sort of how you evolved beyond that. Was it a mindset was a technology, was it sort of cultural? You know, it came from the top as well, but maybe you could describe that a little bit. >>Yeah, the ship comes from our customers because what's happening is customers no longer trying to buy component parts. They're saying it's really about Tesla's like, hey, I want you to deliver this for me. In essence, we're running the data center for them now. We're running their machine learning operations environment for them. Now, you know, we're migrating their mainframe over now. And so what's happening is these sls are really, what matters to customers like that? It's not so much about, hey, what are the speeds and feeds and this and that? And so yes, you can sort of brute force that piece of it. But what you really are having to do is create this deep partnership and relationship with your customer to truly understand their business challenges and then provide them with that capability. Now I think the things that's exciting is yes, the public cloud gives you some some significant benefits for certain workloads and certain capabilities. But what we're hearing from customers is hey, I want to have much more control over my data center. I want to ensure that it has the security required. I want to make sure that I can make the adjustments necessary and so you're doing all that at a lower cost with open platform that I can use a variety of tools and other applications just makes it that much more powerful. So I think that's what we're seeing is we're getting into what our customers really requiring and then you know the most interesting thing is how do you make it work with my entire environment because I am running Azure and I am running A W. S. And I am running google and I'm running some other things. And so how does this cloud really helped me bring all those together to really govern that hybrid estate? And that's where I think Green Lake has really shine. >>So it kind of part of the secret sauce is automation because you've got to be, you still have, you have to be competitive, you know, at least within reason to cloud cost, sometimes it's going to be less expensive, maybe sometimes it can be more expensive. You've got some advantages in certain cases where, you know, there's government governance things and and you know, we don't have to go through all that, but there's the automation but you've got to be profitable at this too. So there's the automation, there's the tooling, there's the openness. So, so that was really a key part of it. Is it not that sort of automating? >>That's right. Automation is key as is really understanding what that customer environment is and optimizing for that piece of it. And so as you heard, we're really excited to announce our Green Lake Lighthouse, which is really providing workload optimized systems that are fully managed for them that provide that capability to run multiple workloads for that customer. But at the same time, to your point, there's a lot of charges that happened on the public cloud side. So, you know, data is the new, you know, gold if you will right, everyone's trying to monetize their data, trying to use it to make decisions and really understand what's happening across their environment and in the cloud. You know, if you put it up in the cloud, you have to pay to get it out. The egress charges can be significant and it's also a bit slower at times because of the latency that happens across that that that connection. And so we are now in a situation where we're seeing a lot of customers that are really trying to analyze their data, leveraging our HPC systems, leveraging our machine learning operation systems in order to really get that data happening, Getting the dancers out much, much faster and a much lower cost than what it would cost them to do that in the cloud. >>So you have some experience at this now. I wonder if we could dig into the customers how customers are using Green Lake. Maybe you can give some examples of success. >>Yeah. Yeah, no. You know it's exciting because you know first off everyone's looking at their digital transformation and that means something different for every single customer, so really understanding what they're trying to do from a transformation standpoint and then saying, okay, well how can we bring a solution to help accelerate that? To help be uh, you know, more connected to your customers to help improve your product delivery. We went to Lyondellbasell for example, one of the largest manufacturers in the world. And you know, they said, hey look, we don't want to run our data center anymore. Most most customers are trying to get out of the data center management business and they're saying, hey, run this for me, uh let me free up resources to go focus on things that really can drive additional value for our customers instead of keeping the lights on patching, blah blah blah. So we have taken their entire environment and moved it to a Coehlo and we're managing it now for them. And so in essence we freed up not just a ton of resources, but they have also been able to drop their carbon footprint, which is also this whole sustainability push is significant as well. And then you look at a customer like care stream, one of the largest medical diagnostic companies in the world, saying hey we gotta be able to allow our doctors to be able to um analyze and diagnose things much much faster through our X ray systems and through our diagnostic machines. And so they have implemented our machine learning operations scenario to dramatically speed up those types of capabilities. So as you go down the list and you start to see these customers really um leveraging technology to meet that digital transformation, saving costs, moving their business forward, creating new business models. It's just, it's really exciting. >>What about partners keith? How how have they responded? I mean, on the one hand, you know, that's great opportunities for them, you know, they're they're transforming their own business model. On the other hand, you know, maybe they were comfortable with the old model, they got a big house, nice, nice boat, you >>know? >>But how are they changing their their their business and how are they leaning in >>similar to what we're seeing? The opportunity for partners is dramatic, right? Because what happens is you have to have a very different relationship with your customer to truly understand their digital transformation. Their business challenges the problems that they're having to address. And so where we're seeing partners really, really sort of the opportunity is where there's the services and that sort of deeper relationship piece of it. So in essence, it's creating much more opportunity because the white spaces dramatic we're seeing, I want to say it's in the 30 to $40 billion worth of market opportunity as we move into an as a service on prem world. So they're seeing that opportunity. They're seeing the ability to add services on top of that and deepen the relationship with our customers. And you know, it's it's from my SVS. We're working closely with S. A. P. For example, to deliver their new rise private cloud customer edition. We're working closely with loosest, for example, who is doing a lot of payment processing type scenarios