(upbeat music) >> Hello, everyone. Welcome to theCUBE's coverage here at AWS re:Invent 2022. This is the Executive Summit with Accenture. I'm John Furrier, your host of theCUBE with two great guests coming on today, really talking about the future, the role of humans. Radically human is going to be the topic. Paul Daugherty, the group Chief Executive Technology and CTO at Accenture. And Jim Wilson, Global Managing Director of Thought Leadership and Technology Research, Accenture. Gentlemen, thank you for coming on theCUBE for this conversation around your new hit book, "Radically Human." >> Thanks, John. It's great to be with you and great to be present at re:Invent. >> We've been following you guys for many, many years now, over a decade. You always have the finger on the pulse. I mean, and as these waves come in, it's really important to understand impact. And more than ever, we're in this, I call it the systems thinking, revolution is going on now where things have consequences and machines are now accelerating their role. Developers are becoming the front lines of running companies, seeing a massive shift. This new technology is transforming the business and shaping our future as as humans. And so I love the book, very, very strong content, really right on point. What was the motivation for the book? And congratulations, but I noticed you got the structure, part one and part two, this book seems to be packing a big punch. What was the motivation, and what was some of the background in putting the book together? >> That's a great question, John. And I'll start, and then, Jim, my co-author and colleague and partner on the book can join in too. If you step back from the book itself, we'd written a first book called "Human + Machine", which focused a lot on artificial intelligence and talked about the potential and future of artificial intelligence to create a more human future for us with the human plus machine pairing. And then when we started working on the next book, it was the COVID era. COVID came on line as we were writing the book. And that was causing really an interesting time in technology for a lot of companies. I mean, think back to what you were doing. Once COVID hit, every company became more dependent on technology. Technology was the lifeline. And so Jim and I got interested in what the impacts of that were on companies, and what was different from the first research we had done around our first book. And what we found, which was super interesting, is that pre-pandemic, the leading companies, the digital leaders that were applying cloud data, AI, and related technologies faster, we're outperforming others by a factor of 2x. And that was before the pandemic. After the pandemic, we redid the research and the gap widened into 5x. And I think that's played a lot into our book. And we talk about that in the opening of our book. And the message there is exactly what you said is technology is not just the lifeline from the pandemic, but now technology is the heart and soul of how companies are driving innovation, how they're responding to global crises around inflation, energy, supply chain crisis because of the war in Ukraine, et cetera. And companies need the technology more than ever. And that's what we're writing about in "Radically Human." And we're taking a step beyond our previous book to talk about what we believe is next. And it's really cloud, data and AI, and the metaverse that signal out as three trends that are really driving transformative change for companies. In the first part of the book, to your question on the structure, talks about the roadmap to that. We talked about the ideas framework, five areas where you need to change your thinking, flip your assumptions on how to apply technology. And then the second part of the book talks about the differentiators that we believe are going to set companies apart as they look to implement this technology and transform their companies for the future. >> Jim, weigh in on this flipping the script, flipping the assumptions. >> You used a really important word there and that is systems. I think when we think about artificial intelligence, and when Paul and I have now talking to companies, a lot of executives think of AI as a point solution. They don't think about AI in terms of taking a systems approach. So we were trying to address that. All right, if you're going to build a roadmap, a technology roadmap for applying intelligent technologies like artificial intelligence, how do you take a holistic systematic view? And that's really the focus of the first section of the book. And then as Paul mentioned, how do you take those systems and really differentiate it using your talent, focusing on trust, experiences and sustainability? >> I like how it reads. It's almost like a masterclass book because you set the table. It's like, 'cause people right now are like in the mode of what's going on around me? I've been living through three years of COVID. We're coming out the other side. The world looks radically different. Humans are much more important. Automation's great, but people are finding out that the human's key, but people are trying to figure out where am I today. So I think the first part really to me hits home. Like, here's the current situation and then part two is here's how you can get better. And it's not just about machines, machines, machines and automation, automation, automation. We're seeing examples where the role of the human, the person in society, whether it's individually or as part of a group, are really now key assets in that kind of this new workforce or this new production system or society. >> Yeah. And just to take a couple examples from the book and highlight that, I think you're exactly right. And that's where "Radically Human", the title came from. And what's happening with technology is that technology itself is becoming more human like in its capability. When you think about the power of the transformer technologies and other things that we're reading about a lot. And the whole hypothesis or premise of the book I should say, is that the more human like the technology is, the more radically human or the more radical the human potential improvement is, the bigger the opportunity. It's pairing the two together rather than, as you said, just looking at the automation or the machine side of it. That's really the radical leap. And one thing Jim and I talked about in context of the book is companies really often haven't been radical enough in applying technology to really get to dramatic gains that they can get. Just a couple examples from the ideas framework, the I in IDEAS. The ideas framework is the first part of the book. The five areas to flip your assumptions. The I stands for intelligence and we're talking about more human and less artificial in terms of the intelligence techniques. Things like common sense learning and other techniques that allow you to develop more powerful ways of engaging people, engaging humans in the systems that we build using the kind of systems thinking that Jim mentioned. And things like emotional AI, common sense AI, new techniques in addition to machine, the big data driven machine learning techniques, which are essential to vision and solving big problems like that. So that's just an example of how you bring it together and enable that human potential. >> I love the idea, go ahead Jim. >> I was going to say we've been used to adapting to technology, and contorting our fingers to keyboards and so on for a long time. And now we're starting to see that technology is in fact beginning to adapt to us and become more natural in many instances. One point that we make is now in the human technology nexus, in fact, the human is in the ascended. That's one of the big ideas that we try to put out there in this book. >> I love the idea of flipping the script, flipping the assumptions, but ideas framework is interesting. I for intelligence, D for data, E for expertise, A for architecture, S for strategy. Notice the strategies last. Normally in the old school days, it's like, hey, strategy first and execution. Really interesting how you guys put that together. It feels like business is becoming agile and iterative and how it's going to be forming. Can you guys, I mean that's my opinion, but I think observing how developers becoming much more part of the app. I mean, if you take digital transformation to its conclusion, the application is the company, It's not a department serving the business, it is the business, therefore developers are running the business, so to speak. This is really radical. I mean, this is how I'm seeing it. What's your reaction to that? Do you see similar parallels to this transformation if you take it down to a conclusion and strategy is just what you do after you get the outcomes you need? What's your reaction to that? >> Yeah, I think one of the most lasting elements of the book might be that chapter on strategy in my opinion, because you need to think about it differently. The old way of doing strategy is dead. You can't do it the way you used to do it. And that's what we tried to lay out with the S in IDEAS, the strategy. The subtitle that chapter is we're all technology companies now. And if you're a technology driven company, the way you need to think about and every company is becoming, that's what I hear when I talk to these suites and CEOs and boards, is everybody's recognizing the essential role that technology plays and therefore they need to master technology. Well, you need to think about strategy differently then because of the pace of technology innovation. And so you need to throw out the old way of doing it. We suggest three new archetypes of how to do strategy that I think are really important. It's about continuous strategy in all cases. An example is one of the techniques we talk about, forever beta, which is, think about a Tesla or companies that it's never quite done. They're always improving and the product is designed to be connected and improving. So it changes along the product and the strategy along how you deploy it to consumers changes as you go. And that's an example of a very different approach to strategy that we believe is essential to consider as you look at the future. Yeah, those multi-month strategy sessions might play out over two or three quarters of going away. And strategy and execution are becoming almost simultaneous these days as Paul was saying. >> It's interesting because that's the trend you're seeing with more data, more automation, but the human plays a much critical role. And just aside on the Tesla example, is well documented. I think I wrote about in a post just this week that during the model three, Elon wanted full automation and had to actually go off scripts and get to humans back in charge 'cause it wasn't working properly. Now they have a balance. But that brings up to part two, which I like, which is this human piece of it. We always talk about skills gaps, there's not enough people to do this, that and the other thing. And talent was a big part of that second half, trust, talent, experiences. That's more of the person's role, either individually as part of a collective group. Is talent the scarce resource now where that's the goal, that's the key 'cause it all could point to that in a way. Skills gap points to, hey, humans are valuable. In fact the value's going up if it's properly architected. What's your reaction to that, guys? Because I think that's something that is not, kind of nuanced point, but it's a feature, not a bug maybe, I don't know. What's your thoughts? >> Yeah, go ahead Jim. >> I was going to say it, we're dramatically underestimating the amount of focus we need to put on talent. That's why we start off that second part of the book, really zooming in on talent. I think you might think that for every hundred dollars that you put into a technology initiative, you might put 50 or 75 into re-skilling initiatives to really compliment that. But what we're seeing is companies need to be much more revolutionary in their focus on talent. We saw economic analysis recently that pointed out that for every $1 you spend on technology, you are likely going to need to spend about $9 on intangible human capital. That means on talent, on getting the best talent, on re-skilling and on changing processes and work tasks. So there's a lot of work that needs to be done. Really that's human focus. It's not just about adopting the technology. Certainly the technology's critical, but we're underestimating the amount of focus that needs to go into the talent factors. >> That's a huge point. >> And I think some of the elements of talent that become really critical that we talked about in the book are becoming a talent creator. We believe the successful companies of the future are going to be able not just to post a job opening and hire people in because there's not going to be enough. And a lot of the jobs that companies are creating don't exist 'cause the technology changing so fast. So the companies that succeed are going to know how to create talent, bring in people, apprentices and such, and shape to tale as they go. We're doing a significant amount of that in our own company. They're going to be learning based organizations where you'll differentiate, you'll get the best employees if you provide better learning environments because that's what employees want. And then democratizing access to technology. Things like Amazon's Honeycode is an example, low-code/no-code development to spread development to wider pools of people. Those types of things are really critical going forward to really unlock the talent potential. And really what you end up with is, yeah, the human talent's important, but it's magnified and multiplied by the power of people, giving them in essence superpowers in using technology in new ways. >> I think you nailed it, that's super important. That point about the force multiplier when you put things in combination, whether it's group constructs, two pizza teams flexing, leveraging the talent. I mean, this is a new configuration. You guys are nailing it there. I love that piece. And I think groups and collectives you're going to start to see a lot more of that. But again, with talent comes trust when you start to have these ephemeral and or forming groups that are forming production systems or experiences. So trust comes up a lot. You guys see the metaverse as an important part there. Obviously metaverse is a pretext to the virtual world where we're going to start to create these group experiences and create new force multipliers. How does the metaverse play into this new radically human world, and what does it mean for the future of business? >> Yeah, I think the metaverse is radically misunderstood to use the word title when we're not with the title of our book. And we believe that the metaverse does have real big potential, massive potential, and I think it'll transform the way we think about digital more so than we've changed our thinking on digital in the last 10 years. So that's the potential of the metaverse. And it's not just about the consumer things, it's about metaverse and the enterprise. It's about the new products you create using distributed ledger and other technologies. And it's about the industrial metaverse of how you bring digital twins and augmented workers online in different ways. And so I believe that it has tremendous potential. We write about that in the book and it really takes radically human to another level. And one way to think about this is cloud is really becoming the operating system of business. You have to build your enterprise around the cloud as you go forward. That's going to shape the way you do business. AI becomes the insight and intelligence in how you work, infused with the human talent and such as we said. And the metaverse then reshapes the experience layers. So you have cloud, AI building on top of this metaverse providing a new way to generate experiences for employees, citizens, consumers, et cetera. And that's the way it unfolds, but trust becomes more important because just as AI raises new questions around trust, every technology raises new questions around trust. The metaverse raises a whole new set of questions. And in the book we outline a five-part framework or five essential parts of the framework around how you establish trust as you implement these new technologies. >> Yeah, we're seeing that about three quarters of companies are really trying to figure out trust, certainly with issues like the metaverse more broadly across their IT so they're focusing on security and privacy, transparency, especially when you're talking about AI systems, explainability. One of the more surprising things that we learned when doing the book, when we were doing the research is that we saw that increasingly consumers and employees want systems to be informed by a sense of humanity. So one company that we've been looking at that's been developing autonomous vehicles, self-driving car systems, they're actually training the system by emulating human behavior. So turning the cameras on test drivers to see how they learn and then training the AI using that sense of humanity 'cause other drivers on the road find human behavior more trustworthy. And similarly, that system is also using explainable AI to actually show which human behaviors that AI system is learning from. Some really interesting innovations happening in that trust space. John. >> Jim, I think you bring up a great point that's worth talking more about. Because you're talking about how human behaviors are being put into the design of new things like machines or software. And we're living in this era of cloud scale, which is compressing this transformation timeline and we've been calling it supercloud, some call it multi-cloud, but it's really a new thing happening where you're seeing an acceleration of the transformation. We think it's going to happen much faster in the next five to 10 years. And so that means these new things are emerging, not just, hey, I'm running a virtual event with chat and some video. It's group behavior, it's groups convening, talking, getting things done, debating, doing things differently. And so this idea of humans informing design decisions or software with low-code/no-code, this completely changes strategy. I mean this is a big point of the book. >> Yeah, no, I go back to one of the, the E in the IDEAS framework is expertise. And we talk about from machine learning to machine teaching, which is exactly that. Machine learning is maybe humans tag data and stuff and feed into algorithms. Machine teaching is how do you really leverage the human expertise in the systems that you develop with AI. One of the examples we give is one of the large consumer platforms that uses human designers to give the system a sense of aesthetic design and product design. A very difficult thing, especially with changing fashion interest and everything else to encode in algorithms and to even have AI do, even if you have fast amounts of data, but with the right human insight and human expertise injected in, you can create amazing new capability that responds to consumers in a much more powerful way. And that's an example of what you just said, John, bringing the two together. >> Well, yeah, it's interesting. I want to to get your thoughts as we get wrap up here soon. How do you apply all these human-centric technologies to the future of business? As you guys talk to leaders in the enterprise of their businesses, as they look at the horizon, they see the the future. They got to start thinking about things like generative AI and how they can bring some of these technologies to the table. We were talking about if open source continues to grow the way it's going, there might not be any code to write, it just writes itself at some point. So you got supply chain issues with security. These are new things you guys are hitting in the book where these are new dynamics, new power dynamics in how things get built. So if you're a business owner and leader, this is a new opportunity, a challenge certainly that is an opportunity. How do you apply all this stuff for business? >> I'll go first then Jim can add in. But the first thing I think starts with recognizing the role that technology does play and investing accordingly in it. So the right technology talent, rethinking the way you do strategy as we talked about earlier and recognizing how you need to build a foundation. That's why the fact you're at re:Invent is so important because companies are, again, rebuilding that operating system of their business in the cloud. And you need that as the foundation to go forward, to do, to build the other types of capabilities. And then I think it's developing those talent systems as well. Do you have the right talent brand? Are you attracting the right employees? Are you developing them in the right way so that you have the right future talent going forward? And then you marry the two together and that's what gives you the radically human formula. >> Yeah. When we were developing that first part of the book, Paul and I did quite a bit of research, and Paul kind of alluded to that research earlier, but one of the things that we saw in really the first year of the pandemic was that there was a lot of first time adoption of intelligent technologies like artificial intelligence. One statistic is that 70% of companies that had never tried AI before went ahead and tried it during the pandemic. So first time adoption rates were way up, but the thing is companies were not trying to do it themselves and to necessarily build an AI department. They were partnering and it's really important to find a partner, often a cloud partner as a way to get started, start small scale, and then scale up doing experiments. So that was one of the key insights that we had. You don't need to do it all yourself. >> If you see the transformation of just AWS, we're here at re:Invent, since we've been covering the events since 2013, every year there's been a thematic thing. It was startups, enterprise, now builders, and now change your company. This year it's continuing that same thing where you're starting to see new things happen. It's not just lift and shift and running a SaaS application on the cloud. People are are changing and refactoring and replatforming categorical applications in for this new era. And we're calling it supercloud, superservices, superapps, 'cause they're different. They're doing different things in leveraging large scale CapEx, large scale talent pools, or talent pools in certain ways. So this is real, something's happening here and we've been talking about it a lot lately. So I have to ask you guys, how does a company know if they're radical enough? Like what is radical? How can I put a pin in that? It's like take a temperature or we like radical enough, what some tell signs can you guys share for companies that are really leaning into this new next inflection point because there are new things happening? How do you know if you're you're pushing the envelope radical enough to take advantage? >> Yeah, I think one. >> You can go ahead, Paul. >> Yeah, I was going to say one of the tests is the impact on your business. You have to start by looking at all this in the context of your business, and is it really taking you to another level? You said it perfectly, John, it used to be we used to talk about migration and workloads to the cloud and things like that. That's still something you need to do. But now our focus with a lot of our customers is on how do you innovate and grow your business in the cloud? What's the platform that you're using for your new digital products and services you're offering to your consumers. I mean it is the business and I think that's the test whether you're being radical enough is on the one hand, are you really using the technology to drive differentiation and real growth and change in your business? And are you equipping people, your human talent with the capabilities they need to perform in very different ways? And those are the two tests that I would give. >> Totally agree. >> Interesting enough, we love this topic and you guys, again, the book is spot on. Very packs of big punch on content, but very relevant in today. And I think one of the things we're looking at is that people who do things differently take advantage of some of these radical approaches like IDEAS, your framework, and understand where they are and what's available and what's coming around the corner. They stand out in the pack or create new business opportunities because the CapEx is taken care of. Now you got your cloud, I mean you're building clouds on top of clouds or something's happening. I think you see it, look at like companies like Snowflake, it's a data warehouse on the cloud. What does that mean? They didn't build a cloud, they used Amazon. So you're starting to see these new things pop up. >> Yeah and that's a good example. And it sounds like a simple thing, data warehouse in the cloud, but the new business capability that a technology like that allows and the portability of being able to connect and use data across cloud environments and such is tremendously powerful. And I think that's why, you talk about companies doing things differently, that's why it's great, again, that you're at re:Invent. If you look at the index of our book, you'll see AWS mentioned a number of times 'cause we tell a lot of customer company stories about how they're leveraging AWS capabilities in cloud and AI to really do transformative things in their business. And I think that's what it's all about. >> Yeah, and one of the things too in the book, it's great 'cause it has the systems thinking, it's got really relevant information, but you guys have seen the movie before. I think one of the wild cards in this era is global. We're global economy, you've got regions, you've got data sovereignty, you're seeing all kinds of new things emerging. Thoughts on the global impact 'cause you take your book and you overlay that to business, like you got to operate all over the world as a human issue, as a geography issue. What's your guys take on the global impact? >> Well that's why you got to think about cloud as one technology. We talked about in the book and cloud is, I think a lot of people think, well, clouds, it's almost old news. Maybe it's been around for a while. As you said, you've been going to re:Invent since 2013. Cloud is really just getting started. And it's 'cause the reasons you said, when you look at what you need to do around sovereign cloud capability if you're in Europe. For many companies it's about multi-cloud capabilities that you need to deploy differently in different regions. And they need to, in some cases for good reason, they have hybrid cloud capability that they match on their own. And then there's the edge capability which comes into play in different ways. And so the architecture becomes very complex and we talk the A in IDEAS is architecture. We talk about all this and how you need to move from the old conception of architecture, which was more static and just modularity was the key thing you thought about. It's more the idea of a living system, of living architecture that's expanding and is what's much more dynamic. And I think that's the way you need to think about it as you manage in a global environment today with the pace of technology advancement. >> Yeah, the innovation is here. It's not stopping. How do you create some defacto standards while not stunting the innovation is going to be a big discussion as these new flipped assumptions start to generate more activity. It's going to be very interesting to watch. Gentlemen, thank you so much for spending the time here on theCUBE as we break down your new book, "Radically Human" and how business leads can flip the script on their business assumptions and put ideas and access to work. This is a big part of the cloud show at re:Invent. Thanks so much for sharing and congratulations on a great book. >> Thanks, John. And just one point I'd add is that one of the things we do talk about in talent is the need to reskill talent. People who need to be relevant in the rapidly changing future. And that's one area where I think we all as institutions, as communities and individuals need to do more is to help those that need to reskilling. And the final point I mentioned is that we've mentioned at the end of the book that all proceeds from the book are being donated to NGOs and nonprofits that are focused on reskilling those who need a skill refresh in light of the radically human change in technology that's happening. >> Great. Buy the book. Proceeds go to a great cause and it's a very relevant book. If you're in the middle of this big wave that's coming. this is a great book. There's a guidepost and also give you some great ideas to reset, reflip the scripts, refactor, replatform. Guys, thanks for coming on and sharing. I really appreciate it. Again, congratulations. >> Thanks, John. >> Thanks, John. Great discussion. >> You're watching theCUBE here covering the executive forum here at AWS re:Invent '22. I'm John Furrier, you're host with Accenture. Thanks for watching. (gentle music)

(upbeat music) >> Hello, everyone. Welcome to theCUBE's coverage here at AWS re:Invent 2022. This is the Executive Summit with Accenture. I'm John Furrier, your host of theCUBE with two great guests coming on today, really talking about the future, the role of humans. Radically human is going to be the topic. Paul Daugherty, the group Chief Executive Technology and CTO at Accenture. And Jim Wilson, Global Managing Director of Thought Leadership and Technology Research, Accenture. Gentlemen, thank you for coming on theCUBE for this conversation around your new hit book, "Radically Human." >> Thanks, John. It's great to be with you and great to be present at re:Invent. >> We've been following you guys for many, many years now, over a decade. You always have the finger on the pulse. I mean, and as these waves come in, it's really important to understand impact. And more than ever, we're in this, I call it the systems thinking, revolution is going on now where things have consequences and machines are now accelerating their role. Developers are becoming the front lines of running companies, seeing a massive shift. This new technology is transforming the business and shaping our future as as humans. And so I love the book, very, very strong content, really right on point. What was the motivation for the book? And congratulations, but I noticed you got the structure, part one and part two, this book seems to be packing a big punch. What was the motivation, and what was some of the background in putting the book together? >> That's a great question, John. And I'll start, and then, Jim, my co-author and colleague and partner on the book can join in too. If you step back from the book itself, we'd written a first book called "Human + Machine", which focused a lot on artificial intelligence and talked about the potential and future of artificial intelligence to create a more human future for us with the human plus machine pairing. And then when we started working on the next book, it was the COVID era. COVID came on line as we were writing the book. And that was causing really an interesting time in technology for a lot of companies. I mean, think back to what you were doing. Once COVID hit, every company became more dependent on technology. Technology was the lifeline. And so Jim and I got interested in what the impacts of that were on companies, and what was different from the first research we had done around our first book. And what we found, which was super interesting, is that pre-pandemic, the leading companies, the digital leaders that were applying cloud data, AI, and related technologies faster, we're outperforming others by a factor of 2x. And that was before the pandemic. After the pandemic, we redid the research and the gap widened into 5x. And I think that's played a lot into our book. And we talk about that in the opening of our book. And the message there is exactly what you said is technology is not just the lifeline from the pandemic, but now technology is the heart and soul of how companies are driving innovation, how they're responding to global crises around inflation, energy, supply chain crisis because of the war in Ukraine, et cetera. And companies need the technology more than ever. And that's what we're writing about in "Radically Human." And we're taking a step beyond our previous book to talk about what we believe is next. And it's really cloud, data and AI, and the metaverse that signal out as three trends that are really driving transformative change for companies. In the first part of the book, to your question on the structure, talks about the roadmap to that. We talked about the ideas framework, five areas where you need to change your thinking, flip your assumptions on how to apply technology. And then the second part of the book talks about the differentiators that we believe are going to set companies apart as they look to implement this technology and transform their companies for the future. >> Jim, weigh in on this flipping the script, flipping the assumptions. >> You used a really important word there and that is systems. I think when we think about artificial intelligence, and when Paul and I have now talking to companies, a lot of executives think of AI as a point solution. They don't think about AI in terms of taking a systems approach. So we were trying to address that. All right, if you're going to build a roadmap, a technology roadmap for applying intelligent technologies like artificial intelligence, how do you take a holistic systematic view? And that's really the focus of the first section of the book. And then as Paul mentioned, how do you take those systems and really differentiate it using your talent, focusing on trust, experiences and sustainability? >> I like how it reads. It's almost like a masterclass book because you set the table. It's like, 'cause people right now are like in the mode of what's going on around me? I've been living through three years of COVID. We're coming out the other side. The world looks radically different. Humans are much more important. Automation's great, but people are finding out that the human's key, but people are trying to figure out where am I today. So I think the first part really to me hits home. Like, here's the current situation and then part two is here's how you can get better. And it's not just about machines, machines, machines and automation, automation, automation. We're seeing examples where the role of the human, the person in society, whether it's individually or as part of a group, are really now key assets in that kind of this new workforce or this new production system or society. >> Yeah. And just to take a couple examples from the book and highlight that, I think you're exactly right. And that's where "Radically Human", the title came from. And what's happening with technology is that technology itself is becoming more human like in its capability. When you think about the power of the transformer technologies and other things that we're reading about a lot. And the whole hypothesis or premise of the book I should say, is that the more human like the technology is, the more radically human or the more radical the human potential improvement is, the bigger the opportunity. It's pairing the two together rather than, as you said, just looking at the automation or the machine side of it. That's really the radical leap. And one thing Jim and I talked about in context of the book is companies really often haven't been radical enough in applying technology to really get to dramatic gains that they can get. Just a couple examples from the ideas framework, the I in IDEAS. The ideas framework is the first part of the book. The five areas to flip your assumptions. The I stands for intelligence and we're talking about more human and less artificial in terms of the intelligence techniques. Things like common sense learning and other techniques that allow you to develop more powerful ways of engaging people, engaging humans in the systems that we build using the kind of systems thinking that Jim mentioned. And things like emotional AI, common sense AI, new techniques in addition to machine, the big data driven machine learning techniques, which are essential to vision and solving big problems like that. So that's just an example of how you bring it together and enable that human potential. >> I love the idea, go ahead Jim. >> I was going to say we've been used to adapting to technology, and contorting our fingers to keyboards and so on for a long time. And now we're starting to see that technology is in fact beginning to adapt to us and become more natural in many instances. One point that we make is now in the human technology nexus, in fact, the human is in the ascended. That's one of the big ideas that we try to put out there in this book. >> I love the idea of flipping the script, flipping the assumptions, but ideas framework is interesting. I for intelligence, D for data, E for expertise, A for architecture, S for strategy. Notice the strategies last. Normally in the old school days, it's like, hey, strategy first and execution. Really interesting how you guys put that together. It feels like business is becoming agile and iterative and how it's going to be forming. Can you guys, I mean that's my opinion, but I think observing how developers becoming much more part of the app. I mean, if you take digital transformation to its conclusion, the application is the company, It's not a department serving the business, it is the business, therefore developers are running the business, so to speak. This is really radical. I mean, this is how I'm seeing it. What's your reaction to that? Do you see similar parallels to this transformation if you take it down to a conclusion and strategy is just what you do after you get the outcomes you need? What's your reaction to that? >> Yeah, I think one of the most lasting elements of the book might be that chapter on strategy in my opinion, because you need to think about it differently. The old way of doing strategy is dead. You can't do it the way you used to do it. And that's what we tried to lay out with the S in IDEAS, the strategy. The subtitle that chapter is we're all technology companies now. And if you're a technology driven company, the way you need to think about and every company is becoming, that's what I hear when I talk to these suites and CEOs and boards, is everybody's recognizing the essential role that technology plays and therefore they need to master technology. Well, you need to think about strategy differently then because of the pace of technology innovation. And so you need to throw out the old way of doing it. We suggest three new archetypes of how to do strategy that I think are really important. It's about continuous strategy in all cases. An example is one of the techniques we talk about, forever beta, which is, think about a Tesla or companies that it's never quite done. They're always improving and the product is designed to be connected and improving. So it changes along the product and the strategy along how you deploy it to consumers changes as you go. And that's an example of a very different approach to strategy that we believe is essential to consider as you look at the future. Yeah, those multi-month strategy sessions might play out over two or three quarters of going away. And strategy and execution are becoming almost simultaneous these days as Paul was saying. >> It's interesting because that's the trend you're seeing with more data, more automation, but the human plays a much critical role. And just aside on the Tesla example, is well documented. I think I wrote about in a post just this week that during the model three, Elon wanted full automation and had to actually go off scripts and get to humans back in charge 'cause it wasn't working properly. Now they have a balance. But that brings up to part two, which I like, which is this human piece of it. We always talk about skills gaps, there's not enough people to do this, that and the other thing. And talent was a big part of that second half, trust, talent, experiences. That's more of the person's role, either individually as part of a collective group. Is talent the scarce resource now where that's the goal, that's the key 'cause it all could point to that in a way. Skills gap points to, hey, humans are valuable. In fact the value's going up if it's properly architected. What's your reaction to that, guys? Because I think that's something that is not, kind of nuanced point, but it's a feature, not a bug maybe, I don't know. What's your thoughts? >> Yeah, go ahead Jim. >> I was going to say it, we're dramatically underestimating the amount of focus we need to put on talent. That's why we start off that second part of the book, really zooming in on talent. I think you might think that for every hundred dollars that you put into a technology initiative, you might put 50 or 75 into re-skilling initiatives to really compliment that. But what we're seeing is companies need to be much more revolutionary in their focus on talent. We saw economic analysis recently that pointed out that for every $1 you spend on technology, you are likely going to need to spend about $9 on intangible human capital. That means on talent, on getting the best talent, on re-skilling and on changing processes and work tasks. So there's a lot of work that needs to be done. Really that's human focus. It's not just about adopting the technology. Certainly the technology's critical, but we're underestimating the amount of focus that needs to go into the talent factors. >> That's a huge point. >> And I think some of the elements of talent that become really critical that we talked about in the book are becoming a talent creator. We believe the successful companies of the future are going to be able not just to post a job opening and hire people in because there's not going to be enough. And a lot of the jobs that companies are creating don't exist 'cause the technology changing so fast. So the companies that succeed are going to know how to create talent, bring in people, apprentices and such, and shape to tale as they go. We're doing a significant amount of that in our own company. They're going to be learning based organizations where you'll differentiate, you'll get the best employees if you provide better learning environments because that's what employees want. And then democratizing access to technology. Things like Amazon's Honeycode is an example, low-code/no-code development to spread development to wider pools of people. Those types of things are really critical going forward to really unlock the talent potential. And really what you end up with is, yeah, the human talent's important, but it's magnified and multiplied by the power of people, giving them in essence superpowers in using technology in new ways. >> I think you nailed it, that's super important. That point about the force multiplier when you put things in combination, whether it's group constructs, two pizza teams flexing, leveraging the talent. I mean, this is a new configuration. You guys are nailing it there. I love that piece. And I think groups and collectives you're going to start to see a lot more of that. But again, with talent comes trust when you start to have these ephemeral and or forming groups that are forming production systems or experiences. So trust comes up a lot. You guys see the metaverse as an important part there. Obviously metaverse is a pretext to the virtual world where we're going to start to create these group experiences and create new force multipliers. How does the metaverse play into this new radically human world, and what does it mean for the future of business? >> Yeah, I think the metaverse is radically misunderstood to use the word title when we're not with the title of our book. And we believe that the metaverse does have real big potential, massive potential, and I think it'll transform the way we think about digital more so than we've changed our thinking on digital in the last 10 years. So that's the potential of the metaverse. And it's not just about the consumer things, it's about metaverse and the enterprise. It's about the new products you create using distributed ledger and other technologies. And it's about the industrial metaverse of how you bring digital twins and augmented workers online in different ways. And so I believe that it has tremendous potential. We write about that in the book and it really takes radically human to another level. And one way to think about this is cloud is really becoming the operating system of business. You have to build your enterprise around the cloud as you go forward. That's going to shape the way you do business. AI becomes the insight and intelligence in how you work, infused with the human talent and such as we said. And the metaverse then reshapes the experience layers. So you have cloud, AI building on top of this metaverse providing a new way to generate experiences for employees, citizens, consumers, et cetera. And that's the way