(bright upbeat music) >> We're back at the Red Hat Summit at the Seaport in Boston, theCUBE's coverage. This is day two. Dave Vellante and Paul Gillin. Chris Wright is here, the chief technology officer at Red Hat. Chris, welcome back to theCUBE. Good to see you. >> Yeah, likewise. Thanks for having me. >> You're very welcome. So, you were saying today in your keynote. We got a lot of ground to cover here, Chris. You were saying that, you know, software, Andreessen's software is eating the world. Software ate the world, is what you said. And now we have to think about AI. AI is eating the world. What does that mean? What's the implication for customers and developers? >> Well, a lot of implications. I mean, to start with, just acknowledging that software isn't this future dream. It is the reality of how businesses run today. It's an important part of understanding what you need to invest in to make yourself successful, essentially, as a software company, where all companies are building technology to differentiate themselves. Take that, all that discipline, everything we've learned in that context, bring in AI. So, we have a whole new set of skills to learn, tools to create and discipline processes to build around delivering data-driven value into the company, just the way we've built software value into companies. >> I'm going to cut right to the chase because I would say data is eating software. Data and AI, to me, are like, you know, kissing cousins. So here's what I want to ask you as a technologist. So we have the application development stack, if you will. And it's separate from the data and analytics stack. All we talk about is injecting AI into applications, making them data-driven. You just used that term. But they're totally two totally separate stacks, organizationally and technically. Are those worlds coming together? Do they have to come together in order for the AI vision to be real? >> Absolutely, so, totally agree with you on the data piece. It's inextricably linked to AI and analytics and all of the, kind of, machine learning that goes on in creating intelligence for applications. The application connection to a machine learning model is fundamental. So, you got to think about not just the software developer or the data scientist, but also there's a line of business in there that's saying, "Here's the business outcomes I'm looking for." It's that trifecta that has to come together to make advancements and really make change in the business. So, you know, some of the folks we had on stage today were talking about exactly that. Which is, how do you bring together those three different roles? And there's technology that can help bridge gaps. So, we look at what we call intelligent applications. Embed intelligence into the application. That means you surface a machine learning model with APIs to make it accessible into applications, so that developers can query a machine learning model. You need to do that with some discipline and rigor around, you know, what does it mean to develop this thing and life cycle it and integrate it into this bigger picture. >> So the technology is capable of coming together. You know, Amanda Purnell is coming on next. >> Oh, great. >> 'Cause she was talking about, you know, getting, you know, insights in the hands of nurses and they're not coders. >> That's right. >> But they need data. But I feel like it's, well, I feel very strongly that it's an organizational challenge, more so. I think you're confirming. It's not really a technical challenge. I can insert a column into the application development stack and bring TensorFlow in or AI or data, whatever it is. It's not a technical issue. Is that fair? >> Well, there are some technical challenges. So, for example, data scientists. Kind of a scarce kind of skillset within any business. So, how do you scale data scientists into the developer population? Which will be a large population within an organization. So, there's tools that we can use to bring those worlds together. So, you know, it's not just TensorFlow but it's the entire workflow and platform of how you share the data, the data training models and then just deploying models into a runtime production environment. That looks similar to software development processes but it's slightly different. So, that's where a common platform can help bridge the gaps between that developer world and the data science world. >> Where is Red Hat's position in this evolving AI stack? I mean, you're not into developing tool sets like TensorFlow, right? >> Yeah, that's right. If you think about a lot of what we do, it's aggregate content together, bring a distribution of tools, giving flexibility to the user. Whether that's a developer, a system administrator, or a data scientist. So our role here is, one, make sure we work with our hardware partners to create accelerated environments for AI. So, that's sort of an enablement thing. The other is bring together those disparate tools into a workflow and give a platform that enables data scientists to choose which, is it PyTorch, is it TensorFlow? What's the best tool for you? And assemble that tool into your workflow and then proceed training, doing inference, and, you know, tuning and lather, rinse, repeat. >> So, to make your platform then, as receptive as possible, right? You're not trying to pick winners in what languages to work with or what frameworks? >> Yeah, that's right. I mean, picking winners is difficult. The world changes so rapidly. So we make big bets on key areas and certainly TensorFlow would be a great example. A lot of community attraction there. But our goal isn't to say that's the one tool that everybody should use. It's just one of the many tools in your toolbox. >> There are risks of not pursuing this, from an organization's perspective. A customer, they kind of get complacent and, you know, they could get disrupted, but there's also an industry risk. If the industry can't deliver this capability, what are the implications if the industry doesn't step up? I believe the industry will, just 'cause it always does. But what about customer complacency? We certainly saw that a lot with digital transformation and COVID sort of forced us to march to digital. What should we be thinking about of the implications of not leaning in? >> Well, I think that the disruption piece is key because there's always that spectrum of businesses. Some are more leaning in, invested in the future. Some are more laggards and kind of wait and see. Those leaning in tend to be separating themselves, wheat from the chaff. So, that's an important way to look at it. Also, if you think about it, many data science experiments fail within businesses. I think part of that is not having the rigor and discipline around connecting, not just the tools and data scientists together, but also looking at what business outcomes are you trying to drive? If you don't bring those things together then it sort of can be too academic and the business doesn't see the value. And so there's also the question of transparency. How do you understand why is a model predicting you should take a certain action or do a certain thing? As an industry, I think we need to focus on bringing tools together, bringing data together, and building better transparency into how models work. >> There's also a lot of activity around governance right now, AI governance. Particularly removing bias from ML models. Is that something that you are guiding your customers on? Or, how important do you feel this is at this point of AI's development? >> It's really important. I mean, the challenge is finding it and understanding, you know, we bring data that maybe already carrying a bias into a training process and building a model around that. How do you understand what the bias is in that model? There's a lot of open questions there and academic research to try to understand how you can ferret out, you know, essentially biased data and make it less biased or unbiased. Our role is really just bringing the toolset together so that you have the ability to do that as a business. So, we're not necessarily building the next machine learning algorithm or models or ways of building transparency into models, as much as building the platform and bringing the tools together that can give you that for your own organization. >> So, it brings up the question of architectures. I've been sort of a casual or even active observer of data architectures over the last, whatever, 15 years. They've been really centralized. Our data teams are highly specialized. You mentioned data scientists, but there's data engineers and there's data analysts and very hyper specialized roles that don't really scale that well. So there seems to be a move, talk about edge. We're going to talk about edge. The ultimate edge, which is space, very cool. But data is distributed by its very nature. We have this tendency to try to force it into this, you know, monolithic system. And I know that's a pejorative, but for good reason. So I feel like there's this push in organizations to enable scale, to decentralize data architectures. Okay, great. And put data in the hands of those business owners that you talked about earlier. The domain experts that have business context. Two things, two problems that brings up, is you need infrastructure that's self-service, in that instance. And you need, to your point, automated and computational governance. Those are real challenges. What do you see in terms of the trends to decentralize data architectures? Is it even feasible that everybody wants a single version of the truth, centralized data team, right? And they seem to be at odds. >> Yeah, well I think we're coming from a history informed by centralization. That's what we understand. That's what we kind of gravitate towards, but the reality, as you put it, the world's just distributed. So, what we can do is look at federation. So, it's not necessarily centralization but create connections between data sources which requires some policy and governance. Like, who gets access to what? And also think about those domain experts maybe being the primary source of surfacing a model that you don't necessarily have to know how it was trained or what the internals are. You're using it more to query it as a, you know, the domain expert produces this model, you're in a different part of the organization just leveraging some work that somebody else has done. Which is how we build software, reusable components in software. So, you know, I think building that mindset into data and the whole process of creating value from data is going to be a really critical part of how we roll forward. >> So, there are two things in your keynote. One, that I was kind of in awe of. You wanted to be an astronaut when you were a kid. You know, I mean, I watched the moon landing and I was like, "I'm never going up into space." So, I'm in awe of that. >> Oh, I got the space helmet picture and all that. >> That's awesome, really, you know, hat's off to you. The other one really pissed me off, which was that you're a better skier 'cause you got some device in your boot. >> Oh, it's amazing. >> And the reason it angered me is 'cause I feel like it's the mathematicians taking over baseball, you know. Now, you're saying, you're a better skier because of that. But those are two great edge examples and there's a billion of them, right? So, talk about your edge strategy. Kind of, your passion there, how you see that all evolving. >> Well, first of all, we see the edge as a fundamental part of the future of computing. So in that centralization, decentralization pendulum swing, we're definitely on the path towards distributed computing and that is edge and that's because of data. And also because of the compute capabilities that we have in hardware. Hardware gets more capable, lower power, can bring certain types of accelerators into the mix. And you really create this world where what's happening in a virtual context and what's happening in a physical context can come together through this distributed computing system. Our view is, that's hybrid. That's what we've been working on for years. Just the difference was maybe, originally it was focused on data center, cloud, multi-cloud and now we're just extending that view out to the edge and you need the same kind of consistency for development, for operations, in the edge that you do in that hybrid world. So that's really where we're placing our focus and then it gets into all the different use cases. And you know, really, that's the fun part. >> I'd like to shift gears a little bit 'cause another remarkable statistic you cited during your keynote was, it was a Forrester study that said 99% of all applications now have open source in them. What are the implications of that for those who are building applications? In terms of license compliance and more importantly, I think, confidence in the code that they're borrowing from open source projects. >> Well, I think, first and foremost, it says open source has won. We see that that was audited code bases which means there's mission critical code bases. We see that it's pervasive, it's absolutely everywhere. And that means developers are pulling dependencies into their applications based on all of the genius that's happening in open source communities. Which I think we should celebrate. Right after we're finished celebrating we got to look at what are the implications, right? And that shows up as, are there security vulnerabilities that become ubiquitous because we're using similar dependencies? What is your process for vetting code that you bring into your organization and push into production? You know that process for the code you author, what about your dependencies? And I think that's an important part of understanding and certainly there are some license implications. What are you required to do when you use that code? You've been given that code on a license from the open source community, are you compliant with that license? Some of those are reasonably well understood. Some of those are, you know, newer to the enterprise. So I think we have to look at this holistically and really help enterprises build safe application code that goes into production and runs their business. >> We saw Intel up in the keynotes today. We heard from Nvidia, both companies are coming on. We know you've done a lot of work with ARM over the years. I think Graviton was one of the announcements this week. So, love to see that. I want to run something by you as a technologist. The premise is, you know, we used to live in this CPU centric world. We marched to the cadence of Moore's Law and now we're seeing the combinatorial factors of CPU, GPU, NPU, accelerators and other supporting components. With IO and controllers and NICs all adding up. It seems like we're shifting from a processor centric world to a connect centric world on the hardware side. That first of all, do you buy that premise? And does hardware matter anymore with all the cloud? >> Hardware totally matters. I mean the cloud tried to convince us that hardware doesn't matter and it actually failed. And the reason I say that is because if you go to a cloud, you'll find 100s of different instance types that are all reflections of different types of assemblies of hardware. Faster IO, better storage, certain sizes of memory. All of that is a reflection of, applications need certain types of environments for acceleration, for performance, to do their job. Now I do think there's an element of, we're decomposing compute into all of these different sort of accelerators and the only way to bring that back together is connectivity through the network. But there's also SOCs when you get to the edge where you can integrate the entire system onto a pretty small device. I think the important part here is, we're leveraging hardware to do interesting work on behalf of applications that makes hardware exciting. And as an operating system geek, I couldn't be more thrilled, because that's what we do. We enable hardware, we get down into the bits and bytes and poke registers and bring things to life. There's a lot happening in the hardware world and applications can't always follow it directly. They need that level of indirection through a software abstraction and that's really what we're bringing to life here. >> We've seen now hardware specific AI, you know, AI chips and AI SOCs emerge. How do you make decisions about what you're going to support or do you try to support all of them? >> Well, we definitely have a breadth view of support and we're also just driven by customer demand. Where our customers are interested we work closely with our partners. We understand what their roadmaps are. We plan together ahead of time and we know where they're making investments and we work with our customers. What are the best chips that support their business needs and we focus there first but it ends up being a pretty broad list of hardware that we support. >> I could pick your brain for an hour. We didn't even get into super cloud, Chris. But, thanks so much for coming on theCUBE. It's great to have you. >> Absolutely, thanks for having me. >> All right. Thank you for watching. Keep it right there. Paul Gillin, Dave Vellante, theCUBE's live coverage of Red Hat Summit 2022 from Boston. We'll be right back. (mellow music)

>>From around the globe. It's the cube with digital coverage of IBM. Think 20, 21 brought to you by IBM. >>Hello everyone. And welcome back to IBM. Think 20, 21, the virtual edition, the cubes continuous coverage. And we're excited to talk about people. How do you align people and technology? Of course, there's a lot of process in between. Those are the hard, hard things. Technology is sometimes easy. Amy Wright is here. She's managing partner of talent transformation at IBM, Amy. Great to see you. >>Thanks. Great to be here. And Dave, >>Yeah, you know, we love to talk tech and sometimes we kind of sweep the really hard stuff under the rug. And we talk about transformation. I mean, it's, it's ongoing. I mean, you think about the pandemic last year, it was sort of this forced March to digital. We had to transform overnight, you know, the vast majority of leaders. I think that in figures like close to 95, 96% say that they've accelerated their digital transformation by half a half a decade. Uh, and of course that was a lot of, it was like I say, it's a forced March, so it wasn't really planful, but now they've got time to plan about a digital first approach and how to deal with remote workers. I wonder if you could talk about the role that people play in that digital transformation, >>Right. Thanks, Dave. Uh, I'm happy to, you know, a lot of people think of digital transformation about being technology oriented. It's a total shift in tech and it is, but it really can't be successful with just tech. So you're right with the pandemic has done for digital transformation. Is it really, it pushed us to these technology extremes more than anyone could have anticipated, particularly with our ways of working, being remote. It also pushed us to extremes and highlighted the role that humanity play played plays will continue to play. So we've been pushed to reimagine jobs, push to re-imagine workplaces, uh, push to re-imagine, how technology can deliver this connected enterprise, um, you know, through, through virtual reality, um, and virtual working, wasn't really something that was accepted before, but now we've been, you know, forced to accept it, which is, which is really great for the digital transformation because it accelerated that. >>So the connected enterprise though, isn't really just working virtually it's these new levels of productivity and decision-making that are enabled by intelligent workflows and cloud and data. And so the technology is absolutely critically important, but automation doesn't have empathy. So it takes people to turn these insights that are brought to us through technology and automation. It takes people to turn them into action and it's that human technology partnership that's required for the digital transformation to get to that desired impact. So when you think about, when we think about people in the role they play and, uh, you know, the pivotal role they play truly, multi-part, it's kind of three parts. One is people are the ones that build tech. And so they influence whether or not the automation is going to work, whether it meets the needs of the enterprise, if it takes advantage of the latest thinking, um, and it fits, you know, it was irresistible. >>If you will. The second is the people use the technology to gain this meaningful insight and turn it into action. And then the third is the people are the ones that embed this tech change into culture. So that's actually sustainable. So to be able to drive this sustainable digital transformation, the people, it requires the people to make it happen. So if you look at healthcare, Dave, think about the dramatic shift in healthcare in the past year where doctors have shifted to telemedicine, nurses have shifted to using iPads as caregivers at the, you know, with their patients that not only required to shift in the tech, but an adoption of caregivers have a new way of working that again, couldn't have been successful unless they adopted an embedded and embedded a different way of working in a different culture in everything that they do. >>You know, what you said is really important, especially we talk a lot about what machines can do that people can't and what people can do that machines can't and you just nailed it with, with empathy. And, and when you think about to the remote work, I think prior to the pandemic, it was probably around 15, 16% of workers were remote. And when you, when we do, we do surveys with a partner ETR out in New York and they, they project based on the surveys that, that that's going to double, but somewhere between 33, 35%, but people don't really know when, when you talk to people, they go, Hey, I kind of like working at home. Other people say, I can't wait to get back to the office. So people obviously critical part of the digital transformation, but how do you think about creating those meaningful experiences at work, whether that's remote, part-time remote, you know, full-time back at work. >>So this is a really great, great question because I think our point of view on this has changed. So first of all, most enterprises we talk to will move back to some hybrid kind of environment. We're never going to be everybody back in the office. Again, that's, that's, that's not who we will be moving forward, but the expectations of employees have changed. Um, we all know that, you know, think about your consumer lives and, and how we experienced that personalized ex that, that, that personalization, when we go to buy something online, that's now bled over into the workplace. So the employees expect that exact same personalized experience at work, but it's now so much more than that. Now it's not only personalization, which, you know, obviously tech enables quite dramatically, but the experience is broader to look at a holistic relationship between the employer and the employee. >>That's a little bit less, it's less transactional. Like I do my job and my company pays me for doing this, this set of activities, but it's more supportive and integrated with their personal selves. So, you know, we did a recent study in which we, uh, looked at consumers and employees and their highest priority areas for the expectations that they now have for their employers is career and skill advancement opportunities with speed. Second is work-life balances that might take the form of what hours they worked, our ability to, um, you know, manage with what they're doing in their home with, with their families and children, uh, you know, their ability to be camera ready or not at all times of day and night and actually where they work from. So people are now working, not only at home, but they're moving to different cities and want that flexibility. >>And then third, a high area of priority now is ethics and values. So not only diversity equity inclusion, obviously critically important, but ways of working and meaningful and purposeful work. So when you look at all of those together, the employee experience has grown to be not only that of personalization, like we have an art consumer world that is, that is critically important, but now, um, it's all of these other things, as well. As a matter of fact, they become so important, Dave, that in our recent research, it shows that one in four employees will change employers in 2021, one in four, one in four will also change professions in 2021. And while about 75% of employers, companies believe that they are doing a good job of meeting the needs. These expanded needs of their employees, less than half of employees feel the same way. So there's a lot of work to be done. So you asked the question, why is this people experience so important? It's important because it's required for the digital transformation. And it's so much broader than what we used to think that it's now a competitive differentiator for employers as they try to not only achieve their digital transformation, but as their organizations disrupt over and over again, um, it's, it's a requirement in order to meet their meet their enterprises objectives. >>So it was a great, uh, great stats, Amy, to just put out there. I mean the career advancement, I think, I feel like it's always been there, but it's now much more front and center employees are more vocal about it, the work life balance, same thing. I mean, you're seeing some organizations, you know, a hundred hour weeks where we're revolting and then, you know, the ethics and values piece to me is one of the most interesting, I often joke Milton Friedman rolling over in his grave because he was the economist that said, Oh, it's just about shareholder value. That's it. And that's not anymore. Um, in fact, there's clearly a relationship between shareholder value in, in ESG and ethics and, and young people are very, very concerned about it. So here's the question who's accountable for making sure that you have a positive employee experiences occur. >>Yeah, really, really, really good question. And the thing is, this is what makes it so hard. There's not one group or one person it's actually all of us. And I know that answer sounds like a little bit like a cop out, but this is why it makes it so hard. Every leader's responsible for the employee experience, every manager is responsible for the employee experience. Every employee is actually not responsible for the experience of their teammates. And actually speaking up if the experience isn't using inclusiveness as a, as an example, if it's not inclusive, every experience, every employee has the responsibility to speak up. So some companies actually have employee experience leaders. Some companies have digital transformation leaders that embodies that, that, that includes that employee experience, but most actually start this journey through the, through the partnership between it and HR. So I teach responsible for this technology architecture, the cloud strategy, the data strategy, architectural framework, all those pieces that put together the foundation and the building brought blocks and the security that helped to Mo um, modernize this employee experience. >>And by the way, they're doing this at the same time where they're modernizing their entire way in which the it function operates. Um, so you got it. That's kind of setting the stage and the foundation for what's possible. And then you have HR who's operating as the steward of the employee experience that those people experiences, um, and putting them in place in a consistent and consistent and a positive way across the entire enterprise. So things like design thinking, um, that puts the employee at the center of the way we, um, architect and create these experiences using rapid iterative design principles with, again, with this, with the employee at the center, making that the cultural norm across the enterprise is a really big deal. So HR is usually in the lead on making that happen. But again, this is a cultural shift, not just I have a project, you know, I'll kind of have a project plan and here's, here's what I'm going to execute on leadership roles. >>So HR is, is the steward of leadership and those characteristics of leaders now are changing very dramatically to be more, even in a big enterprise, large global enterprise entrepreneurial transparent, co-creation really at the core of everything. So being transplant transparent with your teams and be able to co-create, um, you know, procreate for the future. So data and AI, we can use data. And I AI now to actually IM uh, uh, predict the impact that the workforce and the cultural will have on business results, predict attrition, predict what different work workforce design scenarios will look like to the supply chain, um, uh, predict the speed of hiring and how that will impact literally bottom line business results. So you said it right, when, when, when you talked about shareholder value, the P people is at the center of shareholder value now. So, um, our functions need to be modernized, but it's really this partnership between HR and it that's gonna be able to make it happen in a big way. >>It's interesting. I'm just thinking that AI as well, can be a Canary in a coal mine when there's potential problems. Um, and I love this transparency. That's critical. Co-creation so, Oh, okay. So tech is a key part of that, especially in terms of when you go from analog to digital, taking friction out of the system shows the employees that we're investing in, in your experience. Uh, but it's more than that. You're saying it's, it's cultural as it makes it so kind of fuzzy cultural it's it's it takes a village. So that's, that's part of what makes it so hard. How do you think about, you know, the journey? Where do you start and how do you keep iterating? You're never done in this, this world. Are you, >>Yeah, that's a question, uh, everybody's asking now, w w where do I start? So, as you said, this is very hard, um, and it's hard. One of the reasons it's hard is it's because it revolves culture. Um, it's not only about technology. They are hard. Technologies are harder in their own, right? It's not just about data, that's hard in its own, right? But once you involve technology, it makes, it makes it even even harder. And of course the people aspect, unless done very proactively and meaningfully, it can be kind of a wild card right on, who's going to adopt what, so where do you start? So, um, the way we like to think about, um, giving advice to enterprises, uh, regarding where this is, we've seen this work well is to pick a business problem. So what's a business problem that if you solved, you can actually make an impact, not only for your people, but for your people, but for the enterprise. >>So if you could pick a business problem and actually fix it, using data, using cloud, using people, experiences using a cultural shift, then you'll get that. Buy-in, you'll get the buy-in that, yes, we can do this. This is this, this is very doable. We can repeat, it's repeating PETA repeatable over and over again. And it has an impact on our culture. That's a great place to start. Okay. So then you say, if that's a place to start, how do we actually, there's got to be foundational things that have to be in place to make that work. So one of them is a consistency in data and the use of AI and the ability to make insights meaningful, you know, that come through data and AI. And the other part that's really important once you pick your business problem is the shift in the way of working the shifts so that, um, it can impact cultural, um, cultural change, uh, shifts so that there's co-creation with your people and there's transparency. >>So each one of these business problems and the way companies, uh, pick to fix them, they, they won't all work. And the way you get that trust and transparency with your people is as scary as it is to share with them what you're attempting to do and share with them how you're doing along the journey. And if it fails, okay, fails, you know, pick yourself back up and start again, that trust and transparency with your people. That's the way, that's the way we all make this cultural impact. So it, you know, kind of the, none of this is to be, to make sustainable change. We can all make short term change. We can do projects, but to make sustainable change. The humanity aspect has to come to life in these digital transformations. And that only comes to life with this cultural shift, >>Amy, right? You've thought about this a lot deep expertise in the area. Really appreciate your sharing it with our audience. And thanks for coming on the cube, Dave, my pleasure. All right. Keep it right there. But this is Dave Volante. You're watching IBM think 2021, the virtual, is it addition from the queue.