Nutanix and their database as a service scenario and Splunk because again, we went back to the data piece and these guys are doing so much big data type implementations for risk analytics and and regulatory type scenarios. It's just significant. And so because there's such a push to keep things on prem to have the security to reduce the latency to get rid of the egress charges and everything else. There's just a significant white space for both our partners and then from our distributors and resellers, they're getting to change their business model again, to get much deeper in that relationship with our customers >>to be Green Lake is, I mean it's H. P. E. As a service, it's your platform. And so I wonder if you can think about how you're thinking about uh, share with us, How you think about platform innovation? Um, you've got the pricing model, you know, flex up, flex down. Is there other technology we should know about and other things that are going to move you forward in this battle for the next great hybrid cloud and edge platform? >>Yeah, it's a great push because if you think about it, we are Green Lake is the edge to cloud platform And in essence because we have such a strong edge capability with the arab acquisition we made a few years back. That's really significant momentum with the Silver Peak acquisition to give us SD when you've got that edge connectivity all the way up to our high performance computing. And so you'll see us deliver high performance computing as a service. We're announcing that here at discover um you'll see us announced, you know, machine learning ops I mentioned ASAP, but also a virtual desktops. I think the pandemic has brought a lot more work from home type scenarios and customers really want to have that secure desktop. And so, working with partners like Citrix and Nutanix and and VM ware and Crew were able to provide that again, unique scenario for our customers. And so, um, yeah, the innovation is going to keep coming. You know, I mentioned bare metal as a service because many people are starting to really leverage the metal that's out there. You're seeing us also engaged with folks like intel on our silicon on demand. So this is a really exciting technology because what it allows us to do is turn on cores when we need them. So hey, I need additional capacity. I need some power. Let's turn on some cores. But then I turn off those cores when I'm not using them. You go to a software core based software pricing model, like an oracle or a sequel server. I'm saving dramatic cost now because I don't have to pay for all the cores that are on the system. I'm only paying the licenses for the ones that I use. And so that should bring dramatic cost savings to our customers as well. So we're looking from the silicon all the way up. Uh you know, you hear us talk about project Aurora, which is our security capability. We're looking at the silicon level, but we're also looking at the the container and bare metal and then obviously the workloads in the industry solution. So we're sprinting forward. We're listening to our customers were taking their feedback. We're seeing what they're prioritizing and because we have that tight relationship with them as we help move them to the direction they want to go, it's giving us a ton of fantastic inside information for what really matters. >>Right, Thank you for that. So, I want to ask you about data. A lot of organizations are kind of rethinking their ideal data architecture, their organization. They're they're they're seeing the amount of data that is potentially going to be created at the edge, thinking about ai inference and influencing at the edge and maybe reimagining their data organization in this age of insight. I wonder how Green Lake fits into that. How are you thinking about the new era of data and specifically Green Lakes role? >>Yeah, you mentioned the age of insights and and it really is right. So we've moved sort of as the next phase of digital transformation is basically saying, hey look, I've got all this data. I've got to first get my arms around my data estate because in essence it's in all these different pockets around. And so Green Lake gives you that ability to really get that data estate established. Then I want to take and get the answers in the analytics out of it. And then I want to monetize that data either out to my customer set or out to my industry or out to other scenarios as well. And so as we start to deliver our develops capability, our ai and analytics capabilities through HPC. And it's an open platform. So it allows data scientists to easy boot up easily boot up a cluster with which to do their models and their training and their algorithms. But we can also then use and Estancia at that into the business decisions that our customers are trying to make again without the significant cost that they're seeing on that on the public cloud side and in a very secure way because they have the data exactly where they need it. You'll see us continue to do sort of disaster recovery and data protection and those types of scenarios both with our partners and from H P E. So it's exciting to just understand that now you're going to have the tools and resources so you can actually focus on those business outcomes versus how do I protect the data? Where do I start, how do I get my model set up, etcetera. All that becomes automated and self service. You mentioned earlier >>When you talk to customers Keith one of the big sort of challenges that you're addressing. What's the typical, there was no typical but the but the real nuts that they're trying to crack is it financial? We want to move from Capex to opec's is that hey we want this cloud model but we can't do it in the public cloud for a variety of reasons, edicts, organization leaders or we want to modernize our our state. What are the real sort of sticking points that you're addressing with Green Lake? >>Yeah, I think it's threefold and you sort of touched on those. So one is, it really does start with modernization. Hey, you know, we've got to take costs out of the equation. We've got to reduce our carbon footprint. We've got to automate these things because we have limited resources and how do we maximize the ones that we have? And so I mentioned earlier, getting out of the data center, modernizing our apps, really monetizing our data. So I think that's number one. Number two is what you said as well, which is, hey look, I don't need to have all these capital assets. I don't want to be in charge of managing all all these assets. I just want the capability and so being able to sell them that service that says, hey, we can, we can do X number of desktops for your V. D. I. We can run your S. A. P. Environment or we can make sure that you have the, the analytics structure set up to be able to run your models that becomes super compelling and it frees up a lot of resources in cash on that front as well. And then I think the third thing is what you said, which is the world is hybrid. And so I need to find out what's going to run best in my on prem environment and what's going to run best up in the cloud. And I want to be able to optimize that so that I'm not wasting costs in one place or the other, and I want to be able to govern and govern that holistically. So I have the ability to see what's