it unfolds, but trust becomes more important because just as AI raises new questions around trust, every technology raises new questions around trust. The metaverse raises a whole new set of questions. And in the book we outline a five-part framework or five essential parts of the framework around how you establish trust as you implement these new technologies. >> Yeah, we're seeing that about three quarters of companies are really trying to figure out trust, certainly with issues like the metaverse more broadly across their IT so they're focusing on security and privacy, transparency, especially when you're talking about AI systems, explainability. One of the more surprising things that we learned when doing the book, when we were doing the research is that we saw that increasingly consumers and employees want systems to be informed by a sense of humanity. So one company that we've been looking at that's been developing autonomous vehicles, self-driving car systems, they're actually training the system by emulating human behavior. So turning the cameras on test drivers to see how they learn and then training the AI using that sense of humanity 'cause other drivers on the road find human behavior more trustworthy. And similarly, that system is also using explainable AI to actually show which human behaviors that AI system is learning from. Some really interesting innovations happening in that trust space. John. >> Jim, I think you bring up a great point that's worth talking more about. Because you're talking about how human behaviors are being put into the design of new things like machines or software. And we're living in this era of cloud scale, which is compressing this transformation timeline and we've been calling it supercloud, some call it multi-cloud, but it's really a new thing happening where you're seeing an acceleration of the transformation. We think it's going to happen much faster in the next five to 10 years. And so that means these new things are emerging, not just, hey, I'm running a virtual event with chat and some video. It's group behavior, it's groups convening, talking, getting things done, debating, doing things differently. And so this idea of humans informing design decisions or software with low-code/no-code, this completely changes strategy. I mean this is a big point of the book. >> Yeah, no, I go back to one of the, the E in the IDEAS framework is expertise. And we talk about from machine learning to machine teaching, which is exactly that. Machine learning is maybe humans tag data and stuff and feed into algorithms. Machine teaching is how do you really leverage the human expertise in the systems that you develop with AI. One of the examples we give is one of the large consumer platforms that uses human designers to give the system a sense of aesthetic design and product design. A very difficult thing, especially with changing fashion interest and everything else to encode in algorithms and to even have AI do, even if you have fast amounts of data, but with the right human insight and human expertise injected in, you can create amazing new capability that responds to consumers in a much more powerful way. And that's an example of what you just said, John, bringing the two together. >> Well, yeah, it's interesting. I want to to get your thoughts as we get wrap up here soon. How do you apply all these human-centric technologies to the future of business? As you guys talk to leaders in the enterprise of their businesses, as they look at the horizon, they see the the future. They got to start thinking about things like generative AI and how they can bring some of these technologies to the table. We were talking about if open source continues to grow the way it's going, there might not be any code to write, it just writes itself at some point. So you got supply chain issues with security. These are new things you guys are hitting in the book where these are new dynamics, new power dynamics in how things get built. So if you're a business owner and leader, this is a new opportunity, a challenge certainly that is an opportunity. How do you apply all this stuff for business? >> I'll go first then Jim can add in. But the first thing I think starts with recognizing the role that technology does play and investing accordingly in it. So the right technology talent, rethinking the way you do strategy as we talked about earlier and recognizing how you need to build a foundation. That's why the fact you're at re:Invent is so important because companies are, again, rebuilding that operating system of their business in the cloud. And you need that as the foundation to go forward, to do, to build the other types of capabilities. And then I think it's developing those talent systems as well. Do you have the right talent brand? Are you attracting the right employees? Are you developing them in the right way so that you have the right future talent going forward? And then you marry the two together and that's what gives you the radically human formula. >> Yeah. When we were developing that first part of the book, Paul and I did quite a bit of research, and Paul kind of alluded to that research earlier, but one of the things that we saw in really the first year of the pandemic was that there was a lot of first time adoption of intelligent technologies like artificial intelligence. One statistic is that 70% of companies that had never tried AI before went ahead and tried it during the pandemic. So first time adoption rates were way up, but the thing is companies were not trying to do it themselves and to necessarily build an AI department. They were partnering and it's really important to find a partner, often a cloud partner as a way to get started, start small scale, and then scale up doing experiments. So that was one of the key insights that we had. You don't need to do it all yourself. >> If you see the transformation of just AWS, we're here at re:Invent, since we've been covering the events since 2013, every year there's been a thematic thing. It was startups, enterprise, now builders, and now change your company. This year it's continuing that same thing where you're starting to see new things happen. It's not just lift and shift and running a SaaS application on the cloud. People are are changing and refactoring and replatforming categorical applications in for this new era. And we're calling it supercloud, superservices, superapps, 'cause they're different. They're doing different things in leveraging large scale CapEx, large scale talent pools, or talent pools in certain ways. So this is real, something's happening here and we've been talking about it a lot lately. So I have to ask you guys, how does a company know if they're radical enough? Like what is radical? How can I put a pin in that? It's like take a temperature or we like radical enough, what some tell signs can you guys share for companies that are really leaning into this new next inflection point because there are new things happening? How do you know if you're you're pushing the envelope radical enough to take advantage? >> Yeah, I think one. >> You can go ahead, Paul. >> Yeah, I was going to say one of the tests is the impact on your business. You have to start by looking at all this in the context of your business, and is it really taking you to another level? You said it perfectly, John, it used to be we used to talk about migration and workloads to the cloud and things like that. That's still something you need to do. But now our focus with a lot of our customers is on how do you innovate and grow your business in the cloud? What's the platform that you're using for your new digital products and services you're offering to your consumers. I mean it is the business and I think that's the test whether you're being radical enough is on the one hand, are you really using the technology to drive differentiation and real growth and change in your business? And are you equipping people, your human talent with the capabilities they need to perform in very different ways? And those are the two tests that I would give. >> Totally agree. >> Interesting enough, we love this topic and you guys, again, the book is spot on. Very packs of big punch on content, but very relevant in today. And I think one of the things we're looking at is that people who do things differently take advantage of some of these radical approaches like IDEAS, your framework, and understand where they are and what's available and what's coming around the corner. They stand out in the pack or create new business opportunities because the CapEx is taken care of. Now you got your cloud, I mean you're building clouds on top of clouds or something's happening. I think you see it, look at like companies like Snowflake, it's a data warehouse on the cloud. What does that mean? They didn't build a cloud, they used Amazon. So you're starting to see these new things pop up. >> Yeah and that's a good example. And it sounds like a simple thing, data warehouse in the cloud, but the new business capability that a technology like that allows and the portability of being able to connect and use data across cloud environments and such is tremendously powerful. And I think that's why, you talk about companies doing things differently, that's why it's great, again, that you're at re:Invent. If you look at the index of our book, you'll see AWS mentioned a number of times 'cause we tell a lot of customer company stories about how they're leveraging AWS capabilities in cloud and AI to really do transformative things in their business. And I think that's what it's all about. >> Yeah, and one of the things too in the book, it's great 'cause it has the systems thinking, it's got really relevant information, but you guys have seen the movie before. I think one of the wild cards in this era is global. We're global economy, you've got regions, you've got data sovereignty, you're seeing all kinds of new things emerging. Thoughts on the global impact 'cause you take your book and you overlay that to business, like you got to operate all over the world as a human issue, as a geography issue. What's your guys take on the global impact? >> Well that's why you got to think about cloud as one technology. We talked about in the book and cloud is, I think a lot of people think, well, clouds, it's almost old news. Maybe it's been around for a while. As you said, you've been going to re:Invent since 2013. Cloud is really just getting started. And it's 'cause the reasons you said, when you look at what you need to do around sovereign cloud capability if you're in Europe. For many companies it's about multi-cloud capabilities that you need to deploy differently in different regions. And they need to, in some cases for good reason, they have hybrid cloud capability that they match on their own. And then there's the edge capability which comes into play in different ways. And so the architecture becomes very complex and we talk the A in IDEAS is architecture. We talk about all this and how you need to move from the old conception of architecture, which was more static and just modularity was the key thing you thought about. It's more the idea of a living system, of living architecture that's expanding and is what's much more dynamic. And I think that's the way you need to think about it as you manage in a global environment today with the pace of technology advancement. >> Yeah, the innovation is here. It's not stopping. How do you create some defacto standards while not stunting the innovation is going to be a big discussion as these new flipped assumptions start to generate more activity. It's going to be very interesting to watch. Gentlemen, thank you so much for spending the time here on theCUBE as we break down your new book, "Radically Human" and how business leads can flip the script on their business assumptions and put ideas and access to work. This is a big part of the cloud show at re:Invent. Thanks so much for sharing and congratulations on a great book. >> Thanks, John. And just one point I'd add is that one of the things we do talk about in talent is the need to reskill talent. People who need to be relevant in the rapidly changing future. And that's one area where I think we all as institutions, as communities and individuals need to do more is to help those that need to reskilling. And the final point I mentioned is that we've mentioned at the end of the book that all proceeds from the book are being donated to NGOs and nonprofits that are focused on reskilling those who need a skill refresh in light of the radically human change in technology that's happening. >> Great. Buy the book. Proceeds go to a great cause and it's a very relevant book. If you're in the middle of this big wave that's coming. this is a great book. There's a guidepost and also give you some great ideas to reset, reflip the scripts, refactor, replatform. Guys, thanks for coming on and sharing. I really appreciate it. Again, congratulations. >> Thanks, John. >> Thanks, John. Great discussion. >> You're watching theCUBE here covering the executive forum here at AWS re:Invent '22. I'm John Furrier, you're host with Accenture. Thanks for watching. (gentle music)

(upbeat music) >> Hello, everyone. Welcome to theCUBE's coverage here at AWS re:Invent 2022. This is the Executive Summit with Accenture. I'm John Furrier, your host of theCUBE with two great guests coming on today, really talking about the future, the role of humans. Radically human is going to be the topic. Paul Daugherty, the group Chief Executive Technology and CTO at Accenture. And Jim Wilson, Global Managing Director of Thought Leadership and Technology Research, Accenture. Gentlemen, thank you for coming on theCUBE for this conversation around your new hit book, "Radically Human." >> Thanks, John. It's great to be with you and great to be present at re:Invent. >> We've been following you guys for many, many years now, over a decade. You always have the finger on the pulse. I mean, and as these waves come in, it's really important to understand impact. And more than ever, we're in this, I call it the systems thinking, revolution is going on now where things have consequences and machines are now accelerating their role. Developers are becoming the front lines of running companies, seeing a massive shift. This new technology is transforming the business and shaping our future as as humans. And so I love the book, very, very strong content, really right on point. What was the motivation for the book? And congratulations, but I noticed you got the structure, part one and part two, this book seems to be packing a big punch. What was the motivation, and what was some of the background in putting the book together? >> That's a great question, John. And I'll start, and then, Jim, my co-author and colleague and partner on the book can join in too. If you step back from the book itself, we'd written a first book called "Human + Machine", which focused a lot on artificial intelligence and talked about the potential and future of artificial intelligence to create a more human future for us with the human plus machine pairing. And then when we started working on the next book, it was the COVID era. COVID came on line as we were writing the book. And that was causing really an interesting time in technology for a lot of companies. I mean, think back to what you were doing. Once COVID hit, every company became more dependent on technology. Technology was the lifeline. And so Jim and I got interested in what the impacts of that were on companies, and what was different from the first research we had done around our first book. And what we found, which was super interesting, is that pre-pandemic, the leading companies, the digital leaders that were applying cloud data, AI, and related technologies faster, we're outperforming others by a factor of 2x. And that was before the pandemic. After the pandemic, we redid the research and the gap widened into 5x. And I think that's played a lot into our book. And we talk about that in the opening of our book. And the message there is exactly what you said is technology is not just the lifeline from the pandemic, but now technology is the heart and soul of how companies are driving innovation, how they're responding to global crises around inflation, energy, supply chain crisis because of the war in Ukraine, et cetera. And companies need the technology more than ever. And that's what we're writing about in "Radically Human." And we're taking a step beyond our previous book to talk about what we believe is next. And it's really cloud, data and AI, and the metaverse that signal out as three trends that are really driving transformative change for companies. In the first part of the book, to your question on the structure, talks about the roadmap to that. We talked about the ideas framework, five areas where you need to change your thinking, flip your assumptions on how to apply technology. And then the second part of the book talks about the differentiators that we believe are going to set companies apart as they look to implement this technology and transform their companies for the future. >> Jim, weigh in on this flipping the script, flipping the assumptions. >> You used a really important word there and that is systems. I think when we think about artificial intelligence, and when Paul and I have now talking to companies, a lot of executives think of AI as a point solution. They don't think about AI in terms of taking a systems approach. So we were trying to address that. All right, if you're going to build a roadmap, a technology roadmap for applying intelligent technologies like artificial intelligence, how do you take a holistic systematic view? And that's really the focus of the first section of the book. And then as Paul mentioned, how do you take those systems and really differentiate it using your talent, focusing on trust, experiences and sustainability? >> I like how it reads. It's almost like a masterclass book because you set the table. It's like, 'cause people right now are like in the mode of what's going on around me? I've been living through three years of COVID. We're coming out the other side. The world looks radically different. Humans are much more important. Automation's great, but people are finding out that the human's key, but people are trying to figure out where am I today. So I think the first part really to me hits home. Like, here's the current situation and then part two is here's how you can get better. And it's not just about machines, machines, machines and automation, automation, automation. We're seeing examples where the role of the human, the person in society, whether it's individually or as part of a group, are really now key assets in that kind of this new workforce or this new production system or society. >> Yeah. And just to take a couple examples from the book and highlight that, I think you're exactly right. And that's where "Radically Human", the title came from. And what's happening with technology is that technology itself is becoming more human like in its capability. When you think about the power of the transformer technologies and other things that we're reading about a lot. And the whole hypothesis or premise of the book I should say, is that the more human like the technology is, the more radically human or the more radical the human potential improvement is, the bigger the opportunity. It's pairing the two together rather than, as you said, just looking at the automation or the machine side of it. That's really the radical leap. And one thing Jim and I talked about in context of the book is companies really often haven't been radical enough in applying technology to really get to dramatic gains that they can get. Just a couple examples from the ideas framework, the I in IDEAS. The ideas framework is the first part of the book. The five areas to flip your assumptions. The I stands for intelligence and we're talking about more human and less artificial in terms of the intelligence techniques. Things like common sense learning and other techniques that allow you to develop more powerful ways of engaging people, engaging humans in the systems that we build using the kind of systems thinking that Jim mentioned. And things like emotional AI, common sense AI, new techniques in addition to machine, the big data driven machine learning techniques, which are essential to vision and solving big problems like that. So that's just an example of how you bring it together and enable that human potential. >> I love the idea, go ahead Jim. >> I was going to say we've been used to adapting to technology, and contorting our fingers to keyboards and so on for a long time. And now we're starting to see that technology is in fact beginning to adapt to us and become more natural in many instances. One point that we make is now in the human technology nexus, in fact, the human is in the ascended. That's one of the big ideas that we try to put out there in this book. >> I love the idea of flipping the script, flipping the assumptions, but ideas framework is interesting. I for intelligence, D for data, E for expertise, A for architecture, S for strategy. Notice the strategies last. Normally in the old school days, it's like, hey, strategy first and execution. Really interesting how you guys put that together. It feels like business is becoming agile and iterative and how it's going to be forming. Can you guys, I mean that's my opinion, but I think observing how developers becoming much more part of the app. I mean, if you take digital transformation to its conclusion, the application is the company, It's not a department serving the business, it is the business, therefore developers are running the business, so to speak. This is really radical. I mean, this is how I'm seeing it. What's your reaction to that? Do you see similar parallels to this transformation if you take it down to a conclusion and strategy is just what you do after you get the outcomes you need? What's your reaction to that? >> Yeah, I think one of the most lasting elements of the book might be that chapter on strategy in my opinion, because you need to think about it differently. The old way of doing strategy is dead. You can't do it the way you used to do it. And that's what we tried to lay out with the S in IDEAS, the strategy. The subtitle that chapter is we're all technology companies now. And if you're a technology driven company, the way you need to think about and every company is becoming, that's what I hear when I talk to these suites and CEOs and boards, is everybody's recognizing the essential role that technology plays and therefore they need to master technology. Well, you need to think about strategy differently then because of the pace of technology innovation. And so you need to throw out the old way of doing it. We suggest three new archetypes of how to do strategy that I think are really important. It's about continuous strategy in all cases. An example is one of the techniques we talk about, forever beta, which is, think about a Tesla or companies that it's never quite done. They're always improving and the product is designed to be connected and improving. So it changes along the product and the strategy along how you deploy it to consumers changes as you go. And that's an example of a very different approach to strategy that we believe is essential to consider as you look at the future. Yeah, those multi-month strategy sessions might play out over two or three quarters of going away. And strategy and execution are becoming almost simultaneous these days as Paul was saying. >> It's interesting because that's the trend you're seeing with more data, more automation, but the human plays a much critical role. And just aside on the Tesla example, is well documented. I think I wrote about in a post just this week that during the model three, Elon wanted full automation and had to actually go off scripts and get to humans back in charge 'cause it wasn't working properly. Now they have a balance. But that brings up to part two, which I like, which is this human piece of it. We always talk about skills gaps, there's not enough people to do this, that and the other thing. And talent was a big part of that second half, trust, talent, experiences. That's more of the person's role, either individually as part of a collective group. Is talent the scarce resource now where that's the goal, that's the key 'cause it all could point to that in a way. Skills gap points to, hey, humans are valuable. In fact the value's going up if it's properly architected. What's your reaction to that, guys? Because I think that's something that is not, kind of nuanced point, but it's a feature, not a bug maybe, I don't know. What's your thoughts? >> Yeah, go ahead Jim. >> I was going to say it, we're dramatically underestimating the amount of focus we need to put on talent. That's why we start off that second part of the book, really zooming in on talent. I think you might think that for every hundred dollars that you put into a technology initiative, you might put 50 or 75 into re-skilling initiatives to really compliment that. But what we're seeing is companies need to be much more revolutionary in their focus on talent. We saw economic analysis recently that pointed out that for every $1 you spend on technology, you are likely going to need to spend about $9 on intangible human capital. That means on talent, on getting the best talent, on re-skilling and on changing processes and work tasks. So there's a lot of work that needs to be done. Really that's human focus. It's not just about adopting the technology. Certainly the technology's critical, but we're underestimating the amount of focus that needs to go into the talent factors. >> That's a huge point. >> And I think some of the elements of talent that become really critical that we talked about in the book are becoming a talent creator. We believe the successful companies of the future are going to be able not just to post a job opening and hire people in because there's not going to be enough. And a lot of the jobs that companies are creating don't exist 'cause the technology changing so fast. So the companies that succeed are going to know how to create talent, bring in people, apprentices and such, and shape to tale as they go. We're doing a significant amount of that in our own company. They're going to be learning based organizations where you'll differentiate, you'll get the best employees if you provide better learning environments because that's what employees want. And then democratizing access to technology. Things like Amazon's Honeycode is an example, low-code/no-code development to spread development to wider pools of people. Those types of things are really critical going forward to really unlock the talent potential. And really what you end up with is, yeah, the human talent's important, but it's magnified and multiplied by the power of people, giving them in essence superpowers in using technology in new ways. >> I think you nailed it, that's super important. That point about the force multiplier when you put things in combination, whether it's group constructs, two pizza teams flexing, leveraging the talent. I mean, this is a new configuration. You guys are nailing it there. I love that piece. And I think groups and collectives you're going to start to see a lot more of that. But again, with talent comes trust when you start to have these ephemeral and or forming groups that are forming production systems or experiences. So trust comes up a lot. You guys see the metaverse as an important part there. Obviously metaverse is a pretext to the virtual world where we're going to start to create these group experiences and create new force multipliers. How does the metaverse play into this new radically human world, and what does it mean for the future of business? >> Yeah, I think the metaverse is radically misunderstood to use the word title when we're not with the title of our book. And we believe that the metaverse does have real big potential, massive potential, and I think it'll transform the way we think about digital more so than we've changed our thinking on digital in the last 10 years. So that's the potential of the metaverse. And it's not just about the consumer things, it's about metaverse and the enterprise. It's about the new products you create using distributed ledger and other technologies. And it's about the industrial metaverse of how you bring digital twins and augmented workers online in different ways. And so I believe that it has tremendous potential. We write about that in the book and it really takes radically human to another level. And one way to think about this is cloud is really becoming the operating system of business. You have to build your enterprise around the cloud as you go forward. That's going to shape the way you do business. AI becomes the insight and intelligence in how you work, infused with the human talent and such as we said. And the metaverse then reshapes the experience layers. So you have cloud, AI building on top of this metaverse providing a new way to generate experiences for employees, citizens, consumers, et cetera. And that's the way it unfolds, but trust becomes more important because just as AI raises new questions around trust, every technology raises new questions around trust. The metaverse raises a whole new set of questions. And in the book we outline a five-part framework or five essential parts of the framework around how you establish trust as you implement these new technologies. >> Yeah, we're seeing that about three quarters of companies are really trying to figure out trust, certainly with issues like the metaverse more broadly across their IT so they're focusing on security and privacy, transparency, especially when you're talking about AI systems, explainability. One of the more surprising things that we learned when doing the book, when we were doing the research is that we saw that increasingly consumers and employees want systems to be informed by a sense of humanity. So one company that we've been looking at that's been developing autonomous vehicles, self-driving car systems, they're actually training the system by emulating human behavior. So turning the cameras on test drivers to see how they learn and then training the AI using that sense of humanity 'cause other drivers on the road find human behavior more trustworthy. And similarly, that system is also using explainable AI to actually show which human behaviors that AI system is learning from. Some really interesting innovations happening in that trust space. John. >> Jim, I think you bring up a great point that's worth talking more about. Because you're talking about how human behaviors are being put into the design of new things like machines or software. And we're living in this era of cloud scale, which is compressing this transformation timeline and we've been calling it supercloud, some call it multi-cloud, but it's really a new thing happening where you're seeing an acceleration of the transformation. We think it's going to happen much faster in the next five to 10 years. And so that means these new things are emerging, not just, hey, I'm running a virtual event with chat and some video. It's group behavior, it's groups convening, talking, getting things done, debating, doing things differently. And so this idea of humans informing design decisions or software with low-code/no-code, this completely changes strategy. I mean this is a big point of the book. >> Yeah, no, I go back to one of the, the E in the IDEAS framework is expertise. And we talk about from machine learning to machine teaching, which is exactly that. Machine learning is maybe humans tag data and stuff and feed into algorithms. Machine teaching is how do you really leverage the human expertise in the systems that you develop with AI. One of the examples we give is one of the large consumer platforms that uses human designers to give the system a sense of aesthetic design and product design. A very difficult thing, especially with changing fashion interest and everything else to encode in algorithms and to even have AI do, even if you have fast amounts of data, but with the right human insight and human expertise injected in, you can create amazing new capability that responds to consumers in a much more powerful way. And that's an example of what you just said, John, bringing the two together. >> Well, yeah, it's interesting. I want to to get your thoughts as we get wrap up here soon. How do you apply all these human-centric technologies to the future of business? As you guys talk to leaders in the enterprise of their businesses, as they look at the horizon, they see the the future. They got to start thinking about things like generative AI and how they can bring some of these technologies to the table. We were talking about if open source continues to grow the way it's going, there might not be any code to write, it just writes itself at some point. So you got supply chain issues with security. These are new things you guys are hitting in the book where these are new dynamics, new power dynamics in how things get built. So if you're a business owner and leader, this is a new opportunity, a challenge certainly that is an opportunity. How do you apply all this stuff for business? >> I'll go first then Jim can add in. But the first thing I think starts with recognizing the role that technology does play and investing accordingly in it. So the right technology talent, rethinking the way you do strategy as we talked about earlier and recognizing how you need to build a foundation. That's why the fact you're at re:Invent is so important because companies are, again, rebuilding that operating system of their business in the cloud. And you need that as the foundation to go forward, to do, to build the other types of capabilities. And then I think it's developing those talent systems as well. Do you have the right talent brand? Are you attracting the right employees? Are you developing them in the right way so that you have the right future talent going forward? And then you marry the two together and that's what gives you the radically human formula. >> Yeah. When we were developing that first part of the book, Paul and I did quite a bit of research, and Paul kind of alluded to that research earlier, but one of the things that we saw in really the first year of the pandemic was that there was a lot of first time adoption of intelligent technologies like artificial intelligence. One statistic is that 70% of companies that had never tried AI before went ahead and tried it during the pandemic. So first time adoption rates were way up, but the thing is companies were not trying to do it themselves and to necessarily build an AI department. They were partnering and it's really important to find a partner, often a cloud partner as a way to get started, start small scale, and then scale up doing experiments. So that was one of the key insights that we had. You don't need to do it all yourself. >> If you see the transformation of just AWS, we're here at re:Invent, since we've been covering the events since 2013, every year there's been a thematic thing. It was startups, enterprise, now builders, and now change your company. This year it's continuing that same thing where you're starting to see new things happen. It's not just lift and shift and running a SaaS application on the cloud. People are are changing and refactoring and replatforming categorical applications in for this new era. And we're calling it supercloud, superservices, superapps, 'cause they're different. They're doing different things in leveraging large scale CapEx, large scale talent pools, or talent pools in certain ways. So this is real, something's happening here and we've been talking about it a lot lately. So I have to ask you guys, how does a company know if they're radical enough? Like what is radical? How can I put a pin in that? It's like take a temperature or we like radical enough, what some tell signs can you guys share for companies that are really leaning into this new next inflection point because there are new things happening? How do you know if you're you're pushing the envelope radical enough to take advantage? >> Yeah, I think one. >> You can go ahead, Paul. >> Yeah, I was going to say one of the tests is the impact on your business. You have to start by looking at all this in the context of your business, and is it really taking you to another level? You said it perfectly, John, it used to be we used to talk about migration and workloads to the cloud and things like that. That's still something you need to do. But now our focus with a lot of our customers is on how do you innovate and grow your business in the cloud? What's the platform that you're using for your new digital products and services you're offering to your consumers. I mean it is the business and I think that's the test whether you're being radical enough is on the one hand, are you really using the technology to drive differentiation and real growth and change in your business? And are you equipping people, your human talent with the capabilities they need to perform in very different ways? And those are the two tests that I would give. >> Totally agree. >> Interesting enough, we love this topic and you guys, again, the book is spot on. Very packs of big punch on content, but very relevant in today. And I think one of the things we're looking at is that people who do things differently take advantage of some of these radical approaches like IDEAS, your framework, and understand where they are and what's available and what's coming around the corner. They stand out in the pack or create new business opportunities because the CapEx is taken care of. Now you got your cloud, I mean you're building clouds on top of clouds or something's happening. I think you see it, look at like companies like Snowflake, it's a data warehouse on the cloud. What does that mean? They didn't build a cloud, they used Amazon. So you're starting to see these new things pop up. >> Yeah and that's a good example. And it sounds like a simple thing, data warehouse in the cloud, but the new business capability that a technology like that allows and the portability of being able to connect and use data across cloud environments and such is tremendously powerful. And I think that's why, you talk about companies doing things differently, that's why it's great, again, that you're at re:Invent. If you look at the index of our book, you'll see AWS mentioned a number of times 'cause we tell a lot of customer company stories about how they're leveraging AWS capabilities in cloud and AI to really do transformative things in their business. And I think that's what it's all about. >> Yeah, and one of the things too in the book, it's great 'cause it has the systems thinking, it's got really relevant information, but you guys have seen the movie before. I think one of the wild cards in this era is global. We're global economy, you've got regions, you've got data sovereignty, you're seeing all kinds of new things emerging. Thoughts on the global impact 'cause you take your book and you overlay that to business, like you got to operate all over the world as a human issue, as a geography issue. What's your guys take on the global impact? >> Well that's why you got to think about cloud as one technology. We talked about in the book and cloud is, I think a lot of people think, well, clouds, it's almost old news. Maybe it's been around for a while. As you said, you've been going to re:Invent since 2013. Cloud is really just getting started. And it's 'cause the reasons you said, when you look at what you need to do around sovereign cloud capability if you're in Europe. For many companies it's about multi-cloud capabilities that you need to deploy differently in different regions. And they need to, in some cases for good reason, they have hybrid cloud capability that they match on their own. And then there's the edge capability which comes into play in different ways. And so the architecture becomes very complex and we talk the A in IDEAS is architecture. We talk about all this and how you need to move from the old conception of architecture, which was more static and just modularity was the key thing you thought about. It's more the idea of a living system, of living architecture that's expanding and is what's much more dynamic. And I think that's the way you need to think about it as you manage in a global environment today with the pace of technology advancement. >> Yeah, the innovation is here. It's not stopping. How do you create some defacto standards while not stunting the innovation is going to be a big discussion as these new flipped assumptions start to generate more activity. It's going to be very interesting to watch. Gentlemen, thank you so much for spending the time here on theCUBE as we break down your new book, "Radically Human" and how business leads can flip the script on their business assumptions and put ideas and access to work. This is a big part of the cloud show at re:Invent. Thanks so much for sharing and congratulations on a great book. >> Thanks, John. And just one point I'd add is that one of the things we do talk about in talent is the need to reskill talent. People who need to be relevant in the rapidly changing future. And that's one area where I think we all as institutions, as communities and individuals need to do more is to help those that need to reskilling. And the final point I mentioned is that we've mentioned at the end of the book that all proceeds from the book are being donated to NGOs and nonprofits that are focused on reskilling those who need a skill refresh in light of the radically human change in technology that's happening. >> Great. Buy the book. Proceeds go to a great cause and it's a very relevant book. If you're in the middle of this big wave that's coming. this is a great book. There's a guidepost and also give you some great ideas to reset, reflip the scripts, refactor, replatform. Guys, thanks for coming on and sharing. I really appreciate it. Again, congratulations. >> Thanks, John. >> Thanks, John. Great discussion. >> You're watching theCUBE here covering the executive forum here at AWS re:Invent '22. I'm John Furrier, you're host with Accenture. Thanks for watching. (gentle music)