>>mhm Yes. >>Welcome back to the cubes coverage of red hat summit 2021 virtual. I'm john for a host of the cube we're here in Palo alto. Were remote with our great guest here cube alumni. I've been on many times chris wright, Senior vice president and CTO of red hat chris great to see you. Always a pleasure to have you on the screen here too. But we're not in person but thanks for coming in remote. >>Yeah, you bet. Glad to be here. >>Not only were talking about speeds and feeds, digital transformation going under the hood here we're gonna talk about red hats, expanded collaboration with boston University to help fund education and research for open source projects. So you guys have a huge relationship with boston University. Talk about this continued commitment. What's the news, what's the, what's the story? >>Well, we have a couple different things going on uh and and the relationship we have with the EU is many years in. So this itself isn't brand new. Um one of the things that's important to highlight here is we are giving something north of $550 million dollars worth of software to be you really in pursuit of running uh powering and running scaled infrastructure. That's part of the open hybrid class. Um and that's that's an important piece which we can touch on a little bit as we talk to this conversation. The other one is like I said, this isn't a new relationship with the U. And what we're doing now is really expanding the relationship. So we've we've built a great connection directly with the You were substantially expanding that. Um The original relationship we had was a $5 million relationship spread over five years now. We're talking about a $20 million Relationship spread over five years. So really a significant expansion. And of course that expansion is connected to some of the work that we plan to do together in this open hybrid cloud infrastructure and research space. So a lot of things coming together at once to really really advance the red hat ca laboratory at the U. That combined effort in bringing you know, cloud research and open source and all these things together >>and a lot of actually going on. So basically the boston area lot of universities, but I love the shirt you're wearing with his red hat innovation in the open. This is kind of one of those things you also mentioned out of this huge subscription of software grant that's going to be you just a huge number give value for for the boston University. But you also have another project that's been going on the collaborative research and education agreement called red hat collaborative orI Okay, this was in place. You mentioned that. How's that tying in because that was pre existing. Now. You've got the grant, you got your funding more and more research. Talk about how this connects into the open cloud initiative because this is kind of interesting. You're not bringing hybrid cloud kind of research and practical value in A i ops is hot. You can't you can't go anywhere these days without having great observe ability. Cloud native more and more is more complex and you've got these young students and researchers dying and get their hands on it. Take us through the connection between the CA laboratory and open open cloud. >>So the CA laboratory is a clever name that just talks about collaboration and research laboratory type research. And initially the CA laboratory focus was on the infrastructure running the cloud and some of the application workloads that can run on top of an open cloud infrastructure uh that are that's very data centric. And so this is uh an opportunity for multidisciplinary work looking at modeling for um for health care, for example for how you can improve imaging and we've had a great results in this collaboration. Um We've talked at times about the relationship with the boston Children's Hospital and the chris project not related to me, but just similar acronym that spells chris. Um and these things come together in part through connecting relationships to academia, where academia as research is increasingly built in on and around open source software. So if you think of two parallel worlds, open source software development, just the activity of building open source software, it brings so many people together and it moves so quickly that if you're not directly connected to that as an academic researcher, you risk producing academic research results that aren't relevant because it's hard for them to connect back to these large, fast moving projects, which may have invented a solution to the problem you've been focused on as an academic if you're not directly connected. So we see academia and open source coming together to build really a next generation of understanding of the scientific in depth and he's joining the >>train operations you're talking about here though, this is significant because there's dollars behind it, right? There's real money, it's not >>just the right software, >>it's it's a center, it's a joint operation. >>That's right. And so when you think about just the academic research of producing um ideas that manifest themselves as code and software projects, we want to make sure we're first connecting the software projects to open source communities in with our own engineering experience, bringing code into these open, open source projects to just advance the the feeds and speeds and speeds, the kind of functionality the state of the art of the actual project. We're also taking this to a new level with this expanded relationship and that is software today. When you, when you operate software as a cloud, a critical part of the software is the operationalization of that software. So software just sitting there on the shelf doesn't do anybody any good. Even if the shelf is an open source project, it's a tar ball waiting for you to download. If you don't ever grab it and run it, it's not doing anybody any good. And if the challenge of running it is substantial enough that it stops you from using that software, you've created a barrier to the value that's locked inside that project. The focus here is how can we take that the operations experience of running a cloud, which itself is a big complex distributed system, tie some of those experiences back into the projects that are used to build that infrastructure. So you're taking not just the output of the project, but also the understanding of what it takes to run a project and bringing that understanding and even the automation and code associated with that back into the project. So, your operational izing this open source software and you're building deeper understanding of what it means to operate things that scale, including data and data sets that you can use to build models that show how you can create the remediation and closed loop systems with AI and machine learning, you know, sort of synthesizing all the data that you generate out of a big distributed infrastructure and feed that back into the operations of that same infrastructure. So a lot going on there at the same time operationalization as as an open source initiative but also um really the understanding advancement of A I and data centric operations, so ai ops and closed the remediation. >>Yeah, I mean, devops developer and operations to operationalize it and certainly cloud Native put an emphasis on Day two operations, which leads a lot more research, a lot more uh student work on understanding the coding environment. Um so with that I got to ask um I asked you about this uh massachusetts focused or this open cloud initiative because you guys are talking about this open cloud initiative including this massachusetts. Open Cloud, what is that? What is the massachusetts? Open Cloud sounds like you're offering a kind of open person, not just bu but other um Yeah, institutions. >>That's right. So the the M o C massachusetts open cloud is itself a cross um organizational collaboration bringing together five different academic institutions in New England In massachusetts. It's bu it's Harvard mit, its Northeastern and its U. Mass. Coming together to support a common set of infrastructure which is cloud. It's a cloud that runs in a data center and then um it serves a couple of different purposes. One is research on clouds directly. So what does it mean to run a cloud? What does it look like from a research point of view to understand large scale distributed systems? And then the other is more on top. When you have a cloud you can run workloads and those workloads scaled out to do say data processing, looking at the implications of across different fields which could be natural sciences, could be medicine, could be, even political science or social science is really a multidisciplinary view of what it means to leverage a cloud and run data centric workloads on top. So two different areas that are of a focus for the M. O. C. And this becomes this sort of vehicle for collaboration between Red Hat View and the Red Hot Laboratory. >>So I have to ask only because I'm a big fan of the area and I went to one of those schools, is there like a bean pot for technical hackathons where you get all the schools matched up against each other on the mass open cloud and compete for who gets bragging rights and the text city there. >>It's a great question. Not yet. But I'll jot that down here in hell. Up on that. >>Happy to sponsor. We'll we'll do the play by play coverage, you know. Great. >>I love that. Yeah, kind of twitch tv style. The one thing that there is which is very practical is academic research grants themselves are competitive, right? People are vying for research dollars to put together proposals, Bring those proposals to um the agency that's that's that's giving out grants and winning those grants is certainly prestigious. It's important as part of her research institutes continue to fund the work that they're doing. Uh Now we've been associated uh through the work we've done to date with the U. With Yeah almost $15 million 20 papers. So there's there's a lot of work you can't quite call the play by play. It's a >>scoreboard. I mean their numbers you can put numbers on the board. I mean that's what's one of the things you can measure. But let me ask you on those grants. So you're saying this is just the bu you guys actually have data on um the impact of the relationship in terms of grants and papers and stuff like that academic work. >>That's right. That's right. And so those numbers that I'm giving you are examples of how we've worked together with the u to help their faculty generate grant dollars that then fund some of the research that's happening there together with redhead engineers and on and on the infrastructure like the massachusetts Open cloud. >>That's a good way to look at the scoreboard. It's a good point. We have to research that if you don't mind me asking on this data that you have um are all those projects contributing to open source or do they have to be? That's just generic. Is that all of you all papers around bu is part of the research. In other words, I'm trying to think if I'm in open source, has this contributed to me as an >>open source? Yeah, it's a big and complex question because there's so much research that can happen through a research institution. And those research grants tend to be governed with agreements and some of those agreements have intellectual property rights um front and center and might require things like open source software as a result, the stuff that we're working on clearly isn't that focus area of open source software and and research activities that help kind of propel our understanding forward of what does it mean to do large scale distributed systems creation and then operation. So how do you develop software that does it? How do you how do you run the software that builds these big large distributed systems? So we're focused in that area. Um some of the work that we facilitated through that focus includes integrating non open source software that might be part of um same medical imaging. So for example work we've done with the boston Children's Hospital That isn't 100 doesn't require us to be involved 100 of the open source pieces. All the infrastructure there to support it is. And so we're learning how we can build integrated pipelines for data analysis and image analysis and data sharing across different institutions uh at the open source project level. Well maybe we have a specific imaging program that is not generated from this project. And of course that's okay with >>us. You know chris you bring up a good point with all those conversations. I could see this really connecting the dots. Most computer science programs. Most engineering programs haven't really traditionally focused on it at the scale we're talking about because we look at cloud scale but now scaling with hybrid it's real engineering going on to think about the large scale. We know all the big hyper scale ear's right so it's not just I. T. Provisioning you know network connection and doing some I. T. Work. We're talking about large scale. So I have to ask you as you guys look at these relationships with academics uh academia like like bu and others um how are the students responding to this? Are you guys seeing any specific graduate level advancements? Because you're talking about operational roles that are becoming so important whether it's cyber security and as cloud needed because once more data driven you need to have all this new scale engineered up. That's >>what how >>do you look at that? >>There's two different pieces that I would highlight. One is just the data science itself. So schools still need to produce data scientists. And having data is a big part of being a data scientist and knowing what your what your goals are with that data and then experimenting with different techniques, whether it's algorithms or tools. It's a big part of being a data scientist sort of spelunking through the data. So we're helping produce data. We're looking at data science efforts around data that's used to operationalize infrastructure, which is an interesting data science endeavor by itself. The other piece is really what you highlighted, which is there's an emergence of a skill set in the industry, often referred to as SRE site reliability engineering. Um it is a engineering discipline. And if you back up a little bit and you start thinking about what are the underlying principles behind large scale distributed systems, you get to some information theory and computer science. So this isn't just something that you might think of as um some simple training of a few key tools and knowing how to interpret a dashboard. And you're good to go, this is a much more sophisticated view of what does it mean to really operate large scale infrastructure, which to date, there aren't a lot of these large scale infrastructures available to academics to research because their commercial endeavors >>and their new to me. I was talking to some young folks my son's age and daughters age and I was saying, you know, architect in a building, a skyscraper isn't trivial. You can't just do that overnight. There's a lot of engineering that goes on in that science, but you're bringing kind of operating systems theory, systems thinking to distributed computing. I mean that's combination of a interdisciplinary shift and you got, I won't say civil engineering, but like concept is there, you've got structure, you've got networks, they're changing and then you've got software so again completely new area. >>That's right and there's not a lot of even curriculum that explores this space. So one of the opportunity, there's a great program that really focuses on um that that space of site reliability engineering or operational izing software. Um And then the other piece that I'm I'm really excited about is connecting to open source communities so that as we build software, we have a way to run and operationalize that software that doesn't have to be directly tied to a commercial outlet. So products running in the cloud will have a commercial S. L. A. And commercial agreements between the user and the producer of that service. How do you do that in open source context? How do you leverage a community, bring that community software to a community run service, learn through the running of that service. How to best build architect the service itself and then operationalized with the tooling and automation that service? How do you, how do you bring that into the open source community? And that's something that we've been referring to as the operate first initiative. How do you get the operationalization of software? Really thought of as a primary focal point in the software project where you normally think about the internals of software, the features, the capabilities of functionality, less about the operationalization. So important shift at the open source project level, which is something that I think will really be interesting and we'll see a lot of reaping a lot of rewards. Just an open source communities directly. >>Yeah, speed and durability. Certainly having that reliability is great. You know, I love talking with you guys at red hat because, you know, software, you know, open source and you know, operating systems because as it comes together in this modern era, what a great, great fit, great work you're doing with Boston University's and the mass open cloud initiative. Congratulations on that. I got I got to ask you about this Red Hat Graduate Fellows program you have because this kind of speaks to what you guys are doing, you have this kind of this redhead graduate fellows network and the work that's being done. Does that translate into red hat at all? From an engineering standpoint? How does that, how does that work together? >>Basically, what we do is we support um PhD students, we support post docs. So there's a real direct support to the, you know, that is the Red Jack Graduate Fellow program on our focus there is connecting those um uh academics, the faculty members and the students to our engineers to work together on key research initiatives that we think will help drive open source software agendas forward really broad can be in all different areas from security to virtualization too, the operating systems to cloud distributed systems, uh and one of the things that we've discovered is it creates a great relationship with the university and we find students that will be excited to leave university and come into the the industry workforce and work at Red hat. So there is a direct talent relationship between the work that we do at bu and the talent that we can bring into red hat, which is awesome. Uh We know these people we've worked with well with them, but also we're kind of expanding understanding of open source across, you know, more and more of academia, which I think is really valuable and important for red hat. We just go out to the the industry at large, um, and helping bring a set of skills to the industry that whether they're coming, whether these are students that come into red hat or go elsewhere into the industry, these are important skills to have in the industry. So we look at the, how do you work in open source communities? How to operationalize software at scale? These are important things. They didn't >>expand, expand the territory if you will in terms of systems thinking. We just talked about great collaboration. You guys do a great job chris great to have you on a quick final word from you on this year at red hat summer. I know it's virtual again, which we could be in person, but we're starting to come out of the covid kind of post covid right around the corner. Um, what's the update? How would you describe the current state of red hat? Obviously you guys still got that, that vibe. You still pumping strong a lot going on. What's the current? What's the current, uh, bumper sticker? What's the vibe? >>Well, in many ways, because we're so large and distributed. Um, the last year has been, uh, can't say business as usual because it's been an impact on everybody, but it hasn't required us to fundamentally change. And as we work across open source communities, there's been a lot of continuity that's come through a workforce that's gone completely distributed. People are anxious to get to the next phase, whatever back to normal means. Uh, and people at Red Hat are no different. So we're looking forward to what it can mean to spend time with colleagues in offices, were looking forward to what it means to spend time together with our friends and families and travel and all those things. But from a, from a business point of view, Red Hat's focus on the open hybrid cloud and that distributed view of how we work with open source communities. That's something that's, it's only continued to grow and pick up over the course of the last year. So it's clearly an important area for the industry and we've been busier than ever the last year. So, uh, interesting, interesting times for everybody. >>Well, it's great to see and I love how the culture maintains its its relevance, its coolness intersection between software, Open Source and systems. Great, Great working congratulations chris. Thanks for coming on. >>Thank you. >>All right. I'm John for here with the Cube for Red Hat Summit 2021. Thanks for watching. Mhm.

>>from >>around the globe. It's the cube with digital coverage >>of IBM. Think 20 >>21 brought to you by IBM. >>Hello, I want to welcome back to IBM think 2021 the virtual edition. The cubes continuous coverage. And we're excited to talk about people. How do you align people and technology? Of course there's a lot of process in between. Those are the hard, hard things technology sometimes easy amy right is here. She's managing partner of talent transformation at IBM Amy great to see you. >>Thanks Great to be here Dave >>Yeah. You know we love to talk tech and sometimes we kind of sweep the really hard stuff under the rug and we talk about transformation. I mean it's it's ongoing. I mean you think about the pandemic last year was sort of this forced march to digital, we had to transform overnight. You know the vast majority of leaders I think that figures like close to 95 96 say that they've accelerated their digital transformation by half a half a decade. And of course that was a lot of it was like I say, it's a forced march, so it wasn't really planned fel but now they've got time to plan about a digital first approach and how to deal with remote workers. I wonder if you could talk about the role that people play in that digital transformation. >>All right, thanks. Dave I'm happy to, you know, a lot of people think of digital transformation about being technology oriented. It's a total shift in tech and it is but it really can't be successful with just tech. So you're right with the pandemic has done for digital transformation, Is it really it pushed us to these technology extremes more than anyone could have anticipated, particularly with our ways of working being remote. It also pushed us to extremes and highlighted the role that humanity played place, it will continue to play. So we've been pushed to reimagine jobs, pushed to reimagine workplaces, push to reimagine how technology can deliver this connected enterprise. Um you know, through through virtuality. Um and virtual working wasn't really something that was accepted before, but now we've been forced to accept it, which is which is really great for the digital transformation because it accelerated that. So the connected enterprise though isn't really just working virtually. It's these new levels of productivity and decision making that are enabled by intelligent workflows and cloud and data. And so technology is absolutely critically important. But automation doesn't have empathy. So it takes people to turn these insights that are brought to us through technology and automation. It takes people to turn them into action. And it's that human technology partnership that's required for the digital transformation to get to that desired impact. So when you think about, when we think about people in the role they play, and, you know, it's the pivotal role they play. It's really multi part, it's kind of three parts. one is people are the ones that build the tech and so they influence whether or not the automation is going to work, whether it meets the needs of the enterprise, if it takes advantage of the latest thinking, um and if it's, you know, it's irresistible if you will. The second is the people use the technology to gain this meaningful insight and turn into action. And then the third is the people are the ones that embed this tech change into culture, so that's actually sustainable. So to be able to drive this sustainable digital transformation the people, it requires the people to make it happen. So, if you look at health care, Dave think about the dramatic shift in health care in the past year, where doctors have shifted to telemedicine, nurses have shifted to using ipads as caregivers at the, you know, with their patients that not only required to shift in the tech, but an adoption of caregivers of a new way of working that again, could have been successful unless they adopted and embedded, embedded a different way of working in a different culture and everything that they do. >>You know what you said is really important. Especially we talk a lot about what machines can do that people can what people can do that machines can you just nailed it with empathy. And and when you think about to the remote work, I think prior to the pandemic, it was probably around 15 16% of workers were remote. And when you when we do we do surveys with the partner E. T. R. In new york. And the They project based on these surveys that that that's gonna double somewhere between you know, 33 35%. But people don't really know when when you talk to people like I kind of like working at home, other people say I can't wait to get back to the office. So people obviously critical part of the digital transformation. But how do you think about creating those meaningful experiences at work? Whether that's remote? Part time, remote? Full time back at work? >>So this is a really great great question because I think our point of view on this has changed. So first of all, most enterprises we talked to will move back to some hybrid kind of environment. We're never going to be everybody back in the office again. That's that's that's not who we will be moving forward. But the expectations of employees have changed. Um We all know that, you know, think about your consumer lives and and how we experience that personalized that that that personalization when we go to buy something online that's now bled over into the workplace. So the employees expect that exact same personalized experience at work. But it's now so much more than that now. It's not only personalization which you know, obviously tech enables quite dramatically, but the experience is broader to look at a holistic relationship between the employer and employee. That's a little bit less, it's less transactional. Like I do my job and my company pays me for doing this set of activities, but it's more supportive and integrated with their personal cells. So, you know, we did a recent study in which we looked at consumers and employees and their highest priority areas for the expectations that they now have for their employers is career and skill advancement opportunities with speed. Second is work life balance is that might take the form of what hours they work. Their ability to um you know, manage with what they're doing in their home with their families and Children, uh you know, their ability to be camera ready or not at all times of day and night and actually where they work from. So people are now working not only at home, but they're moving to different cities and want that flexibility. And then third, hi, area of priority now is ethics and values. So not only diversity, equity, inclusion, obviously critically important, but ways of working and meaningful and purposeful work. So when you look at all of those together, the employee experience has grown to be not only that of personalization, like we have in our consumer world, that is that is critically important, but now um it's all of these other things as well as a matter of fact, they become so important dave that in our recent research, it shows that one in four Employees will change employers in 2021, 1 and 41 and four will also change professions in 2021. And while about 75 of employers, companies believe that they are doing a good job of meeting the needs, these expanded needs of their employees, less than half of employees feel the same way. So there's a lot of work to be done. So you ask the question why is this? People experience so important? It's important because it's required for the digital transformation and it's so much broader than what we used to think that it's now a competitive differentiator for employers as they try to not only achieve their digital transformation but as their organizations disrupt over and over again. Um It's a requirement in order to meet their meet their enterprises objectives. >>So it was a great great stats and you just put out there in the career advancement. I think I feel like it's always been there but it's now much more front and center employees are more vocal about it. The work like balance, same thing. I mean you're seeing some organizations in 100 hour weeks were revolting and and then you know, the ethics and values piece to me is one of the most interesting I often joke Milton Friedman rolling over in his grave because he was, the economist said uh it's just about shareholder value, that's it and that's not anymore. In fact there's clearly a relationship between shareholder value and E. S. G. And and ethics and young people are very very concerned about it. So here's the question who's accountable for making sure that you have a positive employee experiences occur. >>Yeah. Really really really good question. And the thing is this is what makes it so hard. There's not one group or one person it's actually all of us. And I know that answer sounds like a little bit like a cop out. But this is why it makes it so hard. Every leader is responsible for the employee experience, every manager is responsible for the employee experience. Every employee is actually not responsible for the experience of their teammates. And actually speaking up if the experience isn't using inclusiveness as an example if it's not inclusive. Every experience every employee has the responsibility to speak up. So some companies actually have employee experienced leaders. Some companies have digital transformation leaders that embodies that that that includes that employee experience. But most actually start this journey through the through the partnership between I. T. And H. R. So I. T. Is responsible for this technology architecture, the cloud strategy, the data strategy architectural framework. All those pieces that put together the foundation and the building blocks and the security that helped to um modernize this employee experience and by the way they're doing this at the same time with their modernizing their entire way in which the function operates. Um So you got I. T. That's kind of setting the stage and the foundation for what's possible. And then you have HR. Who's operating as the steward of the employee experience that those people experiences um and putting them in place in a consistent and consistent in a positive way across the entire enterprise. So things like design thinking um that puts the employee at the center of the way we um architect and create these experiences using rapid iterative design principles. With again with this with the employee at the centre making that the cultural norm across the enterprise is a really big deal. So HR is usually in the lead on making that happen. But again this is a cultural shift, not just I have a problem, you know kind of a project plan and here's here's what I'm going to execute on leadership roles. So HR is the steward of leadership and those characteristics of leaders now are changing very dramatically to be more even in a big enterprise large global enterprise entrepreneurial transparent co creation really at the core of everything. So being transplant transparent with your teams and be able to co create um you know co create for the future. So data and ai we can use data and I ai now to actually in uh predict the impact that the workforce and the cultural will have on business results, predict attrition, predict what different work workforce design scenarios will look like to the supply chain um uh predict the speed of hiring and how that will impact literally bottom line business results. So you said it right when when when you talked about shareholder value, the people is at the center of shareholder value now. So our functions need to be modernized. But it's really this partnership between HR. And I. T. That's going to be able to make it happen in a big way. >>It's interesting. I'm just thinking that Ai as well can be a canary in a coal mine when there's potential problems. And I love this transparency. That's critical co creation. So, okay, so tech is a key part of that, especially in terms of when you go from analog to digital, taking friction out of the system shows the employees that we're investing in in your experience. But it's more than that you're saying it's it's cultural and as it makes its kind of fun cultural, it takes a village. So that's that's part of what makes it so hard. How do you think about, you know the journey, where do you start and and how do you keep iterating? You're you're never done in this this world, are you? >>Yeah, that's a question uh everybody's asking now is where do I start? So as you said, this is very hard and and and it's hard. One of the reasons it's hard, it's because it revolves culture. Um it's not only about technology, they are hard. Technologies are hard in their own right. It's not just about data that's hard in its own right. But once you involve technology it makes it makes it even even harder. And of course the people aspect unless done very pro actively and meaningfully, it can be kind of a wild card right on who's gonna adopt what. So where do you start? So um the way we like to think about um giving advice to enterprises regarding where this is, we've seen this work well is to pick a business problem. So what's a business problem that if you solved you can actually make an impact not only for your people but for your people but for the enterprise. So if you could pick a business problem and and actually fix it using data using cloud using people, experiences using a cultural shift, then you'll get that. Buy in, you get the buy in that. Yes we can do this, this is this this is very doable. We can repeat it, repeat peat, repeatable over and over again and it has an impact on our culture. That's a great place to start. Okay so then you say if that's a place to start, how do we actually, there's got to be foundational things that have to be in place to make that work. So one of them is a consistency in data and the use of AI and the ability to make insights meaningful, you know that come through data and AI. And the other part that's really important once you pick your business problem is the shift in the way of working the shift so that it can impact cultural, a cultural change, a shift so that there's co creation with your people and there's transparency so each one of these business problems and the way companies pick to fix them, they won't all work. And the way you get that trust and transparency with your people is as scary as it is to share with them what you're attempting to do and share with them, how you're doing along the journey. And if it fails, okay fails, you know, pick yourself back up and start again that trust and transparency with your people. That's the way that's the way we all make this cultural impact. So, you know, kind of the none of this is to be to make sustainable change. We can all make short term change, we can do projects but to make sustainable change, the humanity aspect has to come to life in these digital transformations and that only comes to life with this cultural shift. >>Amy right? You've thought about this a lot, deep expertise in the area, really appreciate your sharing it with our audience and thanks for coming on the cube. >>Dave my pleasure. >>All right, keep it right there. But this is Dave Volonte. You're watching IBM think 2021 the virtual edition from the Cube. >>Yeah. Mhm.