happening end to end across that so I can manage my business most effectively. So I think those are the three big things that people are really excited about with Green Lake as they enable those things. Um and you know, the reality is that it also means that they have a new partner to help them really think through how can they move forward? So it's not them by themselves. Uh It's really in a one plus one equals three type scenario and then you bring the ecosystem in and now you've got, you know, things working really well. So, >>so big enterprise tech, it's like, it's like the NFL is a sort of a copycat league. And so what, you know what I'm saying? But you guys all got >>big, yeah, >>you've got great resources, hey, this West Coast office exactly is gonna work. We're gonna get a short passing game going. And so that happened. So I feel like, okay, you've raised the bar now on as a service and that's gonna become table stakes. Um you know, it's got a lot of work to get there. I know, and it's a it's a journey, but but when you think about the future uh for H. P. E. Uh what's exciting you the most? >>I think what's exciting me the most is this the reaction that we're seeing with customers because in essence it gets them out of the bits and bytes and speeds and feeds and you know, um >>you >>know, component goo and really gets into business value, business outcomes sls and, and that's what they're looking for because what they're trying to do is break out of, you know that day to day and be able to really focus on the future and where they're going. So I think that's one, I think the second big thing is as you see all these things come together, um you know, we're able to basically provide customers with, I would say a mindset that's like, hey, I can do this holistically, but I can always pick and choose the best that I want and if I ramp up, I have capacity. If I ramp down, I don't have to pay for first scenarios. And so I'm getting the best of both worlds across that piece of it. And then third is I mentioned it earlier. But this whole relationship thing is so important because you know, this isn't about technology anymore. As much as it it is about what's the value that you're going to get out of that technology. And how does that help us move the company and the world forward? Like I love the fact that H. P. E. Was so involved in this pandemic. >>You know, >>with our systems were able to actually uh to run a set of of algorithms and analysis on how to, you know, find a vaccine on how to how to address the things that are going forward. You've seen us now up in space and as we, we broaden our frontier and so as a company you're seeing technology turned into things that are truly helping the world go forward. I think that's exciting as well. >>Yeah. Space. It's like the ultimate edge. >>I >>like you said to me if I take it, it's not not about ports and Mick, nips and gigabytes anymore. It's about the outcome. You mentioned before the S L. A. Um, you know, the thing about, you know, think about virtual, it's great. We have to get in the plane. Its downside. We all know we can't hang out, you know, afterwards, you know, have a drink or you know, chit chat about what's going on in the world, but we can't reach a lot more people. But the other downside of virtual is, you know, you don't have the hallway track. It's not like, hey, did you check out that, that demo on IOT? It's really cool. Where is that? So give us the hallway track. How can folks learn more about discover where would you direct folks? >>You bet. You know, I'm doing a full spot. Obviously let me start with at the top right Antonio Neri our ceo he's going to lay out the whole strategy and then I'll have a spotlight. It's about a 30 minute deep dive on all of these things that that you and I just talked about and then we've got a bunch of breakout sessions were doing some with our partners like Nutanix and others, um, Microsoft as well as we talk about, we didn't really touch on that, but you know, we have a strong partnership with the hyper scholars with Microsoft and with others because in essence customers are expecting an integrated solution that's hybrid. And so, you know, we're showcasing all of that with the with the discover breakouts as well and they're available on demand. We have a huge opportunity with respect to that, so really excited and you know, frankly we're here to help, like I hope people understand this is our opportunity to help you be successful and so please know that our ears are wide open to hear what the challenges are and we're ready to help customers as they needed. >>I'm glad you mentioned the partnership with Microsoft and other hyper skills. I feel like keith, the the Hyper scale is giving us a gift. They've spent last year they spent over $100 billion on Capex build out. That is like, it's like the internet. Thank you. >>Now we're gonna build on >>top of it, we're gonna build an abstraction layer that hides all that underlying complexity. We're gonna connect things and and that's really your job. That's really kind of what you're bringing to the table I think with Green Lake and some of these innovations. So >>I really >>appreciate it. Go ahead please. >>I appreciate the time as well. It's always a pleasure and it's always exciting to get a chance to share with you and and as always, any time you don't want me back, I'm happy to happy to join. Alright, >>would love to do that. So appreciate that. And thank you for spending some time with us. Stay tuned for more great coverage from HPD discovered 21 everything is available on demand as well as the that is the other good thing about virtually go back and watch all this content. This is Dave Volonte for the cube the leader in enterprise tech coverage. Be right back

>>Yeah, >>approximately two years after HP split into two separate companies, antonioni Ranieri was named president and Ceo of Hewlett Packard Enterprise. Under his tenure, the company has streamlined its operations, sharpened his priorities, simplified the product portfolio and strategically aligned its human capital with key growth initiatives. He's made a number of smaller but high leverage acquisitions and return the company to growth while affecting a massive company wide pivot to an as a service model. Welcome back to HPD discovered 2021. This is Dave Volonte for the cube and it's my pleasure to welcome back Antonio Neary to the program. Antonio it's been a while. Great to see you again. >>Hi, Dave. Thanks for having me. >>That's really our pleasure. It was just gonna start off with the big picture. Let's talk about trends. You're a trend spotter. What do you see today? Everybody talks about digital transformation. We had to force marks to digital last year. Now it's really come into focus. But what are the big trends that you're seeing that are affecting your customers transformations? >>Well, Dave, I mean obviously we have been talking about digital transformation for some time uh in our view is no longer a priority is a strategic imperative. And through the last 15 months or so since we have been going through the pandemic, we have seen that accelerated to a level we haven't never seen before. And so what's going on is that we live in a digital economy and through the pandemic now we are more connected