>>Hello and welcome to the Cube's coverage here at AWS Reinvent 2022. This is the Executive Summit with Accenture. I'm John Furry, your host of the Cube at two great guests coming on today, really talking about the future, the role of humans. Radically human is gonna be the topic. Paul Dardy, the group Chief Executive Technology and CTO at Accenture. And Jim Wilson, global managing director of thought Leadership and Technology research. Accenture. Gentlemen, thank you for coming on the cube for this conversation around your new hit book. Radically human. >>Thanks, John. It's great to, great to be with you and great, great to be present at reinvent. >>You know, we've been following you guys for many, many years now, over a decade. You always have the finger on the pulse. I mean, and as these waves come in, it's really important to understand impact. And more than ever, we're kind of in this, I call it the systems thinking, revolutions going on now where things have consequences and, and machines are now accelerating their role. Developers are becoming the front lines of running companies, seeing a massive shift. This new technology is transforming the business and shaping our future as, as humans. And so I love the book. Very, very strong content, really. Right on point. What was the motivation for the book? And congratulations. But, you know, I noticed you got the, the structure part one and part two, This book seems to be packing a big punch. What's, what was the motivation and, and what was some of the background in, in putting the book together? >>That's a great question, John, and I'll start, and then, you know, Jim, my co-author and, and part colleague and partner on this, on the book and join in too. You know, the, if you step back from the book itself, we'd written a first book called, you know, Human Plus Machine, which talked about the, you know, focused a lot on artificial intelligence and talked about the potential and future of artificial intelligence to create a more human future for us with the Human plus machine pairing. And then, you know, when we started, you know, working on the next book, Covid was, you know, it was kinda the Covid era. Covid came online as, as we were writing the book. And, but that was causing really an interesting time in technology for a lot of companies. I mean, think back to what you were doing, you know, once Covid hit, every company became more dependent on technology. >>Technology was the lifeline. And so Jim and I got interested in what the impacts of that were on companies ba, you know, and what was different from the first, you know, research we had done around our first book. And what we found, which was super interesting, is that, is that, you know, pre pandemic, the, the leading companies, the digital leaders that were applying cloud data, AI, and related technologies faster, we're outperforming others by a factor of two x. And that was before the pandemic. After the pandemic. We redid the research and the gap widen into five x. And I think that's, and, and that's kind of played a lot into our book. And we talk about that in the opening of our book. And the message message there is exactly what you said is technology is not just the lifeline, you know, from the pandemic, but now technology is the heart and soul of how companies are driving innovation, how they're responding to global crises around, you know, inflation energy, supply chain crisis because of the war in Ukraine, et cetera. >>And companies need the technology more than ever. And that's what we're writing about in, in Radically Human. And we're taking a step beyond our previous book to talk about what we believe is next. And it's really cloud data and ai and the metaverse that signal out is three trends that are really driving transformative change for companies. And the first part of the book, to your question on the structure, talks about the roadmap to that. We talked about the ideas framework, five areas where you need to change your thinking, flip your assumptions on how to apply technology. And then the second part of the book talks about the differentiators that we believe are gonna set companies apart as they look to, you know, to implement this technology and transform their companies for the future. >>Jim, weigh in on this. Flipping the script, flipping the assumptions. No, >>You, you, you used a really important word there, and that is systems. I think when we think about artificial intelligence, and when Paul and I have now talking to companies, a lot of executives think of AI as kind of a point solution. They don't think of about AI in terms of taking a systems approach. So we were trying to address that, all right, if you're gonna build a roadmap, a technology roadmap for applying intelligent technologies like artificial intelligence, how do you take a holistic systematic view? And that's really the, the focus of the first section of the book. And then as Paul mentioned, how do you take those systems and really differentiate using your talent, focusing on trust, experiences and sustainability. >>You know, I like this, I like how it reads. It's almost like a masterclass book because you kind of set the table. It's like, cuz people right now are like in the mode of, you know, what's going on around me. I'm been living through three years of covid. But coming out the other side, the world looks radically different. Humans are much more important. Automation's great, but people are finding out that the human's key, but people are trying to figure out where I am, where am I today. So I think the first part really to me hits home, like, here's the current situation and then part two is, here's how you can get better. And it's not just about machines, machines, machines and automation, automation, automation. We're seeing examples where the role of the human, the person in society, whether it's individually or as part of a group, are really now key assets in that kind of this new workforce or this new production system or you know, society. >>Yeah. And just to take a couple examples from the book and highlight that, I think you're exactly right. And that's where, you know, radically human, you know, the title came from. And you know, the, what's happening with technology is that technology itself is becoming more human like in its capability. When you think about the power of the transformer technologies and other things that we're reading about a lot and, and that, you know, the whole hypothesis, you know, or premise of the book I should say, is that the more humanlike the technology is, the more radically human or the more radical the, you know, the, the the, the human potential improvement is the more, the bigger the opportunity. It's pairing the two together rather than, as you said, just looking at the automation or the machine side of it. That's really the radical leap. And one thing Jim and I, you know, talk about, you know, talked about, you know, talked about in context of the book is companies really often haven't been radical enough in applying technology to really get to dramatic gains that they can get. >>Just a couple examples from the ideas framework, the eye and ideas is each of the, the ideas framework is the first part of the book, The five areas to flip your Assumptions, The eye stands for intelligence. And we're talking about more, more human and less artificial in terms of the intelligence techniques, things like common sense learning and other techniques that allow you to develop more powerful ways of engaging people, engaging humans in the systems that we build, using the kind of systems thinking that Jim mentioned. And you know, things like emotional ai, common sense ai, new techniques in addition to machine the big data driven machine learning techniques which are essential to vision and solving big problems like that. So that's, that's just an example of, you know, how you bring it together and enable that human potential. >>I love the, we've been, >>We've >>Go ahead Jim. >>I was gonna say we've been used to adapting to technology, you know, and you know, contorting our fingers to keyboards and and so on for a long time. And now we're starting to see that technology is in fact beginning to adapt to us and become more natural in many instances. One point that we make is now in the human technology nexus. In fact, the human is in the ascended. That's one of the, one of the big ideas that we try to put out there in this book. >>You know, I love the idea of flipping the script, flicking assumptions, but, but ideas framework is interesting. I for intelligence, D for data, E for expertise, A for architecture, s for strategy, notice the strategies last. Normally in the old school days, it's like, hey, strategy first and execution really kind of interesting kind of how you guys put that together. It kind of feels like business is becoming agile and iterative and it's how it's gonna be forming. Can you guys, I mean that's my opinion, but I think, you know, observing how developers becoming much more part of, of the app. I mean, if you take digital transformation to its conclusion, the application is the company, It's not a department serving the business, it is the business, therefore developers are running the business, so to speak. This is really radical. I mean, this is kind of how I'm seeing it. What's your reaction to that? Do you see similar parallels to this transformation? If you take it down to a conclusion, strategy is just what you do after you get the outcomes you need. Is that, can you, what's your reaction to that? >>Yeah, yeah, I think, I think one of the most lasting elements of the book might be that chapter on strategy in, in my opinion, because you need to think about it differently. The old, old way of doing strategy is dead. You can't do it the way you used to do it. And that's what we tried to, you know, to lay out with the, the essence ideas, you know, the strategy and the, the, the fun. You know, the, the subtitle that chapter is is we're all technology companies now. And if you're a technology driven company, the way you need to think about and every company is becoming, That's what I hear when I talk to these suites and CEOs and boards, is everybody's recognizing the essential world that technology plays and therefore they need to, to master technology, well, you need to think about strategy differently than because of the pace of technology innovation. >>And so you need to throw out the old way of doing it. We suggest three new archetypes of how to do strategy that I think are really report it's about continuous strategy in all cases. Yet an example is one of the techniques we talk about forever beta, which is, you know, think about a Tesla, you know, companies that, you know, it's never quite done. They're always improving and the product is designed to be connected and improving. So it changes along, you know, the product and the strategy along how you deploy it to consumers changes as you go. And that's an example of a very different approach to strategy that we, we believe is essential to consider as you look at the future. Yeah, those multi-month strategy sessions, you know, might play out over two or three quarters of going away. And strategy and execution are becoming almost simultaneous these days. As Paul was saying, >>It's interesting because that's the kind of the trend you're seeing with more data, more automation. But the human plays a much critical role. And, and just as a side on the Tesla example, you know, is well documented, I think I wrote about in a post just this week that during the model three Elon wanted full automation and had to actually go off script and get to humans back in charge cuz it wasn't working properly. Now they have a balance. But that brings up the, the part two, which I like, which is, you know, this human piece of it, you know, we always talk about skills gaps, there's not enough people to do this, that and the other thing. And talent was a big part of that, that second half, you know, trust, talent experiences, that's the more the person's role, either individually as part of a collective group is talent. The scarce resource now where that's the, that's the goal, that's the, the key because I mean, it all could point to that in a way, you know, skills gap kind of points to, hey, you know, humans are valuable, in fact the value's going up if it's properly architected. What's your reaction to that, guys? Because I think, you know, that's something that is not kind of nuance point, but it's a feature, not a bug maybe, I don't know. What's your thoughts? >>Yeah, it's, go ahead Jim. I was gonna say it, you know, we're, we're dramatically underestimating the amount of focus we need to put on talent. That's why we start off that second part of the book. You know, really zooming in on talent. I think, you know, you might think that for every, you know, a hundred dollars that you put into a technology initiative, you know, you might put 50 or 75 into reskilling initiatives to really compliment that. But what we're seeing is companies need to be much more revolutionary in their focus on talent. We saw a, a economic analysis recently that pointed out that for every $1 you spend on technology, you are likely gonna need to spend about $9 on intangible human capital. That means, you know, on talent, on, on getting the best talent on reskilling and on changing processes and work tasks. So there's a lot of work that needs to be done. Really that's human focus. It's not just about adopting the technology. Certainly the technology's critical, but we're underestimating the amount of focus that needs to go into the talent factors. >>That's a huge point. >>I think some of the elements of talent that become really critical that we, we talked about in the book are, are becoming a talent creator. We believe that the successful companies of the future are gonna be able not, not just to post, you know, post a job opening and hire, hire people in because there's not gonna be enough. And a lot of the jobs that companies are creating don't exist, you know, cause the technology changing so fast. So companies that succeed are gonna know how to create talent, bring in people, apprentices and such and, and, and, you know, shape to tail as they go. We're doing a significant amount of that in our own company. They're gonna be learning based organizations where you'll differentiate, you'll get the best employees if you provide better learning environments because that's what you know, employees want. And then democratizing access to technology, You know, things like, you know, Amazon's honey code is an example, you know, low code, no code development to spread, you know, development to wider pools of people. Those types of things are really critical, you know, going forward to really unlock the talent potential. And really what you end up with is, yeah, the, the human talent's important, but it's magnified to multiplied by the power of people, you know, giving them in essence superpowers in using technology in new >>Ways. I think you nailed it, That's super important. That point about the force multiplier, when you put things in combination with it's group constructs, two pizza teams, flexing, leveraging the talent. I mean, this is kind of a new configuration. You guys are nailing it there. I love that piece. And I think, you know, groups and collectives, you're gonna start to see a lot more of that. But again, with talent comes trust when you start to have these kind of, you know, ephemeral and or forming groups that are forming production systems or, or, or experiences. So trust comes up a lot. You guys see the metaverse as an important part there. Obviously Metaverse is a pretext to the virtual world where we're gonna start to create these group experiences and create new force multipliers. How does the Metaverse play into this new radically human world and and what does it mean for the future of business? >>Yeah, I think the Metaverse is radically, you know, kind of misunderstood to use the word title, word of a, when we're not with the title of our book, you know, and we believe that the metaverse does have real big potential, massive potential, and I think it'll transform the way we think about digital more so than we've changed our thinking on digital in the last 10 years. So, you know, that that's the potential of the metaverse. And it's about, it's not just about the consumer things, it's about metaverse in the enterprise. It's about the new products you create using distributed ledger and other technologies. And it's about the industrial metaverse of how you bring digital twins and augmented workers online in different ways. And so I, I believe you know that it is, has tremendous potential. We write about that in the book and it really takes radically human to another level. >>And one way to think about this is cloud is really becoming the operating system of business. You, you have to build your enterprise around the cloud as you go forward that's gonna shape the way you do business. AI becomes the insight and intelligence in how you work, you know, in infused with, you know, the human talent and such as we said. And the metaverse then reshapes the experience layers. You have cloud AI building on top of this metaverse providing a new way to, to generate experiences for, for employees, citizens, consumers, et cetera. And that's the way it unfolds. But trust becomes more important because the, just as AI raises new questions around trust, you know, every technology raises new questions around trust. The metaverse raises a whole new set of questions. And in the book we outline a five part framework or or five, you know, essential, you know, parts of the framework around how you establish trust as you implement these new technologies. >>Yeah, we're seeing that, you know, about three quarters of companies are really trying to figure out trust, you know, certainly with issues like the metaverse more broadly across their it, so they're, you know, they're focusing on security and privacy transparency, especially when you're talking about AI systems. Explainability. One of the, you know, the more surprising things that we learned when doing the book, when we're doing the research is that we saw that increasingly consumers and employees want systems to be informed by kind of a sense of humanity. So one company that we've been looking at that's been developing autonomous vehicles, self-driving car systems, the, they're, they're actually training the system by emulating human behavior. So kind of turning the cameras on test drivers to see how they learn and then training the AI kind of using that sense of humanity cuz you know, the other drivers on the road find human behavior more trustworthy. And similarly, that system is also using explainable AI to actually show which human behaviors that that AI system is learning from or some really interesting innovations kind of happening in that trust space. John, >>Jim, I think you bring up a great point that's worth talking more about because you know, you're talking about how human behaviors are being put into the, the design of new things like machines or software. And we're living in this era of cloud scale, which is compressing this transformation timeline and you know, we've been calling it super cloud, some call it multicloud, but it's really a new thing happening where you're seeing an acceleration of the transformation. We think it's gonna happen much faster in the next five to 10 years. And so that means these new things are emerging, not just, hey, I'm running a virtual event with Chad and some video, you know, it's, it's group behavior, it's group con groups, convening, talking, getting things done, you know, debating doing things differently. And so this idea of humans informing design decisions or software with low code no code, this completely changes strategy. I mean this is a big point of the book. >>Yeah, no, I go back to, you know, one of the, the, the, the e and the ideas frameworks is expertise. And we talk about, you know, from machine learning to machine teaching, which, which is exactly that, you know, it's, you know, machine learning is, you know, maybe humans tag data and stuff and feed into algorithms. Machine teaching is how do you really leverage the human expertise in the systems that you develop with ai? One of the examples we give is one of the, the large consumer platforms that uses human designers to give the system a sense of aesthetic design and product design. A very difficult thing, especially with changing fashion interest and everything else to code in algorithms and to even have AI do, even if you have fast amounts of data, but with the right human insight and human expertise injected in, you can create, you know, amazing new capability that responds to consumers in a much more powerful way. And that's an example of what you just said, John, bringing the two together. >>Well you, what's interesting is that I wanna to get your thoughts as we can wrap up here soon. How do you apply all these human-centric technologies to the future of business? As you guys talk to leaders in, in the enterprise of their businesses, as they look at the horizon, they see the, the future, they gotta start thinking about things like generative AI and how they can bring some of these technologies to the table where, you know, we were, we were talking about if open source continues to grow the way it's going, there might not be any code to write, it just writes itself at some point. So you got supply chain issues with security. These are, these are new things you guys are hitting on this in the book where these are new dynamics, new power dynamics in how things get built. So if you're a business owner and leader, this is a new opportunity, a challenge, certainly that is an opportunity. How, how do you apply all this stuff for, for business >>Now? I'll go first then Jim Canad. But the, the first thing I think starts with, with recognizing the role that technology does play and investing accordingly in it. So the right, you know, technology, talent, you know, rethinking the way you do strategy as we talked about earlier and recognizing how you need to build a foundation. That's why, you know, the fact you're at reinvent is so important because companies are, you know, again rebuilding that, that operating system of their business in the cloud. And you need that, you know, as the foundation to go forward, to do, you know, to, to build the other, other types of capabilities. And then I think it's developing those talent systems as well. You know, do you, do you have the right the, do you have the right talent brand? Are you attacking the right, attracting the right employees? Are you developing them in the right way so that you have the right future talent going forward and then you marry the two together and that's what, you know, gives you the radically human formula. >>Yeah. When, you know, when we were developing that first part of the book, Paul and I did quite a bit of, of research, and this was ju and Paul kind of alluded to that research earlier, but one of the things that we saw in really the first year of the pandemic was that there was a lot of first time adoption of intelligent technologies like artificial intelligence. You know, one statistic is that 70% of, there was a, there was a of companies that had never tried AI before, went ahead and tried it during the pandemic. So first time adoption rates were way up, but the thing is companies are not, or we're not trying to do it themselves and to, you know, to necessarily, you know, build an it, a AI department. They were partnering and it's really important to, to find a partner, often a cloud partner as a way to get started, start small scale and then scale up doing experiments. So that was one of the, that was one of the key insights that we had. You don't need to do it all yourself. >>If you see the transformation of just aws, we're here at reinvent just since we've been covering the events since 2013, every year there's been kind of a thematic thing. It was, you know, startups, enterprise now builders and now, now change your company this year it's continuing that same thing where you're starting to see new things happen. It's not just lift and shift and, and running a SaaS application on the cloud. People are are changing and refactoring and replatforming, categorical applications in for this new era. And you know, we're calling it super cloud super services, super apps cuz they're different. They're doing different things in leveraging large scale CapEx, large scale talent pools or talent pools in certain ways. So this is real, something's happening here and you know, we've been talking about a lot lately, so I have to ask you guys, how does a company know if they're radical enough? Like when, what is radical? How do, how can I put a pin in that say that could take a temperature or we like radical enough what some tell signs can you guys share for companies that are really leaning into this new next inflection point because there are new things happening. How do you know if you're, you're you're pushing the envelope radical enough to, to take advantage? >>Yeah, I think one, yeah, I was gonna say one of the, one of the tests is is you know, the impact on your business. You have to start by looking at all this in the context of your business and is it really taking you to another level? You said it perfectly, John, it used to be we used to talk about migration and workloads to the cloud and things like that. Yeah. That that's still something you need to do. But now we, our focus, you know, with a lot of our customers is on how do you innovate and grow your business in the cloud? What's, what is, you know, how, how, what's the platform you know, that you're using to, you know, for your, the new digital products and services you're offering to your consumers. I mean it is the business and I think that's the test. Whether being radical, you know, radical enough is on the one hand, is this really, are you really using the technology to drive differentiation and real growth and change in your business? And are you equipping, you know, people, your human talent with the capabilities they need to perform in very different ways? And those are the the two tests that I would give. Totally agree. >>Yeah. You know, interesting enough, we, you know, we, we love this topic and guys, again, the book is spot on. Very packs a big punch on content, but very relevant in today. And I think, you know, one of the things we're looking at is that people who do things differently take advantage of some of these radical approaches like ideas your framework and understand where they are and what's available and what's coming around the corner. They stand out in the, in the pack or create new business opportunities because the CapEx is taken care of. Now you got your cloud, I mean some, you're building clouds on top of clouds or, or something's happening. You can, I think you see it like look at like companies like Snowflake, it's a data warehouse on the cloud. What does that mean? They didn't build a cloud, they used Amazon. So you're starting to see these new things pop up. >>Yeah, and that's a good example and it sounds like a simple thing, data warehouse in the cloud, but the new business capability that a technology like that allows the portability of being able to connect and use data across cloud environments and such is, is, is is tremendously powerful. And I think that's why, you know, you talk about companies doing things differently, that's why it's great again that you're at reinvents. If you look at the index of our book, you'll see, you'll see AWS mentioned a number of times cuz we tell a lot of cus of cus customer and company stories about how they're leveraging aws, AWS capabilities in cloud and AI to really do transformative things in your, in their business. And I, I think that's what it's, that's what it's all about. >>Yeah, and one of the things too in the book, it's great cuz it has kind of a, the systems thinking it's got really relevant information but you know, you guys have seen the, seen the movie before. I think one of the wild cards in this era is global. You know, we're global economy, you've got regions, you've got data sovereignty, you're seeing, you know, all kinds of new things, emerging thoughts on the global impact cuz you, you take your book and you overlay that to business. Like you gotta, you gotta operate all over the world as a human issue. It's a geography issue. What's your guys take on the global impact? >>Well that's, that's why the, the, you gotta think about cloud as as one technology, you know, we talked about in the book and cloud is a lot, I think a lot of people think, well clouds it's almost old news. Maybe it's been around for a while. As you said, you've been going to reinvent since 2013. You know, cloud is really just getting, you know, just getting started. And, and it's cuz the reasons you said, when you look at what you need to do around sovereign cloud capability, if you're in Europe for many companies it's about multi-cloud capabilities. You need to deploy, you know, differently in different, in different regions. And they need to, in some cases for good reason, they have hybrid, hybrid cloud, you know, capability that they, they match on their own. And then there's the edge capability which is comes into play in, in different ways. >>And, and so the architecture becomes very complex and we talk the A in and ideas is architecture. We talk about all this and how you need to move from the old conception of architecture, which was more static and mod and you know, just modularity was kind of the key thing you thought about. It's more the idea of a living system, of living architecture that's, that's expanding and is what's much more dynamic. And I think that's the way you need to think about it as you manage in a global environment today with the, with the pace of technology advancement. >>You know, the innovation is here. It's not stopping. How do you create some defacto standards while not stunting the innovation is gonna be a big discussion as these new flipped assumptions start to generate more activity. It's gonna be very interesting to watch. Gentlemen, thank you so much for spending the time here on the queue as we break down your new book, Radically Human and how it, how business leads can flip the script on their business assumptions and put ideas and access to work. This is a big part of the cloud show at reinvent. Thanks so much for, for sharing and congratulations on a great book. >>You know, Thanks John. And just one point I'd add is that one of the, the things we do talk about in talent is the need to reskill talent. You know, people who need to, you know, be, be relevant to the rapidly changing future. And that's one area where I think we all as institutions, as communities and individuals need to do more is to help those who need to reskilling. And the final point I mentioned is that we mentioned at the end of the book that all proceeds for the book are being donated to not NGOs and nonprofits that are focused on reskilling. Those who need a skill refresh in light of the radically human new, you know, change in technology that's happening >>Great by the book proceeds go to a great cause and it's a very relevant book if you're in the middle of this big way that's coming. This is a great book. There's a guidepost and also give you some great ideas to, to reset re flip the scripts. Refactor, re-platform. Guys, thanks for coming on and sharing, really appreciate it. Again, congratulations. >>Thanks, John. John, great discussion. >>Okay, you're watching the Cube here, covering the executive forum here at AWS Reinvent 22. I'm John Furrier, your host with aen. Thanks for watching.

>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube at ETR. This is "Breaking Analysis" with Dave Vellante. >> The reverse momentum in tech stocks caused by rising interest rates, less attractive discounted cash flow models, and more tepid forward guidance, can be easily measured by public market valuations. And while there's lots of discussion about the impact on private companies and cash runway and 409A valuations, measuring the performance of non-public companies isn't as easy. IPOs have dried up and public statements by private companies, of course, they accentuate the good and they kind of hide the bad. Real data, unless you're an insider, is hard to find. Hello and welcome to this week's "Wikibon Cube Insights" powered by ETR. In this "Breaking Analysis", we unlock some of the secrets that non-public, emerging tech companies may or may not be sharing. And we do this by introducing you to a capability from ETR that we've not exposed you to over the past couple of years, it's called the Emerging Technologies Survey, and it is packed with sentiment data and performance data based on surveys of more than a thousand CIOs and IT buyers covering more than 400 companies. And we've invited back our colleague, Erik Bradley of ETR to help explain the survey and the data that we're going to cover today. Erik, this survey is something that I've not personally spent much time on, but I'm blown away at the data. It's really unique and detailed. First of all, welcome. Good to see you again. >> Great to see you too, Dave, and I'm really happy to be talking about the ETS or the Emerging Technology Survey. Even our own clients of constituents probably don't spend as much time in here as they should. >> Yeah, because there's so much in the mainstream, but let's pull up a slide to bring out the survey composition. Tell us about the study. How often do you run it? What's the background and the methodology? >> Yeah, you were just spot on the way you were talking about the private tech companies out there. So what we did is we decided to take all the vendors that we track that are not yet public and move 'em over to the ETS. And there isn't a lot of information out there. If you're not in Silicon (indistinct), you're not going to get this stuff. So PitchBook and Tech Crunch are two out there that gives some data on these guys. But what we really wanted to do was go out to our community. We have 6,000, ITDMs in our community. We wanted to ask them, "Are you aware of these companies? And if so, are you allocating any resources to them? Are you planning to evaluate them," and really just kind of figure out what we can do. So this particular survey, as you can see, 1000 plus responses, over 450 vendors that we track. And essentially what we're trying to do here is talk about your evaluation and awareness of these companies and also your utilization. And also if you're not utilizing 'em, then we can also figure out your sales conversion or churn. So this is interesting, not only for the ITDMs themselves to figure out what their peers are evaluating and what they should put in POCs against the big guys when contracts come up. But it's also really interesting for the tech vendors themselves to see how they're performing. >> And you can see 2/3 of the respondents are director level of above. You got 28% is C-suite. There is of course a North America bias, 70, 75% is North America. But these smaller companies, you know, that's when they start doing business. So, okay. We're going to do a couple of things here today. First, we're going to give you the big picture across the sectors that ETR covers within the ETS survey. And then we're going to look at the high and low sentiment for the larger private companies. And then we're going to do the same for the smaller private companies, the ones that don't have as much mindshare. And then I'm going to put those two groups together and we're going to look at two dimensions, actually three dimensions, which companies are being evaluated the most. Second, companies are getting the most usage and adoption of their offerings. And then third, which companies are seeing the highest churn rates, which of course is a silent killer of companies. And then finally, we're going to look at the sentiment and mindshare for two key areas that we like to cover often here on "Breaking Analysis", security and data. And data comprises database, including data warehousing, and then big data analytics is the second part of data. And then machine learning and AI is the third section within data that we're going to look at. Now, one other thing before we get into it, ETR very often will include open source offerings in the mix, even though they're not companies like TensorFlow or Kubernetes, for example. And we'll call that out during this discussion. The reason this is done is for context, because everyone is using open source. It is the heart of innovation and many business models are super glued to an open source offering, like take MariaDB, for example. There's the foundation and then there's with the open source code and then there, of course, the company that sells services around the offering. Okay, so let's first look at the highest and lowest sentiment among these private firms, the ones that have the highest mindshare. So they're naturally going to be somewhat larger. And we do this on two dimensions, sentiment on the vertical axis and mindshare on the horizontal axis and note the open source tool, see Kubernetes, Postgres, Kafka, TensorFlow, Jenkins, Grafana, et cetera. So Erik, please explain what we're looking at here, how it's derived and what the data tells us. >> Certainly, so there is a lot here, so we're going to break it down first of all by explaining just what mindshare and net sentiment is. You explain the axis. We have so many evaluation metrics, but we need to aggregate them into one so that way we can rank against each other. Net sentiment is really the aggregation of all the positive and subtracting out the negative. So the net sentiment is a very quick way of looking at where these companies stand versus their peers in their sectors and sub sectors. Mindshare is basically the awareness of them, which is good for very early stage companies. And you'll see some names on here that are obviously been around for a very long time. And they're clearly be the bigger on the axis on the outside. Kubernetes, for instance, as you mentioned, is open source. This de facto standard for all container orchestration, and it should be that far up into the right, because that's what everyone's using. In fact, the open source leaders are so prevalent in the emerging technology survey that we break them out later in our analysis, 'cause it's really not fair to include them and compare them to the actual companies that are providing the support and the security around that open source technology. But no survey, no analysis, no research would be complete without including these open source tech. So what we're looking at here, if I can just get away from the open source names, we see other things like Databricks and OneTrust . They're repeating as top net sentiment performers here. And then also the design vendors. People don't spend a lot of time on 'em, but Miro and Figma. This is their third survey in a row where they're just dominating that sentiment overall. And Adobe should probably take note of that because they're really coming after them. But Databricks, we all know probably would've been a public company by now if the market hadn't turned, but you can see just how dominant they are in a survey of nothing but private companies. And we'll see that again when we talk about the database later. >> And I'll just add, so you see automation anywhere on there, the big UiPath competitor company that was not able to get to the public markets. They've been trying. Snyk, Peter McKay's company, they've raised a bunch of money, big security player. They're doing some really interesting things in developer security, helping developers secure the data flow, H2O.ai, Dataiku AI company. We saw them at the Snowflake Summit. Redis Labs, Netskope and security. So a lot of names that we know that ultimately we think are probably going to be hitting the public market. Okay, here's the same view for private companies with less mindshare, Erik. Take us through this one. >> On the previous slide too real quickly, I wanted to pull that security scorecard and we'll get back into it. But this is a newcomer, that I couldn't believe how strong their data was, but we'll bring that up in a second. Now, when we go to the ones of lower mindshare, it's interesting to talk about open source, right? Kubernetes was all the way on the top right. Everyone uses containers. Here we see Istio up there. Not everyone is using service mesh as much. And that's why Istio is in the smaller breakout. But still when you talk about net sentiment, it's about the leader, it's the highest one there is. So really interesting to point out. Then we see other names like Collibra in the data side really performing well. And again, as always security, very well represented here. We have Aqua, Wiz, Armis, which is a standout in this survey this time around. They do IoT security. I hadn't even heard of them until I started digging into the data here. And I couldn't believe how well they were doing. And then of course you have AnyScale, which is doing a second best in this and the best name in the survey Hugging Face, which is a machine learning AI tool. Also doing really well on a net sentiment, but they're not as far along on that access of mindshare just yet. So these are again, emerging companies that might not be as well represented in the enterprise as they will be in a couple of years. >> Hugging Face sounds like something you do with your two year old. Like you said, you see high performers, AnyScale do machine learning and you mentioned them. They came out of Berkeley. Collibra Governance, InfluxData is on there. InfluxDB's a time series database. And yeah, of course, Alex, if you bring that back up, you get a big group of red dots, right? That's the bad zone, I guess, which Sisense does vis, Yellowbrick Data is a NPP database. How should we interpret the red dots, Erik? I mean, is it necessarily a bad thing? Could it be misinterpreted? What's your take on that? >> Sure, well, let me just explain the definition of it first from a data science perspective, right? We're a data company first. So the gray dots that you're seeing that aren't named, that's the mean that's the average. So in order for you to be on this chart, you have to be at least one standard deviation above or below that average. So that gray is where we're saying, "Hey, this is where the lump of average comes in. This is where everyone normally stands." So you either have to be an outperformer or an underperformer to even show up in this analysis. So by definition, yes, the red dots are bad. You're at least one standard deviation below the average of your peers. It's not where you want to be. And if you're on the lower left, not only are you not performing well from a utilization or an actual usage rate, but people don't even know who you are. So that's a problem, obviously. And the VCs and the PEs out there that are backing these companies, they're the ones who mostly are interested in this data. >> Yeah. Oh, that's great explanation. Thank you for that. No, nice benchmarking there and yeah, you don't want to be in the red. All right, let's get into the next segment here. Here going to look at evaluation rates, adoption and the all important churn. First new evaluations. Let's bring up that slide. And Erik, take us through this. >> So essentially I just want to explain what evaluation means is that people will cite that they either plan to evaluate the company or they're currently evaluating. So that means we're aware of 'em and we are choosing to do a POC of them. And then we'll see later how that turns into utilization, which is what a company wants to see, awareness, evaluation, and then actually utilizing them. That's sort of the life cycle for these emerging companies. So what we're seeing here, again, with very high evaluation rates. H2O, we mentioned. SecurityScorecard jumped up again. Chargebee, Snyk, Salt Security, Armis. A lot of security names are up here, Aqua, Netskope, which God has been around forever. I still can't believe it's in an Emerging Technology Survey But so many of these names fall in data and security again, which is why we decided to pick those out Dave. And on the lower side, Vena, Acton, those unfortunately took the dubious award of the lowest evaluations in our survey, but I prefer to focus on the positive. So SecurityScorecard, again, real standout in this one, they're in a security assessment space, basically. They'll come in and assess for you how your security hygiene is. And it's an area of a real interest right now amongst our ITDM community. >> Yeah, I mean, I think those, and then Arctic Wolf is up there too. They're doing managed services. You had mentioned Netskope. Yeah, okay. All right, let's look at now adoption. These are the companies whose offerings are being used the most and are above that standard deviation in the green. Take us through this, Erik. >> Sure, yet again, what we're looking at is, okay, we went from awareness, we went to evaluation. Now it's about utilization, which means a survey respondent's going to state "Yes, we evaluated and we plan to utilize it" or "It's already in our enterprise and we're actually allocating further resources to it." Not surprising, again, a lot of open source, the reason why, it's free. So it's really easy to grow your utilization on something that's free. But as you and I both know, as Red Hat proved, there's a lot of money to be made once the open source is adopted, right? You need the governance, you need the security, you need the support wrapped around it. So here we're seeing Kubernetes, Postgres, Apache Kafka, Jenkins, Grafana. These are all open source based names. But if we're looking at names that are non open source, we're going to see Databricks, Automation Anywhere, Rubrik all have the highest mindshare. So these are the names, not surprisingly, all names that probably should have been public by now. Everyone's expecting an IPO imminently. These are the names that have the highest mindshare. If we talk about the highest utilization rates, again, Miro and Figma pop up, and I know they're not household names, but they are just dominant in this survey. These are applications that are meant for design software and, again, they're going after an Autodesk or a CAD or Adobe type of thing. It is just dominant how high the utilization rates are here, which again is something Adobe should be paying attention to. And then you'll see a little bit lower, but also interesting, we see Collibra again, we see Hugging Face again. And these are names that are obviously in the data governance, ML, AI side. So we're seeing a ton of data, a ton of security and Rubrik was interesting in this one, too, high utilization and high mindshare. We know how pervasive they are in the enterprise already. >> Erik, Alex, keep that up for a second, if you would. So yeah, you mentioned Rubrik. Cohesity's not on there. They're sort of the big one. We're going to talk about them in a moment. Puppet is interesting to me because you remember the early days of that sort of space, you had Puppet and Chef and then you had Ansible. Red Hat bought Ansible and then Ansible really took off. So it's interesting to see Puppet on there as well. Okay. So now let's look at the churn because this one is where you don't want to be. It's, of course, all red 'cause churn is bad. Take us through this, Erik. >> Yeah, definitely don't want to be here and I don't love to dwell on the negative. So we won't spend as much time. But to your point, there's one thing I want to point out that think it's important. So you see Rubrik in the same spot, but Rubrik has so many citations in our survey that it actually would make sense that they're both being high utilization and churn just because they're so well represented. They have such a high overall representation in our survey. And the reason I call that out is Cohesity. Cohesity has an extremely high churn rate here about 17% and unlike Rubrik, they were not on the utilization side. So Rubrik is seeing both, Cohesity is not. It's not being utilized, but it's seeing a high churn. So that's the way you can look at this data and say, "Hm." Same thing with Puppet. You noticed that it was on the other slide. It's also on this one. So basically what it means is a lot of people are giving Puppet a shot, but it's starting to churn, which means it's not as sticky as we would like. One that was surprising on here for me was Tanium. It's kind of jumbled in there. It's hard to see in the middle, but Tanium, I was very surprised to see as high of a churn because what I do hear from our end user community is that people that use it, like it. It really kind of spreads into not only vulnerability management, but also that endpoint detection and response side. So I was surprised by that one, mostly to see Tanium in here. Mural, again, was another one of those application design softwares that's seeing a very high churn as well. >> So you're saying if you're in both... Alex, bring that back up if you would. So if you're in both like MariaDB is for example, I think, yeah, they're in both. They're both green in the previous one and red here, that's not as bad. You mentioned Rubrik is going to be in both. Cohesity is a bit of a concern. Cohesity just brought on Sanjay Poonen. So this could be a go to market issue, right? I mean, 'cause Cohesity has got a great product and they got really happy customers. So they're just maybe having to figure out, okay, what's the right ideal customer profile and Sanjay Poonen, I guarantee, is going to have that company cranking. I mean they had been doing very well on the surveys and had fallen off of a bit. The other interesting things wondering the previous survey I saw Cvent, which is an event platform. My only reason I pay attention to that is 'cause we actually have an event platform. We don't sell it separately. We bundle it as part of our offerings. And you see Hopin on here. Hopin raised a billion dollars during the pandemic. And we were like, "Wow, that's going to blow up." And so you see Hopin on the churn and you didn't see 'em in the previous chart, but that's sort of interesting. Like you said, let's not kind of dwell on the negative, but you really don't. You know, churn is a real big concern. Okay, now we're going to drill down into two sectors, security and data. Where data comprises three areas, database and data warehousing, machine learning and AI and big data analytics. So first let's take a look at the security sector. Now this is interesting because not only is it a sector drill down, but also gives an indicator of how much money the firm has raised, which is the size of that bubble. And to tell us if a company is punching above its weight and efficiently using its venture capital. Erik, take us through this slide. Explain the dots, the size of the dots. Set this up please. >> Yeah. So again, the axis is still the same, net sentiment and mindshare, but what we've done this time is we've taken publicly available information on how much capital company is raised and that'll be the size of the circle you see around the name. And then whether it's green or red is basically saying relative to the amount of money they've raised, how are they doing in our data? So when you see a Netskope, which has been around forever, raised a lot of money, that's why you're going to see them more leading towards red, 'cause it's just been around forever and kind of would expect it. Versus a name like SecurityScorecard, which is only raised a little bit of money and it's actually performing just as well, if not better than a name, like a Netskope. OneTrust doing absolutely incredible right now. BeyondTrust. We've seen the issues with Okta, right. So those are two names that play in that space that obviously are probably getting some looks about what's going on right now. Wiz, we've all heard about right? So raised a ton of money. It's doing well on net sentiment, but the mindshare isn't as well as you'd want, which is why you're going to see a little bit of that red versus a name like Aqua, which is doing container and application security. And hasn't raised as much money, but is really neck and neck with a name like Wiz. So that is why on a relative basis, you'll see that more green. As we all know, information security is never going away. But as we'll get to later in the program, Dave, I'm not sure in this current market environment, if people are as willing to do POCs and switch away from their security provider, right. There's a little bit of tepidness out there, a little trepidation. So right now we're seeing overall a slight pause, a slight cooling in overall evaluations on the security side versus historical levels a year ago. >> Now let's stay on here for a second. So a couple things I want to point out. So it's interesting. Now Snyk has raised over, I think $800 million but you can see them, they're high on the vertical and the horizontal, but now compare that to Lacework. It's hard to see, but they're kind of buried in the middle there. That's the biggest dot in this whole thing. I think I'm interpreting this correctly. They've raised over a billion dollars. It's a Mike Speiser company. He was the founding investor in Snowflake. So people watch that very closely, but that's an example of where they're not punching above their weight. They recently had a layoff and they got to fine tune things, but I'm still confident they they're going to do well. 