(gentle music) >> Narrator: From around the globe, it's theCUBE with digital coverage of AnsibleFest 2020 brought to you by Red Hat. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. Welcome back to our continuous coverage of AnsibleFest 2020. We're not in person this year, as everybody knows, but we're back covering the event. We're excited to be here and really our next guest we've had him on a lot of times. He's super insightful coming right off the keynote, driving into some really interesting topics that we're excited to get into. It's Chris Wright, he's the Chief Technology Officer of Red Hat Chris, great to see you. >> Hey, great to see you. Thanks for having me on. >> Absolutely. So let's jump into it. I mean, you covered so many topics in your keynote. The first one though, that just jumps off the page, right, is automation and really rethinking automation. You know, and I remember talking to a product manager at a hyperscaler many months ago, and he talked about the process of them mapping out their growth and trying to figure out how are they going to support it in their own data center. And he just basically figured out, we cannot do this at scale without automation. So I think the hyperscaler has been doing it, but really it's kind of a new approach for enterprises to incorporate new and more automation in what they do every day. >> It's a fundamental part of scaling. And I think we've learned over time that one we need programming interfaces on everything. So that's a critical part of beginning of the automation journey. So now you have a programmatic way to interact with all the things out there. But the other piece is just creating really confidence in knowing that when you're automating and you're taking tasks away from humans which are actually error prone and typing on the keyboard is not always the greatest way to get things done. The confidence that those automation scripts or playbooks are going to do the right things at the right time. And so, creating really a business and a mindset around infusing automation everything you do is a pretty big journey for the enterprise >> Right. And that's one of the topics you talked about as well. And you know it comes up all the time with digital transformation or software development. This kind of shift the focus from, you know, kind of it's a destination to it's a journey. And you talked very specifically that you need to think about automation as a journey and as a process and even a language, and really bake it into as many processes as you possibly can. I'm sure that shocks a lot of people and probably scares them but really that's the only way to achieve these types of scales that we're seeing out there. >> Well, I think so. And part of what I was trying to highlight is the notion that a business is filled with people with domain expertise. So everybody brings something to the table. You're a business analyst, you understand the business part of what you're providing. You're the technologist. You really understand the technology. There's a partner ecosystem coming in with a critical parts of the technology stack. And when you want to bring this all together, you need to have a common way to communicate. And the... What I was really trying to point out is a language for communication across all those different cross functional parts of your business is critical. Number one, and number two, that language can actually be an automation language. And so, choosing that language wisely obviously we're talking to AnsibleFest. So we're going to be talking a lot about Ansible in this context. Treating that language wisely is part of how you build the end to end sort of internalized view of what automation means to your business. >> Right. I wrote down a bunch of quotes that you talked about, you know, Ansible is the language of automation, and automation should be a primary communication language. Again, very different kind of language that we don't hear. Now, it's more than a tool but a process a constant process and should be an embedded component of any organization. So, I mean, you're really talking about automation as a first class citizen, not kind of this last thing for the most advanced or potentially last thing for the most simple things where we can apply this process, but really needs to be a fundamental core of the way you think about everything that you do. Really a very different way to think about things and probably really appropriate, you know, as we come out of 2020 in this kind of new world where, you know, everyone like distributed teams, well now you have distributed teams. And so, you know, the forcing function on better tooling, that's really wrapped in better culture has never been greater than we're seeing today. >> I completely agree with that. And that domain expertise, I think we understand well in certain areas. So for example, application developers, they rely on one another. So, you maybe as an application developer consuming a service from somebody else in your microservices architecture, and so you're dependent on that other engineering team's domain expertise. Maybe that's even the database service, and you're not a database DBA or an engineer that really builds schemas for databases. So we kind of get that notion of encapsulating domain expertise in the building and delivering about applications that notion the CICD pipeline, which itself is automating how you build and deliver applications, that notion of encapsulating domain expertise across a series of different functions in your business can go much broader than just building and delivering the application. It's running your business. And that's where it becomes fundamental. It becomes a process. That's the journey, you know, not the end state, but it's the... And it's not the destination, it's the journey that matters. And I've seen some really interesting ways that people actually work on this and try to approach it from the, "How do you change your mindset?" Here's one example that I thought was really unique. I was speaking with a customer who quite literally automated their existing process, and what they did was automate everything from generating the emails to the PDFs, which would then be shared as basically printed out documents for how they walked through business change when they're making a change in their product. And the reason they did that was not because that was the most efficient model at all. It was... That was the way they could get the teams comfortable with automation. If it produced the same artifacts that they were already used to, then it created confidence and then they could sort of evolve the model to streamline it because printing out a piece of paper to review it is not going to be the efficient way to (indistinct) change your business. >> Well, just to follow up on that, right? Cause I think what would probably scares a lot of people about automation, one is exception handling and can you get all the Edge cases in the use cases? So in the one you just talked about, how do they deal with that? And then I think the other one is just simply control. Do I feel confident enough that I can get the automation to a place that I'm comfortable to hand over control? And I'm just curious in that case you just outlined how do they deal with kind of those two factors? >> Well, they always enabled a human checkpoint, so especially in the beginning. So it was sort of trust but verify that model and over time you can look at the things that you really understand well and start with those and the things that have more kind of gray zones, where the exceptions may be the rule or maybe the critical part of the decision making process. Those can be sort of flagged as needs real kind of human intervention. And that's a way to sort of evolve and iterate and not start off with the notion that everything has to be automated. You can do it piecemeal and grow over time and you'll build confidence and you'll understand how to flag those exceptions, where you actually need to change your process itself because you may have bottlenecks that don't really make sense for the business anymore and where you can incorporate the exception handling into the automation essentially. >> Right, that's great. Thank you for sharing that example. I want to shift gears a little bit cause another big topic that you covered in your keynote that we talk about all the time on theCUBE is Edge. So everybody knows what a data center is, everybody knows what a public cloud is, you know lots of conversations around hybrid cloud and multicloud, et cetera, et cetera, et cetera. But this new thing is Edge and I think people talk about Edge in kind of an esoteric way, but I think you just nailed it. I mean you just nailed it very simply, moving the compute to where the data is collected and or consumed. You know I thought that was super elegant, but what you didn't get into on all the complexity is what means. I mean data centers are our pristine environments that they're very, very controlled, the environment's controlled, the network is controlled, the security is controlled and you have the vision of an Edge device and the one everyone always likes to use let's say like a wind farm. Those things are out in crazy harsh conditions and then there's still this balancing act as to what information does get stored and processed and used and then what does have to go back to the data center because it's not a substitute for the data center it's really an extension of the data center or maybe the data center is actually an extension of the Edge. Maybe that's a better way to think of it but we've had all these devices out there, now suddenly we're connecting them and bringing them into a network and add a control. And I just thought the Edge represents such a big shift in the way we're going to see compute change probably as fundamental I would imagine as the cloud shift has been. >> I believe it is, I absolutely believe it's as big a change in the industry as the cloud has been. The cloud really created scale, it created automation, programmatic interfaces to infrastructure and higher level services. But it also was built around a premise of centralization. I mean clouds themselves are distributed and so you can create availability zones and resilient applications, but there's still a sense of centralization. Edge is really embracing the notion that data production is kind of only up into the right and the way to scale processing that data and turning that data into insights and information that's valuable for our business is to bring compute closer to data. Not really a new concept, but the scale at which it's happening is what's really changing how we think about building infrastructure and building the support behind all that processing and it's that scale that requires automation. Because you're just not going to be able to manage thousands or tens of thousands or in certain scenarios even millions of devices without putting automation at the forefront. It's critical. >> Right. And we can't talk about Edge without talking about 5G and I laugh every time I'm watching football on Sundays and they have the 5G commercials on talking about my handset that I can order my food to get delivered faster at my house like completely missing the point. 5G is about machine to machine communication and the scale and the speed and the volume of machine to machine is so fundamentally different than humans talking voice to voice. And that's really this big drivers to instrument as you said, all these machines, all these devices there's already been sensors on them forever but now the ability to actually connect them and pull them into this network and start to use the data and control the machines is a huge shift in the way things are going to happen going forward. >> A couple of things that are important in there. Number one, that data production and sensors and bringing computer closer to data, what that represents is bringing the digital world and the physical world closer together. We'll experience that at a personal level with how we communicate we're already distributed in today's environment and the ways we can augment our human connections through a digital medium are really going to be important to how we maintain our human connections. And then on the enterprise side, we're building this infrastructure in 5G that when you think about it from a consumer point of view and ordering your pizza faster it really isn't the right way to think about it. Couple of key characteristics of 5G. Greater bandwidth, so you can just push more package to the network. Lower latency, so you're closer to the data and higher connection density and more reliable connections. And that kind of combination of characteristics make it really valuable for enterprise businesses. You can bring your data and compute close together you have these highly reliable and dense connections that allow for device proliferation and that's the piece that's really changing where the world's going. I like to think of it in a really simple way which is, 4G and the cloud and the smartphone created a world that today we take for granted, 10 years ago we really couldn't imagine what it looked like. 5G, device proliferation and Edge computing today is building the footprint for what we can't really imagine what we will be taking for granted in 10 years from now. So we're at this great kind of change inflection point in the industry. >> I have to always take a moment to call out (indistinct). I think it's the most underappreciated law and it's been stolen by other people and repackage many ways, but it's basically we overestimate the impact of these things in the short term and we way, way, way, way, kind of underestimate the impact in the longterm and I think your story in they keynote about once we had digital phones and smartphones, we don't even think twice about looking at a map and where are we and where is a store close buy-in are they open and is there a review? I mean the infrastructure to put that together kind of an API based economy which is pulling together all these bits and pieces the stupid relay expectation of performance and how fast that information is going to be delivered to me. I think we still take it for granted, as you said I think it's like magic and we never thought of all the different applications of these interconnected apps enabled by and always on device that's always connected and knows where we are it's a huge change. And as you say that when we think about 5G, 10 years from now, oh my goodness, where are we going to be? >> It's hard to imagine? It really is hard to imagine and I think that's okay. And what we're doing today is introducing everything that we need to help businesses evolve, take advantage of that and that scale of the Edge is a fundamental characteristic of the Edge. And so automating to manage that scale is the only way you're going to be successful and extending what we've learned in the data center, how to the Edge using the same tools, the things we already understand really is a great way to evolve a business. And that's where that common language and the discussions that I was trying to generate around Ansible as a great tool, but it's not just the tool, it's the whole process, the mindset. The culture changed the way you change how you operate your business that's going to allow us to take advantage of the future where my clothes are full of sensors and you can look through a video camera and tell immediately that I'm happy with this conversation. That's a very different kind of augmented reality than we have today and maybe it's a bad example but it's hard to imagine really what it will be like. >> So, Chris, I just want to close on a slight shift. We've been talking a lot about technology, but you talk about culture all the time and really it's about the people and I think a number of times in the keynote you reinforced this is about people and culture. And I just had InaMarie Johnson on the Chief Diversity Officer from Zendesk and she said culture eats strategy for breakfast. Great line. So I wonder if you can talk about the culture because it's very different and you've seen it in opensource from Red Hat for a long time really a shifting culture around opensource the shifting culture around DevOps and continuous delivery and change is a good thing, not a bad thing and we want to be able to change our code frequently and push out new features. So again, as you think of automation and culture, what kind of comes to mind and what should people be thinking about when they think about the people and less about the technology? >> Well, there's a couple of things. Some I'll reinforce what we already touched on which is the notion of creating confidence in the automation. So there's an element of trust associated with that and that's more maybe trusting the technology. So when you're automating something you've already got a process, you already understand how something works, it's turning that something into an automated script or playbook in the Ansible context and trusting that it's going to do the right thing. There's another important part of trust which is getting more to the people part. And I've learned this a lot from open source communities collaboration and communities are fundamentally built around trust and human trust relationships. And the change in process, trusting not only that the tools are going to the right job but that people are really assuming good intent and working with or trying to build for the right outcomes for your business. I think that's a really important part of the overall picture. And then finally that trust is extended to knowing that that change for the business isn't going to compromise your job. So thinking differently about what is your job? Is your job to do the repetitive task or is your job to free up your time from that repetitive task to think more creatively about value you can bring to the business. And that's where I think it's really challenging for organizations to make changes because you build a personal identity around the jobs that you do and making changes to those personal identities really gets to the core of who you are as a person. And that's why I think it's so complicated. The tools almost start the easy part, it's the process changes and the cultural changes, the mindset changes behind that which is difficult but more powerful in the end. >> Well, I think people process tools the tech is always the easy part relative to culture and people in changing the way people do things and as you said, who their identity is, how they get kind of wrapped into what they do and what they think their value is and who they are. So to free them up from that that's a really important point. Well, Chris, I always love having you on, thank you for coming on again, sharing your insight, great keynote. And give your the last word about AnsibleFest 2020. What are you looking forward to take away from this little show? >> Well, number one, my personal hope is that the conversation that I was trying to sort of ignite through the keynote is an opportunity for the community to see where Ansible fits in the Edge and automation and helping really the industry at large scale. And that key part of bringing a common language to help change how we communicate internally is the message I was hoping to impart on the AnsibleFest Community. And so hopefully we can take that broader and appreciate the time here to really amplify some of those messages. >> All right, great. Well, thanks a lot Chris and have a great day. >> Thanks Jeff, thank you. >> All right. He's Chris, I'm Jeff you're watching theCUBE and our ongoing coverage of AnsibleFest 2020. Thanks for watching we'll see you next time. (gentle music)