than ever. We are much more distributed than ever before and an enormous amount of data is being created and that data has tremendous value. And so what we see in our customer's name, more connectivity, they need a platform from the edge to the cloud to manage all the data and most important they need to move faster and extracting that inside that value from the data and this is where HP is uniquely positioned to deliver against those experiences and way we haven't imagined before. >>Yeah, we're gonna dig into that now, of course you and I have been talking about data and how much data for decades, but I feel like we're gonna look back at 2030 and say, wow, we never, we're not gonna do anything like that. So we're really living in a data centric era as the curves are going exponential, What do you see? How do you see customers handling this? How are they thinking about the opportunities? >>Well, I think, you know, customer realized now that they need to move faster, they need to absolutely be uh much more agile and everything. They do, they need to deploy a cloud experience for all the work clothes and data that they manage and they need to deliver business outcomes to stay ahead of the competition. And so we believe technology now plays even a bigger role and every industry is a technology industry in many ways, every company, right, is a technology company, whether your health care, your manufacturer, your transportation company, you are an education, everybody needs more. It no less. It but at the same time they want the way they want to consumer dave is very different than ever before, right? They want an elastic consumption model and they want to be able to scale up and down based on the needs of their enterprise. But if you recall three years ago, I knew and I had this conversation, I predicted that enterprise of the future will be edge century, cloud enable and data driven. The edge is the next frontier, we said in 2018 and think about it, you know, people now are working remotely and that age now is much more distribute than we imagined before. Cloud is no longer a destination, it is an experience for all your apps and data, but now we are entering what we call the edge of insight, which is all about that data driven approach and this is where all three have to come together in ways that customer did envision before and that's why they need help. >>So I see that, I see the definition of cloud changing, it's no longer a set of remote services, you know, somewhere up there in the cloud, it's expanding on prem cross clouds, you mentioned the edge and so that brings complexity. Every every company is a technology company but they may not be great at technology. So it seems that there are some challenges around there, partly my senses, some of some of what you're trying to do is simplify that for your customers. But what are the challenges that your customers are asking you to solve? >>Well, the first they want a consistent and seamless experience, whatever that application and data lives, so, you know, for them, you know, they want to move away from running it to innovate in our 90 and then obviously they need to move much faster. As I said earlier about this data driven approaches. So they need help because obviously they need to digitize every every aspect of the company, but at the same time they need to do it in a much more cost effective way. So they're asking for subject matter expertise on process engineering. They're asking for the fighting the right mix of hybrid experiences from the edge to cloud and they need to move much faster at scale in deploying technologies like Ai deep learning and machine learning and Hewlett Packard Enterprise uh is extremely well positioned because we have been building an age to club platform where you provide connectivity where you bring computing and storage uh in a softer, define scalable way that you can consume as a service. And so we have great capabilities without HP Point next technology services and advice and run inside. But we have a portfolio with HP Green Lake, our cloud services, the cloud that comes to you that are addressing the most critical data driven warlords. >>Probably about 24 months ago you announced that HP was was going to basically go all in on as a service and get there by by 2022 for all your solutions. I gotta get, I gotta say you've done a good job communicating the Wall Street, I think, I think culturally you've really done a good job of emphasizing that to your, to the workforce. Uh, but but how should we measure the progress that you've made toward that goal? How our customers responding? I I know how the markets responding, you know, three or four year big competitors have now announced. But how should we measure, you know, how you're tracking to that goal? >>Well, I think, you know, the fact that our competitors are entering the other service market is a validation that our vision was right. And that's that's that's good because in the end, you know, it tells us we are on the right track. However, we have to move much faster than than ever before. And that's why we constantly looking for ways to go further and faster. You're right. The court of this is a cultural transformation. Engineering wise, once you state, once you state the North Star, we need to learn our internal processes to think Cloud first and data first versus infrastructure. And we have made great progress. The way we measure ourselves. Dave is very simple is by giving a consistent and transparent report on our pivot in that financial aspect of it, which is what we call the annualized revenue run rate, which we have been disclosed enough for more than a year and a half. And this past quarter grew 30% year over year. So we are on track to deliver a 30-40% Kegel that we committed two years ago And this business going to triple more than uh more than one year from now. So it's gonna be three times as bigger as we enter 2022 and 2023. But in the end, it's all about the experience you deliver and that's why architecturally uh while we made great progress. I know there is way more work to be done, but I'm really excited because what we just announced here this week is just simply remarkable. And you will see more as we become more a cloud operating driven company in the in the next months and years to come. >>I want to ask you kind of a personal question. I mean, COVID-19 is you know, sharpened our sensitivity and empathy to to a lot of different things. Uh and I think uh ceos in your position of a large tech company or any large company, they really can't just give lip service to things like E. S. G. Or or ethical uh digital transformation, which is something that you've talked about in other words, making sure that it's inclusive. Everybody is able to participate in this economy and not get left behind. What does this mean to you personally? >>Well, they remember I'm in a privileged position, right? Leading a company like Hewlett Packard Enterprise that has Hewlett and Packard on the brand is an honor, but it's also a big responsibility. Let's remember what this company stands for and what our purpose is, which is to advance the way people live and work, and in that we have to be able to create a more equitable society and use this technology to solve some of the biggest societal