'Cause they're approaching security as a data problem, which is probably people having trouble getting their arms around that. And then again, I see Arctic Wolf. They're not red, they're not green, but they've raised fair amount of money, but it's showing up to the right and decent level there. And a couple of the other ones that you mentioned, Netskope. Yeah, they've raised a lot of money, but they're actually performing where you want. What you don't want is where Lacework is, right. They've got some work to do to really take advantage of the money that they raised last November and prior to that. >> Yeah, if you're seeing that more neutral color, like you're calling out with an Arctic Wolf, like that means relative to their peers, this is where they should be. It's when you're seeing that red on a Lacework where we all know, wow, you raised a ton of money and your mindshare isn't where it should be. Your net sentiment is not where it should be comparatively. And then you see these great standouts, like Salt Security and SecurityScorecard and Abnormal. You know they haven't raised that much money yet, but their net sentiment's higher and their mindshare's doing well. So those basically in a nutshell, if you're a PE or a VC and you see a small green circle, then you're doing well, then it means you made a good investment. >> Some of these guys, I don't know, but you see these small green circles. Those are the ones you want to start digging into and maybe help them catch a wave. Okay, let's get into the data discussion. And again, three areas, database slash data warehousing, big data analytics and ML AI. First, we're going to look at the database sector. So Alex, thank you for bringing that up. Alright, take us through this, Erik. Actually, let me just say Postgres SQL. I got to ask you about this. It shows some funding, but that actually could be a mix of EDB, the company that commercializes Postgres and Postgres the open source database, which is a transaction system and kind of an open source Oracle. You see MariaDB is a database, but open source database. But the companies they've raised over $200 million and they filed an S-4. So Erik looks like this might be a little bit of mashup of companies and open source products. Help us understand this. >> Yeah, it's tough when you start dealing with the open source side and I'll be honest with you, there is a little bit of a mashup here. There are certain names here that are a hundred percent for profit companies. And then there are others that are obviously open source based like Redis is open source, but Redis Labs is the one trying to monetize the support around it. So you're a hundred percent accurate on this slide. I think one of the things here that's important to note though, is just how important open source is to data. If you're going to be going to any of these areas, it's going to be open source based to begin with. And Neo4j is one I want to call out here. It's not one everyone's familiar with, but it's basically geographical charting database, which is a name that we're seeing on a net sentiment side actually really, really high. When you think about it's the third overall net sentiment for a niche database play. It's not as big on the mindshare 'cause it's use cases aren't as often, but third biggest play on net sentiment. I found really interesting on this slide. >> And again, so MariaDB, as I said, they filed an S-4 I think $50 million in revenue, that might even be ARR. So they're not huge, but they're getting there. And by the way, MariaDB, if you don't know, was the company that was formed the day that Oracle bought Sun in which they got MySQL and MariaDB has done a really good job of replacing a lot of MySQL instances. Oracle has responded with MySQL HeatWave, which was kind of the Oracle version of MySQL. So there's some interesting battles going on there. If you think about the LAMP stack, the M in the LAMP stack was MySQL. And so now it's all MariaDB replacing that MySQL for a large part. And then you see again, the red, you know, you got to have some concerns about there. Aerospike's been around for a long time. SingleStore changed their name a couple years ago, last year. Yellowbrick Data, Fire Bolt was kind of going after Snowflake for a while, but yeah, you want to get out of that red zone. So they got some work to do. >> And Dave, real quick for the people that aren't aware, I just want to let them know that we can cut this data with the public company data as well. So we can cross over this with that because some of these names are competing with the larger public company names as well. So we can go ahead and cross reference like a MariaDB with a Mongo, for instance, or of something of that nature. So it's not in this slide, but at another point we can certainly explain on a relative basis how these private names are doing compared to the other ones as well. >> All right, let's take a quick look at analytics. Alex, bring that up if you would. Go ahead, Erik. >> Yeah, I mean, essentially here, I can't see it on my screen, my apologies. I just kind of went to blank on that. So gimme one second to catch up. >> So I could set it up while you're doing that. You got Grafana up and to the right. I mean, this is huge right. >> Got it thank you. I lost my screen there for a second. Yep. Again, open source name Grafana, absolutely up and to the right. But as we know, Grafana Labs is actually picking up a lot of speed based on Grafana, of course. And I think we might actually hear some noise from them coming this year. The names that are actually a little bit more disappointing than I want to call out are names like ThoughtSpot. It's been around forever. Their mindshare of course is second best here but based on the amount of time they've been around and the amount of money they've raised, it's not actually outperforming the way it should be. We're seeing Moogsoft obviously make some waves. That's very high net sentiment for that company. It's, you know, what, third, fourth position overall in this entire area, Another name like Fivetran, Matillion is doing well. Fivetran, even though it's got a high net sentiment, again, it's raised so much money that we would've expected a little bit more at this point. I know you know this space extremely well, but basically what we're looking at here and to the bottom left, you're going to see some names with a lot of red, large circles that really just aren't performing that well. InfluxData, however, second highest net sentiment. And it's really pretty early on in this stage and the feedback we're getting on this name is the use cases are great, the efficacy's great. And I think it's one to watch out for. >> InfluxData, time series database. The other interesting things I just noticed here, you got Tamer on here, which is that little small green. Those are the ones we were saying before, look for those guys. They might be some of the interesting companies out there and then observe Jeremy Burton's company. They do observability on top of Snowflake, not green, but kind of in that gray. So that's kind of cool. Monte Carlo is another one, they're sort of slightly green. They are doing some really interesting things in data and data mesh. So yeah, okay. So I can spend all day on this stuff, Erik, phenomenal data. I got to get back and really dig in. Let's end with machine learning and AI. Now this chart it's similar in its dimensions, of course, except for the money raised. We're not showing that size of the bubble, but AI is so hot. We wanted to cover that here, Erik, explain this please. Why TensorFlow is highlighted and walk us through this chart. >> Yeah, it's funny yet again, right? Another open source name, TensorFlow being up there. And I just want to explain, we do break out machine learning, AI is its own sector. A lot of this of course really is intertwined with the data side, but it is on its own area. And one of the things I think that's most important here to break out is Databricks. We started to cover Databricks in machine learning, AI. That company has grown into much, much more than that. So I do want to state to you Dave, and also the audience out there that moving forward, we're going to be moving Databricks out of only the MA/AI into other sectors. So we can kind of value them against their peers a little bit better. But in this instance, you could just see how dominant they are in this area. And one thing that's not here, but I do want to point out is that we have the ability to break this down by industry vertical, organization size. And when I break this down into Fortune 500 and Fortune 1000, both Databricks and Tensorflow are even better than you see here. So it's quite interesting to see that the names that are succeeding are also succeeding with the largest organizations in the world. And as we know, large organizations means large budgets. So this is one area that I just thought was really interesting to point out that as we break it down, the data by vertical, these two names still are the outstanding players. >> I just also want to call it H2O.ai. They're getting a lot of buzz in the marketplace and I'm seeing them a lot more. Anaconda, another one. Dataiku consistently popping up. DataRobot is also interesting because all the kerfuffle that's going on there. The Cube guy, Cube alum, Chris Lynch stepped down as executive chairman. All this stuff came out about how the executives were taking money off the table and didn't allow the employees to participate in that money raising deal. So that's pissed a lot of people off. And so they're now going through some kind of uncomfortable things, which is unfortunate because DataRobot, I noticed, we haven't covered them that much in "Breaking Analysis", but I've noticed them oftentimes, Erik, in the surveys doing really well. So you would think that company has a lot of potential. But yeah, it's an important space that we're going to continue to watch. Let me ask you Erik, can you contextualize this from a time series standpoint? I mean, how is this changed over time? >> Yeah, again, not show here, but in the data. I'm sorry, go ahead. >> No, I'm sorry. What I meant, I should have interjected. In other words, you would think in a downturn that these emerging companies would be less interesting to buyers 'cause they're more risky. What have you seen? >> Yeah, and it was interesting before we went live, you and I were having this conversation about "Is the downturn stopping people from evaluating these private companies or not," right. In a larger sense, that's really what we're doing here. How are these private companies doing when it comes down to the actual practitioners? The people with the budget, the people with the decision making. And so what I did is, we have historical data as you know, I went back to the Emerging Technology Survey we did in November of 21, right at the crest right before the market started to really fall and everything kind of started to fall apart there. And what I noticed is on the security side, very much so, we're seeing less evaluations than we were in November 21. So I broke it down. On cloud security, net sentiment went from 21% to 16% from November '21. That's a pretty big drop. And again, that sentiment is our one aggregate metric for overall positivity, meaning utilization and actual evaluation of the name. Again in database, we saw it drop a little bit from 19% to 13%. However, in analytics we actually saw it stay steady. So it's pretty interesting that yes, cloud security and security in general is always going to be important. But right now we're seeing less overall net sentiment in that space. But within analytics, we're seeing steady with growing mindshare. And also to your point earlier in machine learning, AI, we're seeing steady net sentiment and mindshare has grown a whopping 25% to 30%. So despite the downturn, we're seeing more awareness of these companies in analytics and machine learning and a steady, actual utilization of them. I can't say the same in security and database. They're actually shrinking a little bit since the end of last year. >> You know it's interesting, we were on a round table, Erik does these round tables with CISOs and CIOs, and I remember one time you had asked the question, "How do you think about some of these emerging tech companies?" And one of the executives said, "I always include somebody in the bottom left of the Gartner Magic Quadrant in my RFPs. I think he said, "That's how I found," I don't know, it was Zscaler or something like that years before anybody ever knew of them "Because they're going to help me get to the next level." So it's interesting to see Erik in these sectors, how they're holding up in many cases. >> Yeah. It's a very important part for the actual IT practitioners themselves. There's always contracts coming up and you always have to worry about your next round of negotiations. And that's one of the roles these guys play. You have to do a POC when contracts come up, but it's also their job to stay on top of the new technology. You can't fall behind. Like everyone's a software company. Now everyone's a tech company, no matter what you're doing. So these guys have to stay in on top of it. And that's what this ETS can do. You can go in here and look and say, "All right, I'm going to evaluate their technology," and it could be twofold. It might be that you're ready to upgrade your technology and they're actually pushing the envelope or it simply might be I'm using them as a negotiation ploy. So when I go back to the big guy who I have full intentions of writing that contract to, at least I have some negotiation leverage. >> Erik, we got to leave it there. I could spend all day. I'm going to definitely dig into this on my own time. Thank you for introducing this, really appreciate your time today. >> I always enjoy it, Dave and I hope everyone out there has a great holiday weekend. Enjoy the rest of the summer. And, you know, I love to talk data. So anytime you want, just point the camera on me and I'll start talking data. >> You got it. I also want to thank the team at ETR, not only Erik, but Darren Bramen who's a data scientist, really helped prepare this data, the entire team over at ETR. I cannot tell you how much additional data there is. We are just scratching the surface in this "Breaking Analysis". So great job guys. I want to thank Alex Myerson. Who's on production and he manages the podcast. Ken Shifman as well, who's just coming back from VMware Explore. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE. Does some great editing for us. Thank you. All of you guys. Remember these episodes, they're all available as podcast, wherever you listen. All you got to do is just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch david.vellante@siliconangle.com. You can DM me at dvellante or comment on my LinkedIn posts and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for Erik Bradley and The Cube Insights powered by ETR. Thanks for watching. Be well. And we'll see you next time on "Breaking Analysis". (upbeat music)

>> From the theCUBE studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Welcome to this week's Wikibon, theCUBE's insights powered by ETR. As we exited the isolation economy last year, supercloud is a term that we introduced to describe something new that was happening in the world of cloud. In this Breaking Analysis, we address the 10 most frequently asked questions we get around supercloud. Okay, let's review these frequently asked questions on supercloud that we're going to try to answer today. Look at an industry that's full of hype and buzzwords. Why the hell does anyone need a new term? Aren't hyperscalers building out superclouds? We'll try to answer why the term supercloud connotes something different from hyperscale clouds. And we'll talk about the problems that superclouds solve specifically. And we'll further define the critical aspects of a supercloud architecture. We often get asked, isn't this just multi-cloud? Well, we don't think so, and we'll explain why in this Breaking Analysis. Now in an earlier episode, we introduced the notion of super PaaS. Well, isn't a plain vanilla PaaS already a super PaaS? Again, we don't think so, and we'll explain why. Who will actually build and who are the players currently building superclouds? What workloads and services will run on superclouds? And 8-A or number nine, what are some examples that we can share of supercloud? And finally, we'll answer what you can expect next from us on supercloud? Okay, let's get started. Why do we need another buzzword? Well, late last year, ahead of re:Invent, we were inspired by a post from Jerry Chen called "Castles in the Cloud." Now in that blog post, he introduced the idea that there were sub-markets emerging in cloud that presented opportunities for investors and entrepreneurs that the cloud wasn't going to suck the hyperscalers. Weren't going to suck all the value out of the industry. And so we introduced this notion of supercloud to describe what we saw as a value layer emerging above the hyperscalers CAPEX gift, we sometimes call it. Now it turns out, that we weren't the only ones using the term as both Cornell and MIT have used the phrase in somewhat similar, but different contexts. The point is something new was happening in the AWS and other ecosystems. It was more than IaaS and PaaS, and wasn't just SaaS running in the cloud. It was a new architecture that integrates infrastructure, platform and software as services to solve new problems that the cloud vendors in our view, weren't addressing by themselves. It seemed to us that the ecosystem was pursuing opportunities across clouds that went beyond conventional implementations of multi-cloud. And we felt there was a structural change going on at the industry level, the supercloud, metaphorically was highlighting. So that's the background on why we felt a new catch phrase was warranted, love it or hate it. It's memorable and it's what we chose. Now to that last point about structural industry transformation. Andy Rappaport is sometimes and often credited with identifying the shift from the vertically integrated IBM mainframe era to the fragmented PC microprocesor-based era in his HBR article in 1991. In fact, it was David Moschella, who at the time was an IDC Analyst who first introduced the concept in 1987, four years before Rappaport's article was published. Moschella saw that it was clear that Intel, Microsoft, Seagate and others would replace the system vendors, and put that forth in a graphic that looked similar to the first two on this chart. We don't have to review the shift from IBM as the center of the industry to Wintel, that's well understood. What isn't as well known or accepted is what Moschella put out in his 2018 book called "Seeing Digital" which introduced the idea of "The Matrix" that's shown on the right hand side of this chart. Moschella posited that new services were emerging built on top of the internet and hyperscale clouds that would integrate other innovations and would define the next era of computing. He used the term Matrix because the conceptual depiction included not only horizontal technology rose like the cloud and the internet, but for the first time included connected industry verticals, the columns in this chart. Moschella pointed out that whereas historically, industry verticals had a closed value chain or stack and ecosystem of R&D, and production, and manufacturing, and distribution. And if you were in that industry, the expertise within that vertical generally stayed within that vertical and was critical to success. But because of digital and data, for the first time, companies were able to traverse industries, jump across industries and compete because data enabled them to do that. Examples, Amazon and content, payments, groceries, Apple, and payments, and content, and so forth. There are many examples. Data was now this unifying enabler and this marked a change in the structure of the technology landscape. And supercloud is meant to imply more than running in hyperscale clouds, rather it's the combination of multiple technologies enabled by CloudScale with new industry participants from those verticals, financial services and healthcare, manufacturing, energy, media, and virtually all in any industry. Kind of an extension of every company is a software company. Basically, every company now has the opportunity to build their own cloud or supercloud. And we'll come back to that. Let's first address what's different about superclouds relative to hyperscale clouds? You know, this one's pretty straightforward and obvious, I think. Hyperscale clouds, they're walled gardens where they want your data in their cloud and they want to keep you there. Sure, every cloud player realizes that not all data will go to their particular cloud so they're meeting customers where their data lives with initiatives like Amazon Outposts and Azure Arc, and Google Anthos. But at the end of the day, the more homogeneous they can make their environments, the better control, security, cost, and performance they can deliver. The more complex the environment, the more difficult it is to deliver on their brand promises. And of course, the lesser margin that's left for them to capture. Will the hyperscalers get more serious about cross-cloud services? Maybe, but they have plenty of work to do within their own clouds and within enabling their own ecosystems. They had a long way to go a lot of runway. So let's talk about specifically, what problems superclouds solve? We've all seen the stats from IDC or Gartner, or whomever the customers on average use more than one cloud. You know, two clouds, three clouds, five clouds, 20 clouds. And we know these clouds operate in disconnected silos for the most part. And that's a problem because each cloud requires different skills because the development environment is different as is the operating environment. They have different APIs, different primitives, and different management tools that are optimized for each respective hyperscale cloud. Their functions and value props don't extend to their competitors' clouds for the most part. Why would they? As a result, there's friction when moving between different clouds. It's hard to share data, it's hard to move work. It's hard to secure and govern data. It's hard to enforce organizational edicts and policies across these clouds, and on-prem. Supercloud is an architecture designed to create a single environment that enables management of workloads and data across clouds in an effort to take out complexity, accelerate application development, streamline operations and share data safely, irrespective of location. It's pretty straightforward, but non-trivial, which is why I always ask a company's CEO and executives if stock buybacks and dividends will yield as much return as building out superclouds that solve really specific and hard problems, and create differential value. Okay, let's dig a bit more into the architectural aspects of supercloud. In other words, what are the salient attributes of supercloud? So first and foremost, a supercloud runs a set of specific services designed to solve a unique problem and it can do so in more than one cloud. Superclouds leverage the underlying cloud native tooling of a hyperscale cloud, but they're optimized for a specific objective that aligns with the problem that they're trying to solve. For example, supercloud might be optimized for lowest cost or lowest latency, or sharing data, or governing, or securing that data, or higher performance for networking, for example. But the point is, the collection of services that is being delivered is focused on a unique value proposition that is not being delivered by the hyperscalers across clouds. A supercloud abstracts the underlying and siloed primitives of the native PaaS layer from the hyperscale cloud and then using its own specific platform as a service tooling, creates a common experience across clouds for developers and users. And it does so in a most efficient manner, meaning it has the metadata knowledge and management capabilities that can optimize for latency, bandwidth, or recovery, or data sovereignty, or whatever unique value that supercloud is delivering for the specific use case in their domain. And a supercloud comprises a super PaaS capability that allows ecosystem partners through APIs to add incremental value on top of the supercloud platform to fill gaps, accelerate features, and of course innovate. The services can be infrastructure-related, they could be application services, they could be data services, security services, user services, et cetera, designed and packaged to bring unique value to customers. Again, that hyperscalers are not delivering across clouds or on-premises. Okay, so another common question we get is, isn't that just multi-cloud? And what we'd say to that is yes, but no. You can call it multi-cloud 2.0, if you want, if you want to use it, it's kind of a commonly used rubric. But as Dell's Chuck Whitten proclaimed at Dell Technologies World this year, multi-cloud by design, is different than multi-cloud by default. Meaning to date, multi-cloud has largely been a symptom of what we've called multi-vendor or of M&A, you buy a company and they happen to use Google Cloud, and so you bring it in. And when you look at most so-called, multi-cloud implementations, you see things like an on-prem stack, which is wrapped in a container and hosted on a specific cloud or increasingly a technology vendor has done the work of building a cloud native version of their stack and running it on a specific cloud. But historically, it's been a unique experience within each cloud with virtually no connection between the cloud silos. Supercloud sets out to build incremental value across clouds and above hyperscale CAPEX that goes beyond cloud compatibility within each cloud. So if you want to call it multi-cloud 2.0, that's fine, but we chose to call it supercloud. Okay, so at this point you may be asking, well isn't PaaS already a version of supercloud? And again, we would say no, that supercloud and its corresponding superPaaS layer which is a prerequisite, gives the freedom to store, process and manage, and secure, and connect islands of data across a continuum with a common experience across clouds. And the services offered are specific to that supercloud and will vary by each offering. Your OpenShift, for example, can be used to construct a superPaaS, but in and of itself, isn't a superPaaS, it's generic. A superPaaS might be developed to support, for instance, ultra low latency database work. It would unlikely again, taking the OpenShift example, it's unlikely that off-the-shelf OpenShift would be used to develop such a low latency superPaaS layer for ultra low latency database work. The point is supercloud and its inherent superPaaS will be optimized to solve specific problems like that low latency example for distributed databases or fast backup and recovery for data protection, and ransomware, or data sharing, or data governance. Highly specific use cases that the supercloud is designed to solve for. Okay, another question we often get is who has a supercloud today and who's building a supercloud, and who are the contenders? Well, most companies that consider themselves cloud players will, we believe, be building or are building superclouds. Here's a common ETR graphic that we like to show with Net Score or spending momentum on the Y axis and overlap or pervasiveness in the ETR surveys on the X axis. And we've randomly chosen a number of players that we think are in the supercloud mix, and we've included the hyperscalers because they are enablers. Now remember, this is a spectrum of maturity it's a maturity model and we've added some of those industry players that we see building superclouds like CapitalOne, Goldman Sachs, Walmart. This is in deference to Moschella's observation around The Matrix and the industry structural changes that are going on. This goes back to every company, being a software company and rather than pattern match an outdated SaaS model, we see new industry structures emerging where software and data, and tools, specific to an industry will lead the next wave of innovation and bring in new value that traditional technology companies aren't going to solve, and the hyperscalers aren't going to solve. You know, we've talked a lot about Snowflake's data cloud as an example of supercloud. After being at Snowflake Summit, we're more convinced than ever that they're headed in this direction. VMware is clearly going after cross-cloud services you know, perhaps creating a new category. Basically, every large company we see either pursuing supercloud initiatives or thinking about it. Dell showed project Alpine at Dell Tech World, that's a supercloud. Snowflake introducing a new application development capability based on their superPaaS, our term of course, they don't use the phrase. Mongo, Couchbase, Nutanix, Pure Storage, Veeam, CrowdStrike, Okta, Zscaler. Yeah, all of those guys. Yes, Cisco and HPE. Even though on theCUBE at HPE Discover, Fidelma Russo said on theCUBE, she wasn't a fan of cloaking mechanisms, but then we talked to HPE's Head of Storage Services, Omer Asad is clearly headed in the direction that we would consider supercloud. Again, those cross-cloud services, of course, their emphasis is connecting as well on-prem. That single experience, which traditionally has not existed with multi-cloud or hybrid. And we're seeing the emergence of companies, smaller companies like Aviatrix and Starburst, and Clumio and others that are building versions of superclouds that solve for a specific problem for their customers. Even ISVs like Adobe, ADP, we've talked to UiPath. They seem to be looking at new ways to go beyond the SaaS model and add value within their cloud ecosystem specifically, around data as part of their and their customers digital transformations. So yeah, pretty much every tech vendor with any size or momentum and new industry players are coming out of hiding, and competing. Building superclouds that look a lot like Moschella's Matrix, with machine intelligence and blockchains, and virtual realities, and gaming, all enabled by the internet and hyperscale cloud CAPEX. So it's moving fast and it's the future in our opinion. So don't get too caught up in the past or you'll be left behind. Okay, what about examples? We've given a number in the past, but let's try to be a little bit more specific. Here are a few we've selected and we're going to answer the two questions in one section here. What workloads and services will run in superclouds and what are some examples? Let's start with analytics. Our favorite example is Snowflake, it's one of the furthest along with its data cloud, in our view. It's a supercloud optimized for data sharing and governance, query performance, and security, and ecosystem enablement. When you do things inside of that data cloud, what we call a super data cloud. Again, our term, not theirs. You can do things that you could not do in a single cloud. You can't do this with Redshift, You can't do this with SQL server and they're bringing new data types now with merging analytics or at least accommodate analytics and transaction type data, and bringing open source tooling with things like Apache Iceberg. And so it ticks the boxes we laid out earlier. I would say that a company like Databricks is also in that mix doing it, coming at it from a data science perspective, trying to create that consistent experience for data scientists and data engineering across clouds. Converge databases, running transaction and analytic workloads is another example. Take a look at what Couchbase is doing with Capella and how it's enabling stretching the cloud to the edge with ARM-based platforms and optimizing for low latency across clouds, and even out to the edge. Document database workloads, look at MongoDB, a very developer-friendly platform that with the Atlas is moving toward a supercloud model running document databases very, very efficiently. How about general purpose workloads? This is where VMware comes into to play. Very clearly, there's a need to create a common operating environment across clouds and on-prem, and out to the edge. And I say VMware is hard at work on that. Managing and moving workloads, and balancing workloads, and being able to recover very quickly across clouds for everyday applications. Network routing, take a look at what Aviatrix is doing across clouds, industry workloads. We see CapitalOne, it announced its cost optimization platform for Snowflake, piggybacking on Snowflake supercloud or super data cloud. And in our view, it's very clearly going to go after other markets is going to test it out with Snowflake, running, optimizing on AWS and it's going to expand to other clouds as Snowflake's business and those other clouds grows. Walmart working with Microsoft to create an on-premed Azure experience that's seamless. Yes, that counts, on-prem counts. If you can create that seamless and continuous experience, identical experience from on-prem to a hyperscale cloud, we would include that as a supercloud. You know, we've written about what Goldman is doing. Again, connecting its on-prem data and software tooling, and other capabilities to AWS for scale. And we can bet dollars to donuts that Oracle will be building a supercloud in healthcare with its Cerner acquisition. Supercloud is everywhere you look. So I'm sorry, naysayers it's happening all around us. So what's next? Well, with all the industry buzz and debate about the future, John Furrier and I, have decided to host an event in Palo Alto, we're motivated and inspired to further this conversation. And we welcome all points of view, positive, negative, multi-cloud, supercloud, hypercloud, all welcome. So theCUBE on Supercloud is coming on August 9th, out of our Palo Alto studios, we'll be running a live program on the topic. We've reached out to a number of industry participants, VMware, Snowflake, Confluent, Sky High Security, Gee Rittenhouse's new company, HashiCorp, CloudFlare. We've hit up Red Hat and we expect many of these folks will be in our studios on August 9th. And we've invited a number of industry participants as well that we're excited to have on. From industry, from financial services, from healthcare, from retail, we're inviting analysts, thought leaders, investors. We're going to have more detail in the coming weeks, but for now, if you're interested, please reach out to me or John with how you think you can advance the discussion and we'll see if we can fit you in. So mark your calendars, stay tuned for more information. Okay, that's it for today. Thanks to Alex Myerson who handles production and manages the podcast for Breaking Analysis. And I want to thank Kristen Martin and Cheryl Knight, they help get the word out on social and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does a lot of editing and appreciate you posting on SiliconANGLE, Rob. Thanks to all of you. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcast. It publish each week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com or DM me @DVellante, or comment on my LinkedIn post. And please do check out ETR.ai for the best survey data. And the enterprise tech business will be at AWS NYC Summit next Tuesday, July 12th. So if you're there, please do stop by and say hello to theCUBE, it's at the Javits Center. This is Dave Vellante for theCUBE insights powered by ETR. Thanks for watching. And we'll see you next time on "Breaking Analysis." (bright music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vallante. >> Welcome to this week's Wikibon CUBE Insights powered by ETR. As we exited the isolation economy last year, Supercloud is a term that we introduced to describe something new that was happening in the world of cloud. In this "Breaking Analysis," we address the 10 most frequently asked questions we get around Supercloud. Okay, let's review these frequently asked questions on Supercloud that we're going to try to answer today. Look at an industry that's full of hype and buzzwords. Why the hell does anyone need a new term? Aren't hyperscalers building out Superclouds? We'll try to answer why the term Supercloud connotes something different from hyperscale clouds. And we'll talk about the problems that Superclouds solve specifically, and we'll further define the critical aspects of a Supercloud architecture. We often get asked, "Isn't this just multi-cloud?" Well, we don't think so, and we'll explain why in this "Breaking Analysis." Now, in an earlier episode, we introduced the notion of super PaaS. Well, isn't a plain vanilla PaaS already a super PaaS? Again, we don't think so, and we'll explain why. Who will actually build and who are the players currently building Superclouds? What workloads and services will run on Superclouds? And eight A or number nine, what are some examples that we can share of Supercloud? And finally, we'll answer what you can expect next from us on Supercloud. Okay, let's get started. Why do we need another buzzword? Well, late last year ahead of re:Invent, we were inspired by a post from Jerry Chen called castles in the cloud. Now, in that blog post, he introduced the idea that there were submarkets emerging in cloud that presented opportunities for investors and entrepreneurs. That the cloud wasn't going to suck the hyperscalers, weren't going to suck all the value out of the industry. And so we introduced this notion of Supercloud to describe what we saw as a value layer emerging above the hyperscalers CAPEX gift, we sometimes call it. Now, it turns out that we weren't the only ones using the term, as both Cornell and MIT, have used the phrase in somewhat similar, but different contexts. The point is, something new was happening in the AWS and other ecosystems. It was more than IS and PaaS, and wasn't just SaaS running in the cloud. It was a new architecture that integrates infrastructure, platform and software as services, to solve new problems that the cloud vendors, in our view, weren't addressing by themselves. It seemed to us that the ecosystem was pursuing opportunities across clouds that went beyond conventional implementations of multi-cloud. And we felt there was a structural change going on at the industry level. The Supercloud metaphorically was highlighting. So that's the background on why we felt a new catch phrase was warranted. Love it or hate it, it's memorable and it's what we chose. Now, to that last point about structural industry transformation. Andy Rapaport is sometimes and often credited with identifying the shift from the vertically integrated IBM mainframe era to the fragmented PC microprocesor based era in his HBR article in 1991. In fact, it was David Moschella, who at the time was an IDC analyst who first introduced the concept in 1987, four years before Rapaport's article was published. Moschella saw that it was clear that Intel, Microsoft, Seagate and others would replace the system vendors and put that forth in a graphic that looked similar to the first two on this chart. We don't have to review the shift from IBM as the center of the industry to Wintel. That's well understood. What isn't as well known or accepted is what Moschella put out in his 2018 book called "Seeing Digital" which introduced the idea of the matrix that's shown on the right hand side of this chart. Moschella posited that new services were emerging, built on top of the internet and hyperscale clouds that would integrate other innovations and would define the next era of computing. He used the term matrix, because the conceptual depiction included, not only horizontal technology rows, like the cloud and the internet, but for the first time included connected industry verticals, the columns in this chart. Moschella pointed out that, whereas historically, industry verticals had a closed value chain or stack and ecosystem of R&D and production and manufacturing and distribution. And if you were in that industry, the expertise within that vertical generally stayed within that vertical and was critical to success. But because of digital and data, for the first time, companies were able to traverse industries jump across industries and compete because data enabled them to do that. Examples, Amazon and content, payments, groceries, Apple and payments, and content and so forth. There are many examples. Data was now this unifying enabler and this marked a change in the structure of the technology landscape. And Supercloud is meant to imply more than running in hyperscale clouds. Rather, it's the combination of multiple technologies, enabled by cloud scale with new industry participants from those verticals; financial services, and healthcare, and manufacturing, energy, media, and virtually all and any industry. Kind of an extension of every company is a software company. Basically, every company now has the opportunity to build their own cloud or Supercloud. And we'll come back to that. Let's first address what's different about Superclouds relative to hyperscale clouds. Now, this one's pretty straightforward and obvious, I think. Hyperscale clouds, they're walled gardens where they want your data in their cloud and they want to keep you there. Sure, every cloud player realizes that not all data will go to their particular cloud. So they're meeting customers where their data lives with initiatives like Amazon Outposts and Azure Arc and Google Antos. But at the end of the day, the more homogeneous they can make their environments, the better control, security, costs, and performance they can deliver. The more complex the environment, the more difficult it is to deliver on their brand promises. And, of course, the less margin that's left for them to capture. Will the hyperscalers get more serious about cross cloud services? Maybe, but they have plenty of work to do within their own clouds and within enabling their own ecosystems. They have a long way to go, a lot of runway. So let's talk about specifically, what problems Superclouds solve. We've all seen the stats from IDC or Gartner or whomever, that customers on average use more than one cloud, two clouds, three clouds, five clouds, 20 clouds. And we know these clouds operate in disconnected silos for the most part. And that's a problem, because each cloud requires different skills, because the development environment is different as is the operating environment. They have different APIs, different primitives, and different management tools that are optimized for each respective hyperscale cloud. Their functions and value props don't extend to their competitors' clouds for the most part. Why would they? As a result, there's friction when moving between different clouds. It's hard to share data. It's hard to move work. It's hard to secure and govern data. It's hard to enforce organizational edicts and policies across these clouds and on-prem. Supercloud is an architecture designed to create a single environment that enables management of workloads and data across clouds in an effort to take out complexity, accelerate application development, streamline operations, and share data safely, irrespective of location. It's pretty straightforward, but non-trivial, which is why I always ask a company's CEO and executives if stock buybacks and dividends will yield as much return as building out Superclouds that solve really specific and hard problems and create differential value. Okay, let's dig a bit more into the architectural aspects of Supercloud. In other words, what are the salient attributes of Supercloud? So, first and foremost, a Supercloud runs a set of specific services designed to solve a unique problem, and it can do so in more than one cloud. Superclouds leverage the underlying cloud native tooling of a hyperscale cloud, but they're optimized for a specific objective that aligns with the problem that they're trying to solve. For example, Supercloud might be optimized for lowest cost or lowest latency or sharing data or governing or securing that data or higher performance for networking, for example. But the point is, the collection of services that is being delivered is focused on a unique value proposition that is not being delivered by the hyperscalers across clouds. A Supercloud abstracts the underlying and siloed primitives of the native PaaS layer from the hyperscale cloud, and then using its own specific platform as a service tooling, creates a common experience across clouds for developers and users. And it does so in the most efficient manner, meaning it has the metadata knowledge and management capabilities that can optimize for latency, bandwidth, or recovery or data sovereignty, or whatever unique value that Supercloud is delivering for the specific use case in their domain. And a Supercloud comprises a super PaaS capability that allows ecosystem partners through APIs to add incremental value on top of the Supercloud platform to fill gaps, accelerate features, and of course, innovate. The services can be infrastructure related, they could be application services, they could be data services, security services, user services, et cetera, designed and packaged to bring unique value to customers. Again, that hyperscalers are not delivering across clouds or on premises. Okay, so another common question we get is, "Isn't that just multi-cloud?" And what we'd say to that is yeah, "Yes, but no." You can call it multi-cloud 2.0, if you want. If you want to use, it's kind of a commonly used rubric. But as Dell's Chuck Whitten proclaimed at Dell Technologies World this year, multi-cloud, by design, is different than multi-cloud by default. Meaning, to date, multi-cloud has largely been a symptom of what we've called multi-vendor or of M&A. You buy a company and they happen to use Google cloud. And so you bring it in. And when you look at most so-called multi-cloud implementations, you see things like an on-prem stack, which is wrapped in a container and hosted on a specific cloud. Or increasingly, a technology vendor has done the work of building a cloud native version of their stack and running it on a specific cloud. But historically, it's been a unique experience within each cloud, with virtually no connection between the cloud silos. Supercloud sets out to build incremental value across clouds and above hyperscale CAPEX that goes beyond cloud compatibility within each cloud. So, if you want to call it multi-cloud 2.0, that's fine, but we chose to call it Supercloud. Okay, so at this point you may be asking, "Well isn't PaaS already a version of Supercloud?" And again, we would say, "No." That Supercloud and its corresponding super PaaS layer, which is a prerequisite, gives the freedom to store, process, and manage and secure and connect islands of data across a continuum with a common experience across clouds. And the services offered are specific to that Supercloud and will vary by each offering. OpenShift, for example, can be used to construct a super PaaS, but in and of itself, isn't a super PaaS, it's generic. A super PaaS might be developed to support, for instance, ultra low latency database work. It would unlikely, again, taking the OpenShift example, it's unlikely that off the shelf OpenShift would be used to develop such a low latency, super PaaS layer for ultra low latency database work. The point is, Supercloud and its inherent super PaaS will be optimized to solve specific problems like that low latency example for distributed databases or fast backup in recovery for data protection and ransomware, or data sharing or data governance. Highly specific use cases that the Supercloud is designed to solve for. Okay, another question we often get is, "Who has a Supercloud today and who's building a Supercloud and who are the contenders?" Well, most companies that consider themselves cloud players will, we believe, be building or are building Superclouds. Here's a common ETR graphic that we like to show with net score or spending momentum on the Y axis, and overlap or pervasiveness in the ETR surveys on the X axis. And we've randomly chosen a number of players that we think are in the Supercloud mix. And we've included the hyperscalers because they are enablers. Now, remember, this is a spectrum of maturity. It's a maturity model. And we've added some of those industry players that we see building Superclouds like Capital One, Goldman Sachs, Walmart. This is in deference to Moschella's observation around the matrix and the industry structural changes that are going on. This goes back to every company being a software company. And rather than pattern match and outdated SaaS model, we see new industry structures emerging where software and data and tools specific to an industry will lead the next wave of innovation and bring in new value that traditional technology companies aren't going to solve. And the hyperscalers aren't going to solve. We've talked a lot about Snowflake's data cloud as an example of Supercloud. After being at Snowflake Summit, we're more convinced than ever that they're headed in this direction. VMware is clearly going after cross cloud services, perhaps creating a new category. Basically, every large company we see either pursuing Supercloud initiatives or thinking about it. Dell showed Project Alpine at Dell Tech World. That's a Supercloud. Snowflake introducing a new application development capability based on their super PaaS, our term, of course. They don't use the phrase. Mongo, Couchbase, Nutanix, Pure Storage, Veeam, CrowdStrike, Okta, Zscaler. Yeah, all of those guys. Yes, Cisco and HPE. Even though on theCUBE at HPE Discover, Fidelma Russo said on theCUBE, she wasn't a fan of cloaking mechanisms. (Dave laughing) But then we talked to HPE's head of storage services, Omer Asad, and he's clearly headed in the direction that we would consider Supercloud. Again, those cross cloud services, of course, their emphasis is connecting as well on-prem. That single experience, which traditionally has not existed with multi-cloud or hybrid. And we're seeing the emergence of smaller companies like Aviatrix and Starburst and Clumio and others that are building versions of Superclouds that solve for a specific problem for their customers. Even ISVs like Adobe, ADP, we've talked to UiPath. They seem to be looking at new ways to go beyond the SaaS model and add value within their cloud ecosystem, specifically around data as part of their and their customer's digital transformations. So yeah, pretty much every tech vendor with any size or momentum, and new industry players are coming out of hiding and competing, building Superclouds that look a lot like Moschella's matrix, with machine intelligence and blockchains and virtual realities and gaming, all enabled by the internet and hyperscale cloud CAPEX. So it's moving fast and it's the future in our opinion. So don't get too caught up in the past or you'll be left behind. Okay, what about examples? We've given a number in the past but let's try to be a little bit more specific. Here are a few we've selected and we're going to answer the two questions in one section here. What workloads and services will run in Superclouds and what are some examples? Let's start with analytics. Our favorite example of Snowflake. It's one of the furthest along with its data cloud, in our view. It's a Supercloud optimized for data sharing and governance, and query performance, and security, and ecosystem enablement. When you do things inside of that data cloud, what we call a super data cloud. Again, our term, not theirs. You can do things that you could not do in a single cloud. You can't do this with Redshift. You can't do this with SQL server. And they're bringing new data types now with merging analytics or at least accommodate analytics and transaction type data and bringing open source tooling with things like Apache Iceberg. And so, it ticks the boxes we laid out earlier. I would say that a company like Databricks is also in that mix, doing it, coming at it from a data science perspective trying to create that consistent experience for data scientists and data engineering across clouds. Converge databases, running transaction and analytic workloads is another example. Take a look at what Couchbase is doing with Capella and how it's enabling stretching the cloud to the edge with arm based platforms and optimizing for low latency across clouds, and even out to the edge. Document database workloads, look at Mongo DB. A very developer friendly platform that where the Atlas is moving toward a Supercloud model, running document databases very, very efficiently. How about general purpose workloads? This is where VMware comes into play. Very clearly, there's a need to create a common operating environment across clouds and on-prem and out to the edge. And I say, VMware is hard at work on that, managing and moving workloads and balancing workloads, and being able to recover very quickly across clouds for everyday applications. Network routing, take a look at what Aviatrix is doing across clouds. Industry workloads, we see Capital One. It announced its cost optimization platform for Snowflake, piggybacking on Snowflake's Supercloud or super data cloud. And in our view, it's very clearly going to go after other markets. It's going to test it out with Snowflake, optimizing on AWS, and it's going to expand to other clouds as Snowflake's business and those other clouds grows. Walmart working with Microsoft to create an on-premed Azure experience that's seamless. Yes, that counts, on-prem counts. If you can create that seamless and continuous experience, identical experience from on-prem to a hyperscale cloud, we would include that as a Supercloud. We've written about what Goldman is doing. Again, connecting its on-prem data and software tooling, and other capabilities to AWS for scale. And you can bet dollars to donuts that Oracle will be building a Supercloud in healthcare with its Cerner acquisition. Supercloud is everywhere you look. So I'm sorry, naysayers, it's happening all around us. So what's next? Well, with all the industry buzz and debate about the future, John Furrier and I have decided to host an event in Palo Alto. We're motivated and inspired to further this conversation. And we welcome all points of view, positive, negative, multi-cloud, Supercloud, HyperCloud, all welcome. So theCUBE on Supercloud is coming on August 9th out of our Palo Alto studios. We'll be running a live program on the topic. We've reached out to a number of industry participants; VMware, Snowflake, Confluent, Skyhigh Security, G. Written House's new company, HashiCorp, CloudFlare. We've hit up Red Hat and we expect many of these folks will be in our studios on August 9th. And we've invited a number of industry participants as well that we're excited to have on. From industry, from financial services, from healthcare, from retail, we're inviting analysts, thought leaders, investors. We're going to have more detail in the coming weeks, but for now, if you're interested, please reach out to me or John with how you think you can advance the discussion, and we'll see if we can fit you in. So mark your calendars, stay tuned for more information. Okay, that's it for today. Thanks to Alex Myerson who handles production and manages the podcast for "Breaking Analysis." And I want to thank Kristen Martin and Cheryl Knight. They help get the word out on social and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does a lot of editing and appreciate you posting on SiliconANGLE, Rob. Thanks to all of you. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search, breaking analysis podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me directly at david.vellante@siliconangle.com. Or DM me @DVallante, or comment on my LinkedIn post. And please, do check out etr.ai for the best survey data in the enterprise tech business. We'll be at AWS NYC summit next Tuesday, July 12th. So if you're there, please do stop by and say hello to theCUBE. It's at the Javits Center. This is Dave Vallante for theCUBE Insights, powered by ETR. Thanks for watching. And we'll see you next time on "Breaking Analysis." (slow music)

>>Hi everyone. And welcome to data citizens. Thank you for making the time to join me and the over 5,000 data citizens like you that are looking to become United by data. My name is Jim Cushman. I serve as the chief product officer at Collibra. I have the benefit of sharing with you, the product, vision, and strategy of Culebra. There's several sections to this presentation, and I can't wait to share them with you. The first is a story of how we're taking a business user and making it possible for him or her data, use data and gain. And if it and insight from that data, without relying on anyone in the organization to write code or do the work for them next I'll share with you how Collibra will make it possible to manage metadata at scales, into the billions of assets. And again, load this into our software without writing any code third, I will demonstrate to you the integration we have already achieved with our newest product release it's data quality that's powered by machine learning. >>Right? Finally, you're going to hear about how Colibra has become the most universally available solution in the market. Now, we all know that data is a critical asset that can make or break an organization. Yet organizations struggle to capture the power of their data and many remain afraid of how their data could be misused and or abused. We also observe that the understanding of and access to data remains in the hands of just a small few, three out of every four companies continue to struggle to use data, to drive meaningful insights, all forward looking companies, looking for an advantage, a differentiator that will set them apart from their peers and competitors. What if you could improve your organization's productivity by just 5%, even a modest 5% productivity improvement compounded over a five-year period will make your organization 28% more productive. This will leave you with an overwhelming advantage over your competition and uniting your data. >>Litter employees with data is the key to your success. And dare I say, sorry to unlock this potential for increased productivity, huge competitive advantage organizations need to enable self-service access to data for everyday to literate knowledge worker. Our ultimate goal at Cleaver has always been to enable this self-service for our customers to empower every knowledge worker to access the data they need when they need it. But with the peace of mind that your data is governed insecure. Just to imagine if you had a single integrated solution that could deliver a seamless governed, no code user experience of delivering the right data to the right person at the right time, just as simply as ordering a pair of shoes online would be quite a magic trick and one that would place you and your organization on the fast track for success. Let me introduce you to our character here. >>Cliff cliff is that business analyst. He doesn't write code. He doesn't know Julian or R or sequel, but is data literate. When cliff has presented with data of high quality and can actually help find that data of high-quality cliff knows what to do with it. Well, we're going to expose cliff to our software and see how he can find the best data to solve his problem of the day, which is customer churn. Cliff is going to go out and find this information is going to bring it back to him. And he's going to analyze it in his favorite BI reporting tool. Tableau, of course, that could be Looker, could be power BI or any other of your favorites, but let's go ahead and get started and see how cliff can do this without any help from anyone in the organization. So cliff is going to log into Cleaver and being a business user. >>The first thing he's going to do is look for a business term. He looks for customer churn rate. Now, when he brings back a churn rate, it shows him the definition of churn rate and various other things that have been attributed to it such as data domains like product and customer in order. Now, cliff says, okay, customer is really important. So let me click on that and see what makes up customer definition. Cliff will scroll through a customer and find out the various data concepts attributes that make up the definition of customer and cliff knows that customer identifier is a really important aspect to this. It helps link all the data together. And so cliff is going to want to make sure that whatever source he brings actually has customer identifier in it. And that it's of high quality cliff is also interested in things such as email address and credit activity and credit card. >>But he's now going to say, okay, what data sets actually have customer as a data domain in, and by the way, why I'm doing it, what else has product and order information? That's again, relevant to the concept of customer churn. Now, as he goes on, he can actually filter down because there's a lot of different results that could potentially come back. And again, customer identifier was very important to cliff. So cliff, further filters on customer identifier any further does it on customer churn rate as well. This results in two different datasets that are available to cliff for selection, which one to use? Well, he's first presented with some data quality information you can see for customer analytics. It has a data quality score of 76. You can see for sales data enrichment dataset. It has a data quality score of 68. Something that he can see right at the front of the box of things that he's looking for, but let's dig in deeper because the contents really matter. >>So we see again the score of 76, but we actually have the chance to find out that this is something that's actually certified. And this is something that has a check mark. And so he knows someone he trusts is actually certified. This is a dataset. You'll see that there's 91 columns that make up this data set. And rather than sifting through all of that information, cliff is going to go ahead and say, well, okay, customer identifier is very important to me. Let me search through and see if I can find what it's data quality scores very quickly. He finds that using a fuzzy search and brings back and sees, wow, that's a really high data quality score of 98. Well, what's the alternative? Well, the data set is only has 68, but how about, uh, the customer identifier and quickly, he discovers that the data quality for that is only 70. >>So all things being equal, customer analytics is the better data set for what cliff needs to achieve. But now he wants to look and say, other people have used this, what have they had to say about it? And you can see there are various reviews for different reviews from peers of his, in the organization that have given it five stars. So this is encourages cliffs, a confidence that this is great data set to use. Now cliff wants to look a little bit more detailed before he finally commits to using this dataset. Cliff has the opportunity to look at it in the broader set. What are the things can I learn about customer analytics, such as what else is it related to? Who else uses it? Where did it come from? Where does it go and what actually happens to it? And so within our graph of information, we're able to show you a diagram. >>You can see the customer analytics actually comes from the CRM cloud system. And from there you can inherit some wonderful information. We know exactly what CRM cloud is about as an overall system. It's related to other logical models. And here you're actually seeing that it's related to a policy policy about PII or personally identifiable information. This gets cliff almost the immediate knowledge that there's going to be some customer information in this PII information that he's not going to be able to see given his user role in the organization. But cliff says, Hey, that's okay. I actually don't need to see somebody's name and social security number to do my work. I can actually work with other information in the data file. That'll actually help me understand why our customers churning in, what can I actually do about it. If we dig in deeper, we can see what is personally identifiable information that actually could cause issues. >>And as we scroll down and take a little bit of a focus on what we call or what you'll see here is customer phone, because we'll show that to you a little bit later, but these show the various information that once cliff actually has it fulfilled and delivered to him, he will see that it's actually massed and or redacted from his use. Now cliff might drive in deeper and see more information. And he says, you know what? Another piece that's important to me in my analysis is something called is churned. This is basically suggesting that has a customer actually churned. It's an important flag, of course, because that's the analysis that he's performing cliff sees that the score is a mere 65. That's not exactly a great data quality score, but cliff has, is kind of in a hurry. His bosses is, has come back and said, we need to have this information so we can take action. >>So he's not going to wait around to see if they can go through some long day to quality project before he pursues, but he is going to come up and use it. The speed of thinking. He's going to create a suggestion, an issue. He's going to submit this as a work queue item that actually informs others that are responsible for the quality of data. That there's an opportunity for improvement to this dataset that is highly reviewed, but it may be, it has room for improvement as cliff is actually typing in his explanation that he'll pass along. We can also see that the data quality is made up of multiple components, such as integrity, duplication, accuracy, consistency, and conformity. Um, we see that we can submit this, uh, issue and pass it through. And this will go to somebody else who can actually work on this. >>And we'll show that to you a little bit later, but back to cliff, cliff says, okay, I'd like to, I'd like to work with this dataset. So he adds it to his data basket. And just like if he's shopping online, cliff wants that kind of ability to just say, I want to just click once and be done with it. Now it is data and there's some sensitivity about it. And again, there's an owner of this data who you need to get permission from. So cliff is going to provide information to the owner to say, here's why I need this data. And how long do I need this data for starting on a certain date and ending on a certain date and ultimately, what purpose am I going to have with this data? Now, there are other things that cliff can choose to run. This one is how do you want this day to deliver to you? >>Now, you'll see down below, there are three options. One is borrow the other's lease and others by what does that mean? Well, borrow is this idea of, I don't want to have the data that's currently in this CRM, uh, cloud database moved somewhere. I don't want it to be persistent anywhere else. I just want to borrow it very short term to use in my Tablo report and then poof be gone. Cause I don't want to create any problems in my organization. Now you also see lease. Lease is a situation where you actually do need to take possession of the data, but only for a time box period of time, you don't need it for an indefinite amount of time. And ultimately buy is your ability to take possession of the data and have it in perpetuity. So we're going to go forward with our bar use case and cliff is going to submit this and all the fun starts there. >>So cliff has actually submitted the order and the owner, Joanna is actually going to receive the request for the order. Joanna, uh, opens up her task, UCS there's work to perform. It says, oh, okay, here's this there's work for me to perform. Now, Joanna has the ability to automate this using incorporated workflow that we have in Colibra. But for this situation, she's going to manually review that. Cliff wants to borrow a specific data set for a certain period of time. And he actually wants to be using in a Tablo context. So she reviews. It makes an approval and submits it this in turn, flips it back to cliff who says, okay, what obligations did I just take on in order to work for this data? And he reviews each of these data sharing agreements that you, as an organization would set up and say, what am I, uh, what are my restrictions for using this data site? >>As cliff accepts his notices, he now has triggered the process of what we would call fulfillment or a service broker. And in this situation we're doing a virtualization, uh, access, uh, for the borrow use case. Cliff suggests Tablo is his preferred BI and reporting tool. And you can see the various options that are available from power BI Looker size on ThoughtSpot. There are others that can be added over time. And from there, cliff now will be alerted the minute this data is available to them. So now we're running out and doing a distributed query to get the information and you see it returns back for raw view. Now what's really interesting is you'll see, the customer phone has a bunch of X's in it. If you remember that's PII. So it's actually being massed. So cliff can't actually see the raw data. Now cliff also wants to look at it in a Tablo report and can see the visualization layer, but you also see an incorporation of something we call Collibra on the go. >>Not only do we bring the data to the report, but then we tell you the reader, how to interpret the report. It could be that there's someone else who wants to use the very same report that cliff helped create, but they don't understand exactly all the things that cliff went through. So now they have the ability to get a full interpretation of what was this data that was used, where did it come from? And how do I actually interpret some of the fields that I see on this report? Really a clever combination of bringing the data to you and showing you how to use it. Cliff can also see this as a registered asset within a Colibra. So the next shopper comes through might actually, instead of shopping for the dataset might actually shop for the report itself. And the report is connected with the data set he used. >>So now they have a full bill of materials to run a customer Shern report and schedule it anytime they want. So now we've turned cliff actually into a creator of data assets, and this is where intelligent, it gets more intelligence and that's really what we call data intelligence. So let's go back through that magic trick that we just did with cliff. So cliff went into the software, not knowing if the source of data that he was looking for for customer product sales was even available to him. He went in very quickly and searched and found his dataset, use facts and facets to filter down to exactly what was available. Compare to contrast the options that were there actually made an observation that there actually wasn't enough data quality around a certain thing was important to him, created an idea, or basically a suggestion for somebody to follow up on was able to put that into his shopping basket checkout and have it delivered to his front door. >>I mean, that's a bit of a magic trick, right? So, uh, cliff was successful in finding data that he wanted and having it, deliver it to him. And then in his preferred model, he was able to look at it into Tableau. All right. So let's talk about how we're going to make this vision a reality. So our first section here is about performance and scale, but it's also about codeless database registration. How did we get all that stuff into the data catalog and available for, uh, cliff to find? So allow us to introduce you to what we call the asset life cycle and some of the largest organizations in the world. They might have upwards of a billion data assets. These are columns and tables, reports, API, APIs, algorithms, et cetera. These are very high volume and quite technical and far more information than a business user like cliff might want to be engaged with those very same really large organizations may have upwards of say, 20 to 25 million that are critical data sources and data assets, things that they do need to highly curate and make available. >>But through that as a bit of a distillation, a lifecycle of different things you might want to do along that. And so we're going to share with you how you can actually automatically register these sources, deal with these very large volumes at speed and at scale, and actually make it available with just a level of information you need to govern and protect, but also make it available for opportunistic use cases, such as the one we presented with cliff. So as you recall, when cliff was actually trying to look for his dataset, he identified that the is churned, uh, data at your was of low quality. So he passed this over to Eliza, who's a data steward and she actually receives this work queue in a collaborative fashion. And she has to review, what is the request? If you recall, this was the request to improve the data quality for his churn. >>Now she needs to familiarize herself with what cliff was observing when he was doing his shopping experience. So she digs in and wants to look at the quality that he was observing and sure enough, as she goes down and it looks at his churn, she sees that it was a low 65% and now understands exactly what cliff was referring to. She says, aha, okay. I need to get help. I need to decide whether I have a data quality project to fix the data, or should I see if there's another data set in the organization that has better, uh, data for this. And so she creates a queue that can go over to one of her colleagues who really focuses on data quality. She submits this request and it goes over to, uh, her colleague, John who's really familiar with data quality. So John actually receives the request from Eliza and you'll see a task showing up in his queue. >>He opens up the request and finds out that Eliza's asking if there's another source out there that actually has good is churned, uh, data available. Now he actually knows quite a bit about the quality of information sturdiness. So he goes into the data quality console and does a quick look for a dataset that he's familiar with called customer product sales. He quickly scrolls down and finds out the one that's actually been published. That's the one he was looking for and he opens it up to find out more information. What data sets are, what columns are actually in there. And he goes down to find his churned is in fact, one of the attributes in there. It actually does have active rules that are associated with it to manage the quality. And so he says, well, let's look in more detail and find out what is the quality of this dataset? >>Oh, it's 86. This is a dramatic improvement over what we've seen before. So we can see again, it's trended quite nicely over time each day, it hasn't actually degraded in performance. So we actually responds back to realize and say, this data set, uh, is actually the data set that you want to bring in. It really will improve. And you'll see that he refers to the refined database within the CRM cloud solution. Once he actually submits this, it goes back to Eliza and she's able to continue her work. Now when Eliza actually brings this back open, she's able to very quickly go into the database registration process for her. She very quickly goes into the CRM cloud, selects the community, to which she wants to register this, uh, data set into the schemas community. And the CRM cloud is the system that she wants to load it in. >>And the refined is the database that John told her that she should bring in. After a quick description, she's able to click register. And this triggers that automatic codeless process of going out to the dataset and bringing back its metadata. Now metadata is great, but it's not the end all be all. There's a lot of other values that she really cares about as she's actually registering this dataset and synchronizing the metadata she's also then asked, would you like to bring in quality information? And so she'll go out and say, yes, of course, I want to enable the quality information from CRM refined. I also want to bring back lineage information to associate with this metadata. And I also want to select profiling and classification information. Now when she actually selects it, she can also say, how often do you want to synchronize this? This is a daily, weekly, monthly kind of update. >>That's part of the change data capture process. Again, all automated without the require of actually writing code. So she's actually run this process. Now, after this loads in, she can then open up this new registered, uh, dataset and actually look and see if it actually has achieved the problem that cliff set her out on, which was improved data quality. So looking into the data quality for the is churn capability shows her that she has fantastic quality. It's at a hundred, it's exactly what she was looking for. So she can with confidence actually, uh, suggest that it's done, but she did notice something and something that she wants to tell John, which is there's a couple of data quality checks that seem to be missing from this dataset. So again, in a collaborative fashion, she can pass that information, uh, for validity and completeness to say, you know what, check for NOLs and MPS and send that back. >>So she submits this onto John to work on. And John now has a work queue in his task force, but remember she's been working in this task forklift and because she actually has actually added a much better source for his churn information, she's going to update that test that was sent to her to notify cliff that the work has actually been done and that she actually has a really good data set in there. In fact, if you recall, it was 100% in terms of its data quality. So this will really make life a lot easier for cliff. Once he receives that data and processes, the churn report analysis next time. So let's talk about these audacious performance goals that we have in mind. Now today, we actually have really strong performance and amazing usability. Our customers continue to tell us how great our usability is, but they keep asking for more well, we've decided to present to you. >>Something you can start to bank on. This is the performance you can expect from us on the highly curated assets that are available for the business users, as well as the technical and lineage assets that are more available for the developer uses and for things that are more warehoused based, you'll see in Q1, uh, our Q2 of this year, we're making available 5 million curated assets. Now you might be out there saying, Hey, I'm already using the software and I've got over 20 million already. That's fair. We do. We have customers that are actually well over 20 million in terms of assets they're managing, but we wanted to present this to you with zero conditions, no limitations we wouldn't talk about, well, it depends, et cetera. This is without any conditions. That's what we can offer you without fail. And yes, it can go higher and higher. We're also talking about the speed with which you can ingest the data right now, we're ingesting somewhere around 50,000 to a hundred thousand records per and of course, yes, you've probably seen it go quite a bit faster, but we are assuring you that that's the case, but what's really impressive is right now, we can also, uh, help you manage 250 million technical assets and we can load it at a speed of 25 million for our, and you can see how over the next 18 months about every two quarters, we show you dramatic improvements, more than doubling of these. >>For most of them leading up to the end of 2022, we're actually handling over a billion technical lineage assets and we're loading at a hundred million per hour. That sets the mark for the industry. Earlier this year, we announced a recent acquisition Al DQ. LDQ brought to us machine learning based data quality. We're now able to introduce to you Collibra data quality, the first integrated approach to Al DQ and Culebra. We've got a demo to follow. I'm really excited to share it with you. Let's get started. So Eliza submitted a task for John to work on, remember to add checks for no and for empty. So John picks up this task very quickly and looks and sees what's what's the request. And from there says, ah, yes, we do have a quality check issue when we look at these churns. So he jumps over to the data quality console and says, I need to create a new data quality test. >>So cliff is able to go in, uh, to the solution and, uh, set up quick rules, automated rules. Uh, he could inherit rules from other things, but it starts with first identifying what is the data source that he needs to connect to, to perform this. And so he chooses the CRM refined data set that was most recently, uh, registered by Lysa. You'll see the same score of 86 was the quality score for the dataset. And you'll also see, there are four rules that are associated underneath this. Now there are various checks that, uh, that John can establish on this, but remember, this is a fairly easy request that he receives from Eliza. So he's going to go in and choose the actual field, uh, is churned. Uh, and from there identify quick rules of, uh, an empty check and that quickly sets up the rules for him. >>And also the null check equally fast. This one's established and analyzes all the data in there. And this sets up the baseline of data quality, uh, for this. Now this data, once it's captured then is periodically brought back to the catalog. So it's available to not only Eliza, but also to cliff next time he, uh, where to shop in the environment. As we look through the rules that were created through that very simple user experience, you can see the one for is empty and is no that we're set up. Now, these are various, uh, styles that can be set up either manually, or you can set them up through machine learning again, or you can inherit them. But the key is to track these, uh, rule creation in the metrics that are generated from these rules so that it can be brought back to the catalog and then used in meaningful context, by someone who's shopping and the confidence that this has neither empty nor no fields, at least most of them don't well now give a confidence as you go forward. >>And as you can see, those checks have now been entered in and you can see that it's a hundred percent quality score for the Knoll check. So with confidence now, John can actually respond back to Eliza and say, I've actually inserted them they're up and running. And, uh, you're in good status. So that was pretty amazing integration, right? And four months after our acquisition, we've already brought that level of integration between, uh, Colibra, uh, data intelligence, cloud, and data quality. Now it doesn't stop there. We have really impressive and high site set early next year. We're getting introduced a fully immersive experience where customers can work within Culebra and actually bring the data quality information all the way in as well as start to manipulate the rules and generate the machine learning rules. On top of it, all of that will be a deeply immersive experience. >>We also have something really clever coming, which we call continuous data profiling, where we bring the power of data quality all the way into the database. So it's continuously running and always making that data available for you. Now, I'd also like to share with you one of the reasons why we are the most universally available software solutions in data intelligence. We've already announced that we're available on AWS and Google cloud prior, but today we can announce to you in Q3, we're going to be, um, available on Microsoft Azure as well. Now it's not just these three cloud providers that were available on we've also become available on each of their marketplaces. So if you are buying our software, you can actually go out and achieve that same purchase from their marketplace and achieve your financial objectives as well. We're very excited about this. These are very important partners for, uh, for our, for us. >>Now, I'd also like to introduce you our system integrators, without them. There's no way we could actually achieve our objectives of growing so rapidly and dealing with the demand that you customers have had Accenture, Deloitte emphasis, and even others have been instrumental in making sure that we can serve your needs when you need them. Uh, and so it's been a big part of our growth and will be a continued part of our growth as well. And finally, I'd like to actually introduce you to our product showcases where we can go into absolute detail on many of the topics I talked about today, such as data governance with Arco or data privacy with Sergio or data quality with Brian and finally catalog with Peter. Again, I'd like to thank you all for joining us. Uh, and we really look forward to hearing your feedback. Thank you..