>> Narrator: From around the globe, it's theCube. With digital coverage of AnsibleFest 2020. Brought to you by Red Hat. (twinkly music) >> Hey, welcome back, everybody. Jeff Frick here with theCube. Welcome back to our continuous coverage of AnsibleFest 2020. We're not in-person this year, as everybody knows, but we're back covering the event. We're excited to be here, and really our next guest... We've had him on a lot of times. He's super insightful. Coming right off the keynote, diving into some really interesting topics that we're excited to get into, and it's Chris Wright. He's the chief technology officer of Red Hat. Chris, great to see you. >> Hey, great to see you. Thanks for having me on. >> Absolutely. So let's jump into it. I mean, you covered so many topics in your keynote. The first one though, that just jumps off the page, right, is automation, and really rethinking automation. And I remember talking to a product manager at a hyperscaler many moons ago, and he talked about the process of them mapping out their growth and trying to figure out how they were going to support it in their own data center. And he just basically figured out we cannot do this at scale without automation. So I think the hyperscalers have been doing it, but really it's kind of a new approach for enterprises to incorporate new, and more, automation into what they do every day. >> It's a fundamental part of scaling, and I think we've learned over time that, one, we need programming interfaces on everything. So that's a critical part of beginning of the automation journey, so now you have a programmatic way to interact with all the things out there. But the other piece is just creating, really, confidence in knowing that when you're automating and you're taking tasks away from humans, which are actually error-prone, and typing on a keyboard is not always the greatest way to get things done, the confidence that those automation scripts, or playbooks, are going to do the right things at the right time. And so creating, really, a business and a mindset around infusing automation into everything you do is a pretty big journey for the enterprise. >> Right. And that's one of the topics you talked about as well, and it comes up all the time with digital transformation or software development; this kind of shift the focus from kind of it's a destination to it's a journey. And you talked very specifically that you need to think about automation as a journey, and as a process, and even a language, and really bake it into as many processes as you possibly can. I'm sure that shocks a lot of people and probably scares them, but really that's the only way to achieve the types of scales that we're seeing out there. >> Well, I think so. And part of what I was trying to highlight is the notion that a business is filled with people with domain expertise. So everybody brings something to the table. You're a business analyst. You understand the business part of what you're providing. You're the technologist. You really understand the technology. There's a partner ecosystem coming in with a critical parts of the technology stack. When you want to bring this all together, you need to have a common way to communicate. What I was really trying to point out is a language for communication across all those different cross-functional parts of your business is critical, number one, and number two, that language can actually be an automation language. And so choosing that language wisely... Obviously, we're talking at AnsibleFest, so we're going to be talking a lot about Ansible in this context. Choosing that language wisely is part of how you build the end-to-end sort of internalized view of what automation means to your business. >> Right. I mean, I wrote down a bunch of quotes that you talked about. "Ansible is the language of automation, and automation should be a primary communication language." Again, very different kind of language that we don't hear. And that it's "more than a tool, but a process, a constant process, and should be an embedded component of any organization." So I mean, you're really talking about automation as a first class citizen, not kind of this last thing for the most advanced, or potentially last thing for the most simple things where we can apply this process, but really needs to be a fundamental core of the way you think about everything that you do. Really a very different way to think about things, and probably really appropriate as we come out of 2020 in this kind of new world where everyone liked distributed teams. Well, now you have distributed teams, and so the forcing function on better tooling that's really wrapped in better culture has never been greater than we're seeing today. >> I completely agree with that. That domain expertise I think we understand well in certain areas. So for example, application developers, they rely on one another. So you're, maybe as an application developer, consuming a service from somebody else in your microservices architecture, and so you're dependent on that other engineering team's domain expertise. Maybe that's even the database service, and you're not a database, a DBA, or an engineer that really builds schemas for databases. We kind of get that notion of encapsulating domain expertise in the building and delivering of applications. That notion, the CI/CD pipeline, which itself is automating how you build and deliver applications, that notion of encapsulating domain expertise across a series of different functions in your business can go much broader than just building and delivering the application. It's running your business. And that's where it becomes fundamental. It becomes a process that's the journey. Not the end state. And it's not the destination. It's the journey that matters. And I've seen some really interesting ways that people actually work on this and try to approach it from the "how do you change your mindset?" Here's one example that I thought was really unique. I was speaking with a customer who quite literally automated their existing process, and what they did was automate everything from generating the emails to the PDFs, which would then be shared as basically printed out documents for how they walked through business change when they're making a change in their product. And the reason they did that was not because that was the most efficient model at all. It was that was the way they could get the teams comfortable with automation. If it produced the same artifacts that they were already used to, then it created confidence, and then they could sort of evolve the model to streamline it, because printing out a piece of paper to review, it is not going to be the efficient way to make changes in your business. >> Well, just to follow up on that, right, cause I think what probably scares a lot of people about automation... One is exception handling, right? And can you get all the edge cases in the use case. So in the one you just talked about, how do they deal with that? And then I think the other one is just simply control. Do I feel confident enough that I can get the automation to a place that I'm comfortable to hand over control? And I'm just curious, in that case you just outlined, how do they deal with kind of those two factors? >> Well, they always enabled a human checkpoint. Especially in the beginning. So it was sort of "trust but verify" that model, and over time you can look at the things that you really understand well and start with those, and the things that have more kind of gray zones, where the exceptions may be the rule, or may be the critical part of the decision making process, those can be sort of flagged as "needs real kind of human intervention," and that's a way to sort of evolve, and iterate, and not start off with the notion that everything has to be automated. You can do it piecemeal and grow over time, and you'll build confidence, and you'll understand where... How to flag those exceptions, where you actually need to change your process itself, because you may have bottlenecks that don't really make sense for the business anymore, and where you can incorporate the exception handling into the automation, essentially. >> Right. That's great. Thank you for sharing that example. I want to shift gears a little bit, cause another big topic that you covered in your keynote that we talk about all the time on theCube is edge, right? So everybody knows what a data center is. Everybody knows what a public cloud is. Lots of conversations around hybrid cloud and multi cloud, et cetera, et cetera, et cetera... But this new thing is edge, and I think people talk about edge in kind of an esoteric way, but I think you just nailed it. I mean, you just nailed it. It's very simply moving the compute to where the data is collected and/or consumed. I thought that was super elegant, but what you didn't get into on all the complexity is what that means, right? I mean, data centers are pristine environments that... They're very, very controlled. The environment's controlled. The network is controlled. The security is controlled, and you have the vision of an edge device. And the one everyone always likes to use is say like a wind farm, right? Those things are out in crazy harsh conditions, and then there's still this balancing act as to what information does get stored, and processed, and used, and then what does have to go back to the data center, because it's not a substitute for the data center. It's really an extension of the data center, or maybe the data center is actually an extension of the edge. Maybe that's a better way to think of it, but we've had all these devices out there. Now, suddenly we're connecting them and bringing them into a network and adding control. And I just thought the edge represents such a big shift in the way we're going to see compute change. Probably as fundamental, I would imagine, as the cloud shift has been. >> I believe it is. I absolutely believe it's as big a change in the industry as the cloud has been. The cloud really created scale. It created automation, programmatic interfaces to infrastructure and higher level services. But it also was built around a premise of centralization. I mean, clouds themselves are distributed, and so you can create availability zones and resilient applications, but there's still a sense of centralization. Edge is really embracing the notion that data production is kind of only up and to the right, and the way to scale, processing that data, and turning that data into insights and information that's valuable for a business, is to bring compute closer to data. It's not really a new concept, but the scale at which it's happening is what's really changing how we think about building infrastructure and building the support behind all that processing. And it's that scale that requires automation, because you're just not going to be able to manage thousands, or tens of thousands, or in certain scenarios even millions of devices, without putting automation at the forefront. It's critical. >> Right. And we can't talk about edge without talking about 5G, and I laugh every time I'm watching football on Sundays and they have the 5G commercials on talking about my handset, that I can order my food to get delivered faster at my house, completely missing the point, right? 5G's about machine-to-machine communication, and the scale, and the speed, and the volume of machine-to-machine is so fundamentally different than humans talking voice-to-voice. And that's really this big driver to instrument, as you said, all these machines, all these devices. There's been sensors on them forever, but now the ability to actually connect them, and pull them into this network, and start to use the data, and control the machines is a huge shift in the way things are going to happen going forward. >> Well, it's a couple of things that are important in there. Number one, that data production, and sensors, and bringing compute closer to data, what that represents is bringing the digital world and the physical world closer together. We'll experience that at a personal level with how we communicate. We're already distributed in today's environment, and the ways we can augment our human connections through a digital medium are really going to be important to how we maintain our human connections. And then on the enterprise side, we're building this infrastructure in 5G that when you think about it from a consumer point of view and ordering your pizza faster, it really isn't the right way to think about it. Couple of key characteristics of 5G: greater bandwidth, so you can just push more packets through the network; lower latency, so you're closer to the data; and higher connection density and more reliable connections, and that kind of combination of characteristics make it really valuable for enterprise businesses. You can bring your data and compute close together. You have these highly reliable and dense connections that allow for device proliferation, and that's the piece that's really changing where the world's going. I like to think of it in a really simple way, which is 4G, and the cloud, and the smartphone created a world that today we take for granted. 10 years ago, we really couldn't imagine what it looked like. >> 5G- >> Jeff: Like tomorrow... Excuse me. >> Device proliferation, and edge computing today is building the footprint for what we can't really imagine what we will be taking for granted in 10 years from now. So we're at this great kind of change in inflection point in the industry. >> Yeah. I have to always take a moment to call out a Amara's law. I think it's the most underappreciated law. It's been stolen by other people and repackaged many ways, but it's basically we overestimate the impact of these things in the short term, and we way, way, way, way kind of underestimate the impact in the longterm. And I think your story in they keynote about once you had digital phones and smartphones, we don't even think twice about looking at a map, and where are we, and where's a store close by, and are they open, and is there a review? I mean, the infrastructure to put that together, kind of an API-based economy, which is pulling together all these bits and pieces... (scoffs) The stupid rely... Expectation, right, of performance, and how fast that information's going to be delivered to me. I think we so take it for granted. As you say, I think it's like magic, and we never thought of all the different applications of these interconnected apps enabled by an always-on device that's always connected and knows where we are. It is a huge change, and as you say that when we think about 5G... (chuckling) 10 years from now. Oh, my goodness. Where are we going to be? >> It's hard to imagine? I mean, it really is hard to imagine, and I think that's okay. And what we're doing today is introducing everything that we need to help businesses evolve. Take advantage of that. And that scale of the edge is... It's a fundamental characteristic of the edge, and so automating to manage that scale is the only way you're going to be successful, and extending what we've learned in the data center out to the edge using the same tools, the things we already understand, really is a great way to evolve a business. And that's where that common language and the discussions that I was trying to generate around Ansible as a great tool. But it's not just the tool, it's the whole process, the mindset, the culture change, the way you change how you operate your business that's going to allow us to take advantage of the future where my clothes are full of sensors and you can look through a video camera and tell immediately that I'm happy with this conversation. That's a very different kind of augmented reality than we have today. Maybe it's a bad example, but it's hard to imagine really what it'll be like. >> So Chris, I just want to close on a slight shift, right? We've been talking a lot about technology, but you talk about culture all the time, and really, it's about the people. And I think a number of times in the keynote you reinforced this is about people and culture. And I just had I'm InaMarie Johnson on, the chief diversity officer from Zendesk. And she said culture eats strategy for breakfast. Great line. So I wondered if you can talk about the culture, because it's very different and you've seen it in opensource from Red Hat for a long time, really, a shift in culture around opensource, the shift in culture around devops, and continuous delivery, and "change is a good thing, not a bad thing," and we want to be able to change our code frequently and push out new features. So again, as you think of automation and culture, what kind of comes to mind, and what should people be thinking about when they think about the people and less about the technology? >> Well, there's a couple of things. I'll reinforce what we already touched on, which is the notion of creating confidence in the automation. There's an element of trust associated with that, and that's more maybe trusting the technology. So when you're automating something, you've already got a process. You already understand how something works. It's turning that something into an automated script, or playbook in the Ansible context, and trusting that it's going to do the right thing. There's another important part of trust, which is getting more to the people part, and I've learned this a lot from opensource communities. Collaboration and communities are fundamentally built around trust, and human trust relationships, and the change in process, trusting not only that the tools are going to do the right job, but the people are really... Assuming good intent, and working with they're trying to build for the right outcomes for your business, I think that's a really important part of the overall picture. And then finally, that trust is extended to knowing that that change for the business isn't going to compromise your job, right? So thinking differently about what is your job. Is your job to do the repetitive task, or is your job to free up your time from that repetitive task to think more creatively about value you can bring to the business? That's where I think it's really challenging for organizations to make changes because you build a personal identity around the jobs that you do, and making changes to those personal identities really gets to the core of who you are as a person. That's why I think it's so complicated. The tools almost are the easy part. It's the process changes and the cultural changes, the mindset changes behind that which is difficult, but more powerful in the end. >> Yeah. Yeah. Well, I think people, process, tools... The tech is always the easy part relative to culture, and people, and changing the way people do things, and as you said, who their identity is, how they get kind of wrapped into what they do, and what they think their value is, and who they are. So to free them up from that, that's a really important point. Well, Chris, I always love having you on. Thank you for coming on again, sharing your insight. Great keynote, and give me the last word about AnsibleFest 2020. What are you looking forward to take away from this little show? >> Well, number one, my personal hope is that the conversation that I was trying to sort of ignite through the keynote is an opportunity for the community to see where Ansible fits in the edge and automation, and helping, really the industry at large, scale. And that key part of bringing a common language to help change how we communicate internally is the message I was hoping to impart on the AnsibleFest community, and so hopefully we can take that broader. Appreciate the time here to really amplify some of those messages. >> All right. Great. Well, thanks a lot, Chris, and have a great day. >> Thanks, Jeff. Thank you. >> All right. He's Chris. I'm Jeff. You're watching theCube, and our ongoing coverage of AnsibleFest 2020. Thanks for watching. We'll see you next time. (twinkly music)

>> If you want to innovate, you must automate at the edge. I'm Chris Wright, chief technology officer at Red Hat. And that's what I'm here to talk to you about today. So welcome to day two of AnsibleFest, 2020. Let me start with a question, do you remember 3G when you first experienced mobile data connections? The first time that internet on a mobile device was available to everyone? It took forever to load a page, but it was something entirely different. It was an exciting time. And then came 4G, and suddenly data connections actually became usable. Together with the arrival of smartphones, people were suddenly online all the time. The world around us changed immensely. Fast forward to today, things are changing yet again, 5G is entering the market. And it's in evolution that brings about fundamental change of how connections are made and what will be connected. Now it's not only the people anymore who are online all the time, devices are entering the stage, sensors, industrial robots, cars, maybe even the jacket you're wearing. And with this revolutionary change and telecommunications technology, another trend moves into the picture, the rise of edge computing. And that's what I'll be focusing on today. So what is edge computing exactly? Well, it's all about data. Specifically, moving compute closer to the producers and consumers of data. Let's think about how data was handled in the past. Previously, everything was collected, stored and processed in the core of the data center. Think of server racks, one after the other. This was the typical setup. And it worked as long as the environment was similarly traditional. However, with the new way devices are connected and how they work, we have more and more data created at the edge and processed there immediately. Gathering and processing data takes place close to the application users, and close to the systems generating data. The fact that data is processed where it is created means that the computing itself now moves out to the edge as well. Outside of the traditional data center barriers into the hands of application users. Sometimes, literally into the hands of people. Look at your smartphone next to you, is one good example. Data sources are more distributed. The data is generated by your mobile phone, by your thermostat, by your doorbell, and data distribution isn't just happening at home, it's happening in businesses too. It's at the assembly line, high on top of a cell tower, by a pump deep down in a well, and at the side of a train track, every few miles for thousands of miles. This leads to more distributed computing overall. Platforms are pushed outside the data center. Devices are spread across huge areas in inaccessible locations, and applications run on demand close to the data. Often even the ownership of the devices is with other parties. And data gathering and processing is only partially under our direct control. That is what we mean by edge computing. And why is this even interesting for us, for our customers? To say it with the words of a customer, edge computing will be a fundamental enabling technology within industrial automation. Transitioning how you handle IT from a traditional approach, towards a distributed computing model, like edge computing, isn't necessarily easy. Let's imagine how a typical data center works right now. We own the machines, create the containers, run the workloads and carefully decide what external services we connect to, and where the data flows. This is the management sphere we know and love. Think of your primary OpenShift cluster for example. With edge computing, we don't have this level of ownership, knowledge or control. The servo motors in our assembly line are black boxes controlled only via special APIs. The small devices next to our train tracks, running embedded operating system, which does not run our default system management software. And our doorbell is connected to a cloud, which we do not control at all. Yet we still need to be able to exercise control our business processes suddenly depend on what is happening at the edge. That doesn't mean we throw away our ways of running the data centers, in fact, the opposite is true. Our data centers are the backbone of our operations. In the data center, we still tie everything together and run our core workloads. But with edge computing, we have more to manage. To do so, we have to leave our comfort zones and reach into the unknown. To be successful, we need to get data, tools and processes under management and connect it back to our data center. Let's take train tracks as an example. We're in charge of a huge network. Thousands of miles of tracks zig-zagging across the country. We have small boxes next to the train tracks every few miles, which collect data of the passing trains. Takes care of signaling and so on. The train tracks are extremely rugged devices and they're doing their jobs in the coldest winter nights and the hottest summer days. One challenge in our operation is, if we lose connection to one box, we have to stop all traffic on this track segment, no signal, no traffic. So we reroute all of the traffic passengers, cargo, you name it, via other track segments. And while the track segments now suddenly have unexpected traffic congestion and so on, we have sent a maintenance team to figure out why we lost the signal, do root cause analysis, repair what needs to be fixed and make sure it all works again. Only then, can we reopen the segment. As you can imagine, just bringing a maintenance team out there takes time, finding the root issue and solving it, also takes time. And all the while, traffic is rerouted. This can amount to a lot of money lost. Now imagine these little devices get a new software update and are now able to report not only signals sent across the tracks, but also the signal quality. And with those additional data points, we can get to work. Subsequently, we can see trends. And the device itself can act on these trends. If the signal quality is getting worse over time, the device itself can generate an event, and from this event, we can trigger followup actions. We can get our team out there in time, investigating everything before the track goes down. Of course the question here is, how do you even update the device in the first place? And how do you connect such an event to your maintenance team? There are three things we need to be able to properly tie events and everything together to answer this challenge. First, we need to be able to connect through the last mile. We need to reach out from our comfort zones, down the tracks and talk to a device, running a special embedded OS on a chip architecture we don't have in our data center. And we have thousands of them. We need to manage at the edge in a way suited to its scale. Besides connecting, we need the skills to address our individual challenges of edge computing. While the train track example is a powerful image, your challenge might be different. Your boxes might be next to an assembly line or on a shipping container or a unit under an antenna. Finally, the edge is about the interaction of things. Without our data center or humans in the equation at all. As I mentioned previously, in the end, there is an event generated by the little box. We have to take the event and first increase the signal strength temporarily between this box and the other boxes on either side, to buy us some more time. Then we ask the corporate CMDB for the actual location of that box, put all this information into a ticket, assign the ticket to the maintenance team at high priority to make sure they get out there soon. As you can see, our success here critically depends on our ability to create an environment with the right management skills and technical capabilities that can react decentrally in a secure and trusted way. And how do we do these three things, with automation. Yeah, it might not come as much of a surprise, right? However, there is a catch. Automation as a single technology product, won't cut it. It's tempting to say that an automation product can solve all these problems. Hey, we're at a tech conference, right? But that's not enough. Edge computing is not simple. And the solution to the challenges is, is not simply a tool where we buy three buckets full, and spread it across our data center and devices. Automation must be more than a tool. It must be a process, constantly evolving, iterating on and on. We only have a chance if we embed automation as a fundamental component of an organization, and use it as a central means to reach out to the last mile. And the process must not focus on technology itself, but on people. The people who are in charge of the edge IT as well as the people in charge of the data center IT. Automation can't be a handy tool that is used occasionally, it should become the primary language for all people involved to communicate in. This leads to a cooperation and common ground to further evolve the automation. And at the same time, ensure that the people build and improve the necessary skills. But with the processes and the people aligned, we can shed light on the automation technology itself. We need a tool set that is capable of doing more than automating an island here and a pocket there. We need a platform powerful enough to write the capabilities we need and support the various technologies, devices, and services out at the edge. If we connect these three findings, we come to a conclusion. To automate the edge, we need a cultural change that embraces automation in a new and fundamental way. As a new language, integrating across teams and technology alike. Such a unified automation language, speaks natively with the world out there as well as with our data centers at any scale. And this very same language is spoken by domain experts, by application developers and by us as automation experts, to pave the way for the next iteration of our business. And this language has the building blocks to create new interfaces, tools and capabilities, to integrate with the world out there and translate the events and needs into new actions, being the driving motor of the IT at the edge and evolving it further. And yes, we have this language right here, right now. It is the Ansible language. If we come back to our train track, one more time, this Ansible that can reach out and talk to our thousands of little boxes sitting next to the train tracks. The Ansible language, the domain experts of the boxes can natively work together with the train operations experts and the business intelligence people. Together, they can combine their skills to write workflows in a language they can all understand and where the deep down domain knowledge is encapsulated away. And the Ansible platform offers the APIs and components to react to events in a secure and trusted way. If there's one thing I'd like you to take away from this, it is edge computing is complex enough. But luckily we do have the right language, the right tools, and here with you and awesome community at our fingertips, to build upon it and grow it even further. So let's not worry about the tooling, we have that covered. Instead, let's focus on making that tool great. We need to become able to execute automation anywhere we need. At the edge, in the cloud, in other data centers, in the end, just like serverless functions, the location where the code is actually running, should not matter to us anymore. Let's hear this from someone who is right at the core of the development of Ansible, over to Matt Jones, our automation platform architect.