challenge you have been facing The last 18 months has been really hard on a number of dimensions, not just for the business but for their communities. Uh, we saw disruption, we saw hardships on the financial side, we saw acts of violence and hatred. Those are completely unacceptable. But if we work together, we can use these technologies to bring the community together and to make it equitable. And that's one is one of my passion because as we move into this digital economy, I keep saying that connecting people is the first step and if you are not connected, you're not going to participate. Therefore we cannot afford to create a digital economy for only few. And this is why connectivity has to become an essential service, not different than water and electricity. And that's why I have passion and invest my own personal time working with entities like World Economic Forum, educating our government, right, Which is very important because both the public sector and the private sector have to come together. And then from the technology standpoint, we have to architect these things that are commercially accessible and viable to everyone. And so it's uh it's I will say that it's not just my mission. Uh this is top of mind for many of my colleagues ceos that talked all the time and you can see of movement, but at the same time it's good for business because shareholders now want to invest in companies that take care about this, how we make, not just a word more inclusive and equitable, but also how we make a more sustainable and we with our technologies, we can make the world way more sustainable with circular economy, power, efficiency and so forth. So a lot of work to be done dave but I'm encouraged by the progress but we need to do way way more. >>Thank you for that Antonio. I want to ask you about the future and I want to ask you a couple of different angles. So I want to start with the edge. So it seems to me that you're you're building this vision of what I call a layer that abstracts the underlying complexity of the whether it's the public cloud across clouds on prem and and and the edge and it's your job to simplify that. So I as the customer can focus on more strategic initiatives and that's clearly the vision that you guys are setting forth on. My question is is how far do you go on the edge? In other words, it seems to me that Aruba for example, for example, awesome acquisition could go really, really deep into the far edge, maybe other parts of your portfolio, you're kind of more looking at horizontal. How should we think about HP. Es, positioning and participation in that edge opportunity? >>Well, we believe we are becoming one of the merger leaders at the intelligent edge. Right? These edges becoming way more intelligent. We live in a hyper connected world and that will continue to grow at an exponential pace. Right? So today we we may have billions of people and devices pursue. We're entering trillions of things that will be connected to the network. Uh, so you need a platform to be able to do with the scale. So there is a horizontal view of that to create these vertical experiences which are industry driven. Right? So one thing is to deliver a vertical experience in healthcare versus manufacturer transportation. And so we take a really far dave I mean, to the point that we just, you know, put into space 256 miles above the Earth, a supercomputer that tells you we take a really far, but in the end, it's about acting where the data is created and bringing that knowledge and that inside to the people who can make a difference real time as much as possible. And that's why I start by connecting things by bringing a cloud experience to that data, whatever it lives because it's cheaper and it's way more economical and obviously there's aspects of latest in security and compliance. They have to deal with it and then ultimately accelerate that inside into some sort of outcome. And we have many, many use cases were driving today and Aruba is the platform by the way, which we have been using now to extend from the edge all the way to the core into the cloud business. And that's why you HP has unique set of assets to deliver against that opportunity. >>Yes, I want to talk about some of the weapons you have in your arsenal. You know, some people talk about, hey, well we have to win the architectural battle for hybrid cloud. I've heard that statement made, certainly HP is in that battle. It's not a zero sum game, but you're a player there. And so when I, when I look at as a service, great, you're making progress there. But I feel like there's more, there's, there's architecture there, you're making acquisitions, you're building out as moral, which is kind of an interesting data platform. Uh, and so I want to ask you how you see the architecture emerging and where H. P. S sort of value add, I. P. Is your big player and compute you've got actually, you've got chops and memory disaggregate asian, you've done custom silicon over the years. How how should we think about your contribution to the next decade of innovation? >>Well, I think it's gonna come different layers of what we call the stock, right? Obviously, uh, we have been known for an infrastructure company, but the reality is what customers are looking for. Our integrated solutions that are optimized for the given world or application. So they don't have to spend time bringing things together. Right? And and spend weeks sometimes months when they can do it in just in a matter of minutes a day so they can move forward innovative on I. T. And so we were really focused on that connectivity as the first step. And Aruba give us an enormous rich uh through the cloud provisioning of a port or a wifi or a one. As you know, as we move to more cloud native applications. Much of the traffic through the connectivity will go into the internet, not through the traditional fixed networks. And that's what we did acquisitions like Silver Peak because now we can connect all your ages and all your clouds in an autonomous softer. The final way as we go to the other spectrum. Right? We talk about one load optimization and uh for us H. P. S my role is the recipe by which we bring the infrastructure and the software in through that integrated solution that can run autonomously that eventually can consume as a service. And that's why we made the introduction here of HP Green like Lighthouse, which is actually a fully optimised stack. They with the push of a bottom from HP Green Lake cloud platform, we can deploy whatever that that is required and then be able to Federated so we can also address other aspects like disaster recovery and be able to share all the knowledge real time. Swarm learning is another thing that people don't understand. I mean if you think about it. So I'm learning is a distributed Ai learning ecosystem and think about what we did with the D. C. Any in order to find cures for Alzheimer's or dementia. But so I'm learning is going to be the next platform sitting on this age to cloud architecture. So that instead of people worrying about sharing data, what we're doing is actually sharing insights And be able to learn through these millions of data points that they can connect with each other in a secure way. Security is another example, right? So today on an average takes 28 days