>>Okay, welcome back to the cubes coverage of Del tech world. I'm john for your host of the cube we're here for virtual coverage were not yet face to face as we start to come out of covert, we're still doing the remote but we got the cube virtual. We're here with Dennis Hoffman, senior Vice President, General Manager for the telecom Systems business group within Dell Technologies dead. It's great to see you. Thanks for coming in CUba alumni. Thanks for coming on. >>My pleasure, john great to see you and look forward to the days when we can stop doing this virtually. >>Well, you guys have been certainly pumping out a lot of content and right now telco cloud telco disruption is big. We heard Michael Dell last event and even when we were in person in real life, we he was really laying down the five G leadership now with hybrid cloud, um, standardized, pretty much I mean, consensus is no, no debate really. It's hybrid multi cloud on the horizon. That's still just a subsystem of basically distributed computing A. K. A hybrid cloud makes the edge a huge part of the story this year. And the innovations all around telecom, Edge in five G have been around and they're changing really fast. What's how are these Edge in five G technologies impacting the market today? >>Yeah, it's uh is fascinating times, I'll tell you they are providing really the ultimate carrots, you know, the catalyst for um innovation in the market and really driving the world's network operators To uh want to take advantage of all the opportunity that the edge presents and that 5G enables. And it's, you know, at the end of the day, it's really forcing folks to think hard about if they have the right network architectures to enable that to capture that opportunity to have the right kind of capabilities. And so we're seeing an awful lot of interest in network desegregation, network modernization, various forms of adopting the technology is you and I are familiar with from years of what's going on in data center evolution are really starting to hit the telco network now at a really, really interesting time >>while we're on the landscape. Do you want to get your opinion on something? I've been hearing a lot, certainly in interviewing other folks here at Dell tech world and in the industry about how the edge and the data compute equation and the connectivity has changed how they're going to lay out essentially their factory, their plants, their operations and certainly covid pushing everyone at home has changed the game on how data is being computed on and how apps are being built. This is a huge five G opportunity certainly when you start to get into the business impact, autonomous vehicles, I've been doing stories about autonomous boats and everything we could have an autonomous cube soon. So, you know, everything is autonomous which drives to this whole edge piece, What's your take on that? >>Yeah, you know, it's, it's funny for years we've been talking about on prem and off prem, like there's two problems there turns out there's a third Prem, right? There is the other premises and that is not the private data center and not the public cloud. And when you stop and think about it, it it makes sense because at the end of the day, wherever we can get data, we can create digital advantage and it's always been cheaper and more effective and faster to move compute to data than to move data to compute. So technology is like 5G are beginning to make it possible to run very interesting applications in very different places and capture what is predicted to be some 3/4 of the data created over the next decade is going to get created somewhere other than a private data center or a public cloud. And that's the edge, you know, in telcos, look at that third premises as their opportunity to get another bite of the apple on services. Four G was kind of a story of the over the top. Players really took the profit pool and made a lot of money from the over to the netflix is to the itunes and so on and so forth. But when you come back to Five G and think of it kind of as the Enterprise G, it's a chance now for the world's network operators to really get a chunk of that profit pool that comes from the emergence of this third premises called the edge >>Enterprise G. I love that, I'm gonna steal that from you. It's a great, great uh >>somebody else >>uh Yeah, the new trend, but it's a business, it's a business opportunity again, totally cool. And consumers to um okay, so you got your out on the road a lot. I know that we've talked in the past on the cube. There's a lot of discussions in the industry, as well as customers that you're having. What are you hearing? What are the some of the pain points are, see Covid has unveiled unveiled new use cases, people had had adapted to it. There's adaptations that are out there that are new and then things that might not happen again. What are you hearing from customers? >>Yeah, I would say in summary, we're hearing a mix of optimism and uncertainty, optimism around all the stuff we just talked about and that you mentioned, you know, it's it's a blank from anywhere. World right work from anywhere, learn from anywhere. Medicine from anywhere. And you know, if the pandemic has taught us anything, it's about the absolute necessity of communications technology to the world we live in today. The uncertainty comes from this question of, okay, so I know that there's this big opportunity and I know that I need to modernize my network architecture and kind of change the way I operate to capture it all. But the architecture is I run on today, make that really hard. And the architecture is that that the modern data center is built on, We know they work. But how do I get them in a way that allows me to build a resilient, high performance agile communications network. Um, you know, today we uh we face a world in which we see, we have a world in which solutions are delivered very fairly monolithically in the network uh for network operators but going forward, the power to potentially decompose all of that is wonderful provided it can be recomposed in a way they can consume. And I think that's where the uncertainty lies. There's a lot of testing and trialing of pieces of applications of underlying hardware, infrastructure, servers, accelerators, um certainly different types of virtualization and container ization technologies. But in the end these networks need to run it many many many nines um and they need to be extremely robust and pulling together a lot of different components from the open ecosystem is a daunting challenge for most of the network operators. >>You know, I hear you saying about the opportunity recognition and the re factoring how we called re composing this opportunity here and again. I like this enterprise G angle because what it means is that it's not the consumer the only it's it's everything. It's a complete consumer ization of I. T. So it's a whole another edge landscape. Prem third, the third premise is the edge. All good. I've always so set on the cube and certainly Dave and I have David and I have riffed on this is that you know, everything is now cloud operations and the data center is a big edge and then you've got other pieces that are just edges. A distributed system kind of sounds like a computer in the cloud. So this is kind of operating model. So I have to ask the question which is in telco, if it's gonna be distributed like that and it's going to be operated at scale, how is Dell responding to capture the mind share and customers using Dell in this new telco disruption? Because it's kind of you got to keep the lights on and you gotta also get them in a position to take advantage of the new opportunity. How are you responding? >>Yeah, Well, we're trying to we're literally trying to fill that gap, you know, the talking to the world's uh modern or say the world's telecom network operations leaders. We've uh we've had a lot of conversations with folks about what they need to do and what's holding them back from really in many ways taking advantage of the digital transformation that that's kind of rippling through the economy. And as they kind of laid that out to us, we decided that it was an enormous opportunity for Dell that this this uh you know, this new network will be fundamentally built on computer technology uh and it will be open industry standard computer technology. And on top of that we will use virtualization. And if this begins to sound like the way data centers are being built, because that's exactly what's happening. But more than that, I think there's a need for an at scale substantial provider that the world's biggest carriers can bet on and feel they can trust as a strategic partner to not only pull the ecosystem together, validated, certified, curated a little bit uh, and deliver it as an outcome, but then stand behind it running and importantly, do all of that in a way that doesn't constrain the continuous innovation. That's really the hallmark of some of these modern architecture. So for us, we see, you know, an opportunity that is literally perfectly built for a company like dealt and that's why we decided to invest in it. That's why you hear Michael talking about it a lot. Uh it's um, you know, it's it's really super well aligned with our strategy, we think it's actually key to winning the edge. Uh and and it's also really well aligned with our purpose, you know what this company exists to accelerate human progress through technology. And this little slice of it is all about accelerating communications and the transformation of modern networks to do exactly that right, To help close the digital divide, to bring fair and equitable medicine and learning to all, um and to allow us all to work from wherever we're working. So it's uh it's something that we're excited about on multiple levels and we think the company is really built for the distributed computing environment that a modern telco network represents. >>Yeah, what's interesting is that the value that you guys can enable at the edge, his real impact, It's not just data center and compute and have applications. Remember the old days I got my crm in my E. R. P and I got my apps on my systems and it's all good now. Business is completely software enables, it's the entire business and the business is software naval, which means that you have to have that edge. So I totally love of the positioning and strategy. I have to ask you if you don't mind, where is the residents with customers when you look at the telco enablement there that you're enabling them to do what's resonating the most, what's jumping out from the telescopes in terms of what Dell's doing for them And the customers, you mentioned tele medicine, which by the way, is an amazing impact to the world. Just one example. But where's the residence? >>Yeah. You know, first we we are what we are. Right. So it's, I think with a lot of conversations, it begins with, um, the telecommunications network needs server technology, but it needs very specific kinds of server technology built in very specific ways. Um, the, you know, the needs of compute at the base of a cell tower on a hill in Montana in the middle of winter are different than we've been building for data centers for years. So I think the first thing that resonates it, I need it, I need a very specific kind of open compute, uh, infrastructure hardware foundation that is industry standard. And, and we turn to somebody like Delta do do exactly that. But what we've learned is there's so much more than that because really we need to begin to deliver outcomes on top of that foundation. Uh, First outcome, we need to deliver his modern operations and maintenance of a distributed network. Zero touch provisioning, zero touch upgrading. How can we impact the total cost of maintenance and ownership in a meaningful way, um, for a network that is in fact constructed out of a fabric of server. On top of that there's the actual network core network services, Edge, the radio access network. And how do we successively open up each section of the network, driving computing storage all the way to the edge? Because for many organizations in the world, many enterprises, their edge will actually be on the telco premises. Right. The telco edge will be their edge. Some of the bigger companies certainly can build their own. But as you get in the world of medium and small business, the person they buy their circuits from and their communications from. If they have the ability to deliver them private slices of networks and virtual compute and storage, that's going to be how they get after it. So you know for us that next piece that resonates is the ability to pull together solutions like we've been doing for years with the ex rail hyper converged the stuff we did with the C. E. Back in the day and then last >>I'm just saying that you know you're bringing up things that kind of sound. It's super complex physical plant and equipment. You're talking about real hard and purpose built devices in the past very operational technology oriented stuff and then that has to have I. T. Agility right? And then have scalability behind it and complete you know integration this is not obvious and easy. It's hard. >>Yeah. No I mean software doesn't run on software right? Software runs on hardware and so as much as a lot of the power and the interest comes from what the application can do underlying it all is a capability to distribute, compute and storage to where the application or the software wants to run or runs best. That's what's really cool about five G is its ability to do the stuff you mentioned earlier on, you know, the, the G Wiz stuff, drones and autonomous and a AR and VR and all the things that ultra reliable, low latency communication would make possible on a grand scale that really bring the machines into the picture, not just humans on the edge. It's the stuff, right? That that's on the edge and we've been talking about it for a long time, but none of it's gonna matter if we don't put this infrastructure foundation in place. Then we got to lay an open marketplace of containerized network functions. Virtualized network functions on top of that all to enable our network operators to deliver interesting services to end users. It's >>super exciting. I got to say that it's a super exciting because you know, it's coming it's like the energies there, it's like the, you know, the storm's coming of disruption in the innovation because you think about what containers and cloud native kubernetes the cloud native technologies can do for legacy because its shelf life and more headroom, right? So you can you can win these telcos can actually not only pivot but line extension into new capabilities. So they tend to be very strong technically is an operator, operator networks, the hard tech stuff, physical stuff and software but not known for it. I mean but now there's a huge opportunity that's gonna come around the corner. I'm bullish on Iot and edge where you have the O. T. And I. T. Coming together. It's really compelling And it's going to be radically different I think in the next 5 to 10 years what's your take on that in terms of outlook? >>Couldn't agree more. Yeah I mean it's you know it's for those of us are in the industry always the knowledge of what's coming or the belief in what's coming. The hype precedes the actual development. But you know just as I don't know 15 20 years ago the idea that you can completely disrupt the taxi industry with an app and a four G smartphone service was in nobody's mind except maybe a couple of people. You >>know it >>makes you wonder what is the what is the uber equivalent of a business service that will be fundamentally enabled by the architecture we just described that we're not thinking about right now and that's why every time we move from a centralized computing model to a decentralized computing models that decentralized computing models dramatically larger than a centralized, >>way >>bigger than mainframe. Edge, way bigger than client server, which is already way bigger than cloud, Public. Cloud. And so I think it's, you know, there's a, there's a lot of promise, a lot of excitement. Still a long way to go though. A lot of the stuff we're talking about still is not actually rolled out into the network. Um and that's kind of the opportunity for somebody like them. >>Yeah. And decentralized and open winds. It's funny you mentioned high, we were talking David was just talking with Michael Dell and Pat Gelsinger in 2013. We're talking hybrid cloud, that's 78 years ago. Okay, so good stuff. Let's get into the news real quick. Um Deltek World, you've got some news coming. Uh Let's dig into it. Please share some of the outlook of the news. You're gonna be you're you're announcing here? >>Yeah, thanks. Sure, john, I mean, we're gonna be announcing two things relative to the telecom portfolio. Uh and they're both reference architectures with VM ware. One is the second edition of the telco cloud platform for five G. Um, so that's a Delvian where reference architecture, that is exactly what we just talked about. It's this open software defined on industry standard hardware platform, um for running 5G applications. And then the other one is the first version of the telco cloud platform for the radio access network, TCP ran as we would call it. Um and as we start to push this technology from the core out towards the edge of the telecom network. So to really interesting developments in in deep partnership with VM ware and stuff, we've been working on for a while stuff, we are in fact working on with customers and delivering today and we'll be making formal announcements about those at the D T W show. >>Awesome. Dennis, thanks for coming on the Cuban, sharing the update and thanks for the industry insight. Uh, I love the telco shift that's going on. It's an extension of existing, I think cloud native saves the day here with telco and allows the completely different landscape to evolve. So you guys were on top of it. Thanks for sharing S VP and general manager, the telecom systems business with Dell Dennis. Hoffman. Thanks for coming on. >>Thanks john Okay >>cube coverage here. Del Tech world. I'm john for a year. Thanks for watching. Yeah.

(upbeat music) >> Hello, and welcome to this Cube special presentation of Cube On CloudStartups with AWS Showcase. I'm John Furrier, your host of theCUBE. This session is the accelerate digital transformation and simplify AWS with autonomous cloud operations with Venkat Krishnamachari, who's the CEO and co-founder here with me on remote. Venkat, good to see you. >> Great to see you, John. >> So this is a session on, essentially DAY2 operations. Something that we've been covering on theCUBE as you know, for a long time. But the big trend is as DevOps becomes much more mainstream, intelligent applications or agile applications, have to connect with intelligent infrastructure and your company MontyCloud has the solution that literally turns IT pros into cloud powerhouses as you guys say, it's your tagline. This is a super important area. I want to get your thoughts and showcase what you guys are doing as one of the hot 10 startups. Thanks for coming on. So take a minute to explain real quick. What is MontyCloud all about? >> Great, thank you again for the opportunity. Hey everybody, I'm Venkat Krishnamachari. I represent mandate team at MontyCloud. We are an intelligent cloud management platform company. What we help customers do, is we help them simplify their cloud operations so they can go innovate and develop intelligent applications. Our platform is called DAY2, because everything after the day one of going to Cloud, needs a lot of expertise and we decided that's a fun area to go solve for our customers. We solve everything on starting DAY2 from simplifying provisioning, to management, to operations, to autonomous cloud operations. Our platform does this for our customers so they can innovate faster and they can close the cloud skills gap that is required to empower the developers. >> Venkat, I want to get your thoughts on DAY2 operations. There's been a trend that people talk about for a long time. As people move to the cloud and see the economic advantage of certainly with COVID-19, the market has said, "Hey, if you're on cloud native, you win." Andy Jassy at re:Invent last Keynote really laid out how companies can be proficient in becoming cloud-scale advantages. One of them was have expertise in cloud. So everyone is kind of doing that. You're starting to see enterprises all build the muscle for cloud operations. That's day one, they get started. Then that's kind of the challenges and the opportunities kick in when you have to continue in production. You have things that go on in the software. The underlying scaling infrastructure needs to be scaled out or all these kinds of things happen. This is what DAY2 is all about, keeping track of and maintaining high availability, uptime and keep the cost structure in line. This is what people discover. If they don't think properly about the architecture, they have huge problems. You guys solve this problem. Could you explain why this is important. >> Sure thing, John. So cloud operations, as you described, it's a continuous operations and continuous improvement in cloud environments. What efficient cloud operations does for customers is it accelerates innovation, reduces the risk, and more importantly, all the period of time that they are using their applications in the cloud, which is future, reduces the total cost of cloud operations. This is important because there is a huge gap in cloud skills. The surface area of cloud that customers need to manage is growing by the day. And most importantly, developers are increasingly and rightfully so, getting a seat at the table in defining and accelerating company's cloud journey. Which means, now they're proposing, microservices based application, container based application. Traditional applications are still in the mix. Now the surface area becomes a challenge for the IT operators to manage. That's why it's very important to start right. See, we ask this question to our customers. Having listened to our customers as hundreds of them, one thing is clear, when we ask this question to our customers, ever wonder why and how large scale companies like AWS are able to deliver massively scalable services and operate massive data centers with fewer people? Because it's automation. And it's important to think about, as you scale, automate a way things that must be automated, eliminate undifferentiated heavy lifting and help your developers move fast. All of this is vital in the day and age we live in, John. >> Yeah, I want to double down on that because I think this idea of integrating into operations is a critical key point for where success and failure kind of happen. We've seen with cloud, certainly IT departments and enterprise is going okay, cost optimization, check. Get cloud native, getting the cloud, lift and shift, I thought it through, I put some stuff in the cloud and then they go great, now I need resilience. I need resiliency, and I want to make sure things are now working okay, water flowing through the pipes, cloud's working. Then they say, "Well this is good, I got to need to integrate in with my own premises or edge or other things that are happening." Then they try to integrate into their core operations. McKinsey calls this the value driver three, integrating into core operators. We heard from them earlier in the program here at this event. This is key, it's not trivial to integrate cloud into your operations. This is what DAY2 and beyond is all about. Talk more about that. >> Yeah, that's a great point. And that's something that we've been working with customers to hands-on help learn and build it for them, right? So the acceleration of cloud adoption during the pandemic and ongoing adoption, it's going to shift the software security compliance and operational landscape dramatically. There's no escaping it. Cloud operations will no longer be an afterthought. DevOps will integrate with CloudOps. It'll provide a seamless feedback loop so that a box can be found sooner, fixed sooner, and uptime can be guaranteed. I'll give an example. One of our customers is a university. During the pandemic, their core examination application went down and they couldn't fix it on time because of lack of resources. For them, it's vital to have adopted cloud operations sooner but the runway they had was very little. Fortunately, we had the solution for them there. Within a week, they were able to take their entire on-prem application online, not just take the application but provide an autonomous cloud operations layer to their existing IT team with our platform, upscale them, and then about 14,000 students took their exams without any disruption. Now this customer and customers such as themselves have come to expect that level of integrated cloud operations into their application portfolio. It's important to address that with a platform that simplifies it. >> Venkat, real quick. Define, what is autonomous CloudOps platform? What does that mean? >> So let's take an example here, right? Customers who are trying to move an existing workload to cloud bring a traditional set of application. Then customers who are born in the cloud build microservices or server less based applications. Then there is containers. Now, all three the person surface areas that customers, particularly the IT teams have to manage. With the growing surface area, with the adoption of infrastructure as core, it becomes more nuanced to think about, how do we simplify? And in simplification comes automation. When a developer provision certain resource, previously, they used to be filing a ticket. Central IT team has to respond. Developers don't want that anymore. They want to innovate faster but at the same time Central IT team wants to have some governance in play. The best way to get out of the way of developers is automating it. And providing autonomous cloud operations means developers can deploy newer workloads faster, but with a level of guaranteed guardrail on security compliance and costs that sets them free. This is what we mean by autonomous cloud operations, closing the gap in skills, closing the gap in tooling, empowering your developers without thinking about the traditional model but enabling them to do things that's more in a rapid pace. That's what we mean by autonomous cloud operations. >> You had a great market opportunity. I think this is obviously a no brainer. As people say in the industry "cloud is scale is proven". Even post COVID if people don't have a cloud growth strategy they're pretty much going to be toast. McKinsey calls this a trillion dollar at a minimum not including potential new use cases, new pioneering applications coming. So pretty much, well the verdict is there, this is cloud. I got to ask you about MontyCloud as you guys have a business. Give or take a quick minute to explain the business of MontyCloud, some vitals or how people buy the product, the business model. Take a quick minute to explain MontyCloud business. >> Sure thing. John, see, our entire goal is to simplify cloud operations. Because what we learned is what seems to be complex about cloud adoption is that everybody is expected to be an expert on everything in the new era, but most teams are not ready to run efficient cloud operations at scale, as the cloud footprint is growing. This means we have to redefine certain conversations here. We talk directly to infrastructure architects, cloud architects, application owners. And in general, we talk to people who are leading their IT digital transformation for their companies. What we are enabling our customers is, they must demand that the traditional operation model must change to enable newer application patterns. For this, we are expecting customers want to standardize things, right? IT leaders are beginning to say, "All right, I got to standardize my provisioning, standardize my operations, reduce the heavy lifting that comes with infrastructure's code, and enable the business team and the application team to work closely together." The best way to do that is to go solve this problem with automation. So our platform is able to go help such customers, particularly leaders who demand digital transformation. With clear KPIs, our platform can help them ask the why question easily. And then our platform can also go perform, the how part of automation. That's what we solve. Those are the kinds of customers we really have been working with, John. >> So if I'm a customer, how do I know when I need to call MontyCloud? Is it because my cloud footprint is growing which is a natural sign of growth, or is it because I have more events happening, more things to manage? When do I know I have the need to call you guys? What's the signal? What's the sign? >> So we call it the day one mindset, and also the DAY2 mindset. Customers deciding to go to cloud on day one, should think about DAY2. Because without thinking about DAY2, it can become very expensive, right? When a customer's thinking about digital transformation, could be a lift and shift or it could be starting a new application pattern in the cloud, we can certainly help starting right that day because there are a couple of things they have to do, right? They have to standardize the cloud operations which means setting up the cloud accounts, setting up guardrails, enabling teams to go provision with self service. You want to start the right way. So we are happy to help on the day one journey itself and we can automate DAY2 along with it. So standardizing infrastructure operations, standardizing provisioning, security, visibility, compliance, cost. If any of this is an important milestone that customers have to achieve in their cloud journey, we can help. >> By the way, I would just point out that we were just talking on another session around lift and shift is not a no-brainer either if not thought through and remediated correctly that cost could go through the roof. I mean, we've seen evidence of lift and shift fails just because they didn't think it through. Just to your point. I mean, that's not a no brainer. Quickly explain why lift and shift is not as easy as it looks. >> Sure thing. So lift and shift is great to get started, but why sometimes it fails is that the connotations about wanting to keep your Opex down while giving up CapEx is at odds with each other, right? Cloud is great for reducing your Capex. But ongoing operations, of the DAY2 operations, can add a lot of burden to the operational expenses. What customers find out is after moving to the cloud, the cost overruns are happening because of resources that are not provisioned correctly, resources that should not be running. Wild Wild West kind of scenarios, where everybody has access to everything and they over provision. All of this together end up impacting customers' ability to go control the Opex. Then digital transformation projects are looked at from three different angles at least, right? Cost is definitely one, security is another, and then the ongoing operational tax with respect to monitoring, governance, remediation. All three when it simultaneously hits our customers, they look at lift and shift and saying, "Hey, this was cheaper on prem." But actually in the long run, this will be not just cheaper on the cloud, it can also be more efficient if they do it right. We can talk about some examples on how we help some customers with that helpful, John. >> Well, I want to get into the cloud operations, the whole dashboard in cloud operation administration. Is there anything that you could share because people are wanting more and more analytics. I mean, they're buying everything in sight. I mean, cyber security, you name it. There's more and more dashboards. No one wants another dashboard. So this is something that you guys have a strong opinion on how to think this through. Because again, at the end of the day, if you're instrumenting your network properly and your applications, your intelligence, things are changing, where's the data? Take us through your thinking around that. >> Sure thing. You are spot on. Nobody wants another dashboard that is just spewing data at them because data, without context is irrelevant in our mind, right? We want to be able to provide context, we want to be able to provide data within the context. And the dashboard to us means a customer that's looking at it, an IT leader looking at it should be able to ask the why question without working too hard at it, right? Let's bring up our dashboard. I would love to show and tell, although it's a dashboard, it is a tool that can enable IT leaders do things differently. >> John: Right, here it is. This is it right here. Okay, so this is the dashboard. Take me through it, what does it mean? >> Venkat: Yeah, let's (indistinct) right? The chart in the middle is the most important piece there. What we help our leaders, IT leaders do is, all the fullness of time of cloud adoption, we know the cloud's footprint is going to grow. The gray chart in the back, the stock chart represents the cloud footprint. As the cloud footprint continues to grow, we would like our leaders to demand that their security issues go down, their compliance issues go down and their costs to become more and more optimum. When leaders demand this, they can make things happen and our platform can help reduce all three and leaders can have this kind of dashboard to ask the why question. For example, they can compare one department with another department, ask that why question. They can compare an application that is similar in one department in another department and ask the why question, why is it more expensive? Why is it having more compliance issues? This is the kind of why questions our dashboard helps our customers perform and ask those questions, and they don't have to lift a finger, right? This entire dashboard comes to life within few minutes of them connecting their cloud accounts, where we provide visibility into operational issues, trend lines of data on how much consumption happens. And over a couple of months, they can see for themselves, make overall operation cost going down. Is my IT infrastructure now in cloud more resilient? And doesn't take more people to do it or am I able to turn on MontyClouds DAY2 bonds to go start reducing that burden or the period of time. This is what we mean by putting the power of autonomous CloudOps in our hands for customers. >> And this is what you mean by the IT powerhouse for the cloud. Is this on Amazon? So if I want to consume the product, what do I need to do to engage with you guys? What does it mean to me? Am I buying a service? Is it native? Is there agents involved? Take me through, what do I need to do? >> It's a great question. We are born in the cloud startup, which means we are super thankful for amazing technologies like Amazon infrastructure as core and the venting platform that's out there. So our platform is fully hosted, managed SaaS platform. A customer does not need to do anything but log onto montycloud.com, click a bunch of buttons, and connect their database account. They get started in under five minutes, self-service. And as they go through the platform, the guided experience where they can get to that dashboard I showed you in just a few clicks. They can get visibility, security posture assessment, compliance posture assessment, all in those few clicks. And when they decide to start using the platform more to automate and leverage the bots, they can always buy into additional services in the platform. So it's a easy to use get started in 10 minutes tops, if you will, that kind of platform >> Okay, great stuff. I want you to take me through the intelligent application flywheel that's going on here. So I can imagine that as the flywheel of success happens. Okay, got some intelligent apps, I see the dashboard, I'm getting some more visibility on the value creation, unlocking more value, new use case, all the things that happen in cloud, all good. And then I start growing, but I got builders trying to build more applications, more demand for more applications, more pressure on the infrastructure. The next question's, how do you guys simplify the cloud operation equation? Because I got to add more VPCs, I got to do more infrastructure, is it more EC Two? It can get complicated. How do you guys solve that problem? Because if the cloud footprint starts to grow because of more intelligent applications, how do you guys make it easier and simpler to scale up the intelligent infrastructure? >> Oh, that's a great question again, John. I'm going to go into a little bit of a detailed slide here. But before I do that, let's talk about two customers that we helped, right? This slide on the left, talks to those, both the customers. So what we have learned working with customers is, they have to build cloud accounts, manage cloud regions, user onboarding. Then they have to build networking infrastructure. Then they have to enable application infrastructure on top of the networking infrastructure. Application infrastructure could mean they want high-performance computing workloads or elastic services, such as queuing services, storage, or traditional VMS databases. That's a lot to build in the application infrastructure with infrastructure scope. On top of that, our customers have to deal with visibility, security, compliance costs. You get it, right? The path to intelligent applications is not easy because cloud is powerful, but it's broad, and the talent required is deep. We are able to say, how can we help our customers automate everything below the intelligent application layer. If we can do that, which we do, we can now propel our developers to go build intelligent applications without having the of also managing the underlying infrastructure. And we can help the IT operations team become cloud powerhouses because they get out of the way and enabled. Give you two examples here, right? One of our customers is a fortune 200 large ISP. They have about 10,000 servers in a particular department. And previously, when the servers were on premises, they had about a four member team managing compliance for it. When they lifted and shifted these servers into the cloud, the same model they wanted to... There are leaders that asked "Why should we continue with the same model?" They wanted MontyCloud. Now there is a DAY2 compliance board that's running, managing the 10,000 servers automatically watching on for compliance drifts, notifying them in a Slack channel, gets approval, remediates and fixes it. They were able to take those four folks and put them on the intelligent application side, I suppose to continuous infrastructure management site. Another example, a fortune 200 global networking company. It's an interesting situation, John. So on cyber Monday, they wanted to go big of obviously the cyber Monday was very important for them. The Thursday before cyber Monday, their on-premises data center and application went down and their teams wanted to move the application to cloud. And the partner that we work with, that brought this challenge to us saying hey, this fortune customer wants to go to cloud and we have this weekend. Well, we were able to go guide the partner and with our platform they were able to not only take their application from on-prem to cloud, they set up the cloud infrastructure, the networking, the application layer, the monitoring layer, the operations layer, all of that within a day. And on Monday that application delivered three X sales for this customer, without that partner or the customer being a cloud expert. That's what we mean by putting that kind of power in the hands of customers. >> Yeah, and I want to go back to that slide 'cause I think there's a second section I want to look at because what you just referred to is, I think this builds into the next comment on the right-hand side, this DAY2 kind of console vision here. The idea of getting in the weeds and getting into the troubleshooting of say, that cyber Monday example is exactly the non agility scenario, right? Because, if anyone's ever worked in tech knows when you have to get to root cause on something, it can take a while, right? So you need to have the system architecture built out. So here, classic cloud architecture on the left moves to a simple kind of console model. That's kind of what you guys are offering. Am I getting that right, Venkat? Is that kind of how this works? >> Yeah, that's kind of how it works, but the path to that maybe, a quick explanation though. We look at what's on the right--- >> Put that slide back up, let's get that slide back. Okay, there it is. >> Venkat: So what's on the right side here is, every layer on the left requires specialized talent and specialized tooling. That's all customers are currently experienced in the cloud. They either have to buy into a expensive monitoring tool or buy into an expensive security posture management tool. They have to hire, you know... It's hard to find cloud talent, right? And then they have to use infrastructure as code solutions. Sometimes that is, that can get more complex to maintain. What we have in MontyCloud is that, every layer there, they can provision by clicking away. For example, when they provision their cloud accounts setting up AWS best practices, budget guardrails, security, logging and monitoring, they can click away and do it. Setting up network infrastructure like VPC is setting up AWS transit gateway, VPNs, there's templates they can click and do it. The application infrastructure, which is a growing set of application infrastructure. Imagine this John, if a developer can come in and request the IT team they would like to set up an RDS database, right? The IT team can now with DAY2, can provide the developer options of, do you want it in dev stage prod? And do you want snapshots, backup, high availability? These are all check boxes and the developer can pick and choose and they can provision what they want without additional help from the IT team. And the IT team does not have to automate any one of those because it's pre automated in our platform. >> Yeah, this is the promise of infrastructure as code. You don't got to get in to the architecture and start throwing switches and all kinds of weird stuff can happen. Someone doesn't turn off, they don't enable auto-scale and they tested for this they forgot to revert back. I mean, there's a zillion things that could go wrong, human error, as well as automation. So once you set it up, then you provide a consumable developer friendly approach. That seems to be what's happening. Okay, cool. All right, well Venkat, this is fantastic. Final minutes we have left. I want to get your thoughts on the momentum and the vision. Talk about the momentum that you guys have now in the marketplace and what's the vision for the next five years. >> Great, it's a great question. From a momentum perspective John, we take an approach of, let's work with customers and understand that we can solve some problems for them. We've been working backups with customers. We have customers that are startups, that are born in the cloud, we have customers that are enterprise customers who are having a large footprint on-prem. Then we have everybody in between like university customers who are transitioning off. So what we did is from a momentum perspective, we worried more about, do we understand the talent gap and the tooling gap that exists across the board of all customers? Because every customer, once they go to cloud, they look to achieving the same level of efficiency and simplicity like modern cloud companies. A traditional company that moves to cloud wants to act and behave like the one in the cloud customer. For us it was very important to understand a variety of customers, a variety of use cases, and then automated away. So momentum is that we are able to go help a customer that is a Greenfield customer to go to cloud easily. And we're also able to go help brownfield customers, ensure they can reduce the total cost of cloud operations on an ongoing basis. So we've been seeing customers of all sizes, even helping customers of all sizes move fast. And there's a bunch of case studies out there in our website. We are a startup, so we've been able to help those customers and earn their trust by delivering results for them. So the momentum is that, we are able to go scale up now, and scale up fast for our customers without us being in the way, technically. Or customers can go to our platform help themselves and accelerate the platform. That's the momentum we have. From a future perspective, you asked, where things are headed, right? There are a couple of things. First things first, it's important to not just predict the future, we got to create it, right? About two years back when we founded MontyCloud, the question my team asked me, my CTO asked me is, what really matters in cloud ranking, right? So we said, all right, this is provisioning automation management. Yeah, they all matter. But what seemed to really matter is there are three things that matter. That's how we came to... One is events. The cloud itself is an eventing machine, right? More than ever, the cloud infrastructure emits events at every turn, every resource, every activity is expressed as an event. So we made an early bet on building an event driven platform from the ground up. We are the only platform that is even driven. Every other platform is seen to try and solve problems which is awesome to have, but they take an approach of an API based model or an inference into log based model. So the future, we believe, belongs to eventing model because it's lightweight on the customer's infrastructure, it goes easy on the cloud providers. More importantly, it gets the customer as close as possible to when the event happens, right? That's very important, to be able to be even event-driven. If you noticed Cloud Native Foundation came up and announced recently cloud events is the right way to deal with modern SaaS platforms. We've been in cloud events from day one for us, right? So the future is in eventing model. >> And that's where the data angle, I think, connects here for this event and why you guys are a hot startup is, observability, all these things. It's all about a event driven infrastructure. It's all events. It's monitoring, it's management, it's data. At the end of the day, the data is the instrumentation, is what it is. Developers are coding. Media's data. Everything's data. Everything has to do with data. You guys have a unique approach. Venkat Krishnamachari, thank you for coming on. Appreciate it, and thanks for sharing your story here at the AWS Showcase. First inaugural Cube On CloudStartups, part of the 10 hot startups categories. Thanks for sharing. >> Thanks for the opportunity. And we hope to help a lot more customers, simply for the cloud operations and innovate with some intelligent applications that's going to change the world. >> Check out Venkat and his company all on Twitter, on Facebook, they're on every channel, all the channels are open, of course. theCUBE we're bringing you all the hot startups, extracting the signal from the noise. I'm John furrier. Thanks for watching. (Upbeat music)