from around the globe it's the cube with digital coverage of Red Hat summit 2020 brought to you by Red Hat welcome back this is the cubes coverage of Red Hat summit 2020 of course the event happening digitally we're bringing in the guests from where they are around the globe happy to welcome back to the program and he's one of the keynotes because he's also many times cube alumni chris wright is the senior vice president and chief technology officer at Red Hat chris it is great to see you and we've got almost matching hats you have a real red hat fedora I've got one that the you know kubernetes Red Hat team OpenShift team gives out in Europe so in case anybody in the Red Hat community goes yes I've been a longtime member of the community I got you know I think my original Red Hat baseball cap probably 15 years ago but the Hat that I had is not one of the nice felt one it is they're pretty good to see here all right so we've gotta wait a little bit to get your keynote but so many topics I want to get to with you but you know of course as I mentioned me open and it's pretty obvious everyone's remote right now is kind of you know special times we are living in so bring us inside a little bit you know your your organization your group or community you know what what this means and how's everybody doing well I mean it'd be hard not to sort of acknowledge that there's a major global event happening right now and and kovetz really changing how we operate how we work from a RedHat perspective our number one priority is just employee safety and employ health and so we we were quick to send our folks home and have everybody to work from home and so what's interesting from a RedHat point of view I think and then even if you broaden that out to open-source communities the the distributed nature of open-source development and and specifically the engineering teams Red Hatter are pretty distributed kind of mirroring those open-source communities that we participate in so in the one hand you can kind of say well things haven't changed substantially in the sense of how do we how do we operate in upstream communities but on the other hand people working from home is it's a whole new set of challenges I mean my kids are 12 and 14 but you know say you have toddlers that's a real distraction or you have a working environment at home that's crowded with multiple people I mean it can really change how you approach your daily your your your daily work life um so creating that balance has been really important and for our teams we talk a lot about just think empathy think about how you're supporting one another and again when you broaden that out to the larger communities I think probably a really important aspect of open-source development is crossing corporate boundaries and being inclusive of such a broad set of contributors that there's a built-in resiliency associated with open source communities which i think is fantastic and then when you add to that sort of the the enthusiasm around just doing great things there's a lot of interesting activities that are collaborative in nature that are community based that are trying to address the Kovach crisis whether it's 3d printing of supplies or whether it's contact tracing applications that help people understand where they become across kovat or anything like that I mean a lot of cool stuff happening that's inspired by a real challenge to the entire globe yeah okay Chris one of my favorite things the last few years that summit has you know talk and he's cut talking to companies that are going through their journey of you know what we usually call digital transformation what we have always said from the research side is what separates you know people that have successfully gone through this is that data and they become data-driven and data is such an important piece of what they're doing well I think everyone has been getting a real crash course on data because not only businesses but you know governments and you know the entire globe now is you know watching the daily data trying to understand data sources you know bring us inside is to you know really the importance of data and you know where that intersects with everything that red hat is well the those are great examples I mean it's sometimes a little depressing but the the notion that data is a critical part of decision-making and access to quality data in real time is what helps us make better decisions more effective decisions and more efficient decisions and so when you when you look at the amount of data being produced it just keeps growing you know it's sort of on the exponential growth curve and when you look at the commensurate amount of compute power associated with all of that data it's also growing which is maybe an obvious statement what it says is we are gathering more and more data and the degree to which we can pull meaningful insights out of that data is really how much we can impact our companies you know value and differentiation and in the context of something like Cova that means vaccine discoveries and you know shortening times to field trials in in a more business context it's talking about how quickly you can respond to your customers needs and we see a really dynamic shift and the work force all working from home that puts a real strain on the infrastructure we're here supporting infrastructure builders and the amount of data that they can collect to efficiently operate infrastructure is critical at a time when people are distributed and getting access into the lab environments is challenging and so it you know I think there's a lot to be said for the amount of data that's being produced and then how we analyze it we think of it in terms of bringing data to applications and historically they kind of lived in separate I'd call them silos bringing the data sources and data processing and model development all onto a common platform is a really powerful thing that's happening in the industry today which is which is exciting so you know we were bringing data to be a central actors how I like to describe it yeah well look I'm really glad how you connected that discussion of data to the applications we as you know my background really is on the infrastructure side and the concern I have a lot of times as infrastructure people you know we talk about the bits and bytes we talk about the infrastructure but the only reason we have infrastructure is to run those applications and you know deal with that data it was hoping you can connect the dots for us the key note that all gave one of the main things he's talking about it where's the open hybrid cloud and I had a great discussion with him on the cube so with that setup of applications and data you know how does that intersect you know with what Red Hat calls the open hybrid cloud and what differentiates Red Hat's position there from some of the other discussions that we hear in the industry about cloud whether the open hybrid cloud is is a platform I think that's the best way to think of it and that platform it's a it's a platform that spans different types of infrastructures so that's public clouds that's on-premises data centers you know the enterprise zones themselves and I think important increasingly out to the edge so the notion of where you deploy isn't also coupled to what platform do I have to develop to in order to do that deployment and you know when we talk about the edge extending out to the edge that means you're getting closer to those data sources so bringing the data in doing the Associated inference and making decisions close to that data where latency really can matter is a big part of what that open hybrid cloud platform brings to to the market or to our customers and when you think about an application developer typically an application developer is trying to in a you know enable some some behavior or feature or functionality and the more we can drive use data to drive the behavior or drive the functionality the more personalized and application is the more intelligent the application is and so the connection between data the data sources the data processing the data science behind data cleansing and model generation and the associated models that can be easily accessed by applications that's the real power that's the real value that works to help develop for our customers so they can change their business we actually do this internally it's how we operate you know we collect data we use data to make decisions we use data in our product release process and the platform that we've created is a data processing and analytics and machine learning platform that we use internally and we also make that externally available as an open source project the open data hub so open and data and hybrid cloud are all intertwined at this point yeah one of the things that really has been highlighted to me at Summit this year is that connection you know we always knew Red Hat had you know strong developer community out there but you know you think back to Linux Linux has eyes directly into the application you look across the portfolio and it's not the app dev team over here and the infrastructure team over here and you know how do we operate all of these various pieces you know ansible you know has connections into all the various roles so what want you to just comment you know with kind of your you know CTO role and you you look over the entire portfolio but that discussion of you know how roles are changing how organization and make sure that they're not a bunch of various functions that aren't in sync but you know we're really coming together to help respond to the business needs and move forward in the speed that is needed in today's world well I think the the early stages of that were well captured with the DevOps phrase so bringing developers and operations closer together it's not always clear what that means and in some cases that the the notion of a of a platform and the notion of operating an application and then who operates the platform I think there there's been some question in the industry about exactly what that means we're thinking of it today to sort of stick with the buzzwords in the dev sac ops context and even what I would call AI dead set cops so in data and intelligence infused obses cops and the idea is developers are just trying to move rapidly so the degree to which the underlying infrastructure is just there to support application development is the operations teams need yeah that's what the operation seems trying to provide developers need at the same time access to tooling to consistency from test environments through to production environments and also access to those data models that I was talking about earlier so bringing that all together I think on the DevOps side or the dev Sackhoff side it's how can you build a platform that gives the right business specific guidelines and sort of guardrails that allow developers to move as quickly as possible without getting themselves into trouble and you know inadvertently creating a security vulnerability by pulling in an old dependency as a concrete example so bringing these things together I think is what's really important and it's a big part of what we're focused on the so operational side being infused with intelligence that's data in telemetry you're gathering from at the platform level and using models to inform how you operate the system and then if you go up a level to the application development sort of CIC deep pipeline where can you make intelligent recommendations to developers as they're pulling in dependencies or even writing code and then give easy access to the data science workflow to intercept so that what you're delivering is a well integrated model with an application that you know has a lifecycle and a maintenance that is well understood yeah so so Chris you know we've watched this is the seventh year we've had the cubit at Red Hat summit of course Red Hat itself has a large portfolio but not only Red Hat but you know the open source communities there are so many you know countless projects out there and you have a huge partner ecosystem you were just talking a bunch about DevOps you know I've got sitting at my desk you know one of those charts that shows you know DevOps tooling and it here's some of the platforms and here's all the various pieces and it's like you know I think there's only you know 50 or 80 different rules on that but how's Red Hat and the community overall how are you helping customers you know deal with this you know challeng world is you know we've got the paradox in place out there on it you know we understand that you know everybody's needs something a little bit different but how are we helping to give a little bit of structure and guidance in the the ever-changing world well I think it's one of the values of pulling content together if you think of a set of components being brought together as curation then we're helping curate the content and assembling pieces together it turns out is a is a lot of work especially when you want a lifecycle manage those components together so one basic thing that we're doing is bringing together an entire distribution of content so it's not just a single it's not just Linux it's not just kubernetes it's Linux and kubernetes engineered together with a set of supporting tooling for logging and monitoring and CI pipelines and all of that we bring together in a context that we opinionated or prescriptive what we also focus on is understanding that every Enterprise has a as its own legacy and history and set of investments that they've made so that process where we bring together an opinionated stack also needs to incorporate the flexibility so where can we plug in a CI pipeline that your your enterprise already has or where can we plug in your monitoring logging tools so that kind of flexibility allows us to bring together some best-of-breed components that we're finding in the open-source communities with flexibility to bring a whole set of ecosystem partners and if we go back to that open data have conversation there are a lot of data centric tools that we put in the open data have open source project we have commercial partners that can support things like say spark as a concrete example or tensorflow and so you know combine those those are open source projects but they're not coming from Red Hat they're coming from our ecosystem partners combine that all together into something that's engineered to work together and you're taking a lot of the friction out of the system so that developers can just move quickly all right so Chris give us a little bit of preview what what are people gonna see in the keynote and you know there's some people that are going to be watching this interview live but others will be efforts though I believe edge is one of the pieces we'll be touching on in the keynote but give us a little bit of what will we can expect well whatever you'll have to come to the keynote to really get the full full experience but what we're trying to to talk through is how data is really fundamentally changing business and if and we talk through that that's sort of story line starting with how it impacts red hats but you know at one level we're an enterprise we have our own business needs we use data to drive how we operate we also see that the platforms that we're building are really helpful for our customers to harness the value of data and change their own business and in the context of doing that we get to take a look at some ways where those business changes have industry-wide effects you know that we talk about things like 5g and artificial intelligence and where these things come together especially in edge computing really interesting space for these things all kind of converge and you know so kind of that that broad broad story line of data something that we use to change how we operate something that we build is from a platform point of view of our customers change how they operate and ultimately those changes have major impacts across the industry which is was which is pretty exciting pretty cool yeah I'm curious Chris you know I think back a few years ago I would have been interviewing you about like NFB and many of the themes it feels like we were talking about there we're really setting the table for the discussion we've been having for 5b is is that you know do you agree with that you know what would what's kind of the same and different from what we might have been looking at five years ago this it's very much and I love that question because it touches on something I think is really important it's very much an evolution and so in the tech world we talked so much about disruption and I think we overplay disruption I think what's interesting is technology evolution just consistently changing and moving forward gives rise at points in time to really interesting convergence of change that can be disruptive so as a concrete example NFV historically was about really improving the operational efficiencies of the service providers building networks and helping them move more rapidly so they could introduce new services most of that was focused on 4G most of that was focused on the core of the network today we're introducing 5g across the industry the discussions are moving technology wise into where do containers fit into this new world and the discussion at the network level is not only in the core but all the way out to the edge and then when you look at the edge where you have a portion of the network operating as software you have a platform like open ship that can also host enterprise or consumer facing education so this is really all of those early stages of NFV are culminating in this in a place today where the technology supports total software infrastructure for the network and utilizing that same cloud that you're running using to run the network to power enterprise or consumer facing applications that's pretty far away from where we were in the early days of NFB very much in evolution and then if you take it one step further and say orgy smart devices and cloud computing gave rise to a set of disruptive businesses ten years ago those businesses did not exist today we can't imagine life without them 5g device proliferations and not just smartphones but a whole set of new devices and edge computing are the ingredients that give rise to that same next wave of innovation where 10 years from now we can't really imagine what are the businesses that in 10 years we won't be able to imagine our lives without so we're at a really interesting inflection point and it's it's partially through this evolution of technology I think it's really exciting all right Chris last question for you there's always so many different pieces going on you know red hats really striking a nice balance there's not really as much of the habla and announcements but you know so much you know everything that does is built on open source so you know there's always things I run across it's like oh I need to you know look down the rabbit hole a little bit and what was that Farkas thing I think I'd heard that word before where all of the projects at the CN CF where you know Red Hat's involved in so you know in the last minute he or give us you know any areas where people said hey you know go google this go look up this you know project other cool things that you know you and your team are working on that you want to make sure to highlight well you you've mentioned one which is Korkis and not often time we talk about infrastructure I think it's a really cool project that is developer focus it's it's in the Java space and it's really bringing Java from an enterprise development platform into a modern language that can be used to build cloud native applications or even serverless functions I think serverless is a critical space so we've been talking for quite some time about all the ways serverless can be impactful we're in a place now where K native as a project is maturing and the the kind of world around it is getting more sophisticated so we have a serverless offer and as part of part of the open shift platform so you know making sure you're paying attention to what's happening in the K native space I think is is really important there's a whole new set of management challenges that will be in the security and a multi cluster space we're bringing those we're bringing technology to bear in this space and as RedHat we will bring those out as open source projects so looking for the open source communities around where you hear things like ECM or advanced container management or multi cluster managed environments which are the norm at this point you know those are some examples of things I think are important and then there's a world of stuff that's data focused there's all of the data science tools you know too many to really enumerate but that I think is an example where open-source is leading the space leading the industry in terms of where all where all those tools are developed and how the coverage and access developers have to data science tools all right well thank you so much Chris right always a pleasure to catch up with you and definitely looking forward to your your you know alright thank you all right lots more coverage check out the cube dotnet you can see all the interviews after they've gone out live they will be on demand all those projects Chris mentioned I've had deep dives on all of them so also hit up Chris square myself on Twitter if you have any follow up always love to hear the feedback I'm Stu minimun and as always thank you for watching the cube [Music]

la from Las Vegas it's the cube covering AWS reinvent 2019 brought to you by Amazon Web Services and Vinum care along with its ecosystem partners Oh welcome back to the sands here we are live here in Las Vegas along with Justin Warren I'm John wall's you're watching the Cuban our coverage here of AWS rain vut 2019 day one off in Rowan and EJ on the keynote stage this morning for a couple of hours and now a jam-packed show for Chris Wright joins us the CTO and Red Hat waking his way toward Cube Hall of Fame status we're getting there this is probably worth 50 of the parents I think good to see you good to see you yeah always a pleasure first off let's just let's just talk about kind of the broad landscape right now the pace of innovation that's going on what's happening in the open cloud you know catching up to that acceleration if you're if you're a legacy enterprise you know you got all these guys that are born over here and they're moving at warp speed you got to be you've got to play catch-up and and talk about maybe that friction if you will and and what people are learning about that in terms of trying to get caught up to the folks that have two head start well I think number one the way I like to frame it is open source is the source of innovation for the industry and part of that is you look at the collaborative model bringing different people together across industry to build technology together it's hard to compete with that pace and speed the challenge of course is as you describe how do you how do you consume that how do you bring it into the enterprise which is you know got a whole business that's running off of infrastructure that has been sustaining their business for potentially decades so there's that impedance mismatch of needing to go quickly to keep abreast of of the technology changes while honoring the fact that your core business is running already on key technology so I think looking at how you bring platforms in that support the newer technologies as well as create connections or even support existing applications is a great way to kind of bridge that gap and then partnering with people who can build a bridge like an impedance match between your speed and the speed of innovation is a great way to kind of you know harness the power without exposing yourself to the ragged edges as much sure yeah talk to us a bit more about it about enterprise experience with open source a Red Hat has a long heritage of providing open source to enterprise and couldn't pretty much sits out as a unique example of how you make money with open source so enterprises have lots of open source that they're using every day now you know Linux has come into the enterprise left right and center but there's a lot more open source technologies that enterprises are using today so give us a bit of a flavor of how enterprises are coming to grips with how open source helps sustain their business well in one sense it's that innovation engine so it's bringing new technology and in another sense it's what we've experienced in the in the Linux space is post driving a kind of commoditization of infrastructure so switching away from the traditional vertically integrated stack of a RISC UNIX environment to providing choice so you have a common platform that you can target all your applications do that creates independence from the underlying hardware that's that's something that provide a real value to the enterprise that notion continues to play out today as infrastructure changes it's not just hardware it's virtualized data centers it's public clouds how do you create that consistency for developers to target their applications too as well as the operation seems to manage well you know it's through leveraging open source and bringing a common platform in into your environment as you go up the stack I think you get more and more proliferation of ideas and choices from developer tools and modules and dependencies you know most software stacks today have some open source even included inside whether you're building exclusively on top of a platform that's open source based you're probably also including open source into your application so it's a whole variety from building your key infrastructure to supporting your your enterprise applications and you mentioned openness which y'all know is a big very important thing to Red Hat and one thing that red has been speaking of lately is open hybrid cloud so maybe you can explain that to us what what he is open hybrid cloud what does red head mean by that sure so open hybrid cloud for us start with open that's our platforms are built from open source project so we work across like literally thousands of open source projects bring those together into products that build our platform also we create an open ecosystem so you know we're really fostering partnerships and collaboration at every level from the developer level up through our commercial partnerships the hybrid piece is talking about where you deploy this infrastructure inside your data center on bare metal servers inside your data center virtualized in a private cloud across multiple public clouds and increasingly out to the edge so that that notion of what is the data center - to me it really encompasses all those different footprints so the hybrid cloud cloud meaning give a cloud like experience from an Operations point of view simple to operate meaning you know we're doing everything we can to help operators manage that infrastructure from a developer point of view surface scene functionality as services Nate the eyes and you know how do you give a self-service environment to developers like you know like a cloud so it's across all that first you talk about data in the edge which you know the fact that there's so much the computing that's going on out there and staying closer to the source right we're not bringing it back in you're leaving it out there that adds a whole new level of complexity - I would think and scale you know massive amounts what everything is happening out there so what are you seeing in that in that in terms of handling that complexity and addressing challenges that you see coming as this growth is tremendous growth continues well one it's how do you manage all of that infrastructure so I think having some consistency is a great way to manage that so using the same platform across all of those different environments including the edge that's really going to give you a direct benefit to targeting your applications to that same common platform having the ability to recognize some dependencies so maybe you have a dependency on a data set and that data sets supplied from sources that are in an edge location we can codify that and then enable developers to build applications you know do test dev Prada cross a variety of environments pushing all the way out to an edge deployment where you know thinking you're taking in a lot of data you may be building models in a scale out environment internally in your private cloud or out in the public cloud taking those models deploying those to the edge for inference in real time to make real-time decisions based on data flows through the system and that's that's the world that we live in today so managing that complexity is critical automation for managing that consistency common platforms I think are key tools that we can use to to help build up that that rich in person just from an industry perspective so who does who's that applied to in your mind right what kind of industry is looking at this and saying all right this is this is a an opportunity but also a challenge for us and something we really need to address what's the array there do you think honestly I see it across almost all market verticals so we look at the world or a platform centric view from from a RedHat perspective so we look at the world across industries what I find interesting in the edge use cases is they tend to get more vertically specific so in a manufacturing case you know maybe you're dealing with a manufacturing line which is a set of applications and a set of devices which looks quite different from a retail office or branch office environment some similar problems but very different environments and then you take the service providers networks the telco network out of the edge and that looks quite different from a manufacturing floor so you know it's a it's a wide variety of vertically oriented solutions drawing from some common platform technologies containers Linux you know how do you do automation across all of those environments that machine learning tools those are the things that I think are consistent but you get all a lot of very vertically focused use cases yeah I'm now in the canine today that that Andy was mentioning that they love open source and when we're here at Amazon and and he likes to talk about the compatibility that and customer choice is also very important to Amazon's wit tell us a little bit about how openness interacts with somewhere like ADA we're actually we're here at reinvent which is an ADA where show so how does Red Hat and AWS work together how do you coexist in this ecosystem and get the benefits of open source technologies we could exist in a number of different ways one would be as engineers working together in open source communities building technology another is we have commercial partnerships so we run our platforms on top of AWS so we bring customers to AWS which is a shared you know we have a shared benefit there and then there's also areas where we have competitive offerings so it's you know it's a full spectrum kind of the modern world of the buzzword co-op petitioner or whatever you know it I really think when you look in the open source communities engineers thrive on building great technologies together independent of any kind of corporate boundaries commercially people develop relationships that are complicated today and we have a great working relationship we've run a lot of our cloud customers on Amazon but again there's there's areas where we're both invested in kubernetes ours is openshift there's a zk s so customers have a choice in that context yeah sorry is that in that context that there are some in the open-source community who view cloud as possibly a bit of a villain and certain things we've seen some some dynamics around some particular providers around the debt the database face I went I went name 50 particular players but we've seen some competitive moves in in that place so do you see cloud is it the villain or is it an enabler of open-source technologies well it's definitely an enabler now there's a complicated scenario and this like is it a villain which is how do we create sustainable communities and in the context where a technology is developed largely by one vendor and it's monetized largely by another vendor it's not going to be a very sustainable model so we just have to focus on how are we building technology together and building it in a sustainable way and part of that is making the contributions back into the community to help the project's themselves grow and thrive part of it is having a great diversity of contributors into the into the project and recognizing that business models change and you know the world evolves yeah that doesn't introduce an element of risk it's been around for a while that enterprise are a little bit concerned about open source oh well who's really behind this will this project or software still be here in six months that seems to be decreasing as as the commercial support for particular open source projects and initiatives come to me and we see the rise of foundations and so on that try to give a little bit of an underpinning to some of these projects particularly ones that are critical for the supportive of enterprise technologies do you see enterprises maturing in their view of open source do they do they see it as no no that we understand that this is definitely a sustainable technology whereas these other ones like yeah that one's not quite there yet or do they still need a lot of assistance in making that kind of decision I've been at it for a couple of decades so in the beginning there was a lot of evangelism that this is safe it's consumable by the enterprise it's not some kind of crazy idea to bring open-source you're not gonna lose your intellectual property or things like that those days I mean I'm sure you could find an exception but those days are largely over in this in the sense that open source has gone mainstream so I would say open source is one most large enterprises have an open-source strategy they consider open source as critical to not only how they source software from vendors but also how they build their own applications so the world has really really evolved and now it's really a question of where are you partnering with vendors to build infrastructure that's critical to your business but not your differentiator and where are you leveraging open source internally for your to differentiate your business I think that's a more sophisticated view it's not the safety question it's not is it is it legally you know that you're bringing legal concerns into the picture it's really a much different conversation and people in the enterprise are looking how can we contribute to these projects so that's really it's pretty exciting actually so so what do you think it is then in the maturation process then as it did is it in the adolescent years is it growing into young adulthood you said you've been at it for a long time and it's more acceptable but where are we you think on that in that arc you know what in terms of adapting or or adopting if you will that philosophy probably depends on where you are in the layer of the stack and so the lower you get into the infrastructure the more commonplace it is the closer you get to differentiated value and something that's really unique there's less reason to even build those applications as open source if it's only you consuming it you know pretty pretty broad spectrum there I think that in general we're in some level of adulthood it's a very mature world in the open-source communities and what's interesting today is how we change business models around deploying and consuming open source technologies and then a next generation of technology will be very data-centric data drives a whole set of questions there's policy and governance around data placement there's model training and model exchanging and where models come from data or the models open source is the data shareable you know that it sets a whole new wave of questions that I think in that context it's much earlier so that's our next interview by the way with Chris next time down the road thanks for the time as always really good to see you and I know you're you're awfully busy this week so we really do appreciate you carving out a little slice of time glad to do face press yeah thank this right over Red Hat CTO back with Justin and John live on the cube here at AWS reinvent 2019