to find a bridge in your enterprise with project Aurora, which we're going to make available at the end of the year by the end of the year. We actually can address zero day attacks within seconds. And then we're work in other areas like disaster recovery when you get attacked. Think about the ransom ramp somewhere that we have seen in the last few weeks, right? You know, God forbid you have to pay for it. But at the same time, recovery takes days and weeks. Sometimes we are working on technology to do it within 23 seconds. So this is where HP can place across all spectrums of the stack And at the same time of course people expect us to innovate in infrastructure layer. That's why we also partner with companies like Intel were with the push of a bottom. If you need more capacity of the court, you don't have to order anything. She's pushed the bottle, we make more calls available so that that warlord can perform and when you don't need it, shut it off so you don't have to pay for it. And last finalist, you know, I will say for us is all about the consumption availability of our solutions And that's what I said, you know, in 2019 we will make available everything as a service by 2022. You know, we have to say as you know, there is no need to build the church for Easter Sunday when you can rent it for that day. The point here is to grow elastically. And the fact that you don't need to move the data is already a cost savings because cost of aggression data back and forth is enormous and customers also don't want to be locked in. So we have an open approach and we have a true age to cloud architecture and we are focusing on what is most valuable aspect for the customer, which is ultimately the data. >>Thank you for that. One of the other things I wanted to ask you about, again, another weapon in your arsenal is you mentioned supercomputing before. Up in space, we're on the cusp of exa scale and that's the importance of high performance computing. You know, it used to be viewed as just a niche. I've had some great conversations with DR go about this, but that really is the big data platform, if you will. Uh can I wonder if you could talk a little bit about how that fits into the future. Your expertise in HPC, you're obviously a leader in that space. What's the fit with this new vision you're laying out? >>Well, HPC, high performance computing in memory computer are the backbone to be able to manage large data sets at massive scale. Um, and, you know, deployed technologies like deep learning or artificial intelligence for this massive amount of data. If we talked about the explosion of data all around us and uh, you know, and the algorithms and the parameters to be able to extract inside from the day is getting way more complex. And so the ability to co locate data and computed a massive scale is becoming a necessity, whether it's in academia, whether it's in the government obviously to protect your, your most valuable assets or whether it is in the traditional enterprise. But that's why with the acquisition of cray as G. I. And our organic business, we are absolutely the undisputed leader to provide the level of capabilities. And that's why we are going to build five of the top six exa scale systems, which is basically be able to process the billion billion, meaning billion square transactions per second. Can you imagine what you can do with that? Right. What type of problems you can go solve climate problems? Right. Um you know, obviously be able to put someone back into the moon and eventually in mars, you know, the first step to put that supercomputer as an edge computer into the international space station. It's about being able to process data from the images that take from the ice caps of the of the earth to understand climate changes. But eventually, if you want to put somebody in in into the Marks planet, you have to be able to communicate with those astronauts as they go and you know, you can't afford the latency. Right? So this is what the type of problems we are really focused on. But HPC is something that we are absolutely super committed and it's something that honestly, we have the full stack from silicon to software to the system performance that nobody else has in the industry. >>Well, I think it's a real tailwind for you because the industry is moving in that direction and everybody talks about the data and workloads are shifting. We used to be uh I got O. L. T. P. And I got reporting. Now you look at the workloads, there's so much diversity so I'll give you the last word. What what really is the most exciting to you about the future of HPV? >>Well, I'm excited about the innovation will bring it to the market and honestly as the Ceo I care about the culture of the company. For me, the last almost 3.5 years have been truly remarkable. As you said at the beginning, we are transforming every aspect of this company. When I became Ceo I had three priorities for myself. One is our customers and partners. That's why we do these events right to communicate, communicate, communicate. They are our North Star, that's why we exist. Second is our innovation right? We compete and win with the best innovation, solving the most complex problems in a sustainable and equitable way. And third is the culture of the company, which are the core is how we do things in our Team members and employees. You know, I represent my colleagues here, the 60,000 strong team members that had incredible passion for our customers and to make a contribution every single day. And so for me, I'm very optimistic about what we see the recovery of the economy and the possibilities of technology. Uh, but ultimately, you know, we have to work together hand in hand and I believe this company now is absolutely on the right track to not just be relevant, but really to make a difference. And remember That in the end we we have to be a force for good. And let's not forget that while we do all of this, we have some farm with technology. We have to also help some, uh, to address some of the challenges we have seen in the last 18 months and H. P. E. is a whole different company uh, that you knew 3.5 years ago. >>And as you said, knowledge is the right thing to do. It's good. It's good for business Antonio. Neary, thanks so much for coming back to the cube is always a pleasure to see you. >>Thanks for having me. Dave and >>thank you for watching this version of HP discover 2021 on the cube. This is David want to keep it right there for more great coverage. Mm