>>Yeah, >>yeah. Hello and welcome to this track to creating engaging analytics experiences for all. I'm Hannah Sinden Thought spots Omiya director of marketing on. I'm delighted to have you here today. A boy Have we got to show for you now? I might be a little bit biased as the host of this track, but in my humble opinion, you've come to a great place to start because this track is all about everything. Thought spot. We'll be talking about embedded search in a I thought spot one spot I. Q. We've got great speakers from both thoughts about andare customers as well as some cool product demos. But it's not all product talk. We'll be looking at how to leverage the tech to give your users a great experience. So first up is our thoughts about one deep dive. This session will be showing you how we've built on our already superb search experience to make it even easier for users across your company to get insight. We've got some great speakers who are going to be telling you about the cool stuff they've been working on to make it really fantastic and easy for non technical people to get the answers they need. So I'm really delighted to introduce Bob Baxley s VP of design and experience That thought spot on Vishal Kyocera Thought spots director of product management. So without further ado, I'll hand it over to Bob. Thanks, >>Hannah. It's great to be here with everybody today and really excited to be able to present to you thought spot one. We've been working on this for months and months and are super excited to share it before we get to the demo with Shawl, though, I just want to set things up a little bit to help people understand how we think about design here. A thought spot. The first thing is that we really try to think in terms of thought. Spot is a consumer grade product, terms what we wanted. Consumer grade you x for an analytics. And that means that for reference points rather than looking at other enterprise software companies, we tend to look at well known consumer brands like Google, YouTube and WhatsApp. We firmly believe that people are people, and it doesn't matter if they're using software for their own usage or thought are they're using software at work We wanted to have a great experience. The second piece that we were considering with thoughts about one is really what we call the desegregation of bundles. So instead of having all of your insights wraps strictly into dashboards, we want to allow users to get directly to individual answers. This is similar to what we saw in music. Were instead of you having to buy the entire album, of course, you could just buy individual songs. You see this in iTunes, Spotify and others course. Another key idea was really getting rid of gate keepers and curators and kind of changing people from owning the information, helping enable users to gather together the most important and interesting insights So you can follow curator rather than feeling like you're limited in the types of information you can get. And finally, we wanted to make search the primary way, for people are thinking about thought spot. As you'll see, we've extended search from beyond simply searching for your data toe, also searching to be able to find pin boards and answers that have been created by other people. So with that, I'll turn it over to my good friend Rachel Thio introduce more of thought, spot one and to show you a demo of the product. >>Thank you, Bob. It's a pleasure to be here to Hello, everyone. My name is Michelle and Andy, product management for Search. And I'm really, really excited to be here talking about thoughts about one our Consumer analytics experience in the Cloud. Now, for my part of the talk, we're gonna first to a high level overview of thoughts about one. Then we're going to dive into a demo, and then we're gonna close with just a few thoughts about what's coming next. So, without any today, let's get started now at thought spot. Our mission is to empower every user regardless of their expertise, to easily engage with data on make better data driven decisions. We want every user, the nurse, the neighborhood barista, the teacher, the sales person, everyone to be able to do their jobs better by using data now with thoughts about one. We've made it even more intuitive for all these business users to easily connect with the insights that are most relevant for them, and we've made it even easier for analysts to do their jobs more effectively and more efficiently. So what does thoughts about one have? There's a lot off cool new features, but they all fall into three main categories. The first main category is enhanced search capabilities. The second is a brand new homepage that's built entirely for you, and the third is powerful tools for the analysts that make them completely self service and boost their productivity. So let's see how these work Thought Spot is the pioneer for search driven analytics. We invented search so that business users can ask questions of data and create new insights. But over the years we realized that there was one key piece off functionality that was missing from our search, and that was the ability to discover insights and content that had already been created. So to clarify, our search did allow users to create new content, but we until now did not have the ability to search existing content. Now, why does that matter? Let's take an example. I am a product manager and I am always in thought spot, asking questions to better understand how are users are using the product so we can improve it now. Like me, A lot of my colleagues are doing the same thing. Ah, lot of questions that I asked have already been answered either completely are almost completely by many of my colleagues, but until now there's been no easy way for me to benefit from their work. And so I end up recreating insights that already exists, leading to redundant work that is not good for the productivity off the organization. In addition, even though our search technology is really intuitive, it does require a little bit of familiarity with the underlying data. You do need to know what metric you care about and what grouping you care about so that you can articulate your questions and create new insights. Now, if I consider in New employees product manager who joins Hotspot today and wants to ask questions, then the first time they use thought spot, they may not have that data familiarity. So we went back to the drawing board and asked ourselves, Well, how can we augment our search so that we get rid off or reduced the redundant work that I described? And in addition, empower users, even new users with very little expertise, maybe with no data familiarity, to succeed in getting answers to their questions the first time they used Hot Spot, and we're really proud and excited to announce search answers. Search answers allows users to search across existing content to get answers to their questions, and its a great compliment to search data, which allows them to search the underlying data directly to create new content. Now, with search answers were shipping in number of cool features like Answer Explainer, Personalized search Results, Answer Explorer, etcetera that make it really intuitive and powerful. And we'll see how all of these work in action in the demo. Our brand new homepage makes it easier than ever for all these business users to connect with the insights that are most relevant to them. These insights could be insights that these users already know about and want to track regularly. For example, as you can see, the monitor section at the top center of the screen thes air, the KP eyes that I may care most about, and I may want to look at them every day, and I can see them every day right here on my home page. By the way, there's a monitoring these metrics in the bankrupt these insights that I want to connect with could also be insights that I want to know more about the search experience that I just spoke about ISS seamlessly integrated into the home page. So right here from the home page, I can fire my searchers and ask whatever questions I want. Finally, and most interestingly, the homepage also allows me to connect with insights that I should know about, even if I didn't explicitly ask for them. So what's an example? If you look at the panel on the right, I can discover insights that are trending in my organization. If I look at the panel on the left, I can discover insights based on my social graph based on the people that I'm following. Now you might wonder, How do we create this personalized home page? Well, our brand new, personalized on boarding experience makes it a piece of cake as a new business user. The very first time I log into thought spot, I pay three people I want to follow and three metrics that I want to follow, and I picked these from a pool of suggestions that Ai has generated. And just like that, the new home page gets created. And let's not forget about analysts. We have a personalized on boarding experience specifically for analysts that's optimized for their needs. Now, speaking of analysts, I do want to talk about the tools that I spoke off earlier that made the analysts completely self service and greatly boost their productivity's. We want analysts to go from zero to search in less than 30 minutes, and with our with our new augmented data modeling features and thoughts about one, they can do just that. They get a guided experience where they can connect, model and visualize their data. With just a few clicks, our AI engine takes care off a number of tasks, including figuring out joints and, you know, cleaning up column names. In fact, our AI engine also helps them create a number of answers to get started quickly so that these analysts can spend their time and energy on what matters most answering the most complicated and challenging and impactful questions for the business. So I spoke about a number of different capabilities off thoughts about one, but let's not forget that they are all packaged in a delightful user experience designed by Bob and his team, and it powers really, really intuitive and powerful user flows, from personalized on boarding to searching to discover insights that already exist on that are ranked based on personalized algorithms to making refinements to these insights with a assistance to searching, to create brand new insights from scratch. And finally sharing all the insights that you find interesting with your colleagues so that it drives conversations, decisions and, most importantly, actions so that your business can improve. With that said, let's drive right into the demo for this demo. We're going to use sales data set for a company that runs a chain off retail stores selling apparel. Our user is a business user. Her name is Charlotte. She's a merchandiser, She's new to this company, and she is going to be leading the genes broader category. She's really excited about job. She wants to use data to make better decisions, so she comes to thought spot, and this is what she sees. There are three main sections on the home page that she comes to. The central section allows you to browse through items that she has access to and filter them in various ways. Based for example, on author or on tags or based on what she has favorited. The second section is this panel on the right hand side, which allows her to discover insights that are trending within her company. This is based on what other people within her company are viewing and also personalized to her. Finally, there's this search box that seamlessly integrated into the home page. Now Charlotte is really curious to learn how the business is doing. She wants to learn more about sales for the business, so she goes to the search box and searches for sales, and you can see that she's taken to a page with search results. Charlotte start scanning the search results, and she sees the first result is very relevant. It shows her what the quarterly results were for the last year, but the result that really catches her attention is regional sales. She'd love to better understand how sales are broken down by regions. Now she's interested in the search result, but she doesn't yet want to commit to clicking on it and going to that result. She wants to learn more about this result before she does that, and she could do that very easily simply by clicking anywhere on the search result card. Doing that reveals our answer. Explain our technology and you can see this information panel on the right side. It shows more details about the search results that she selected, and it also gives her an easy to understand explanation off the data that it contains. You can see that it tells her that the metrics sales it's grouped by region and splitter on last year. She can also click on this preview button to see a preview off the chart that she would see if she went to that result. It shows her that region is going to be on the X axis and sales on the Y axis. All of this seems interesting to her, and she wants to learn more. So she clicks on this result, and she's brought to this chart now. This contains the most up to date data, and she can interact with this data. Now, as she's looking at this data, she learns that Midwest is the region with the highest sales, and it has a little over $23 million in sales, and South is the region with the lowest sales, and it has about $4.24 million in sales. Now, as Charlotte is looking at this chart, she's reminded off a conversation she had with Suresh, another new hire at the company who she met at orientation just that morning. Suresh is responsible for leading a few different product categories for the Western region off the business, and she thinks that he would find this chart really useful Now she can share this chart with Suresh really easily from right here by clicking the share button. As Charlotte continues to look at this chart and understand the data, she thinks, uh, that would be great for her to understand. How do these sales numbers across regions look for just the genes product category, since that's the product category that she is going to be leading? And she can easily narrow this data to just the genes category by using her answer Explorer technology. This panel on the right hand side allows her to make the necessary refinements. Now she can do that simply by typing in the search box, or she can pick from one off the AI generated suggestions that are personalized for her now. In this case, the AI has already suggested genes as a prototype for her. So with just a single click, she can narrow the data to show sales data for just jeans broken down by region. And she can see that Midwest is still the region with the highest sales for jeans, with $1.35 million in sales. Now let's spend a minute thinking about what we just saw. This is the first time that Charlotte is using Thought spot. She does not know anything about the data sources. She doesn't know anything about measures or attributes. She doesn't know the names of the columns. And yet she could get to insights that are relevant for her really easily using a search interface that's very much like Google. And as she started interacting with search results, she started building a slightly better understanding off the underlying data. When she found an insight that she thought would be useful to a colleague offers, it was really seamless for her to share it with that colleague from where she Waas. Also, even though she's searching over content that has already been created by her colleagues in search answers. She was in no way restricted to exactly that data as we just saw. She could refine the data in an insight that she found by narrowing it. And there's other things you can do so she could interact with the data for the inside that she finds using search answers. Let's take a slightly more complex question that Charlotte may have. Let's assume she wanted to learn about sales broken down by, um, by category so that she can compare her vertical, which is jeans toe other verticals within the company. Again, she can see that the very first result that she gets is very relevant. It shows her search Sorry, sales by category for last year. But what really catches her attention about this result is the name of the author. She's thrilled to note that John, who is the author of this result, was also an instructor for one off for orientation sessions and clearly someone who has a lot of insight into the sales data at this company. Now she would love to see mawr results by John, and to do that, all she has to do is to click on his name now all of the search results are only those that have been authored by John. In fact, this whole panel at the top of the results allow her to filter her search results or sort them in different ways. By clicking on these authors filter, she can discover other authors who are reputed for the topic that she's searching for. She can also filter by tags, and she can sort these results in different ways. This whole experience off doing a search and then filtering search results easily is similar to how we use e commerce search engines in the consumer world. For example, Amazon, where you may search for a product and then filter by price range or filter by brand. For example, Let's also spend a minute talking about how do we determine relevance for these results and how they're ranked. Um, when considering relevance for these results, we consider three main categories of things. We want to first make sure that the result is in fact relevant to the question that the user is asking, and for that we look at various fields within the result. We look at the title, the author, the description, but also the technical query underpinning that result. We also want to make sure that the results are trustworthy, because we want users to be able to make business decisions based on the results that they find. And for that we look at a number of signals as well. For example, how popular that result is is one of those signals. And finally, we want to make sure the results are relevant to the users themselves. So we look at signals to personalize the result for that user. So those are all the different categories of signals that we used to determine overall ranking for a search result. You may be wondering what happens if if Charlotte asks a question for which nobody has created any answer, so no answers exist. Let's say she wants to know what the total sales of genes for last year and no one's created that well. It's really easy for her to switch from searching for answers, which is searching for content that has already been created to searching the data directly so she can create a new insight from scratch. Let's see how that works. She could just click here, and now she's in the search data in her face and for the question that I just talked about. She can just type genes sales last year. And just like that, she could get an answer to her question. The total sales for jeans last year were almost $4.6 million. As you can see, the two modes off search searching for answers and searching, the data are complementary, and it's really easy to switch from one to the other. Now we understand that some business users may not be motivated to create their own insights from scratch. Or sometimes some of these business users may have questions that are too complicated, and so they may struggle to create their own inside from scratch. Now what happens usually in these circumstances is that these users will open a ticket, which would go to the analyst team. The analyst team is usually overrun with these tickets and have trouble prioritizing them. And so we started thinking, How can we make that entire feedback loop really efficient so that analysts can have a massive impact with as little work as possible? Let me show you what we came up with. Search answers comes with this system generated dashboard that analysts can see to see analytics on the queries that business users are asking in search answers so it contains high level K P. I is like, You know how many searches there are and how many users there are. It also contains one of the most popular queries that users are asking. But most importantly, it contains information about what are popular queries where users are failing. So the number on the top right tells you that about 10% off queries in this case ended with no results. So the user clearly failed because there were no results on the table. Right below it shows you here are the top search queries for original results exist. So, for example, the highlighted row there says jean sales with the number three, which tells the analysts that last week there were three searches for the query jean sales and the resulted in no results on search answers. Now, when an analyst sees a report like this, they can use it to prioritize what kind of content they could be creating or optimizing. Now, in addition to giving them inside into queries which led to no results or zero results. This dashboard also contains reports on creatives that lead to poor results because the user did get some results but didn't click on anything, meaning that they didn't get the answer that they were looking for. Taking all these insights, analysts can better prioritize and either create or optimize their content to have maximum impact for their business users with the least amount of for. So that was the demo. As you can see with search answers, we've created a very consumer search interface that any business user can use to get the answers to their questions by leveraging data or answers that have already been created in the system by other users in their organization. In addition, we're creating tools that allow analysts toe create or optimized content that can have the highest impact for these business users. All right, so that was the demo or thoughts about one and hope you guys liked it. We're really excited about it. Now Let me just spend a minute talking about what's coming next. As I've mentioned before, we want to connect every business user with the insights that are most relevant for them, and for that we will continue to invest in Advanced AI and personalization, and some of the ways you will see it is improved relevance in ranking in recommendations in how we understand your questions across the product within search within the home page everywhere. The second team that will continue to invest in is powerful analyst tools. We talked about tools and, I assure you, tools that make the analysts more self service. We are committed to improving the analyst experience so that they can make the most off their time. An example of a tool that we're really excited about is one that allows them to bridge the vocabulary difference that this even business user asks questions. A user asked a question like revenue, but the column name for the metric in the data set its sales. Now analysts can get insights into what are the words that users air using in their questions that aren't matching anything in the data set and easily create synonyms so that that vocabulary difference gets breached. But that's just one example of how we're thinking about empowering the analysts so that with minimal work, they can amplify their impact and help their business users succeed. So there's a lot coming, and we're really excited about how we're planning to evolve thoughts about one. With all that said, Um, there's just, well, one more thing that my friend Bob wants to talk to you guys about. So back to you, Bob. >>Thanks, Michelle. It's such a great demo and so fun to see all the new work that's going on with thought. Spot one. All the happenings for the new features coming out that will be under the hood. But of course, on the design side, we're going to continue to evolve the front end as well, and this is what we're hoping to move towards. So here you'll see a new log in screen and then the new homepage. So compared to the material that you saw just a few minutes ago, you'll notice this look is much lighter. A little bit nicer use of color up in the top bar with search the features over here to allow you to switch between searching against answers at versus creating new answers, the settings and user profile controls down here and then on the search results page itself also lighter look and feel again. Mork color up in the search bar up the top. A little bit nicer treatments here. We'll continue to evolve the look and feel the product in coming months and quarters and look forward to continue to constantly improving thoughts about one Hannah back to you. >>Thanks, Bob, and thank you both for showing us the next generation of thought spot. I'd love to go a bit deeper on some of the points you touched on there. I've got a couple of questions here. Bob, how do you think about designing for consumer experience versus designing for enterprise solutions? >>Yes, I mentioned Hannah. We don't >>really try to distinguish so much between enterprise users and consumer users. It's really kind of two different context of use. But we still always think that users want some product and feature and experience that's easy to use and makes sense to them. So instead of trying to think about those is two completely different design processes I think about it may be the way Frank Lloyd Wright would approached architecture. >>Er I >>mean, in his career, he fluidly moved between residential architecture like falling water and the Robie House. But he also designed marquis buildings like the Johnson wax building. In each case, he simply looked at the requirements, thought about what was necessary for those users and designed accordingly. And that's really what we do. A thought spot. We spend time talking to customers. We spend time talking to users, and we spent a lot of time thinking through the problem and trying to solve it holistically. And it's simply a possible >>thanks, Bob. That's a beautiful analogy on one last question for you. Bischel. How frequently will you be adding features to this new experience, >>But I'm glad you asked that, Hannah, because this is something that we are really really excited about with thoughts about one being in the cloud. We want to go really, really fast. So we expect to eventually get to releasing new innovations every day. We expect that in the near future, we'll get to, you know, every month and every week, and we hope to get to everyday eventually fingers crossed on housing. That can happen. Great. Thanks, >>Michelle. And thank you, Bob. I'm so glad you could all join us this morning to hear more about thoughts about one. Stay close and get ready for the next session. which will be beginning in a few minutes. In it will be introduced to thoughts for >>everywhere are >>embedded analytics product on. We'll be hearing directly from our customers at Hayes about how they're using embedded analytics to help healthcare providers across billing compliance on revenue integrity functions. To make more informed decisions on make effective actions to avoid risk and maximize revenue. See you there.

>> From the CUBE studios in Palo Alto in Boston, bringing you data-driven insights from the CUBE in ETR. This is breaking analysis with Dave Vellante. >> Earlier these week, the U S department of justice, along with attorneys general from 11 States filed a long expected antitrust lawsuit, accusing Google of being a monopoly gatekeeper for the internet. The suit draws on section two of the Sherman antitrust act, which makes it illegal to monopolize trade or commerce. Of course, Google is going to fight the lawsuit, but in our view, the company has to make bigger moves to diversify its business and the answer we think lies in the cloud and at the edge. Hello everyone. This is Dave Vellante and welcome to this week's Wiki Bond Cube insights powered by ETR. In this Breaking Analysis, we want to do two things. First we're going to review a little bit of history, according to Dave Vollante of the monopolistic power in the computer industry. And then next, we're going to look into the latest ETR data. And we're going to make the case that Google's response to the DOJ suit should be to double or triple its focus on cloud and edge computing, which we think is a multi-trillion dollar opportunity. So let's start by looking at the history of monopolies in technology. We start with IBM. In 1969 the U S government filed an antitrust lawsuit against Big Blue. At the height of its power. IBM generated about 50% of the revenue and two thirds of the profits for the entire computer industry, think about that. IBM has monopoly on a relative basis, far exceeded that of the virtual Wintel monopoly that defined the 1990s. IBM had 90% of the mainframe market and controlled the protocols to a highly vertically integrated mainframe stack, comprising semiconductors, operating systems, tools, and compatible peripherals like terminal storage and printers. Now the government's lawsuit dragged on for 13 years before it was withdrawn in 1982, IBM at one point had 200 lawyers on the case and it really took a toll on IBM and to placate the government during this time and someone after IBM made concessions such as allowing mainframe plug compatible competitors to access its code, limiting the bundling of application software in fear of more government pressure. Now the biggest mistake IBM made when it came out of antitrust was holding on to its mainframe past. And we saw this in the way it tried to recover from the mistake of handing its monopoly over to Microsoft and Intel. The virtual monopoly. What it did was you may not remember this, but it had OS/2 and Windows and it said to Microsoft, we'll keep OS/2 you take Windows. And the mistake IBM was making with sticking to the PC could be vertically integrated, like the main frame. Now let's fast forward to Microsoft. Microsoft monopoly power was earned in the 1980s and carried into the 1990s. And in 1998 the DOJ filed the lawsuit against Microsoft alleging that the company was illegally thwarting competition, which I argued at the time was the case. Now, ironically, this is the same year that Google was started in a garage. And I'll come back to that in a minute. Now, in the early days of the PC, Microsoft they were not a dominant player in desktop software, you had Lotus 1-2-3, WordPerfect. You had this company called Harvard Presentation Graphics. These were discreet products that competed very effectively in the market. Now in 1987, Microsoft paid $14 million for PowerPoint. And then in 1990 launched Office, which bundled Spreadsheets, Word Processing, and presentations into a single suite. And it was priced far more attractively than the some of the alternative point products. Now in 1995, Microsoft launched Internet Explorer, and began bundling its browser into windows for free. Windows had a 90% market share. Netscape was the browser leader and a high flying tech company at the time. And the company's management who pooed Microsoft bundling of IE saying, they really weren't concerned because they were moving up the stack into business software, now they later changed that position after realizing the damage that Microsoft bundling would do to its business, but it was too late. So in similar moves of ineptness, Lotus refuse to support Windows at its launch. And instead it wrote software to support the (indistinct). A mini computer that you probably have never even heard of. Novell was a leader in networking software at the time. Anyone remember NetWare. So they responded to Microsoft's move to bundle network services into its operating systems by going on a disastrous buying spree they acquired WordPerfect, Quattro Pro, which was a Spreadsheet and a Unix OS to try to compete with Microsoft, but Microsoft turned the volume and kill them. Now the difference between Microsoft and IBM is that Microsoft didn't build PC hardware rather it partnered with Intel to create a virtual monopoly and the similarities between IBM and Microsoft, however, were that it fought the DOJ hard, Okay, of course. But it made similar mistakes to IBM by hugging on to its PC software legacy. Until the company finally pivoted to the cloud under the leadership of Satya Nadella, that brings us to Google. Google has a 90% share of the internet search market. There's that magic number again. Now IBM couldn't argue that consumers weren't hurt by its tactics. Cause they were IBM was gouging mainframe customers because it could on pricing. Microsoft on the other hand could argue that consumers were actually benefiting from lower prices. Google attorneys are doing what often happens in these cases. First they're arguing that the government's case is deeply flawed. Second, they're saying the government's actions will cause higher prices because they'll have to raise prices on mobile software and hardware, Hmm. Sounds like a little bit of a threat. And of course, it's making the case that many of its services are free. Now what's different from Microsoft is Microsoft was bundling IE, that was a product which was largely considered to be crap, when it first came out, it was inferior. But because of the convenience, most users didn't bother switching. Google on the other hand has a far superior search engine and earned its rightful place at the top by having a far better product than Yahoo or Excite or Infoseek or even Alta Vista, they all wanted to build portals versus having a clean user experience with some non-intrusive of ads on the side. Hmm boy, is that part changed, regardless? What's similar in this case with, as in the case with Microsoft is the DOJ is arguing that Google and Apple are teaming up with each other to dominate the market and create a monopoly. Estimates are that Google pays Apple between eight and $11 billion annually to have its search engine embedded like a tick into Safari and Siri. That's about one third of Google's profits go into Apple. And it's obviously worth it because according to the government's lawsuit, Apple originated search accounts for 50% of Google search volume, that's incredible. Now, does the government have a case here? I don't know. I'm not qualified to give a firm opinion on this and I haven't done enough research yet, but I will say this, even in the case of IBM where the DOJ eventually dropped the lawsuit, if the U S government wants to get you, they usually take more than a pound of flesh, but the DOJ did not suggest any remedies. And the Sherman act is open to wide interpretation so we'll see. What I am suggesting is that Google should not hang too tightly on to it's search and advertising past. Yes, Google gives us amazing free services, but it has every incentive to appropriate our data. And there are innovators out there right now, trying to develop answers to that problem, where the use of blockchain and other technologies can give power back to us users. So if I'm arguing that Google shouldn't like the other great tech monopolies, hang its hat too tightly on the past, what should Google do? Well, the answer is obvious, isn't it? It's cloud and edge computing. Now let me first say that Google understandably promotes G Suite quite heavily as part of its cloud computing story, I get that. But it's time to move on and aggressively push into the areas that matters in cloud core infrastructure, database, machine intelligence containers and of course the edge. Not to say that Google isn't doing this, but there are areas of greatest growth potential that they should focus on. And the ETR data shows it. But let me start with one of our favorite graphics, which shows the breakdown of survey respondents used to derive net score. Net score remembers ETR's quarterly measurement of spending velocity. And here we show the breakdown for Google cloud. The lime green is new adoptions. The forest green is the percentage of customers increasing spending more than 5%. The gray is flat and the pinkish is decreased by 6% or more. And the bright red is we're replacing or swapping out the platform. You subtract the reds from the greens and you get a net score at 43%, which is not off the charts, but it's pretty good. And compares quite favorably to most companies, but not so favorite with AWS, which is at 51% and Microsoft which is at 49%, both AWS and Microsoft red scores are in the single digits. Whereas Google's is at 10%, look all three are down since January, thanks to COVID, but AWS and Microsoft are much larger than Google. And we'd like to see stronger across the board scores from Google. But there's good news in the numbers for Google. Take a look at this chart. It's a breakdown of Google's net scores over three survey snapshots. Now we skip January in this view and we do that to provide a year of a year context for October. But look at the all important database category. We've been watching this very closely, particularly with the snowflake momentum because big query generally is considered the other true cloud native database. And we have a lot of respect for what Google is doing in this area. Look at the areas of strength highlighted in the green. You've got machine intelligence where Google is a leader AI you've got containers. Kubernetes was an open source gift to the industry, and linchpin of Google's cloud and multi-cloud strategy. Google cloud is strong overall. We were surprised to see some deceleration in Google cloud functions at 51% net scores to be on honest with you, because if you look at AWS Lambda and Microsoft Azure functions, they're showing net scores in the mid to high 60s. But we're still elevated for Google. Now. I'm not that worried about steep declines, and Apogee and Looker because after an acquisitions things kind of get spread out around the ETR taxonomy so don't be too concerned about that. But as I said earlier, G Suite may just not that compelling relative to the opportunity in other areas. Now I won't show the data, but Google cloud is showing good momentum across almost all interest industries and sectors with the exception of consulting and small business, which is understandable, but notable deceleration in healthcare, which is a bit of a concern. Now I want to share some customer anecdotes about Google. These comments come from an ETR Venn round table. The first comment comes from an architect who says that "it's an advantage that Google is "not entrenched in the enterprise." Hmm. I'm not sure I agree with that, but anyway, I do take stock in what this person is saying about Microsoft trying to lure people away from AWS. And this person is right that Google essentially is exposed its internal cloud to the world and has ways to go, which is why I don't agree with the first statement. I think Google still has to figure out the enterprise. Now the second comment here underscores a point that we made earlier about big query customers really like the out of the box machine learning capabilities, it's quite compelling. Okay. Let's look at some of the data that we shared previously, we'll update this chart once the company's all report earnings, but here's our most recent take on the big three cloud vendors market performance. The key point here is that our data and the ETR data reflects Google's commentary in its earning statements. And the GCP is growing much faster than its overall cloud business, which includes things that are not apples to apples with AWS the same thing is true with Azure. Remember AWS is the only company that provides clear data on its cloud business. Whereas the others will make comments, but not share the data explicitly. So these are estimates based on those comments. And we also use, as I say, the ETR survey data and our own intelligence. Now, as one of the practitioners said, Google has a long ways to go as buddy an eighth of the size of AWS and about a fifth of the size of Azure. And although it's growing faster at this size, we feel that its growth should be even higher, but COVID is clear a factor here so we have to take that into consideration. Now I want to close by coming back to antitrust. Google spends a lot on R&D, these are quick estimates but let me give you some context. Google shells out about $26 billion annually on research and development. That's about 16% of revenue. Apple spends less about 16 billion, which is about 6% of revenue, Amazon 23 billion about 8% of the top line, Microsoft 19 billion or 13% of revenue and Facebook 14 billion or 20% of revenue, wow. So Google for sure spends on innovation. And I'm not even including CapEx in any of these numbers and the hype guys as you know, spend tons on CapEx building data centers. So I'm not saying Google cheaping out, they're not. And I got plenty of cash in there balance sheet. They got to run 120 billion. So I can't criticize they're roughly $9 billion in stock buybacks the way I often point fingers at what I consider IBM's overly wall street friendly use of cash, but I will say this and it was Jeff Hammerbacher, who I spoke with on the Cube in the early part of last decade at a dupe world, who said "the best minds of my generation are spending there time, "trying to figure out how to get people to click on ads." And frankly, that's where much of Google's R&D budget goes. And again, I'm not saying Google doesn't spend on cloud computing. It does, but I'm going to make a prediction. The post cookie apocalypse is coming soon, it may be here. iOS 14 makes you opt in to find out everything about you. This is why it's such a threat to Google. The days when Google was able to be the keeper of all of our data and to house it and to do whatever it likes with that data that ended with GDPR. And that was just the beginning of the end. This decade is going to see massive changes in public policy that will directly affect Google and other consumer facing technology companies. So my premise is that Google needs to step up its game and enterprise cloud and the edge much more than it's doing today. And I like what Thomas Kurian is doing, but Google's undervalued relative to some of the other big tech names. And I think it should tell wall street that our future is in enterprise cloud and edge computing. And we're going to take a hit to our profitability and go big in those areas. And I would suggest a few things, first ramp up R&D spending and acquisitions even more. Go on a mission to create cloud native fabric across all on-prem and the edge multicloud. Yes, I know this is your strategy, but step it up even more forget satisfying investors. You're getting dinged in the market anyway. So now's the time the moon wall street and attack the opportunity unless you don't see it, but it's staring you right in the face. Second, get way more cozy with the enterprise players that are scared to death of the cloud generally. And they're afraid of AWS in particular, spend the cash and go way, way deeper with the big tech players who have built the past IBM, Dell, HPE, Cisco, Oracle, SAP, and all the others. Those companies that have the go to market shops to help you win the day in enterprise cloud. Now, I know you partner with these companies already, but partner deeper identify game-changing innovations that you can co-create with these companies and fund it with your cash hoard. I'm essentially saying, do what you do with Apple. And instead of sucking up all our data and getting us to click on ads, solve really deep problems in the enterprise and the edge. It's all about actually building an on-prem to cloud across cloud, to the edge fabric and really making that a unified experience. And there's a data angle too, which I'll talk about now, the data collection methods that you've used on consumers, it's incredibly powerful if applied responsibly and correctly for IOT and edge computing. And I don't mean to trivialize the complexity at the edge. There really isn't one edge it's Telcos and factories and banks and cars. And I know you're in all these places Google because of Android, but there's a new wave of data coming from machines and cars. And it's going to dwarf people's clicks and believe me, Tesla wants to own its own data and Google needs to put forth a strategy that's a win-win. And so far you haven't done that because your head is an advertising. Get your heads out of your ads and cut partners in on the deal. Next, double down on your open source commitment. Kubernetes showed the power that you have in the industry. Ecosystems are going to be the linchpin of innovation over the next decade and transcend products and platforms use your money, your technology, and your position in the marketplace to create the next generation of technology leveraging the power of the ecosystem. Now I know Google is going to say, we agree, this is exactly what we're doing, but I'm skeptical. Now I think you see either the cloud is a tiny little piece of your business. You have to do with Satya Nadella did and completely pivot to the new opportunity, make cloud and the edge your mission bite the bullet with wall street and go dominate a multi-trillion dollar industry. Okay, well there you have it. Remember, all these episodes are available as podcasts, so please subscribe wherever you listen. I publish weekly on Wikibond.com and Siliconangle.com and I post on LinkedIn each week as well. So please comment or DM me @DVollante, or you can email me @David.Vollante @Siliconangle.com. And don't forget to check out etr.plus that's where all the survey action is. This is Dave Vollante for the Cube Insights powered by ETR. Thanks for watching everybody be well. And we'll see you next. (upbeat instrumental)