>> live from Boston, Massachusetts. It's the you covering your red have some twenty nineteen rots. You buy bread hat. >> Good to have you back here on the Cube as we continue our coverage. Live at the Red Had Summit twenty nineteen, Day three of our coverage with you since Tuesday. And now it's just fresh off the keynote stage, joining stew, Minutemen and myself. Chris. Right? VP and chief technology officer at Red Hat. Good job there, Chris. Thanks for being with us this morning. Yeah. >> Thank you. Glad to be here. >> Great. Right? Among your central things, you talked about this, this new cycle of innovation and those components and how they're integrating to create all these great opportunities. So if you would just share for those with those at home who didn't have an opportunity to see the keynote this morning, it's what you were talking about. I don't think they play together. And where that lies with red hat. Yeah, you bet. >> So, I think an important first kind of concept is a lot of what we're doing. Is lane a foundation or a platform? Mean red hats focuses in the platform space. So we think of it as building this platform upon which you build an innovate. And so what we're seeing is a critical part of the future is data. So we're calling it a Kino data centric. It's the data centric economy. Along with that is machine learning. So all the intelligence that comes, what do you dividing? The insights you're grabbing from that data. It introduces some interesting challenges data and privacy and what we do with that data, I mean, we're all personally aware of this. You see the Cambridge Analytica stuff, and you know, we all have concerns about our own data when you combine all of us together techniques for how we can create insights from data without compromising privacy. We're really pushing the envelope into full distributed systems, EJ deployments, data coming from everywhere and the insights that go along with that. So it's really exciting time built on a consistent platform like lycopene shift. >> So, Chris, I always loved getting to dig in with you because that big trend of distributed systems is something that you know we've been working on for quite a long time. But, you know, we fully agree. You said data at the center of everything and that roll of even more distributed system. You know, the multi cloud world. You know, customers have their stuff everywhere and getting their arms around that, managing it, being about leverage and take advantage. That data is super challenging. So you know where where, you know, help us understand some of the areas that red hat in the communities are looking to solve those problems, you know, where are we and what's going well and what's still left to work on. >> Well, there's a couple of different aspect. So number one we're building these big, complex systems. Distributed systems are challenging distribute systems, engineers, air really solving harder problems. And we have to make that accessible to everybody operations teams. And it's one of the things that I think the cloud taught us when you sort of outsource your operations is somebody else. You get this encapsulated operational excellence. We need to bring that to wherever your work clothes are running. And so we talked a lot about a I ops, how you harness the value of data that's coming out of this complex infrastructure, feed it through models and gain insights, and then predict and really Ultimately, we're looking at autonomic computing how we can create autonomous clouds, things that really are operating themselves as much as possible with minimal human intervention. So we get massive scale. I think that's one of the key pieces. The other one really talking about a different audience. The developers. So developers air trying to incorporate similar types of intelligence into their applications were making recommendations. You're tryingto personalize applications for end users. They need easy access to that data. They need easy access to train models. So how do we do that? How do we make that challenging data scientist centric workflow accessible to developers? >> Yeah, just some of the challenges out there. I think about, you know, ten, fifteen years ago, you talk to people, it was like, Well, I had my central source of truth and it was a database. And you talk to most companies now and it's like, Well, I've got a least a dozen different database and you know, my all my different flavors of them and whether in the cloud or whether I have them in my environment, you know, things like a ops trying to help people get involved with them. You talked a little bit in your keynote about some of the partners that you're working on. So how do you, you know, bring these together and simplify them when they're getting, you know, even more and more fragmented? >> Well, it's part of the >> challenge of innovation. I mean, I think there's a there's a natural cycle. Creativity spawns new ideas. New ideas are encapsulated in projects, so there's a wave of expansion in any kind of new technology time frame. And then there's ultimately, you see some contraction as we get really clear winners and the best ideas and in the container orchestration space communities is a great example of that. We had a lot of proliferation of different ways of doing it. Today we're consolidating as an industry around Cooper Netease. So what we're doing is building a platform, building a rich ecosystem around that platform and bringing our partners in who have specific solutions. They look at whether it's the top side of the house, talking to the operations teams or whether it's giving developers easy access to data and training models through some partners that we had today, like perceptive labs and each to a A I this partnership. Bringing it to a common platform, I think, is a critical part of helping the industry move forward and ultimately will see where these best of breed tools come into play. >> Here, uh, you know, maybe help a little bit with with in terms of practical application, you got, you know, open source where you've got this community development going on and then people customized based on their individual needs all well, great, right? How does the inverse happen? Where somebody who does some custom ization and comes up with a revelation of some kind and that applies back to the general community. And we can think of a time where maybe something I'm thinking like Boston children, their imaging, that hospital we saw actually related to another industry somehow and gave them an ah ha moment that maybe they weren't expecting an open source. Roy was the driver that >> Yeah, I think what we showed today were some examples of what If you distill it down to the core, there's some common patterns. There's data, they're streaming data. There's the data processing, and there's a connection of that processed data or train model to an application. So we've been building an open source project called Open Data Hub, where we can bring people together to collaborate on what are the tools that we need to be in this stack of this kind of framework or stack And and then, as we do, that we're talking to banks. They're looking at any money laundering and fraud detection. We're talking to these hospitals that were looking at completely different use cases like HC Healthcare, which is taking data to reduce the amount of time nurses need to spend, gathering information from patients and clearly identify Septus sepsis concerns totally different applications, similar framework. And so getting that industry level collaboration, I think is the key, and that having common platforms and common tools and a place to rally around these bigger problems is exactly how we do that through open source. >> So Lynn exits and an interesting place in the stack is you talked about the one commonality and everything like that. But we're actually at a time where the proliferation of what's happen to get the hardware level is something that you know of an infrastructure and harbor guy by background, and it was like, Oh, I thought We're going to homogenize everything, standardize everything, and it's like, Oh, you're showing off Colin video stuff. And when we're doing all these pieces there, there's all these. You know, new things, Every been things you know you work from the mainframe through the latest armed processors. Give us a little insight as to how your team's geeking out, making sure that they provide that commonality yet can take advantage of some of the cool, awesome stuff that's out there that's enabling that next wave of innovation. >> Yeah, so I share that infrastructure geek nous with you. So I'm so stoked the word that we're in this cycle of harbor innovation, I'll say something that maybe you sounds controversial if we go back in time just five years or a little, a little more. The focus was around cloud computing and bringing massive number of APS to the cloud, and the cloud had kind of a T shirt size, small, medium, large view of the world of computer. It created this notion that Khun computers homogenous. It's a lie. If you go today to a cloud provider and count the number of different machine types they have or instance types it's It's not just three, it's a big number. And those air all specialized. It's for Io throughput. It's for storage acceleration. It's big memory, you know. It's all these different use cases that are required for the full set of applications. Maybe you get the eighty percent in a common core, but there's a whole bunch of specific use cases that require performance optimization that are unique. And what we're seeing, I think, is Moore's law. The laws of physics are kind of colliding a little bit, and the way to get increased acceleration is through specialized hardware. So we see things like TP use from Google. We see until doing deal boost. We've got GPS and even F p G A s and the operating system is there TIO give a consistent application run time while enabling all those hardware components and bringing it all together so the applications can leverage the performance acceleration without having to be tied directly to it. >> Yeah, you actually think you wrote about that right now, one of your a block post that came about how hardware plays this hugely important role. You also talked about innovation and change happening incrementally and And that's not how we kind of think about like big Banks, right? Yeah. Wow, this is But you pointed out in the open source, it really is step by step by step. Which way? Think about disruption is being very dramatic. And there's nothing sexy about step by step. Yeah, that's how we get to Yeah, disruption. I kind of >> hate this innovation, disruption and their buzz words. On the one hand, that's what captures attention. It's not necessarily clear what they mean. I like the idea that, you know, in open source, we do every day, incremental improvements. And it's the culmination of all these improvements over time that unlock new opportunities. And people ask me all the time, where is the future? What do we do and what's going on? You know, we're kind of doing the same thing we've been doing for a long time. You think about micro services as a way to encapsulate functionality, share and reuse with other developers. Well, object oriented programming decades ago was really tryingto tryingto established that same capability for developers. So, you know, the technologies change we're building on our history were always incrementally improving. You bring it all together. And yes, occasionally you can apply that in a business case that totally disrupts an industry and changes the game. But I really wanted encourage people to think about what are the incremental changes you can make to create something fundamentally new. >> All right, I need to poke it that a little bit, Chris, because there's one thing you know, I looked back in my career and look back a decade or two decades. We used to talk about things like intelligence and automation. Those have been around my entire career. Yeah, you look it today, though, you talk about intelligence and talk about automation, it's not what we were doing, you know, just the amount of degrees, what we're having there. It is like if we'd looked at it before, it was like, Oh, my gosh, science fiction's here so, you know, way sometimes lose when we're doing step by step, that something's there making step function, improvements. And now the massive compact, massive changes. So love your opinions there. >> Yeah, well, I think it's a combination, so I talk about the perpetual pursuit of excellence. So you pick up, pick a field, you know, we're talking about management. We got data and how you apply that data. We've been working towards autonomic computing for decades. Concepts and research are old, the details and the technologies and the tools that we have today are quite different. But I'm not. You know, I'm not sure that that's always a major step function. I think part of that is this incremental change. And you look at the number for the amount of kind of processing power and in the GPU today No, this is a problem that that industry has been working on for quite a long time. At some point, we realize, Hey, the vector processing capabilities in the GPU really, really suit the machine learning matrix multiplication world real world news case. So that was a fundamental shift which unlocked a whole bunch of opportunity in terms of how we harness data and turn it into knowledge. >> Yes. So are there any areas that you look at? Now that we've been working at that, you feel we're kind of getting to those tipping points or the thie waves of technology or coming together to really enable Cem Cem massive change? >> I do think our ability to move data around, like generate data. For one thing, move data around efficiently, have access to it from a processing capability. And turning that into ah, >> model >> has so fundamentally changed in the past couple of decades that we are tapping into the next generation of what's possible and things like having this. This holy grail of a self healing, self optimizing, self driving cluster is not as science fiction as it felt twenty years ago. It's >> kind of exciting. You talk about you've been there in the past, the president, but there is very much a place in the future, right? And how would that future looks like just from from again? That aye aye perspective. It's a little scary, sometimes through to some people. So how are you going about, I guess, working with your partners to bring them along and accept certain notions that maybe five six years ago I've been a little tough to swallow or Teo feel comfortable with? >> Yeah, well, there's a couple of different dimensions there. One is, uh, finding tasks that air computers are great at that augment tasks that humans were great at and the example we had today. I love the example, which was, Let's have computers, crunch numbers and nurses do what they do best, which is provide care and empathy for the patients. So it's not taking the nurse's job away. In fact, is taking the part that is drudgery ITT's computation >> and you forget what was the >> call it machine enhanced human intelligence right on a couple of different ways of looking at that, with the idea that we're not necessarily trying to eliminate humans out of the loop. We're trying to get humans to do what they do best and take away the drudgery that computers air awesome at repetitive tasks. Big number crunching. I think that's one piece. The other pieces really, from that developer point of view, how do you make it easily accessible? And then the one step that needs to come after that is understanding the black box. What happens inside the machine learning model? How is it creating the insights that it's creating and there's definitely work to be done there? There's work that's already underway. Tto help understand? Uh, the that's really what's behind the inside so that we don't just trust, which can create some problems when we're introducing data that itself might already be biased. Then we assumed because we gave data to a computer which is seemingly unbiased, it's going to give us an unbiased result, right? Garbage in garbage out. >> So we got really thoughtful >> about what the models are and what the data is that we're feeding >> It makes perfect sense it. Thanks for the time. Good job on the keynote stage again this morning. I know you've got a busy afternoon scheduled as well, so yeah, I will let you. We'Ll cut you loose. But thank you again. Always good to see you. >> Yeah. I always enjoy being here >> right at that's right. Joining us from red hat back with Wharton Red Hat Summit forty nineteen. You're watching live here on the Cube?

>> Live from Seattle, Washington, it's the Cube, covering KubeCon andCloudNativeCon North America 2018. Brought to you buy Red Hat, the CloudNative computing foundation and it's ecosystem partners. (background crowd chatter) >> Okay welcome back everyone, live here in Seattle forKubeCon and CloudNativeCon. This is the Cube's coverage, I'm John Furrier with Stu Miniman. We've got two great guests, Chris Wright CTO of Red Hat, Scott Sneddon who's the senior director ofcloud at Juniper Networks, breaking down, windingdown day one of three days of coverage here. Rise of kubernetes, rise of cloudnatives, certainly impacting IT,open source communities, and developers. Guys, thanks for coming on the Cube. Appreciate it. It's good to see you. >> Yeah, good to see you. >> Welcome to the Cube. Okay, so, talk aboutthe relationship between Red Hat and Juniper. Why we're here, what are we talking about? >> Well, we're here to talkabout a combined solution. So, Red Hat's bringingkind of the software platform infrastructure piece and Juniper's bringinga networking component that ties it together.>> Yeah. >> So, we do have a fairly, well, in tech terms arelatively long history of working together. We've had a partnership for a little more than two years on sometelco Cloud initiatives around OpenStack, using the right OpenStackplatform with Contrail Juniper's contrail solutionas an SDN layer for these telco Cloud deployments. And have had a lot of successwith that partnership. A lot of large and smallto medium telco's around the world have deployed that. Earlier this year at theOpenStack summit in Vancouver, we announced an expandedpartnership to start to address some enterprise use cases. And, you know, naturallyopen shift is the lead technology that we wanted to tie in with around enterpriseadoption of cloud and some alternatives to someof the legacy platforms that are out there. >> And we were talkingearlier in the Cube here, we always get kind ofthe feel of the show, kubernetes maturing? But it kind of two worlds colliding and working together. A systems kind of view,almost like operating systems. The network systems, allkind of systems thinking. And then just apps. Okay, the old app thing. So these old legacy worldthat we all lived in kind of happening in really dynamic ways with the apps aren't thinkingabout what's below it. This is really kind of whereyou guys have a tailwind with Juniper.>> Yeah. Because you still gotto make things dynamic, you still got latency, onpremises not going away. You got IOT, so networkingplays a really big thing as software starts figuringthings out as kubernetes. Let's talk about that. Where is that value? How's it expanding? Cause clearly you stillneed to move packets from A to B.>> Yeah. Be more efficient with it. Apps going to have policy. >> The, well, I mean you've still got to, the network is always been the foundation of technology or at least for the last 20 plus years. And as cloud has been adopted, really we've seen network scale drive in different ways. The mega scalers thathave built infrastructure that we've been enabling for quite a while and have been working withthose customers as well. We've been developing a lot of simplified architecture just forthe physical plumbing to connect these things together. But what we've seen andis more and more important is, you know, it's all about the app, the app is the thing that'sgoing to consume these things. And the app developerdoesn't necessarily want to worry about IP addresses and port numbers and firewall rules and things like that, so how could we justmore simply extract that? And so, you know, we'vebeen developing automation and aimed at the networkfor quite a while, but I think more andmore it's becoming more important that theapplication can just consume that without having to directthe automation at the app. And so, you know, groupslike CloudNative foundation and a lot of the workwith kubernetes are on network policy, let's us use CloudNativeprivatives and then we can translate into the network primitives that we need to deploy to move packets, you know, IP addresses and subnets. >> And Chris, talk aboutthe multi cloud dynamic here because again, the dayof things are moving around the standardizationaround those core value propositions, youmentioned about networking and software networks, all kinds of software, you know, venations under the covers. I'm a customer, I havemultiple clouds now. This is going to be a core requirement. So you got to have a a clean integration between it. >> There's really two things. If you look at a modern application, you got your traditionalmonolithic application and as you tease itapart and into components and services, there's only one thingthat reconnects them and that's the network and so insuring that that's as easy to use as an applicationdevelopers focus is around the app and not aroundnetwork engineering is fundamental to a single cluster. And then if you have multiple clusters and you're trying to take advantage of different specialtiesin different clouds or geo replication or things like this that also require thenetwork to reconstitute those applications across thedifferent multiple clouds. If you expect your applicationengineers to become experts in networking, you're just sort ofsetting everybody up with misset expectations. >> It slows things down,requires all these other tasks you got to do. I mean it's like a rock fetch. You don't want to do it. Okay, stack a bunch of rocks, move them from there to there. I mean, this is whatthe holy grail of this infrastructure's code really is. >> Yeah.>> Yeah. I mean, that's the goal. >> Help connect the dots for us. When you look at multicloud networking obviously is a very critical component, what're your customers looking for? How does this solution goto market for your company? >> Absolute ease ofuse is top of the list. So, it can't be overly complicated. Because we're alreadybuilding complex systems, these are big distributive systems and you're adding multipleclusters and trying to connect them together. So ease of use is important. And then something that'sdynamic and reflects the current application requirements, I think is also really important. So that you don't over utilize resources in a cloud to maintainsort of a static connection that isn't actually needed at that moment. I'm sure you probably havea different perspective. >> Yeah, I mean, this isthe whole concept of SDN and network virtualization, a lot of the buzzwordsthat have been around for a few years now, is the ability to deliveron demand network services that are turned on whenthe application asks for it and are turned off when the application's done with it. We can create dynamic connectionsas applications scale. And then with a lot of thenewer things we've been doing around contrailand with Red Hat are the ability to extend thoseapplications environments with networking andsecurity into various cloud platforms. So, you know, if it's runningon top of an openstack environment or in a public cloud or, some other bare metal infrastructure, we're going to make surethat the network and security primitives are inplace when the application needs it and then get deepervisioned or pulled out when they go away. >> Being at a show like this, I don't think we need to talktoo much about open source, because that's reallycore and fundamental, but what we're doing here, but I guess, how doesthat play into customers? We've been watching the slow change in the networking world, you know, I'm a networking guy by background, used to measure changesin networks in decades and now it feels like we'removing a tiny bit faster, >> Little bit. >> What're we seeing is--? >> Well, I mean the historyof openness in networking was the ITF>> Standards. >> and IEEE and standards bodies, right? How do we interact? We're going to have ourlittle private playground and then we'll makesure to protocol layer, we can interact with each otherand we call that openness. But the new openness is open source and transparency into the platform and the ability tocontribute and participate. And so Juniper shifted a lot of our focus, I mean we still haveour own silicone and the operating system we built on our routers and switches, but we'vealso taken the contrail platform, open sourced it a few years ago, it's now called thetungsten fabric project under the Linux foundation. And we're activeparticipants in a community. And our customers really demand that. The telco's are drivingtowards an open source model, more and more enterpriseswant to be able to consume open source software with support, which is where we come in, but also be able to have an understanding of what's going on under the covers to participate if that's a possibility. But really drivinginteroperability through a different way then justa protocol interaction and a standards body. >> I can see how kubernetescan be a great fit for you guys at Juniper, clearly out of the boxyou have this kind of inter cloud, inter networking, paradigm that you're used to, right? How does the relationshipof Red Hat take it to the next level? What specifically areyou guys partnering on, where's that, what'sthat impact on customers? Can you just give a quick explanation, take a minute to explainthe Juniper Red Hat-- >> Well a lot of itcomes down to usability and ease of use, right? I mean what Red Hat's done with open shift is developed a platformleveraging kubernetes heavily, to make kubernetes easierto use with the great support model and a lot of tooling built on top of that to make thatmore easily deployable, more easily developersto develop on top of. What we're doing withcontrail is providing a supported version ofour open source project and then by tying thesethings together with some installation tools and packaging and most importantly a support model, that let's a customer have the proverbial single throat to choke. >> Have you ever hadcustomers that can run beautifully on your platform? >> Yeah yeah, and theinstallation process is seamless, it's a nob that installtime to consume contrail or some other networking stack and they can call Red Hat for support and they'll escalate toJuniper when appropriate and vice versa. And we've got all those things in place. >> I think one of the things that we have like shared vision on is, the ease of use andthen if you think about two separate systems with a plug in, there's going to be someintegration that needs to happen and we're lookingat how much automation can we do to keep thoseintegrations always functional so that ifwe need to do upgrades, we can do those together instead of abandoning one side or the other. And I think another areawhere we have shared vision is the multi cloud space where we really see the importance for our customer base toget applications deployed to the right locations. And that could be takingadvantage of different pricing structures in different clouds or it could be hardwarefeatures of functionality. Especially as we getinto edge computing and really creating a differentview of computing fabric, which isn't quite so, you know, client serveror cloud centralized, but much more distributed. >> I like how you said that Chris, earlier about how when you decomposethat monolithic app it connects with the network. That's also the other way around. Little pieces can cometogether and work with the network and then form in real time, whether it's an IOT datacoming into the data center, or pushing computdata to the edge, you got to have that network interaction. This is a real CloudNative evolution, this is the core. >> Yeah, and I think anotherpiece that we haven't touched on as much, Scott mentioned it, was the security component. >> Yeah, explain that. >> Again, with as youdecompose that application into components, you surface those components with APIs, those were internal APIswhen they're now exposed externally security really matters. And having simple policythat describes not just the connectivity topologybut who can speak to whom is pretty fundamentally important. So that you maintainsecurity posture and a risk profile that's acceptable. >> And then I think it'sreally important is, your traditionalenterprise starts to adopt these CloudNative models. You've got a securityteam there that might not necessarily be up to speed or on board. So you've got to havetooling and visualization and analytics to beable to present to them that policies are being enforced correctly and are compliant and all those things so. >> Yeah and they're tough customers too. They're not going to, they expectreally rock solid capability. >> They don't let youjust deploy a big flat network with no policy-- >> Hey what about the APIs? Service areas exposed in the IOT space. >> Yeah.>> Right. >> You got to nail it down. >> Yeah absolutely, sothat's a lot of what we're bringing to the table here, is a lot of Juniper'shistory around developing security products. >> Take a minute to explain,I want to give you some time to get a plug in for Juniper. I've been following youguys for a long time. Junos back on the old days, contrail. Juniper's has had a software, big time software view. >> Yeah. >> Explain the DNA of software at Juniper. >> You know the earlydays of Juniper were, we weren't the first networkvendor on the market. There was already somebodyon the market in the mid 90s that had a pretty solid stronghold on carrier and enterprise networking. We had to come in with a better model. Let's make the box easierto use and simpler. Let's make the interfacea little more structured and understandable. Let's make it programmable, right? I mean the first feature request for Junos was to have a CLI becausethe first interaction to it was just an API call. And that was out of the box from day one. We had to write a user interface to it just to fit in to theexisting network world in the mid 90s. And so we've alwaysbeen really proud of the Junos operating systemthat runs on our boxes. We've really been proudthat we've had this one Junos concept of a commonoperating system on every network device that we deliver. As we've started tovirtualize those network devices for NFE and things like that, it's again that same operatingsystem that we deliver. Contrail came to us through acquisition, so it's not Junos in and of itself, but still leveraging a lot of those same fundamentals around,model driven configuration management, understandableAPIs, and openness that we've always had. >> Cloud operating modelthat everyone's going to, the common operating modelfits in that unification vision that you guys have had. >> Yeah absolutely. >> And really early, by the way, was before SDN was SDN, I think that was SDN's kind of like-- >> I like to dry, I-- >> Should have called it SDN. >> Right, I described SDN as just a big distributed router andreally we've had big distributed routers for a long time. >> John, we are in Seattle, everything we're talkingabout in tech is hipster. >> Chris, great stuff. Great to have you on, Scott. Great smart commentary. CTO Red Hat, you guys are winning. Congratulations on the betsyou made at kubernetes early, >> Yeah. >> CoreOS great acquisition,great team there, and some news there aboutsome dealings out back into the C and CF, soI mean, you've got it-- >> A lot going on. >> A lot going on. And yeah, big news with that other things, I can't remember what it was, it was some big-->> Something in there. >> Something for a million dollars. >> Great news out there. Thanks for coming out, appreciate it. Good to see you.>> Good to see you. >> Alright, breakingdown day one coverage. I'm John Furrier, Stu Miniman. Day two starts tomorrow. Three days of wall towall coverage of KubeCon. And they're shutting down the hall. Be right back and see you tomorrow. Thanks for watching. (techy music)

>> From the Aria Resort in Las Vegas, it's theCUBE. Covering AWS Marketplace. Brought to you by Amazon Web Services. >> Hey welcome back everybody, Jeff Frick here with theCUBE, we're at AWS re:Invent in Las Vegas, I don't know, 50,000, 60,000. I can't wait 'til the number comes in, there's a lot of people at this event. Been coming for years, we actually have nine days of coverage here spread out over three sets in four different locations, but we're kickin' it off tonight, at the AWS Marketplace and Service Catalog event here at the Aria. Come on by there's no lines over here. I'm sure there's giant lines over at the Sands. We're excited to see an old friend and make a new friend, and talk about the service catalogs from some of the experts and Dave Wright, Chief Innovation Officer from ServiceNow, has been on many, many times. >> I have. >> Dave great to see you. >> No, good to be here. >> And our new friend MaSonya Scott, she's Senior Business Development Manager, at AWS Service Catalog. >> Hi, how are you? >> Welcome. >> Thank you. >> So there's a lot of talk about service catalog. We know about the ServiceNow Service Catalog. We got the AWS Service Catalog. But now you guys have brought these two things together. >> Yes. >> Why did you bring it together? How did it happen? How did we get here today? >> So, AWS, 95% of our features are based on what customer feedback. And so we were listening to our customers tell us that, hey it's your innovation in AWS Service Catalog, where you provide the governance, the guardrails, the launch constraints is great but we already have a service catalog with the ideas and tools such as ServiceNow and how can federate that ingest the details and information into ServiceNow so that we can do it in one place? So our developers don't have to swivel chair between two different systems. >> Right. >> So we listened to that feedback. Got requirements, started a proof of concept, and then we built on it and we're now on our third iteration. >> And Dave, what were you hearing your side of the chair? >> So we were getting, customers were coming to us saying they wanted a similar experience they were getting using regular service catalog. So they wanted a unified experience. They wanted the ability to have governance and control over what was happening. But most importantly they wanted us to integrate because they'd say, hey, you two are both strategic platforms for us. We don't want to go to the last mile after to aid the integration so you guys need to work together and sort it out. So, it was very much customer driven. It was customers that were saying, We want you guys to work together more closely. >> I love it. I just love the customer-centric nature because we hear it over and over again. >> Yes. >> And this is kind of a great example of a real instance. And you didn't really care, per se, whose was kind of on top. What are the logging into initially. >> Right. >> You just wanted to get the integration done. >> Yes. >> And it sounds like today, right now it's a ServiceNow log in and then it integrates with the AWS Service catalog-- >> Yes. >> Underneath. >> Right. So for the AWS Service Catalog we state the source of truth for all the resources, the products and the portfolios. And then we sync to ServiceNow Service Catalog through their scheduled job process and we expose products that the ServiceNow administrator wants their end-users to see. So they can order an iPhone and now they can order a web server. >> And where's the identity? Is that in the ServiceNow platform now in terms the rights and access that an individual person has inside of your catalog. >> So we have a scoped app in ServiceNow where you correlate the identities in AWS to a role in ServiceNow. >> Okay. >> So that's that. And then the best thing about the app is that the ServiceNow end users doesn't have direct access to the AWS console. >> Right. They just order what is a compliant, secure product product that they need to have. And then even in the AWS console, the connection only gives the end user role that's assumed access to the AWS Service Catalog. So not even direct resources to EC2 or S3. And so what that enables is that segregation of duty. >> Right. >> And so we put the permissions on the launch constraints and then they deplore products and then you can give the evidence in an audit to what you've provisioned. >> Okay. And then where is this in the life cycle? You said, I think we're kind of past POCs getting into production? >> Yeah our customers are starting to move to production, doing POCs, giving us feedback and as they give us more feedback, we're creating more releases. We've launched our third iteration of features last Monday, the 19th and what we did was we integrated not only just AWS Service Catalog but the ability through AWS Systems Manager to do SSM actions so if you provision a web server, now you can start, stop, reboot it. And so, then once you do that we create a change in ServiceNow's IT change management. So we're moving more from provisioning as well as into operational actions as well. >> So that's what was great for us was if you've got that point of initiation where you know something's happening, we can then update the CMDB in real time, we can start looking at software asset management so we can see what's deployed. And then we've also found this great use cases where we can look at how we actually map the service to then be able to use Amazon's cloud migration products to be able to speed up migrations as well. >> Yes. Yes, and so customers are not only just provisioning storage or EC2's but anything from a cloud formation template workspace, an Amazon workplace. >> Right. >> We can also send those requests through ServiceNow we got a lot of feedback on that. >> Right. >> We have sessions and builder projects today at re:Invent that are showing how that works. >> So it begs the question obviously down the road, you know will kind of the priority switch from the customer perspective? You know, will ServiceNow be integrated in through the marketplace of the service catalog and people access it through that way? >> I think there's no reason why it couldn't be at some point. The only challenge we have at the moment is obviously people using the service catalog to provision all kinds of things as well as AWS components >> Right, right. >> But if you're a core AWS shopper and that's what you're using everything for there's no reason why you couldn't flip that around. >> Right, I was just thinking you know what screen are you on all day, right? You know everybody wants your attention. They want that screen and if that's your work screen, that's your work screen so know you're opening this up, really adds a whole lot of power to the ServiceNow work screen that wasn't there before. >> We've always focused on can it change what the employee experience is like to just try and give them, ironically an Amazon-like experience. >> Yeah. (laughs) >> Before we said it's easy for me to order stuff in the real world for me to just get something from Amazon why couldn't I have exactly that same experience when I wanted to order any piece of IT equipment whether it be physical, virtual, peripherals, whatever. So that's why we're trying to change the experience but it's funny that it's come through that full circle of them going, oh, it would be good if it was like that, and then we end up working with these guys anyway. >> So did you get a cake? (laughing) >> Did I get a cake? I've seen many cakes in my career. But no, I think you need to send me a cake. >> We need to do it, get a go live cake. >> I know, I know >> Yeah you got to get a Go Live cake once you get that first one up and official and ready to roll. >> We ought to do that, take a picture. >> See your product manager. >> It's a huge tradition. It's one of the coolest things of the ServiceNow culture I have to say. >> Yes, yes, yes. >> All right. So any final words on this partnership beyond just continuing to make the improvements getting closer to production and build more functionality? Anything you want to highlight as you turn the calendar on 2018 and 2019's just around the corner? >> So we're looking to get feedback on not just provisioning but going through other management tool services and trying to see what the customers are looking for. So that's one of those key features is just building bigger not just AWS Service Catalog but a connector between the two platforms so that we can continue to create that synergy. >> Excellent. All right, well thanks for taking a few minutes of your day and we'll see you I think in May. (laughing) Is usually when we see you. And we see AWS, I think we had six shows this year. You guys just keep rolling. >> Yes. >> So thanks for taking a few minutes and have a terrific show. >> No great to be here. >> Thank you. >> All right, she's MaSonya, he's Dave and I'm Jeff, you're watching theCUBE. It's the AWS Marketplace Service Catalog Enterprise at the Aria, come on by. Thanks for watching. (upbeat music)