>>Kirk focuses on the approach to modern data quality and how it can enable the continuous delivery of trusted data. Take it away. Kirk >>Trusted data has been a focus of mine for the last several years. Most particularly in the area of machine learning. Uh, I spent much of my career on wall street, writing models and trying to create a healthy data program, sort of the run the bank and protect the franchise and how to do that at scale for larger organizations. Uh, I'm excited to have the opportunity today sitting with me as Victor to have a fireside chat. He is an award-winning and best-selling author of delete big data and most currently framers. He's also a professor of governance at Oxford. So Victor, my question for you today is in an era of data that is always on and always flowing. How does CDOs get comfortable? You know, the, I can sleep at night factor when data is coming in from more angles, it's being stored in different formats and varieties and probably just in larger quantities than ever before. In my opinion, just laws of large numbers with that much data. Is there really just that much more risk of having bad data or inaccuracy in your business? >>Well, thank you Kirk, for having me on. Yes, you're absolutely right. That the real problem, if I were to simplify it down to one statement is that incorrect data and it can lead to wrong decisions that can be incredibly costly and incredibly costly for trust for the brand, for the franchise incredibly costly, because they can lead to decisions that are fundamentally flawed, uh, and therefore lead the business in the wrong direction. And so the, the, the real question is, you know, how can you avoid, uh, incorrect data to produce incorrect insights? And that depends on how you view trust and how you view, uh, data and correctness in the first place. >>Yeah, that's interesting, you know, in my background, we were constantly writing models, you know, we're trying to make the models smarter all the time, and we always wanted to get that accuracy level from 89% to 90%, you know, whatever we could be, but there's this popular theme where over time the models can diminish an accuracy. And the only button we really had at our disposal was to retrain the model, uh, oftentime I'm focused on, should we be stress testing the data, it almost like a patient health exam. Uh, and how do we do that? Where we could get more comfortable thinking about the quality of the data before we're running our models and our analytics. >>Yeah, absolutely. When we look at the machine learning landscape, even the big data landscape, what we see is that a lot of focus is now put on getting the models, right, getting it worked out, getting the kinks worked out, but getting sort of the ethics, right. The value, right. That is in the model. Um, uh, and what is really not looked at what is not focused enough that, um, is the data. Now, if you're looking at it from a compliance viewpoint, maybe it's okay if you just look at the model, maybe not. But if you understand that actually using the right data with the right model gives you a competitive advantage that your competitors don't have, then it is far more than compliance. And if it is far more compliance, then actually the aperture for strategy opens up and you should not just look at models. You should actually look at the data and the quality and correctness of the data as a huge way by which you can push forward your competitive advantage. >>Well, I haven't even trickier one for you. I think, you know, there's so much coming in and there's so much that we know we can measure and there's so much we could replay and do what if analysis on and kind of back tests, but, you know, do you see organizations doing things to look around the corner? And maybe an interesting analogy would be something like with Tesla is doing whether it's sensors or LIDAR, and they're trying to bounce off every object they know, and they can make a lot of measurements, but the advancements in computer vision are saying, I might be able to predict what's around the corner. I might be able to be out ahead of the data error. I'm about to see tomorrow. Um, you know, do you see any organizations trying to take that futuristic step to sort of know the unknown and be more predictive versus reactive? >>Absolutely. Tesla is doing a bit Lincoln, uh, but so are others in that space and not autonomous driving space, um, uh, Waymo, the, uh, the, the, uh, Google company that is, uh, doing autonomous driving for a long period of time where they have been doing is collecting training data, uh, through their cars and then running a machine learning on the training data. Now they hit a wall a couple of years ago because the training data wasn't diverse enough. It didn't have that sort of Moore's law of insight anymore, even though it was more and more training data. Um, and so the, the Delta, the additional learning was just limited. So what they then decided to do was to build a virtual reality called car crafting, which were actually cars would drive around and create, uh, uh, predictive training data. Now, what is really interesting about that is that that is isn't a model. It is a model that creates predictive data. And this predictive is the actual value that is added to the equation here. And with this extra predictive data, they were able to improve their autonomous driving quite significantly. Uh, five years ago, their disengagement was, uh, raped was every, uh, 2000 miles on average. And, uh, last year, uh, five years later, it was every 30,000 miles on average, that's a 15 K improvement. And that wasn't driven by a mysterious model. It was driven by predictive data. >>Right, right. You know, that's interesting. I, I'm also a fan of trying to use data points that don't exist in the data sets. So it sounds like they were using more data data that was derived from other sources. And maybe the most simple format that I usually get started with was, you know, what, if I was looking at data from Glassdoor and I wanted to know if it was valid, if it was accurate, but of course there's going to be numbers in the age, field and salary and years of experience in different things. But what if the years of experience and age and academic level of someone no longer correlates to the salary yet that correlation component is not a piece of data that even lives in the column, the row, the cell. So I do think that there's a huge area for improvement and just advancement in the role data that we see in collect, but also the data science metrics, something like lift and correlation between the data points that really helped me certify and feel comfortable that this data makes sense. Otherwise it could just be numbers in the field >>Indeed. And, and this challenge of, of finding the data and focusing on the right subset of the data and manipulating it, uh, in the right, in a qualitatively right way is really something that has been with us for quite a number of years. There's a fabulous, uh, case, um, a few years back, uh, when, um, in Japan, when there was the suspicion that in Sumo wrestling, there was match fixing going on massive max fiction. Um, and, and so investigators came in and they took the data from the championship bouts and analyzed them and, uh, didn't find anything. And, uh, what was, what was really interesting is then later researchers came in and read the rules and regulations of Sumo wrestling and understood that it's not just the championship bouts that matter, but it's also sometimes the relegation matches that matter. And so then they started looking at those secondary matches that nobody looked at before and that subset of data, and they discovered there's massive match fixing going on. It's just, nobody looked at it because nobody just, as you said, that connection, uh, between th those various data sources or the sort of causal connectivity there. And so it's, it's, it's really crucial to understand, uh, that, uh, driving insight out of data, isn't a black box thing where you feed the data in and get it out. It really requires deep thinking about how to wire it up from