(upbeat electronic music) >> From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation! >> Hi, everybody, this is Dave Vellante, and welcome to this CUBE conversation. You know, in this COVID-19 pandemic, we've been reaching out to folks that really have good visibility on what's going on out there. Tim Conley is here, he's a principal with the ATS Group, and partner of IBM. Tim, good to see you again man, thanks for comin' on! >> You got it, Dave, how are you today? >> Not too bad, you hangin' in there with all this craziness? How are things where you are? >> Yeah, we sure are, it's like groundhog day everyday, right? >> I know, the family's goin' crazy. They want to get out, and, well summer's comin', so hopefully the pandemic is going to calm down a little bit here, give us a breather. >> I hear that. >> But so, tell us what's goin' on these days with your company, with the ATS Group, what are you seein' in the marketplace? Give us the update. >> Sure, Dave. We've been in business 19 years now as a IBM systems integrator. Doin' a lot of work around storage. There's a lot of shiny new nickels out there these days that we're trying to make sure that we stay ahead of the game on. You know, our customers demand excellence from us, because that's what we've been giving them the last, you know, 19 years. So, they demand that from us, which is actually a great position for us to be in, but you know, with a lot of the new, shiny new nickels out there today, takes a lot of energy to focus on those, make sure we're talkin' to our customer about the right things, at the right times in the marketplace. >> I had Ed Walsh on the other day, and actually a couple times within the last six months, and he shared with us, actually in studio, when we didn't have to be six feet apart, the new announcements, the simplification of the portfolio. Presumably you've seen that. What was your reaction, how do you think the customer will react? >> That's a good question. Like I said, we're always looking to be bleeding edge, that's actually where we got our name from, Advanced Technology Services Group. So, IBM consistently comes out with some really good products and solutions, and we're constantly vetting that in our innovation center, in beta programs and things like that. A couple key things that are working now with us is Hybrid Multicloud. You know, IBM comes out, like I said, with some good solutions. We vet them out, and we're real excited about Spectrum Virtualize for Public Cloud. We've been using that for probably the last 12, 14 months, so trying to get the word out our customers on what it means, for partners as well, we can have a simple 10 minute conversation with our customers and our partners, kind of describe it at a high-level, and then they can gain interest at that point. It can be a little tricky, but we try to take that trickiness out of it, and let our customers know what's really goin' on, how it works for disaster recovery, for data protection to the cloud. Customers always want to talk about those things, but a lot of them really don't know those specifics, so we literally in 10 to 15 minutes can simply it to them, let 'em know how it works, and what scenarios it might work for them. Again, doing tests, and PoCs, things like that, it's really easy for us to do. One of our big federal customers want to call today at 12 o'clock, going over that implementation. They're pretty excited about tryin' it out, 'cause everybody thinks they want to move some things to the cloud, so Spectrum Virtualize allows us to do that pretty transparently. In fact, we used it ourselves last year, 'cause we took the journey to the cloud for SaaS offering. Took us over a year to do it, let me tell ya, it's not easy. You know, people make it sound like goin' to cloud is a snap, you know, spin up some OS instances, some EBS storage, and away we go. It's not that easy. >> I was just talkin' to a software executive who started his company 37 years ago, we both agreed, that's kind of when I started in this business, we both agreed that it just keeps getting more and more complicated. So, firms like yours are, but okay, so you talk about Hybrid Multicloud, of course IBM has cloud, but IBM itself says, "Hey, we hope people put their data into our cloud, "but we know there's other clouds out there." Well, hence Multicloud. So, what do you see as going on in the marketplace, specifically as it relates to Multicloud? And I wonder if we could weave in the COVID-19. Are you seeing people more receptive to cloud? >> Yeah, I'll tell ya, with COVID-19 we've had some opportunities delayed, because customers don't quite know where the market's going to go for themselves. We actually had one customer go out of business. So, that ultimately delayed a deal forever, right? But overall, things aren't that bad, but we do see customers, you know, lookin' to make some things easier for themselves. They might have been thinking about the cloud, but COVID's kind of brought it to the forefront, and they want to make things easier right away. Maybe you can save some money, right? So, we have a calculator we created for our customers to really go measure things to see what actually would it cost to go to cloud? You know, a lot of customers have no clue what it is. We could do that in five minutes for them, really interesting so, again we'll give them that information that hey, going to cloud might be an opportunity that they didn't think might be existent 'til now. >> So, Spectrum Virtualize, otherwise known you know, for those who have been around for a while like I have as kind of the roots of the SVC, the SAN Volume Controller, and the history of that product is software that enables you to virtualize, not just IBM storage, but anybody's storage, and of course one of the major use cases has been migration. So, in downturns, people want to get more value out of existing system. You know, maybe they come off lease, or maybe they want to elongate the life, and they may not have all the function so they can plug it into an SVC, and they get all the wonderful new bells and whistles, and the capabilities there. I wonder if we could talk about that, and again, what you're seeing just in terms of the current, you know, economic situation, and then specifically as it relates to cloud? >> That's a really a good point. So, you're tying to key things in today, right? Customers are looking to save money, because they don't want their financial outlook is based on COVID-19, so being able to help customers, and you nailed it, right? SVCs, Spectrum Virtualize has been around for, gosh probably 11 or 12 years now, 13 years actually. Right? So, we pride ourselves on bringing that to customers. Showing them how they can virtualize their environments in the storage arena. And we have some gigantic customers in the federal space, commercial space, so we don't just bring out white paper, say, "Eh, well it kind of looks good." Right? We actually have distinct customers, and talk to them about how they can drive their storage efficiencies up with IBM technologies, especially virtualization. And then, you know, reducing their overall cost. That's key, especially now. Customers are constantly looking to reduce their costs and whatnot with their storage, so that's a perfect inroad to that, and then bringin' in the Multicloud part of it, you're just extending Spectrum Virtualize to the cloud. You know, it was in IBM cloud first, it was in AWS back June of last year, and now we're working at IBM on puttin' that out into Azure. You know, so we can bring those savings to customers in the cloud, which they didn't know they could do that before. >> All right Tim, talk a little bit more about Multicloud, because you know, a joke recently, up until recently anyway, that Multicloud is more of a symptom of multi vendor, as opposed to a strategy, but with shadow IT, and sort of rogue systems, and the marketing department, the sales, everybody doing their own cloud, essentially Multicloud has become a strategy that the CIO has been asked to come in, "Hey, we got all these clouds." Clean up the crime scene I call it! What are you seeing today around Multicloud? >> That's a great point, I like that term, I'm going to steal it if you don't mind. Multicloud's customers are very much interested in, we have several customers doing Multicloud, IBM, Amazon, Azure. We actually did a study for an Azure customer, where we actually projected him to go to AWS with substantial cost savings. Some of that had to do with right-sizing their environment, where they weren't right-size in azure today. But I got to tell ya, you know, Cloud's not simple. It's not easy, again I mentioned earlier, we took that journey ourselves, spent a lot of time and energy with some really smart guys on my team to take that journey. So, Multicloud is a really great idea, and should be looked at, but I'm tellin ya' it's not quite that easy to just shift around, but there are definitely things to move to different cloud vendors. Again, if we bring it back to the storage arena, right? Spectrum Virtualize today's in IBM and Amazon, it's not in other clouds, so if you want to go that route, perfect opportunity to go Multicloud. >> Yeah, I mean I think you're makin' a good point. Let's face it, for our audience, we're in the early days of Multicloud. Yes, everybody has multiple clouds, everybody talks about having multiple clouds, but to be able to run applications natively in all these different clouds, whether it's the control plane, the data plane, the transport plane, all these disparate systems, and really be able to take native advantage of the local cloud services. That's not only very complex, it's really not fully baked out here today, but you know, we heard this week at IBM saying a lot of talk about Red Hat, containers, and Open Shift. So, we're starting on that journey, and that's really the promise of Multicloud, to be able to ultimately run applications anywhere, but as you point out, that's a very complex situation today for customers. >> Yeah, that's a good point. So, I totally would follow up with you on that, that's Multicloud, customers are looking at it, and their are some distinct advantages to the different cloud vendors. One could even say on-prem is a form of cloud, right? That's just your private cloud. So, keeping things on-prem for certain scenarios makes sense, be able to tie that back to the big cloud vendors, IBM, Amazon, Azure, right? Tying them together is the direction people are looking to go, and are kind of, some of them are there and have done it, but I'd say some, or more of them are in the infancy stage of that. >> What are you seeing in terms of, just kind of switching topics on you, in terms of things like governments, compliance, a lot of talk about cyber resiliency, especially given the pandemic. What are you seeing there with customers? >> Wow, that's a big topic. It's interesting, data classification, you think it'd be that easy, especially for some of our fed' customers, it's not that easy, right? Tryin' to classify the data, they just don't know, they might know the applications, but they don't know the content of that data. Is it able to be, what is it, section 126? Something like that. Is it able to go to the cloud? So, customers have a struggle on their hands tryin' to do that, right? The technology, groups within the customers, the storage folks, the OS folks, the Apps folks, they're all about the cloud, move things to cloud, but at the end of the day, it's the security folks that need to be able to do that data classification to see can the data even go there? Let alone the application or whatnot. Fairly easy to do that kind of stuff, but the data classification, we see that's the hard part. >> Okay, so you talked about shiny new toys at the beginning of this conversation. You know, IBM, you're tryin' to be a shiny old toy, (Tim laughing) they've been around you know, a century. >> Yeah. >> Why IBM though? What is it about IBM that you choose to partner with them? Give us the good, the bad, and the what you'd like to see improve. >> I would say, we've been a partner for IBM a long time, I used to work for IBM a million years ago. At the end of the day, our customers demand excellence from us, and they demand things to work, right? So, for me to put my company, and my resources into an opportunity for my customers, we can count on IBM. One, we have a great relationship with them, they have fantastic solutions, and then we vet them out. Our customers demand that of us, and I can give real world examples of one customer to another. So again, it's not like a white paper, I read it from vendor XYZ, at the end of the day we're implementing these solutions at our customers. A lot of times we're doing em in our lab first to make sure it works as designed, figure out with the shiny new nickels, you know, what's broken with that nickel? Why's it not so shiny? Or is it really as shiny as it appears to be, right? So, being able to do that stuff in-house is great, but at the end of the day, our customers demand excellence, and you know, we have to be bringing solutions to our customers, and IBM provides quite a few solutions, especially around the storage arena, where we live and breathe, that instant marketplace. So, we have to use great solutions that we can trust, and know work. >> So, my last question is what have you learned in the last, you know, couple of months with this pandemic. Now that we start to hopefully come out of it, at least for a little while, what are you learning? What's been accelerated, or pulled forward, and we're obviously not just goin' to 2019. So, how are you seeing your business, and your customers responding, what's the sort of mindset going forward? >> I'd say two things, so there's the COVID stuff, and then I talk about ransomware, cyber security, that could be another whole topic, right? But at the end of the day, I've been on a lot of webinars, and things of last three, four weeks, five weeks, listenin' to vendors talk about their shiny new nickels, and it's, quite frankly it's a bunch of mumbo jumbo, and that's not the world we live in, 'cause that's not what our customers are asking from us. But a lot of customers are really concerned about cyber security, ransomware. I have two customers locally that got hit with ransomware last fall, and let me tell ya, it's not a pretty scene, and they were not prepared for it, right? So, one of our jobs is to really help our customers understand where their gaps are within their organization, so that if they do get hit by cyber crime, or ransomware, that they can actually survive that, and not actually have to pay for it, then be up and running in a very small amount of time, which is key. Like I said, two customers got hit, just of mine, within 20 miles of our business, and they weren't prepared for it. >> I can't leave it there Tim, what do I got to do, if I'm an organization that's concerned about ransomware, probably every organization, what are the steps that I should take, like immediately? >> I would say a health assessment, and it doesn't have to be from ATS, it could be from anybody that's got the experience, and whatnot. We do health checks for customers consistently, and they don't have to be expensive, they don't have to be like, months. People always think, "A health check, oh my god, it's going to take so much time." It really doesn't. It's a quick health check, and we can look at those key things within your organization to see where you might not be prepared. And I'm talking like not prepared, like if you get ransomware tomorrow, you very well could be out of business. It's not hard to see those kinds of things. And you can make it more detailed if customers want that, right? But I would definitely have customers, if you're interested in that, call us, call any other vendor out there that's doin' those kinds of things. But it's fairly easy for folks like us and other vendors to be able to do those health checks, just take a quick look in your environment, see where your gaps are that you could literally go out of business tomorrow. >> Okay so, first pass is you're lookin' for open chest wounds that you got to close immediately and stop the bleeding, and then what? You start implementing things, you know, best practices, air gap. >> Air gap, you stole the word right out of my mind, air gap, right? You have to start, you know, look and see where, what's the requirements? First of all, make sure you can survive the event, and get back up and running in a reasonable amount of time, right? That one customer I mentioned was probably four or five weeks before they were able to restore all their servers, and they were fortunate that a lot of those were test thing that they could kind of wait a little bit long, but the other one they nearly went out of business, 'cause they just weren't prepared for it, right? So yeah, air gapping is a key thing, right? You know, where I put my data that it can't be touched, right? That's a fairly easy thing to start off with. >> Yeah, and then the whole process of recovery, who's on deck, you know, et cetera, et cetera. How communications occurs, there's technology, and of course as always, there's people in process. Well, Tim, I'll give you the last word, bring us home! >> Bring us home. Hey, but Dave, thanks very much for your time today. This is was a great time talking to you about some key things that we've worked with day in and day out over the last couple months. Again, bringing our solutions to our customers, that they demand that excellence from us. Bringin' IBM solutions that we natively know and love, and trust, because we've done 'em many, many times with other customers. So, pretty excited about what's goin' on in the industry, lookin' at all those shiny new nickels, and see which ones are actually shiny at the end of the day. >> All right, Tim, well listen, thanks for comin' back on theCUBE, it's great to see ya. I hope we get to see each other face to face. Stay safe. >> Sounds good Dave, thanks for your time, thank you. >> All right, you're welcome, and thank you for watching, everybody. This is Dave Vellante with theCUBE. Go to http://www.siliconangle.com to check out all the news, for thecube.net, where all these videos live, and http://www.wikibon.com, where I publish weekly. We'll see you next time on theCUBE. (relaxing instrumental music)

hello everyone I'm Jean Faria we are reporting on the ground near Puerto Rico for blockchain unbound exclusive conversations at coinage end of covering all the action restart week of ten of events cryptocurrency blockchain all the people are here with the local ecosystem the cube is here it's great to have you on thanks for joining blockchain innovation is today global this is a revolution way bigger than the Internet itself programmable money programmable contracts that wipes out finance it wipes out legal it wipes out governance in many ways there's no central authority you have access to open source software it's fully connected so now is the time to make it translate we've all heard about the steam digital transformation its businesses that if they don't evolve and adopt blockchain AI all these other things they have a threat of being put out of business it is extremely competitive a new set of stakeholders investors global players governments are it's happening now you have a chance to be a part of an economy without a permission of a centralized organization have to pay 200 people in 40 countries and it's an unholy mess with withholding taxes and concerns around money transfer costs a hassle it's a nightmare like all currency control so you're only allowed to move a certain amount of capital out the country legally so what happens in all your backups our currency and you can effectively invest in assets around the world this is making it much easier to contribute to help people to get healthy and you don't have to go to school there's a very big influx of young and talented minds at that right and this is really changing the revolution landscape you've got the radical Burning Man hippie guy all the way to a three-piece suit yeah and that diversity is very very rich a lot of people are scared I like whoa hold on slow down we're not gonna prove it the other half saying no this is the future so you have two competing forces colliding for some reason crypto really pokes at people's biases you know why does it have any value and I go well why does the United States dollar have any value I mean you've got Full Faith and Credit of the government that's in debt by 20 trillion dollars you know is that a good idea most people that come here sorry with the what the how and people are scared but the young people are like yo this is happening this is not a moment this is a movement is definitely oh say 1996-97 of the internet bubble it's just starting people know there's something really magical they don't quite know what you know America really grew because you're abused to have all the controls and so the capital by sea left Europe and away in America and now it's happening 300 years later as America has all the controls and the capital starting to go away so a new Liberation's happening incredible resources are now being poured in problems that were ignored for many many and what is beautiful is that block Candy's doing it open-source is accelerating the tech these ideas are being freely shared whereas before there's bottlenecks in the collaboration aspect if we're able to write a contract in a thousand people be able to verify that contract and we're able to transfer money from one person to another without the two parties being involved we've got a perfect scenario security and speed and fairness all at the same time you can create these chains of trust and that can happen anywhere in the world you're on a level playing field if you have 4G connectivity now you can compete globally and be a part of the global economy so if you're someone who's in the emerging developing world and you want to begin to build wealth and you'd like to own a piece of first world real estate and today the minimum is about a thousand dollars but by implementing the Plott chain further they won't eventually get down to one dollar you can buy a piece of real estate and enjoy the returns on that I want to solve the wealth gap and I truly believe we can do it when we can allow anyone anywhere to invest in good quality assets a conduit with the current system there's too many friction costs the killer app right is money it's paying people that is the killer app of the block type right now let's say that money is software and it is software so if you buy something with a credit card what do you think's happening it's all software and what has happened is open-source software has always eventually won with respect to close source software so proprietary money is probably back on its heels because open-source money's coming in something like that will give liquidity to a lot of small business owners America is a country of small business owners across the globe it supports small business owners it's an interesting model yeah you don't have to give up any equity you don't have to give up any poor seats yeah right it's much leaner my super if you're an investor you gotta get a pound of flesh somewhere is it's just getting it on the discounted tokens is there a little liquidity going on when you think about you know private sale presale is 99% a token deal right although equities coming in because a lot of more venture capital is coming in and they're demanding a piece of the action from a company and equity perspective its equity might be future revenue sometimes as dividends or the opportunity get dividends so it's a combination of you have a preference you care you know at the other day equity is I was always preferable there is a provision in the 1934 Securities Act called section 12 G it allows us Spacely to go public by telling the SEC we're doing it without having to delay it to wait for their permission after 60 days it's a derivative so we'll continue to clear comments but but the thing is with tokens who knows how long that'll take I mean is the SEC gonna Shepherd something through with crypto 1 or do they gonna make it take 5 years I don't know [Music] all over the island this is the new Oliver field the world is moving too fast today for a big country to keep up it's all gonna happen now in this next century at the city level and so we work a lot with four smaller countries or small countries because I know estonia armenia baja rains got you know dubai envy so i mean every country wants to be the crypto country multiple small countries are going to come into the space which they know now they can get the capital flowing into that company and they're gonna allow their rules to be lacs they're gonna let capital flow through and then us will have to change or maybe UK will have to change orders against us will have to change in the first world a lot of what we're talking about is a nice-to-have it's it's sort of a bit of a game and if i can participate but where I come from an emerging war that's a necessity they are no other solutions so if you live in South Africa or China or India and you want to get your money into a first world country like England Australia America it's very very difficult and virtually no one can do it but it's a major problem because you want wealth preservation you want but Plan B you want your children to be able to go to a first world university etc etc etc Puerto Rico being a free associated States of the United States of America is like the best place to actually test this possibly some push for that for infrastructure for you know internet for all sorts of different things in terms of building the best infrastructure the new newest best-in-class for your business it's four percent corporate taxes and individual it's zero percent now that's what you got to move here you gotta move here okay but you don't have to give you deliver your US citizenship no taxes are great at the same time they fall in love with the islands so it's amazing because to me Puerto Rico is a combination of LA's whether San Francisco's open-mindedness and Barcelona's you know deep European history it's just a really beautiful place and it's US territory so it's a short hop and a jump to the States if you need to most people in America mainland sort of think they're going to a foreign country because it's treated that way by our government how do I come to Puerto Rico do it right not offend the culture in abil them together what's your experience with the play ball stay good friends lost their relocation services for their business and themselves so they write a big check to you guys for the service but it's you guide them through the entire process and there's real energy here because there's a social movement underneath the entire cryptocurrency movement and that's to basically help your fellow man or women all these activity is really going to give a a shot in the arm to the Puerto Rican economy and we're bringing our funds and we're bringing our advisory the radar Thank You exponent there the hurricane was a horrible atrocity that happened and now we have this blank canvas to create a vision for Puerto Rico so what we're doing is we're connecting every single University on the island to work on open source projects to like make solutions for the private sector they know that if they can buy power on a cellphone like they're already doing for other goods and services now we've got a game-changer this is restart week and one of the other things that we've done is help all of the conference's come together collaborate rather than compete so go into the same week and put all of these satellite groups around it and then we blanket it a week around it so that we had one place for people to go and look for all of the events and then also for some for them to understand a movement about the education piece it's very difficult for people that kind of get caught up to speed because there's some technical things that need to understand to really apply this technology into the business world the other day we had an event where we talked 50 people how to create a smart contract from scratch those are 50 people who are not the same anymore ecosystems developing yet entrepreneurs you got projects you got funding coming in but as it's gonna be a fight for the ecosystem because you can't have zillion ecosystems there are definitely some you know the galaxies and you know regulatory aspects that you know put some concerns and a lot of you know people's mind since its inception you've seen people and media and mainstream media in particular target Bitcoin and they're just adopting the government narrative saying oh everyone in this industry is corrupt Oh everyone in this industry is an ICS camera Oh everyone in this industry is a a drug runner and they have all selling drugs on the dark web and and it's like you know what like you can do some research and don't get better than that traditional media they want to take down everybody that they don't consider you know like a birds of the same feather there actually are a lot of scammers and a lot of like dark forces inside of the cryptocurrency movement so that's why I think we welcome kind of more regulatory influence because you know none of us want to see bad actors in the space we've seen folks go out raise you know really big about to capital with no product roadmap no business talking roadmap no real way to get from zero to X what are they trying to shoehorn a regular business onto the blockchain and just assume that by adding crypto at the end of you know toilet paper they're gonna get something I had another founder tell me that you know Mike tokens are worth 100 million humming yep you don't have a user you just have a product you're tokens I've hiked if you ask me it's it's what little I can tell my house is 100 million dollars it's only worth as much as the top buyer how much we really need hardcore reputation systems in our industry and in the for the world I think 2018 is going to be the year of clarity on regulation and I think that's where Puerto Rico comes in and plays a major role just to see the thousands of people who have come here to support these several conferences has been amazing my most surprising thing though is the amount of people that have told me that they bought a one-way ticket and have no intention of going home so to make Puerto Rico your home I think is a really amazing first step when I go to the supermarket and where I go it's full of American and people from outside and when you ask them where you're from and they will tell you from Puerto Rico this is gonna become the epicenter of this multi-billion dollar market we need to have people prepared for this you have to create the transparency the beauty of the transparency is there's actually privacy baked in and that's what I love about blockchain is it has all of the good things all communities need to evolve in my opinion between technology communities open networks of governance where we have peer-to-peer distribution of finance and of resources in a way that allows people to aggregate around the marketplaces that are actually benefitting the way that they believe the world should work we're going to be tools that far surpassed what's currently available in terms of the messages the websites all these things for 20 years the Internet has been free it's a really beautiful thing for consumption and open-source is the absolute right methodology for software when it comes to your own content a reward it makes sense everybody is going to get to play together across every device the developers are going to get rewarded for creating content people are going to be rewarded for creating things inside the games and the players are going to get rewarded for getting to the top levels of all the games and we're going to reward them through our cryptocurrency if we begin to own ourself sovereign identity then when we're owning our data that's the foundation for universal basic income communications completely frictionless payment completely frictionless and governance completely frictionless and we have to put this all together who wins here the average citizen entrepreneur that is leveraged citizen player that wants to start something whether it's a banking a service provider of some sort an entrepreneur or a new financial instrument or firm you all have greenfield opportunity here the first thing I would tell found us is to reach out ok this community is very very supportive like you can reach out to me you can reach out to other guys LinkedIn Facebook or come to these events and say your idea and you need help because you will need help you cannot run this alone ok you are running a company you're running your team have a good team that's the first thing you got to be vigilant and you keeping your money in a hard wallet not keeping your private keys on your computer if you're using a centralized system those centralized systems are really easily exploitable strategic partnerships Advisors founding team and then show the idea to the people explain yourself frankly and honestly and I think the community will reward you to go and find it ring whether you're a fortune 500 company or a startup it's all about building the community and I believe that whether it's utility Target or security or combination of the two it provides an incredible vehicle to ultimately be the catalyst to your community and if you the to community adding value then you're going to build a company event it's always gonna be led by the business model because you need something to act as the power pull to pull the thing along right and you can continuously pump capital into something but if the model is wrong it's just going to drain and it's going to go to inefficient systems and in the end maybe do some help but but a very small percentage of the capacity of what it could do then the advice would be to entrepreneurs don't fret about the infrastructure just nail your business models right and because the switching cost might not be as high as you think that's right we're in the old days when we grew up yeah you made a bad technology decision you're out of business yeah but the first advice that I give my clients is to stomp this is this business that's too much formal in it yeah right if you're missing out so no just because everybody's out there Nico you should be doing an SEO right yeah 46% of I SEOs have already failed already failed start with the business gather this in the counties down right so free cash flow unique value proposition Prada market fit what sits under business think about the token model right the token model has to go in handy now with your business model and revenue model and once you figure out that business and took the models now it's time to think about compliance I'm gonna raise money in the US and abroad I've decided to go to security choking hypothetical instance absolute what do I do is there for you an incentive mechanism or is a fundraising mechanism or both who's gonna be my user who's gonna use this token right there aren't gonna be moms dads hospitals they was my target and then how they're gonna use it and are they gonna hold it I'm gonna sell it are they gonna trade it so all these different things define that oh c'mon once you get your token actually authenticated realized everything's transparent and it gets on that secondary market it's better to use that to invest in anything you need investment get everybody incentivized around your token all your employees all your vendors everybody incentivize around that token it's a thousand percent more powerful than a dollar so the dollar doesn't go up in value in your token your token can go up and down and as soon as you find just one spark it blows up everybody boats rise equal it's pasta Sara Lee the time to crack open the champagne you still have to demonstrate product market fit you have to help build a market in our particular case so there's a lot of hard work launch it's a start line it's just like it's only a step along the whole process you know what made people get it you showed them the money yeah you showed them the money sometimes people don't you can explain these concepts that are world-changing super high level or whatever people were not actually gonna get it until it's useful to them average business people and senior business people who have typically been shut off to the idea of blockchain are now seeing this as very real and here to stay momentum is just beginning it's gonna be amazing what these guys come up with that's one of the things I love about doing this thing right I'm an old guy and I get to hang around these smart young people makes me feel young again yeah but the other thing that we have and I think you should share it as well as we have to offer to these young guys experience thing we just invented a new category in the ico category an advisor token and a you have to have the stomach for it and I think you just have to be as educated and as you can what government entity can resist for the long term something that's actually trying to provide a better and better and better financial infrastructure you should be able to participate in many different nations who have many different economies that are all really cooperating interdependently to create the best possible life for all human good one dollar will not change your life but if you change your habits you'll change your financial destiny and so my philosophy is get it to a dollar so that every single person can participate and once you start to learn good habits around money and wealth the rest it's a formula like it's a flywheel instead the world will become a better place we'll have better companies positive impact is not counter to profit they go hand in hand the Puerto Rico movement it's a movement while Czech entrepreneurs capital investors the pioneers in the blockchain decentralized Internet are all here this is like the Silicon Valley of the crypto right I think they're calling it crypto island yes TV show we should be honest like it's not lost its crypto island exclusive coverage for Puerto Rico's - Cuba I'm John Ferrari getting the signal here out of all the noise in the market this is what we do this is the cube mission great strip we start week Point agenda open content community thanks for watching [Music]

>> Announcer: Live from San Juan, Puerto Rico, it's theCUBE, covering Blockchain Unbound. Brought to you by Blockchain Industries. (Caribbean music) >> Welcome back, everyone. We're here for exclusive CUBE coverage in Puerto Rico for Blockchain Unbound, a great conference where entrepreneurs and leaders are all here, coming together at a global level. You've got investors, you've got entrepreneurs, you've got the ecosystem developing. We've got it covered for you, I'm John Furrier, your host of theCUBE. Next guest, Joe Mohen, CEO of Chimes, industry executive, a lot of experience doing an ICO, doing some great work, Joe welcome to theCUBE. >> Thank you, it's a pleasure to be here. >> So, tell us first what Chimes is doing. You've got an interesting approach with music. What are you guys doing? Is there an ICO in the future? Have you done an ICO? Give the quick update. >> Okay, sure. Chimes is a digital media company, and we are consolidating music-related search results on Google in a similar way to what Amazon did with IMDB, consolidating film and television results many years ago. Amazon built an audience of about quarter of a billion to half a billion monthly users, and we expect we can create an audience on that order of magnitude over time. Just like IMDB is the third largest entertainment website in the world, it is our objective to create the fourth largest one. >> What's the value proposition there? Acquire audience, use that audience to tokenize? How does the token economics fit into all this? >> Well, first, like any media company, the first thing you have to get is an audience, right? I remember I interviewed for a job at CBS when I was out of college, and in the interview they said, "Do you know what we make here?" And I said, "You make TV shows." They go, "No, we make audiences." So we have to make an audience with a good product. The audience will be driven primarily by search, okay? But we also do have a double ICO in our future. First, we monetize the big audience. You can monetize with advertising, but that's not enough to make big money anymore, right, we all know that. So we have a layer of crypto products over and above that that we're going to be launching, including, for example, inter-country commerce, hiring producers in another country, hiring songwriters, et cetera, but automating that so we can do it on scale with smart contract. So we are creating a micro-currency that we can use on the website. We're doing an ICO for that but that's not for the purpose of raising capital. >> That's more part of the business model. >> That's part of the business model. >> That's not the financial aspect of it. >> Correct, and that's done so we can scale international commerce with automation. We're doing an actual ICO for the equity, for securities tokens as well. I've done a full IPO myself. My first company, I had Microsoft and Novell as my shareholders and it was a full S1, full registration. >> Interviewer: You went through the whole process. >> Yeah, but I also did a Form 10 once, ten years ago, for another reason. So what we're doing is possibly the first, certainly one of the first, but I think the first registration with the SEC of a company actually doing an ICO. And we're doing that using, I don't want to call it a loophole in securities laws, but there is a provision in the 1934 Securities Act called Section 12G. And what this does is it allows us basically to go public by telling the SEC we're doing it without having to delay it to wait for their permission. A Form 10 looks just like an S1, but when you file it, it's automatically effective 60 days after you file it, period. And so what we're doing is-- >> Period, full stop, no issues, no questions. >> Joe: No issue, right. >> So do you have to fill out all the same paperwork, the S1, >> Correct. >> the normal format, do the business plan, the normal paperwork? >> Joe: No, right, in 1930-- >> But there's no comments coming back? You just chip it to them? >> Comments come back and you have to clear them, just like with a prospectus, just like with an S1, however that doesn't delay it becoming effective. It's effective 60 days later. >> So they can be commenting during the 60 day time clock going on, but after 60 days, you're in. >> It's effective. So we'll continue to clear comments, but the thing is, with tokens, who knows how long that'll take? Is the SEC going to shepherd something through with crypto, or are they going to make it take five years? I don't know! Who knows? So, the thing is, we are complying with all of the laws for registration, but 60 days after we file it, it's effective. What we're doing is, in the pre-sale for the tokens, we're not issuing the tokens themselves to the buyers of the pre-sale for six months. The reason for that is they will have met the statutory holding period. So once the Form 10 is effective, those buyers can sell freely on token exchanges-- >> And what's the statutory holding period, six months? >> Generally six months. There's a few exceptions for affiliates, like an insider like me. >> I'm confused, a holding period kicks in before or after six months? >> After six months, the statutory holding period is satisfied. >> So you're going to wait to delay them anyway six months. >> Joe: Yes. >> So that covers the holding period. >> Correct, and then we file the Form 10, and 60 days later, they can trade and anybody can buy them. >> So do you file a Form 10 before the six month holding period? >> It'll be at about the same time. The reason being is because we have to get all the ducks in a row to be a public company. >> Cutting edge advice here, this is fantastic. So you're basically going to be the first ICO that actually files with the SEC. >> Correct. >> I mean, who does that, nobody. You! >> Watch us! >> John: That's awesome. >> Basically, we're using a provision, it's like we went back in time to 1934, got them to put something in the 1934 Securities Act for the purposes of ICO's, and then we came back to 2018 with the time machine-- >> Are you from the future? Back to the future! You went back and jerry rigged it. Hey, we should put this Form 10 in there! >> Joe: There you go! That's right. >> It could come in handy some day during the crypto bubble. >> Joe: That's right. >> So let's back to the cryptocurrency thing. I think you're onto something that I think is a tell sign that I haven't seen yet. I've been seeing some formation of it. You are using two types of tokens. Your business model is do security token for funding, trade that puppy through the Form 10. Utility token, a separate ICO for the product, and that's going to have one token, two tokens? >> There's one utility token, so to speak, one currency token, and that has its own regulations that you have to manage to also. But that's designed to appreciate, but not to go up 17 times. >> Okay, I want to dig into that for a second, because you mentioned scale. You're going to scale your business model with the utility token. That's the purpose of the utility token. So let's get into how you're going to do these smart contracts. Let's just say that a producer in Europe somewhere, in Italy, says, "Hey, I'm going to do something "with Joe in the UK." And they form a collaboration. >> Joe: That's right. >> Do they use that utility token or a new token gets created? >> No, that utility token. It's called a Chime, the Chime token. And what happens with that token is you can build in the contract administration through the token. Right now, you can do international deals. People do them every day. The difficulty is if you've got an audience of a half a billion people a month, for example, to do that on scale and automate it... Right now, if you do a deal with somebody in Japan, you, the American, has to have an American lawyer and a Japanese lawyer. And if there's a dispute, good luck suing. I, one time, a customer in Hong Kong, owed me a million and a half bucks and he's like, "Sue me." I'm in New York, he's in Hong Kong, and good luck. >> Did you do the New York thing? I'm flying over there and going to break your legs! >> We bitched and complained, threatened them, and ultimately we settled on 30 cents on the dollar, so we did, that's exactly what happened. With a situation like this, with smart contracts, neither side has to hire two sets of lawyers in the other country-- >> So Chime takes care of that. You want Chime to take care of that administrative inefficiency? >> Correct. The company might still get involved in administering exceptions but not everyone single one. What the smart contract does is it allows you to scale international business. The key is international business, and that's a new efficiency into the market, and that's a great-- >> And in the business model, what does that scale mean to you for operationalizing it? More people, do you have to hire them? >> More cash. No, less people and more cash because there's more automation, right? It means more software development-- >> Where's the cash coming from? >> We have a lot of revenue products. Like the obvious, like every other website, we have subscription revenue and advertising revenue. Subscription revenue comes from like... You know how IMDB is the LinkedIn of the TV and film business? So we'll have that too. >> It's not really large, though. It can be. >> Amazon could make it larger if they wanted to. They have their reasons for doing it the way they do it. But, in our case, I'll give you an example of some revenue products. Let's say you want to crowdfund a project. So let's say you want a bunch of Taylor Swift fans to crowdfund a project for her to do a duet with Kanye West. Sounds preposterous, but it's goofy enough. You'd be amazed, Stormy Daniels is crowdfunding a project for her legal bills with Donald Trump, and I betcha it's going to get funded, right? >> John: I would agree. >> So there's a lot of nutty stuff that gets crowdfunded. >> The wisdom of the crowd is actually efficient. >> Yes, that's right, and the whims of the crowd. But also, I'll give you another example. Let's say people want, if they go to a webpage about an artist, the band All American Rejects, for example, and Wheeler, one of the band members... Ten years ago, you could have given your niece a gift of a CD of All American Rejects. Well, good luck now. They wouldn't even know what a CD is in many cases, right? But what you could do is say, "Hey, you know what? "I'll give you a gift of a Google Hangouts chat with him, "And I'll pay $200 for that, or $500 for it." >> It's probably a bot, but anyway, how do you make this happen? This is really important. You're creating value by allowing people to collaborate in a way that's different, so that scales. Is that going to be done in the Chime contract or it's all going to be part of one currency? >> One currency, that's right. We're very careful. We brought in as an advisor, Rod Garrett, who gave one of the keynotes here yesterday. Rod Garrett is the money supply economist from UCSB, but he was also former VP of the New York Fed, he was the leader at the New York Fed for cryptocurrency. Rod is one of the smartest people I've ever met. >> You know him? >> Very well now, and you know what, Rod can explain the most complex things in simple words, which means he actually understands them. So we've actually used Fisher's equation to help model the utility token value over time. And, again, it's designed to appreciate, but we don't want nutty appreciation because then it'll be useless as a currency, right? We have fixed supply, the Bitcoin principle, the fixed supply and stable market so we can keep it reasonably stable. >> You're using the utility token to create value on your network so the creators can capture that value. >> Correct. >> That's what you're doing with the utility. The security is the money making side. How are you backing the security token, with equity or cash flow? >> Equity, and very important, really important, if you did a percentage of revenue or royalties, it wouldn't work, and I'll tell you why. It wouldn't scale, because we're looking five years out, 10 years out, for this to be a good investment. We want investors to buy it. And if you, let's say you need to do a secondary, because an acquisition becomes available, because you're low on money or whatever. Then how do you do a secondary if you've already given away 20% of your revenue to token holders. What if you have to do a secondary or tertiary capital round? How many rounds were necessary for Spotify, I happen to know Spotify, it was six, right? Facebook, Google, how many founds of financing did they do? A lot, and by the way, they still might do more. >> So basically the revenue share is hair on the deal. It really puts a lot of hair on the deal. >> Destroys it, in my opinion, destroys it. It's a dressing thing, but look, if you're really going to grow to a major company and have, be it five or 10 year success, it kills it. This is my opinion. >> What percentage of equity, say they're going to do a 50 million dollar raise, hard cap, soft cap, say 25, that's what seems to be the norm right now, what would be a percentage of equity converting to tokens that you'd see? >> In Chimes' case, we have a Common A class of stock. We're creating a preferred class of stock called a Series T which, if fully sold, would be about 43% of the equity of the company. They had to do it preferred stock, because there's too many, in Delaware Corporate Law, which all the tech companies are all Delaware, common stock would be very difficult to make a token. You can do whatever you want with preferred. So the preferred is more flexible, so it's actual equity, actual shares, it's not a derivative, it's not a rev share, it's not a royalty, it's actual equity. >> It's paper that converts nicely and it scales on the business side. >> So you say, "What's the evaluation?" >> We're selling 100 million dollars worth of the equity, or we're offering 100 million dollars of the equity, the pre-sale evaluation is a little over 200 million. In Chimes' cases, that's because we're not a startup, we're an early stage company. >> How old is the company? >> Pardon me? >> How old is the company? >> Three and a half years. >> So you weren't born yesterday. >> We acquired music databases that were built at a cost of tens of millions of dollars in Europe, funded by the richest guy in Europe, who built it out and then got tired of it, tired of funding it, and then we were able to pick it up basically for equity deals. We picked it up and we're buying a second music database also that's a very big one. So it's not like we're a startup with an idea and a business plan. >> No, you've got assets, and you've got momentum, good management, you obviously know what you're doing. It's awesome. You've got a great scalability mindset. You've got a nicely packaged, clear target. >> That's right, so we're probably a little bit different than a lot of crypto startups, in that, a lot of brilliant entrepreneurs that you see here, but we've been around the block with having to do IPO's, having to do exits, having to do... And you know, I'm a contrarian, right? I was getting a lot of advice yesterday from a lot of really smart people saying, "Hey, raise the money overseas through a foundation." >> "Everyone's doing it!" >> Look, I'm going to take a contrarian approach. >> I'm just going to comply with the law, by doing the registration. And they say, "What if your utility token has to comply "with money transfer laws?" Then we'll comply with them! It's like look, the contrarian approach is, whatever the law is, follow it! It gives us the flex-- >> The thing is you're actually doing what they want you to do, notifying them of what you're doing, and you have a utility! >> By separating out the token into two, one that has the attributes of currency, one that has the attributes of an equity, neither one is screwing up the other. >> I agree, that's really smart, and very novel. A lot of smart people are going down that road because it's actually known things people can understand. Security token is paperwork that you can do. >> Yes, but I'll tell you the other thing that feels very important, a pretty important point to make. By doing registration, the resale can go to anybody. My personal opinion, is you know these second market type of approaches that you can only resale them to accredited investors or to foreign investors or whatever, I think that's mistake. I think what happens is people who take that approach are going to find that the resale value of the token, or the token that has securities is going to be about 10% of what it would have been otherwise. >> If they only do accredited? >> Well yeah, because here's the thing. First, it's not only that they got to be accredited-- >> How do you get around the security token? >> Because it's registered. The waitress working the bar here can buy a publicly traded equity if it's registered, right? She can buy a publicly traded token-- >> That's the Form 10 that you were talking about. >> Right, Form 10 registers the company. The initial batch of trading will be done under 144 because the token holds will evolve over six months, so they can sell them at their leisure, right? There are exceptions, by the way, like an affiliate might have to do some form filing. I would have to file a Form 3, you know, the usual stuff. But, a regular token investor, he can do whatever he wants. And I can call them investors. I can do business in the United States. I don't have to pretend I'm domiciled in a country you've never heard of, right? So it's like look, I'm an American, my staff is mostly American, we do business in America, let's follow American law instead of-- >> Joe, this is a great conversation. We're getting down and dirty under the hood, capital structure, business models, Chimes' really interesting approach. Joe, thanks for sharing that great data here on theCUBE. Section 12G of the 1934 Securities Act. Form 10 is the secret weapon that was built by aliens before us to allow us to get this special clause in there for crypto. I'd love to continue this conversation another time. I think there's four or five things we just identified, great great topics, thanks for sharing. It's theCUBE's coverage here in Puerto Rico, I'm John Furrier, we'll be back with more after this short break. (digital jingle)