>> Narrator: Live from Las Vegas, it's theCube covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back everyone to theCube's live coverage of ServiceNow Knowledge18 here in Las Vegas. I'm your host Rebecca Knight along with my cohost Dave Vellante. We're joined by Dave Wright. He is the chief innovation officer at ServiceNow. Thanks so much for coming on the program. >> It's a pleasure, always a pleasure. >> Good to see you again Dave. >> Good to see you as well. >> Yeah, you've been around the block. You've been around theCube a few times. >> Around the block, a bad way of putting it but yeah. (laughing) >> So chief innovation officer, we've had a lot of great new product launches at this show. What are you most excited about, and what are you already thinking about when you go back to your office? >> So I think what's been interesting to me is the different way of engaging now, we've got the concept of virtual agent technology and I don't just mean the fact that we've got virtual agents. The fact that it starts to give people alternatives and it gives people alternative ways to come into the system, whether it be through our interface or whether it be through someone else's interface, I start to wonder, what'll happen going forward as we get more and more bot type technologies out. How will you have that one interface that works with all those to get that information back of the chain? How will you almost have a single interface that allows you to connect to all these bots and solve your problems? Because the benefits kind of two fold. One is the bot technology you get from being a customer to coming in and actually doing a request. But the other is you'll eventually be able to take that same technology and apply it to the fulfilled user so the power user because if I'm doing something and I can have an agent that's helping me do it, almost like the agent assist concept, you saw this morning. If I can take that to a next level and have AI running on top of that, then I can make work easier for the people coming in but I can actually improve the people that are in the system and make them more effective. >> Go ahead. >> Go ahead, follow up please. >> No, I was just going to ask about, how you get your ideas? So you're here, you're interacting with customers, you're seeing how they're using your product. So is it interviewing customers to find out their pain points? Is it really just watching, I mean you're the chief innovation officer. How do you spark your own creativity? >> It's a really weird answer. I get most of it off kids, most of it off my kids. So I can tell you a story. We were in Barnes and Noble the other week and they had albums in the, plastic twelve inch albums. >> Rebecca: They're coming back. >> And they cost more than they use to. >> Dave Vellante: Yeah really. >> So I called the kids over, I said look, let's get educated. This is what I use to play music on. And now we moved to CD's and you guys miss CD's and this is why you guys buy music. Now I've got a 12 year old and seven year old. And the 12 year old was saying, well, we don't buy music. And I said yeah, and I thought, no you don't, you rent music. And then my youngest daughter said, why would you want to own a song forever? And I was like, this is interesting. We started having a discussion, >> These are deep, these are deep questions. >> It was while other kids we're over having a sleepover and they're all eating pizza and they were talking about the concept of having a job. They said, how do you decide what you want to do for the rest of your life and how do you do that? And we we're talking about how you do something, you get better. You go to another company, you get better at doing it. You go to another company. And one of them said, it sounds really boring just like doing the same thing. And then one of them had the best answer. She said, don't you think it's a waste of your time? And I said, why is it a waste? And she said, because if you're really good at something, why should you just do it for one company? And I was like, oh so, you're going to be a contactor. (laughing) But what I realize was because this whole generation don't need to own things, they just need to use things, so they don't need to know how to do something, they just know they want to do it. And they don't need to own something, they just need temporary access to it. Then it got me thinking when you talk about where could work go to. Do you get a whole concept of the gig economy becoming a gig enterprise. Because we've got a lot of people in work who've got all these different skills but we force them to do one job. And it might be that someone's doing a job but they've got skills that would be applicable outside of that job but they never get to use them. So have we seen the first generation arrive now where they expect everything to be tass based? And then it gives you control over your own career. Because then you say, well, actually I'm not good at this but I can start a bid for work. I can say to people, hey I'm only a three on a skills racing but if you don't need any complex, I'll take it cause I get to learn. So it's a whole new dynamic and I think when you ask whereabout ideas come from, some of the first stage ideas or the first horizon, I think they come form customers. Some of the second horizon, they come from people who aren't working. It's just trying to imagine how they all develop and whereabout that all goes. >> So you surround yourself with millennials? >> Not even millennials. >> Dave Vellante: They're kind of pre post millennials. >> Almost like the linksters, almost the people who've been born connected. It's definitely a Gen Z thing but it's beyond millennials. I think the millennials had a certain expectation around well it's kind of a negative connotation but before they were called millennials, people use to refer to it as the entitlement generation. And it wasn't because they were entitled, it was because they felt they just got access to everything. So it's like with my kids, they're kind of Gen Z and one of them is a linkster, but you never go to them and say, they never come to you and say, hey, I want to watch a movie and you go, great, let's go to Blockbuster's, let's rent it. They expect it to be just available on demand, available all the time. And that was what I think the kind of millennial generation started being entitled to access to data, then I think you went to the generation where it was everything always connected, no concept of banword. But now I think it's the, the real thing that's changing is the concept of ownership and I think that's where you start to see things like, will the car industry ever be the same because if you don't need to own a car because you're not driven by the same passions that people who own cars are driven by, it's just a way of communicating you don't need a garage on your house, you may as well park the car somewhere else. It comes when you need it. It can change the way cities are laid out. I mean there's so many different routes you can go down with this. >> SO how does that innovation, how does that knowledge that you gain get into ServiceNow products and services? >> That all comes back then to how you, how people are going to face new management dynamics or how people are going to manage things like IOT devices? How are people going to deal with managing work if it is a task based economy? How are people going to start to think about not just working in a system of record, or not just working in a system of engagements, but how are they going to start to build that mesh or that web that links all these different things together? And I think that's where our strand comes. Our strand comes in the ability to be able to link systems of records together. To link users with those backend systems, to be able to manage those complex work processes. That's kind of the core elements. Also I think when you look at what Fred Crasick when he built the platform and he had the whole work flow engine and it is that engine that's kind of the key pallet to the whole company. >> I think the metaphor of mesh, sometimes we talk about a matrix of digital services that becomes ubiquitous beyond a cloud of remote services, is really transforming to this mesh of digital capabilities that are everywhere that do things that Clouds don't do. They sense, they react, they respond, they read, they hear. It's an amazing time that we're entering in innovation. Andy McAfee and Erik Brynjolfsson when they wrote the book Second Machine Age talked about Moore's Law, power innovation but they also talked about the innovation curve reshaping from going from Linears Moore's Law which we've marched to the cadence of Moore's Law for decades in this industry to reshaping, to an expediential curve. And I wonder if we could get your thoughts. We've paused that it's accommodation of sort of data applying machine intelligence to that data and then leveraging Cloud economics at scale is really where the innovation is going to come from in the future. What are your thoughts on that? >> So let me try to understand the question. So you're talking about not actually the way that you've seen the growth from a process prospective but the way you actually see the growth from a machine learning capability being able to analyze that data? >> Applying that layer of machine learning. Maybe use that mesh metaphor, that top level. You know you've got horizontal technology services but then there's this new AI layer on top. The data is the fuel for that AI. >> Absolutely, I think it's the I think people can't even imagine what they can do with that data, people can't even contemplate some of the decisions they can make and it's when people start to look at things in completely different ways, it's when people start to say, well, if we apply machine learning to a user interface for example, could we come up with a better user interface because now if we understand how people interact with the system, could we actually build a better system? Or you see people starting to have this whole butterfly effect around the way that artificial intelligence works. So the best example I heard was from, I was actually at a convention with a girl called Louis Chang and she was talking to me about it. But they were speaking to hospitals. They we're talking about self drive cars and the application machine learning of being able to help cars drive. And they were saying the interest in knock on effect of this was a hospital saying it was going to be a real problem for them having self drive cars. And she said, why's it going to be a problem? And the problem was, if you look across the whole America you have about 20 people a day die because they can't get replacement organs. But 37 percent of the organs come from car crashes. So if you take car crashes out of the equation. So what they were investing in was actually looking at how they do cloning technology for organs. So no one would ever imagine (mumbled speaking) and this is going to improve cloning technology so much. And I think AI's in the same place. Everyone's using it in such a small area that they don't even see the potential of what they could do with it, they don't have any concept of what they could be starting to look at and how they could start to spot transvaterian people. Even on a base level, I was speaking to one of our customers the other night, and they managed to put an AI system in place that when they got a call in off the description of the call they could work out what the customer satisfaction was going to be and if it was going to be a bad satisfaction figure, they could preemp that and put different agents that were more skilled on that particular issue. And they said a few years ago all they were interested in was maybe one day we'll be able to categorize something asymmetrically. But now they can predict how well something's going to be resolved. >> It's very hard to predict isn't it? I mean who would of thought that Alexa would of emerged as one of the best if not the best natural language processing systems or that images of cats on the internet would lead to facial recognition in technology. >> That one especially. >> Could of never predicted that. So, but because you're such a clear thinker and a strategic thinker, I want to ask you to make some predictions. I'm going to run some things by you. You talked about autonomous vehicles for awhile. Do you believe that owning in the future, pick whatever time frame you want, that owning and driving your own car will become the exception? >> Yeah I think it will probably be the people who, well okay, so I definitely think driving your own car will become the exception. I think some people will always want that sense of ownership until we get to a generation that doesn't. I think they'll always be a hard core of people who do want to own and do want to drive and do want that experience, but I think you've already got the issue where congestion's such a level in most areas. Is there any enjoyment out of driving? So I love driving, I love sports cars, I collect them. But if someone said, hey you've got two options, you can sit in a high performance sports car to go to LA or you can sit in a Tesla and it will drive itself and you can read a book. I'm getting in the Tesla. (laughing) >> How about retail? Right for disruption, do you think that large retail stores will essentially, not essentially, it's never complete, but will largely go away? >> I think it depends on the nature of the experience. So I think for a lot of goods that are consumable goods, I can kind of see that going away. I don't think it will go away for luxury goods. I don't think it will go away fully for fashion. I think people always like to look at things and understand things and check fits but for some things that are high consumables maybe even for electronics, I can see those going or I can see it going for things where it's worn product. So something like a shop that just sells sneakers. I can see someone could easily offer a range and say, well look, here's what we sell. When you order something, we'll automatically ship you one size up, one size down, or two variations of color and it will be a free system return the ones you don't want. I think the whole experience of ordering one thing and hoping it works out, I think that will go away. It will be concept of ordering a group of things or maybe it will be applying to artificial intelligence to say, hey you've asked for this color, but we know that people who also ask for that color like this color as well. We're going to ship you them both. You can see how it goes and send us the one back you don't like. >> Okay, let's see. Will machines make better diagnosis than doctors? I've got to say I think you will get to a point where that will happen. Especially if it's things where it's image processing, where it's x-ray processing, MRI processing. Where it's something like process of mental health, then I don't know. Maybe, I'd probably rather have my mental health treated by a person than a questionnaire. But yeah I think the things we're using, image recognition, or things where you're looking at patent distribution or you're looking at even like virus distribution or virus structure, then I think those areas I think you will get to a point where diagnostic issue is better. But you look at where artificial intelligence is from diagnostics now and you go on doctor google and search for something, you know, everything finished with the bottom line, or it could be cancer. >> Dave Vennari: Yeah, you're dead. >> What about will there ever be a revolt, you know in the sense of, technology is so pervasive, and people just say forget it, I'm sick of just being tracked, I just kind of want to have a human to human connection and, >> Dave Vellante: Dream on. >> So are we done for? >> I was speaking to a girl who works for me, Menesha, and she was saying, we were talking on Friday and she said, hey, I was having a coffee with nother girl Cass, and Menesha's in Seattle and Cass in is San Francisco, and I said, oh was she in Seattle or were you in San Francisco and Menesha's a lot younger than me, and she went, no we weren't in the same room. We were just like doing it over video like a virtual coffee. And I was like what, so you both get coffee and sit and have a conversation? And she was like, oh yeah. >> Dave Vellante: Alright, I've got one more, I've got one more. >> Okay, let's hear it, let's hear it. >> Alright last one, it's great, thanks for playing along. >> I know this is fun. >> Financial services is an industry that really hasn't been disrupted. DO you feel like the banks will lose control, the major banks will lose control of payment systems? >> I think there's a lot of conversations now around how much those payment systems open up. Because if you, let's say you do a transaction with Amazon, you do a transaction with Google, how hard would it be for every transaction to be done that way? So rather than, if your setting off a payment for I don't know, gas bills or a car loan payments, rather than giving your bank details, why not give your PayPal details or your Amazon account details or your Google details? That could be, reduce all the banking transactions down to one interface. I think that could happen. I think you could get, well obviously you're already seeing the rise of Blockchain and I'm not a Blockchain expert. I'm itching to find a used case for us with Blockchain but I can't find it yet. But for direct transactions, if both sources can trust each other than yeah, that direct transaction between those two sources, I think that's completely possible. I think there's also areas where, you've seen happen in the past where a banking faces issues from retail coming into banking, so sometimes you'll get the big supermarket chains, especially in Europe they say, okay you're going to get (foreign name) or you're going to get Tesco's Bank, because they've got all our customer loyalty, they've got people waiting to give discounts to if they bank with them, so they can instantly bring, if you said to your shopping account base, hey, if you bank with me we'll give you 20 dollars a week off your grocery shopping, you could probably ring 10 million customers straight away. So I think banking's challenged from other industries that want to get into it, from places where you'll actually go and do each transactions now and from where places where you'll just go and order stuff online and you could filter all that through one place, I think they'll still always be the commercial side of banking. There's always going to be the stocks and bonds, there's still going to be the wealth management, but props for transactional banking, you could start to see a decline. >> Fantastic, thank you. >> I love this futurist talk, it's been a lot of fun. Thank you so much for coming on theCube Dave. >> Alright, thanks for having me, always a pleasure. >> Dave Vellante: Great to see you. >> We will have more from ServiceNow Knowledge18 theCube's live coverage just after this. (upbeat music)