the very beginning. >>No, that's an interesting story. I kind of wonder if the model in that case is almost the, the wrestlers themselves or the output, but definitely the, the data that goes into it. Um, yeah. So, I mean, do you see a path where organizations will achieve a hundred percent confidence? Because we all know there's a, I can't sleep at night factor, but there's also a case of what do I do today. It's, I'm probably not living in a perfect world. I might be sailing a boat across an ocean that already has a hole in it. So, you know, we can't turn everything off. We have to sort of patch the boat and sail it at the same time. Um, what do you think the, a good approaches for a large organization to improve their posture? >>You know, if you focus on perfection, you never, you never achieved that perfection a hundred percent perfection or so is never achievable. And if you want some radical change, then that that's admirable. But a lot of times it's very risky. It's a very risky proposition. So rather than doing that, there is a lot of low hanging fruit than that incremental, pragmatic step-by-step approach. If I can use an analogy from history, uh, we, we, we talk a lot about, um, the data revolution and before that, the industrial revolution, and when we think about the industrial revolution, we think about the steam engine, but the reality is that the steam engine, wasn't just one radical invention. In fact, there were a myriad of small incremental invade innovations over the course of a century that today we call the industrial revolution. And I think it's the various same thing when the data revolution where we don't have this one silver bullet that radically puts us into data Nirvana, but it is this incremental, pragmatic step-by-step change. It will get us closer. Um, pragmatic, can you speak in closer to where we want to be, even though there was always more work for us left? >>Yeah, that's interesting. Um, you know, that one hits home for me because we ultimately at Collibra take an incremental approach. We don't think there's a stop the world event. There's, you know, a way to learn from the past trends of our data to become incrementally smarter each day. And this kind of stops us from being in a binary project mode, right. Where we have to wait right. Something for six months and then reassess it and hope, you know, we kind of wonder if you're at 70% accuracy today is being at 71% better tomorrow, right? At least there's a measurable amount of improvement there. Uh, and it's a sort of a philosophical difference. And it reminds me of my banking days. When you say, uh, you know, past performance is no guarantee of future results. And, um, it's a nice disclaimer, you can put in everything, but I actually find it to be more true in data. >>We have all of these large data assets, whether it's terabytes or petabytes, or even if it's just gigabytes sitting there on all the datasets to learn from. And what I find in data is that the past historical values actually do tell us a lot about the future and we can learn from that to become incrementally smarter tomorrow. And there's really a lot of value sitting there in the historical data. And it tells me at least a lot about how to forecast the future. You know, one that's been sitting on the top of my mind recently, especially with COVID and the housing market a long time back, I competed with automation, valuation modeling, which basically means how well can you predict the price of a house? And, you know, that's always a fun one to do. And there's some big name brands out there that do that pretty well. >>Back then when I built those models, I would look at things like the size of the yard, the undulation of the land, uh, you know, whether a pool would award you more or less money for your house. And a lot of those factors were different than they are now. So those models ultimately have already changed. And now that we've seen post COVID people look for different things in housing and the prices have gone up. So we've seen a decline and then a dramatic increase. And then we've also seen things like land and pools become more valuable than they were in the housing model before, you know, what are you seeing here with models and data and how that's going to come together? And it's just, is it always going to change where you're going to have to constantly recalibrate both, you know, our understanding of the data and the models themselves? >>Well, indeed the, the problem of course is almost eternal. Um, oftentimes we have developed beautiful models that work really well. And then we're so wedded to this model or this particular kind of model. And we can fathom to give them up. I mean, if I think of my students, sometimes, you know, they, they, they, they have a model, they collect the data, then they run the analysis and, uh, it basically, uh, tells them that their model was wrong. They go out and they collect more data and more data and more data just to make sure that it isn't there, that, that, that their model is right. But the data tells them what the truth is that the model isn't right anymore that has context and goals and circumstances change the model needs to adapt. And we have seen it over and over again, not just in the housing market, but post COVID and in the COVID crisis, you know, a lot of the epidemiologists looked at life expectancy of people, but when you, when you look at people, uh, in the intensive care unit, uh, with long COVID, uh, suffering, uh, and in ICU and so on, you also need to realize, and many have that rather than life expectancy. >>You also need to look at life quality as a mother, uh, kind of dimension. And that means your model needs to change because you can't just have a model that optimizes on life expectancy anymore. And so what we need to do is to understand that the data and the changes in the data that they NAMIC of the data really is a thorn in our thigh of revisiting the model and thinking very critically about what we can do in order to adjust the model to the present situation. >>But with that, Victor, uh, I've really enjoyed our chat today. And, uh, do you have any final thoughts, comments, questions for me? >>Uh, you know, Kirk, I enjoyed it tremendously as well. Uh, I do think that, uh, that what is important, uh, to understand with data is that as there is no, uh, uh, no silver bullet, uh, and there is only incremental steps forward, this is not actually something to despair, but to give and be the source of great hope, because it means that not just tomorrow, but even the day after tomorrow and the day after the day after tomorrow, we still can make headway can make improvement and get better. >>Absolutely. I like the hopeful message I live every day to, uh, to make data a better place. And it is exciting as we see the advancements in what's possible on what's kind of on the forefront. Um, well with that, I really appreciate the chat and I would encourage anyone. Who's interested in this topic to attend a session later today on modern data quality, where I go through maybe five key flaws of the past and some of the pitfalls, and explain a little bit more about how we're using unsupervised learning to solve for future problems. Thanks Victor. Thank you, Kurt. >>Thanks, Kirk. And Victor, how incredible was that?