>> Narrator: Live from San Francisco. It's theCUBE! Covering RedHat Summit 2018. Brought to you by Red Hat. >> Alright welcome back, this is theCUBE's exclusive coverage of Red Hat 2018. I'm John Furrier, the co host of theCUBE with John Troyer, co-founder of TechReckoning Advisory Firm. Next guest is Chris Wright, Vice President and CTO Chief of Technology of his Red Hat. Great to see you again, thanks for joining us today. >> Yeah, great to be here. >> Day one of three days of CUBE coverage, you got, yesterday had sessions over there in Moscone South, yet in classic Red Hat fashion, good vibes, things are rocking. Red Hat's got a spring to their step, making some good calls technically. >> Chris: That's right. >> Kubernetes' one notable, Core OS Acquisition, really interesting range, this gives, I mean I think people are now connecting the dots from the tech side, but also now on the business side, saying "Okay we can see now some, a wider market opportunity for Red Hat". Not just doing it's business with Linux, software, you're talking about a changing modern software architecture, for application developers. I mean, this is a beautiful thing, I mean. >> Chris: It's not just apps but it's the operator, you know, operation side as well, so we've been at it for a long time. We've been doing something that's really similar for quite some time, which is building a platform for applications, independent from the underlying infrastructure, in the Linux days I was X86 hardware, you know, you get this HeteroGenius hardware underneath, and you get a consistent standardized application run time environment on top of Linux. Kubernetes is helping us do that at a distributive level. And it's taken some time for the industry to kind of understand what's going on, and we've been talking about hybrid cloud for years and, you really see it real and happening and it's in action and for us that distributed layer round Kubernetes which just lights up how do you manage distributed applications across complex infrastructure, makes it really real. >> Yeah it's also timing's everything too right? I mean, good timing, that helps, the evolution of the business, you always have these moments and these big waves where you can kind of see clunking going on, people banging against each other and you know, the glue layers developing, and then all of a sudden snaps into place, and then it just scales, right? So you're starting to see that, we've seen this in other ways, TCPIP, Linux itself, and you guys are certainly making that comparison, being Red Hat, but what happens next is usually an amazing growth phase. Again, small little, and move the ball down the field, and then boom, it opens up. As a CTO, you have to look at that 20 mile stair now, what's next? What's that wave coming that you're looking at in the team that you have on Red Hat's side and across your partners? What's the wave next? >> Well there's a lot of activity going on that's beyond what we're building today. And so much of it, first of all, is happening in Open Source. So that itself is awesome. Like we're totally tuned into these environments, it's core to who we are, it's our DNA to be involved in these Open Source communities, and you look across all of the different projects and things like machine learning and blockchain, which are really kind of native Open Source developments, become really relevant in ways that we can change how we build functionality and build business, and build business value in the future. So, those are the things that we look at, what's emerging out of the Open Source communities, what's going to help continue to accelerate developers' ability to quickly build applications? Operations team's ability to really give that broad scale, policy level view of what's going on inside your infrastructure to support those applications, and all the data that we're gathering and needing to sift through and build value from inside the applications, that's very much where we're going. >> Well I think we had a really good example of machine learning used in an everyday enterprise application this morning, they kicked off the keynote, talking about optimizing the schedule and what sessions were in what rooms, you know, using an AI tool right? >> Chris: That's right. >> And so, that's reality as you look at, is that going to be the new reality as you're looking into the future of building in these kind of machine learning opportunities into everyday business applications that, you know, in the yesteryear would've been just some, I don't know, visual basic, or whatever, depending on how far back you look, right? You know, is that really going to be a reality in the enterprise? It seems so. >> It is, absolutely. And so what we're trying to do is build the right platforms, and build the right tools, and then interfaces to those platforms and tools to make it easier and easier for developers to build, you know, what we've been calling "Intelligent Apps", or applications that take advantage of the data, and the insights associated with that data, right in the application. So, the scheduling optimization that you saw this morning in the keynote is a great example of that. Starting with basic rules engine, and augmenting that with machine learning intelligence is one example, and we'll see more and more of that as the sophisticated tools that are coming out of Open Source communities building machine learning platforms, start to specialize and make it easier and easier to do specific machine learning tasks within an application. So you don't have to be a data scientist and an app developer all in one, you know, that's, there's different roles and different responsibilities, and how do we build, develop, life cycle managed models is one question, and how do we take advantage of those models and applications is another question, and we're really looking at that from a Red Hat perspective. >> John F: And the enterprises are always challenged, they always (mumbles), Cloud Native speaks to both now, right? So you got hybrid cloud and now multi-cloud on the horizon, set perfectly up with Open Shift's kind of position in that, kind of the linchpin, but you got, they're still two different worlds. You got the cloud-native born in the cloud, and that's pretty much a restart-up these days, and then you've got legacy apps with container, so the question is, that people are asking is, okay, I get the cloud-native, I see the benefits, I know what the investment is, let's do it upfront, benefits are horizontally scalable, asynchronous, et cetera et cetera, but I got legacy. I want to do micro-servicing, I want to do server-less, do I re-engineer that or just containers, what's the technical view and recommendation from Red Hat when you say, when the CIO says or enterprise says, "Hey I want to go cloud native for over here and new staff, but I got all this old staff, what do I do?". Do I invest more region, or just containerize it, what's the play? >> I think you got to ask kind of always why? Why you're doing something. So, we hear a lot, "Can I containerize it?", often the answer is yes. A different question might be, "What's the value?", and so, a containerized application, whether it's an older application that's stateful or whether it's a newer cloud-native application (mumbles), horizontally scalable, and all the great things, there's value potentially in just the automation around the API's that allow you to lifecycle manage the application. So if the application itself is still continuing to change, we have some great examples with some of our customers, like Keybank, doing what we call the "Fast moving monolith". So it's still a traditional application, but it's containerized and then you build a CICD model around it, and you have automation on how you deliver and deploy production. There's value there, there's also value in your existing system, and maybe building some different services around the legacy system to give you access, API access, to data in that system. So different ways to approach that problem, I don't think there's a one size fits all. >> So Chris, some of this is also a cultural and a process shift. I was impressed this morning, we've already talked with two Red Hat customers, Macquarie and Amadeus, and you know Macquarie was talking about, "Oh yeah we moved 40 applications in a year, you know, onto Open Shift", and it turns out they were already started to be containerized and dockerized and, oh yeah yeah you know, that is standard operating procedure, for that set of companies. There's a long tail of folks who are still dealing with the rest of the stuff we've had to deal, the stack we've had to deal with for years. How is Red Hat, how are you looking at this kind of cultural shift? It's nice that it's real, right? It's not like we're talking about microservices, or some sort of future, you know, Jettison sort of thing, that's going to save us all, it's here today and they're doing it. You know, how are you helping companies get there? >> So we have a practice that we put in place that we call the "Open Innovation Lab". And it's very much an immersive practice to help our customers first get experience building one of these cloud native applications. So we start with a business problem, what are you trying to solve? We take that through a workshop, which is a multi-week workshop, really to build on top of a platform like Open Shift, real code that's really useful for that business, and those engineers that go through that process can then go back to their company and be kind of the change agent for how do we build the internal cultural shift and the appreciation for Agile development methodologies across our organization, starting with some of this practical, tangible and realist. That's one great example of how we can help, and I think part of it is just helping customers understand it isn't just technology, I'm a technologist so there's part of me that feels pain to say that but the practical reality is there's whole organizational shifts, there's mindset and cultural changes that need to happen inside the organization to take advantage of the technology that we put in place to build that optimize. >> John F: And roles are changing too, I'll see the system admin kind of administrative things getting automated way through more operating role. I heard some things last week at CubeCon in Copenhagen, Denmark, and I want to share some quotes and I want to get your reaction. >> Alright. >> This is the hallway, I won't attribute the names but, these were quotes, I need, quote, "I need to get away from VP Engine firewalls. I need user and application layer security with unfishable access, otherwise I'm never safe". Second quote, "Don't confuse lift and shift with running cloud-native global platform. Lot of actors in this system already running seamlessly. Versus say a VM Ware running environment wherein V Center running in a data center is an example of a lift and shift". So the comments are one for (mumbles) cloud, you need to have some sort of security model, and then two, you know we did digital transformation before with VM's, that was a different world, but the new world's not a lift and shift, it's re-architect of a cloud-native global platform. Your reaction to those two things, and what that means to customers as they think about what they're going to look like, as they build that bridge to the future. >> Security peace is critical, so every CIO that we're talking to, it's top of mind, nobody wants to be on the front page of The Wall Street Journal for the wrong reasons. And so understanding, as you build a micro-services software architected application, the components themselves are exposed to services, those services are API's that become potentially part of the attack surface. Thinking of it in terms of VPN's and firewalls, is the kind of traditional way that we manage security at the edge. Hardened at the edge, soft in the middle isn't an acceptable way to build a security policy around applications that are internally exposing parts of their API's to other parts of the application. So, looking at it for me, application use case perspective, which portions of the application need to be able to talk to one another, and it's part of why somebody like Histio are so exciting, because it builds right in to the platform, the notion of mutual authentication between services. So that you know you're talking to a service that you're allowed to talk to. Encryption associated with that, so that you get another level of security for data and motion, and all of that is not looking at what is the VPN or what is the VLAN tag, or what is the encapsulation ID, and thinking layer two, layer three security, it's really application layer, and thinking in terms of that policy, which pieces of the application have to talk to each other, and nobody else can talk to that service unless it's, you know, understood that that's an important part for how the application works. So I think, really agree, and you could even say DevSecOps to me is something that I've come around to. Initially I thought it was a bogus term and I see the value in considering security at every step of build, test and deliver an application. Lift and shift, totally different topic. What does it mean to lift and shift? And I think there's still, some people want to say there's no value in lift and shift, and I don't fully agree, I think there's still value in moving, and modernizing the platform without changing the application, but ultimately the real value does come in re-architecting, and so there's that balance. What can you optimize by moving? And where does that free up resources to invest in that real next generation application re-architecting? >> So Chris, you've talked about machine learning, right? Huge amounts of data, you've just talked about security, we've talked about multi-cloud, to me that says we might have an issue in the future with the data layer. How are people thinking about the data layer, where it lives, on prem, in the cloud, think about GDPR compliance, you know, all that sort of good stuff. You know, how are you and Red Hat, how are you asking people to think about that? >> So, data management is a big question. We build storage tooling, we understand how to put the bytes on disc, and persist, and maintain the storage, it's a different question what are the data services, and what is the data governance, or policy around placement, and I think it's a really interesting part of the ecosystem today. We've been working with some research partners in the Massachusetts Open Cloud and Boston University on a project called "Cloud Dataverse", and it has a whole policy question around data. 'Cause there, scientists want to share data sets, but you have to control and understand who you're sharing your data sets with. So, it's definitely a space that we are interested in, understand, that there's a lot of work to be done there, and GDPR just kind of shines a light right on it and says policy and governance around where data is placed is actually fundamental and important, and I think it's an important part, because you've seen some of the data issues recently in the news, and you know, we got to get a handle on where data goes, and ultimately, I'd love to see a place where I'm in control of how my data is shared with the rest of the world. >> John F: Yeah, certainly the trend. So a final question for you, Open Source absolutely greatness going on, more and more good things are happening in projects, and bigger than ever before, I mean machine learning's a great example, seeing not just code snippets, code bases being you know, TensorFlow jumps out at me (mumbles), what are you doing here this year that's new and different from an Open Source standpoint, but also from a Red Hat standpoint that's notable that people should pay attention to? >> Well, one of the things that we're focused on is that platform layer, how do we enable a machine learning workload to run well on our platform? So it starts actually at the very bottom of the stack, hardware enablement. You got to get GPUs functional, you got to get them accessible to virtual machine based applications, and container based applications, so that's kind of table stakes. Accelerate a machine learning workload to make it usable, and valuable, to an enterprise by reducing the training and interference times for a machine learning model. Some of the next questions are how do we embed that technology in our own products? So you saw Access Insights this morning, talking about how we take machine learning, look at all of the data that we're gathering from the systems that our customers are deploying, and then derive insights from those and then feed those back to our customers so they can optimize the infrastructure that they're building and running and maintaining, and then, you know, the next step is that intelligent application. How do we get that machine learning capability into the hands of the developer, and pair the data scientist with the developers so you build these intelligent applications, taking advantage of all the data that you're gathering as an enterprise, and turning that into value as part of your application development cycle. So those are the areas that we're focused on for machine learning, and you know, some of that is partnering, you know, talking through how do we connect some of these services from Open Shift to the cloud service providers that are building some of these great machine learning tools, so. >> Any new updates on (mumbles) the success of Red Hat just in the past two years? You see the growth, that correlates, that was your (mumbles) Open Shift, and a good calls there, positioned perfectly, analysts, financial analysts are really giving you guys a lot of props on Wall Street, about the potential revenue growth opportunities on the business side, what's it like now at Red Hat? I mean, do you look back and say, "Hey, it was only like three years ago we did this", and I mean, the vibes are good, I mean share some inside commentary on what's happening inside Red Hat. >> It's really exciting. I mean, we've been working on these things for a long time. And, the simplest example I have is the combination of tools like the JBoss Middleware Suite and Linux, well they could run well together and we have a lot of customers that combine those, but when you take it to the next step, and you build containerized services and you distribute those broadly, you got a container platform, you got middleware components, you know, even providing functionality as services, you see how it all comes together and that's just so exciting internally. And at the same time we're growing. And a big part of-- >> John F: Customers are using it. >> Customers are using it, so putting things into production is critical. It's not just exciting technology but it's in production. The other piece is we're growing, and as we grow, we have to maintain the core of who we are. There's some humility that's involved, there's some really core Open Source principles that are involved, and making sure that as we continue to grow, we don't lose sight of who we are, really important thing for our internal culture, so. >> John F: Great community driven, and great job. Chris, thanks for coming on theCUBE, appreciate it. Chris Wright, CTO of Red Hat, sharing his insights here on theCUBE. Of course, bringing you all a live action as always here in San Francisco in Moscone West, for Red Hat Summit 2018, we'll be right back. (electronic music) (intense music)

(lively, bouncy music) >> Host: Live from Los Angeles, it's The Cube, covering Open Source Summit North America 2017, brought to you by the.

>> Announcer: Live from Orlando, Florida, it's The Cube. Covering Service Now Knowledge 17. Brought to you by Service Now. >> we're back, welcome to Orlando, everybody, this is Service Now Knowledge 17, #Know17. I'm Dave Vellante with my cohost, Jeff Frick. Dave Wright is here, he's the chief strategy officer of Service Now and a long time Cube friend. Good to see you again, David. >> Good seeing you again, guys. So off the keynote, we were just talking about intelligent automation and what's new in your world. New way to work is really kind of the broader theme here, people are changing the way they work. So what is intelligent automation and how does it fit in? >> So what we did when we built intelligent automation is we wanted to come at it from a different angle. So we didn't want to build a product and then look for a solution that it'd work with, we wanted to go out and speak to people and see what are the challenges that they faced. So what we did was we came up with kind of four key areas where people wanted to be able to improve or do things differently. We wanted the capability to be able to predict when something was going to happen from an event perspective. We wanted to be able to use machine learning to be able to augment it. So to be able to perhaps order, categorize, or provide severity, or in the case of change, provide risk analysis. We wanted to be able to do that at a machine level rather than use a human triage level. Then people were coming back saying we feel we're doing a good job, but we want to understand if we're doing a good job, so that was the concept of expanding out the benchmarks program to include more and more benchmarks for people to see how they compared against their peers. And the final element was people wanted to set themselves performance targets, but then they wanted to understand when am I going to get to that target. So what we have to do then was augment the whole performance analytics suite to be able to do predictive analytics. So they're kind of the four core areas that sit in the intelligent automation engine. We can go into as much detail as you want around them, but it's pretty interesting. >> So help us understand, 'cause I get a little confused about, you know, when I hear something like a big announcement coming up at Jakarta, platform, but then I see bits and pieces hit the various products. Can you maybe set that up for us and help us understand. >> Yeah, so what'll happen is the benchmarking, the predictive analytics capability, and the ability to do predictive service usage, they will all appear in Jakarta. And then the actual ML side where we can do the auto-categorization, that will appear in the Kingston release. So by the end of the year, everything that's shown will be available. >> And it hits the platform and then the modules take advantage of that, is that correct? >> Yes, so what is happening at the moment is the initial use cases have gone through around IT. So it's IT looking at well how do we process events so that we can get a precursor to a bigger issue and predict the bigger issue. How do we categorize when someone comes in with an IT request or an IT incidence, how do we make sure it goes to the right people and gets the right categorization. And then what'll happen over time is we'll be able to use that for the security module, we'll be able to use it for customer service, for human resources, because it's all, in the same way we said, it's all a different type of service, it's exactly the same process to be able to categorize, to prioritize, to put a severity on something. And then more long term, we can use this technology to look at all kinds of different files on the system. >> And when you say IT first, it's ITSM and ITOM, is that right? >> Yes, ITSM and ITOM. >> Okay, and so good, I like this, this is a very practical example of, generally, AI, as people don't really know what it is. You're going to tell us that something's going to break before it breaks is usually the use case here. >> What we realized is because we can now start to look at time series data and analyze time series data, there's a few things we can do. So the first thing is we can do corelation, so we can start to link events together, so people didn't spend ages just trying to fix the symptoms, they could go right down to the disease and say well, this is what's causing everything else. The other thing we could build in because we could understand what normal looked like is we could build an anomaly detection. So normally, an event says hey, this has got a high CPU, or this switch has gone down. Now we could say this just looks weird. We've got an activity that never normally happens to this level, or it never normally happens at this time of day, or we've never seen this before on a Saturday. And we can actually generate an anomaly alert at that point. Now, the anomaly alert might be a precursor to a traditional alert where you might get. I think the example used in the actual keynote was we get a large number of user threads on a system, that's probably a precursor to high CPU. So once we've started to be able to do that correlation, the more and more examples you get, the more you can start to predict. So you can say as soon as I get that precursor, I have a level of confidence of when we're going to see the next event. So now you get a brand new type of incidence, you'll get an incident for a predicted failure. So the system will say I've seen this, this, and this, I'm 86% confident we've got two hours and we're going to lose this service. So the whole concept of this was how do you work at light speed. And my whole challenge was what happens when you do it before it happens, is that beyond light speed, it was very difficult to try and wrap your mind around it. >> The speed of light is too damn slow. >> Yeah, it's too slow, no one's going to wait for it. >> I did get a tweet back where someone said if you fix everything before it happens, we'll get no budget because everyone will say nothing ever happens. >> If a tree falls and nobody's around. And so there's a risk, sort of risk scoring algorithm in there that helps you say okay, this one is going to fail and you better take advantage of it. >> Yeah, so if you imagine seeing a precursor to something, you look how many times that precursor has caused that event, that allows you to give a degree of probability as to how likely you think it's going to happen. And it might be you decide to set a threshold and say look, if it's below 50%, don't bother doing it. But if it's above 70%, do it. Or if it's a specific type of issue, if it's something around security, and you're above 90% confidence, I want it flagged as a priority one issue. >> Yeah, but if it's my picnic wiki, so can you inject the notion of value in there, I guess the question. >> Dave: Yes, yeah, you can. >> I want to ask you about this categorization piece, even though it's coming down the road with Kingston. That's been a challenge for organizations in so many different use cases. I mean, the one I can think of, you know, is like email archiving and the federal rules of civil procedure, all that stuff when electronic records became admissible. And everybody sort of scrambled to categorize. But it was manual, they were using tags, it just didn't work, it didn't scale. So the answer was always technology to auto-categorize at the point of creation or use. But even then, it was complicated and the math kind of worked but you couldn't apply it. What's changed now and what's the secret sauce behind it? Was that part of the DX Continuum acquisition, maybe you can explain that. >> So we acquired DX Continuum, that gave us eight really bright math Ph.Ds who were data scientists, who could come in, who could look at data in a different way. But I think technology also drove it. So you've got the ability to have the compute power to be able to do the number crunching, but you've got the volume of data as well, I think the more volume of data you get, the more accurate it is. So we found if we're going to train auto-categorization, we need between 50 and 100,000 records to be able to get to a degree of accuracy. And then obviously, we can just keep on doing it again and again and that accuracy gets better and better over time. But even when we ran this out of the box on our system for the very first time before we'd rewritten it on the platform, first time we ran it through, it was 82% accurate straight off. Now, the real interesting thing about when you do something like categorization, it's almost as important what you get right as not guessing when you're going to get it wrong. So we wanted to be be very sure that they system would say I am 100% confident that this is where this is. But if I don't know it, I'm not going to guess. I'm not going to say well, it's 75% confident, so I'm going to say it's this. At that point, you want to say I just don't know. So these, 18%, for example, in this case, I don't know. And then over time, you get to reprocess the things that you don't know, and that percentage gradually goes up. So now, I think in-house, we're running into the 90% region. >> So the math, though, has been around forever. I mean, things like support vector machines and there are other techniques. What is it about this day and age that has allowed us to effectively apply that math and solve this problem? >> So I think what you get now, if you look at the DX Continuum technology used, I think it was five different methodologies for being able to interrogate. And it was neural nets, it was using base, but I think what gives you the big advantage is people have always taken live data and then tried to do this prediction. That's probably the wrong way to do it. If you take historical data and then run it, you just find out which one works. And if this algorithm is working the best for you based on the way you structure your data, then that's the algorithm you focus on. And that's exactly the way predictive analytics works. What we do is we were initially looking, saying okay, well we've got these three different models we can use. We can use projection, we can use seasonal trend lows, we can use AREMA with the auto-regressive moving average type solution. Which one are we going to use? And then we realized we didn't need to guess. What we could do is we could give the system historical data and say which one of these most accurately maps and then use that algorithm for that data set. Because every data set is different, so you might look at one data set where it's really spiky, so you don't want to use projection because if you choose the wrong points, your projection of them is effectively out. So it might be, in that case, you want to use STL and be able to smooth out some of the curves. So you have to, every time you want to do predictive analytics around a specific data set, you need to work out what mathematical model you need to use. >> So the data is then training the models and the models are your models, correct? >> Yes, yeah. >> And now you tell the customer, and I'm sure you do, that this is your data and your data is not going to be shared with anybody outside of your instance. But the model, the gray area between the model and the data, they start to blend together. Is there concern in your customer base about oh, I don't want the model that you train going to my competitors, or is this a different world where they feel as though hey, I want to learn, like, security. What are you seeing there? >> So this is the uniqueness that we, you don't get a generic ML where we look at everyone's instance and train across that. We can only train for your instance. And that's because everyone does things differently. You go to some companies where their highest priority issue is a sev-9, whereas another customer would have sev-1, so you've got people doing different implementations like that. But let's say I tried to do everyone's, and I went through and I said look at this description, this is a networking issue, so I'm going to categorize it as networking. And you haven't got a networking category, you've got networking infrastructure or networking hardware, then it fails. So I have to build a model that's very specific to your instance. So every time we do this, we'll build it for each customer. So it's kind of customized artificial intelligence machine learning models that sit within your instance. >> So my data, your model that you're basically applying for me and only me. Period, the end. >> Yeah, so we do the training on your data and we inject that model, which is your model, back into your instance. >> And now, the benchmarks, you guys have been talking about benchmarks for a while, this is sort of taken it to a new level. So how do you roll that out, how do you charge for it, what's the strategy there? >> So what people do is they effectively subscribe to it. So they're willing to share their data, we're at that point, allowing them, so it's almost a community issue, at this point, everyone is sharing data across the systems. Now, we added another nine benchmarks in the Jakarta release and now I think there's 16 benchmarks. Ive been mainly focused around IT and ITOM, but as we get more and more customers coming on in CSM and more on HR and more on security, we'll be able to start to introduce the whole concept of benchmarking those as well. But the thing you can do now is you don't just see the benchmark and how you perform, we can also use analytics to show how you're trending as well. So you might be better than people of a similar size or people in the same industry, but it might be that you're trending down and you're actually going to start to get close to being worse than them. So the concept here is you can take corrective measures. But also, it gives a lot of power to customers, not just to be able to say I think I'm doing a good job, but to be able to go to senior management and say this is how customers that look like us are currently performing. This is how customers in the finance sector perform. This is how customers with 100,000 people or more perform. And they can see look, we're leading in this, this, and this area, and they can see where they're not leading, and they can actually start to see how they'd address that. Or it might even be that you start to build relationships where they could say to their account manager who are the people who have got this best in performance type thing, could we meet with them, could we exchange with them? The evolution of this will be on the performance analytics side when we start to get to Kingston and beyond will be to be able to do not just the predictive analytics, but to be able to do modeling and to be able to do what-if. And the end goal is we've gotten to the point where we've got predictive, you want to get to the point where you get to prescriptive. Where the system says this is where you are, if you do this, this is where you'll get. >> That's what I was going to ask you, is it intuitive to the client, what they should do, and what role does Service Now play in advising them. And you're saying in the future, the machine is actually going to-- >> Yeah, could be able to say hey, well, if you want to, let's say you want to improve your problem closure rates, you could say well, when you look at other customers, an indicator of this is people have gotten much better first call incident closure. So what you need to do is you need to focus on closing first call incidents because that's going to then have the knock on effect to driving down the way you resolve problems. So we'll be able to get to that, but we'll also be able to allow people to actually model different things. So they could say what happens if I increase this by 10%? What happens if I put another 10 people working on this particular assignment group, what's the effect going to be, and actually start to do those what-if models, and then decide what you're going to do. >> To prioritize the investment to get the numbers down. It's interesting too, 'cause it's a continuous process, as you mentioned, it's this whole do the review once a year, do your KPIs. That's just not the way it works anymore, you don't have time. And to use the integration of the real time streaming data, which is interesting that you said not necessarily always what you want to use first compared to the historical data that's driving the actual business models and the algorithms. >> I think the thing about the whole benchmark concept is it's constantly being updated. So it's not like you take a snapshot and you say okay, we can improve and move here, you see if everyone else is improving at the same time. So there might just be a generic industry trend that everyone is moving in a certain direction. It might be that as we start to see more things coming online from an IOT perspective, I'll be interested to see whether people's CMDBs start to expand. Because I don't know if people have yet established whether IT is going to be responsible for IOT. Because it's using the same protocol for its messaging, how are you going to process those events, how are you going to deal with all that. >> So I guess it's the man versus machine, machines have always replaced humans. But for the first time, it really is happening quickly with cognitive functions. And one of your speakers at the CIO event, Andrew McCafee and his colleague Erik Brynjolfsson have written a book. And in that book, they talked about the middle class getting kind of hollowed out and they theorize that a big part of that is machines replacing them. One of the stats is the median income for U.S. workers has dropped from $55,000 to $50,000 over the last decade. And they posited that cognitive functions are replacing humans, and you see it everywhere. Billboards, the kiosks at airports, et cetera. Should we be alarmed by that? What is your personal opinion here? And I know it's a scary topic for a lot of IT vendors, but it's reality and you're a realist and you're a futurist. What are your thoughts, share them with us. >> People have different views on this. If you look at the view of executives, they see this see this as potentially creating more jobs. If you look at the workforce, I completely agree with you, there's a massive fear that yeah, this is going to take my job away. I think what happens over time is jobs will shift, people will start doing different things. You can go back 150 years and find that 90% of America is working farmland. And you can come now and you can find out they're like 2%. >> Not too many software engineers either back then. >> Not too many. Hard to get that mainframe in the field. What I think you can do is you can not just use AI or machine learning to be able to replace the mundane jobs or the very repetitive jobs, you can actually start to reverse that process. So one of the things we see is initially, when people were talking about concepts like chat bots, it was all about how do you externalize it, how do you have people coming in and being able to interface to a machine. But you can flip that and you can actually have a bot become a virtual assistant. Then what you're doing is you're enabling the person who's dealing with the issue to actually be better than they were. An interesting example is if you look at something like the way people analyze sales prospects. So in the past, people would have a lot of different opportunities they were working on. And the good sales guys would be able to isolate what's going to happen, what's not going to happen. What I can do is can run something like a machine learning algorithm across that and predict which deals are most likely to come in. I then can have a sales guy focusing on those, I've actually improved the skills of that sales guy by using ML and AI to actually get in there. I think a lot of times, you'll be able to move people from a job that was kind of repetitive and dull and be able to augment their skills and perhaps allow them to do a job that they couldn't have done before. So I'm pretty confident just based on the impact that this is going to have from a productivity perspective, where this is going to go from a job perspective. There's a really cool McKinsey report and it talks about the impact of the steam engine on what that drove on productivity and that was a .3% increase in productivity year and year over 50 years. But the prediction around artificial intelligence is it'll produce a productivity increase of 1.4% for the next 50 years. So you're looking at something that people are predicting could be five times as impactful as the industrial revolution. That's pretty significant. >> Next machine age, this is a huge topic. We're out of time, but I would love for you, Dave, to come back to our Silicon Valley studio and maybe talk about this in more depth because it's a really important discussion. >> I'm always around, happy to do it. >> Thanks very much for coming on The Cube it's great to see you again. >> All right, thanks, guys. >> All right, keep it right there, everybody, we're back with our next guest right after this short break. Be right back.