>>In late 2009 when the industry was just beginning to offer so-called converged infrastructure, CI Nutanix was skating to the puck, so to speak, meaning unlike conversion infrastructure, which essentially bolted together compute and networking and storage into a single skew that was very hardware centric. Nutanix was focused on creating HCI hyperconverged infrastructure, which was a software led architecture that unified the key elements of data center infrastructure. Now, while both approaches saved time and money, HCI took the concept to new heights of cost savings and simplicity. Hyperconverged infrastructure became a staple of private clouds creating a cloudlike experience. OnPrem. As the public cloud evolved and grew, more and more customers are now taking a cloud first approach to it. So the challenge becomes how do you remodel your IT house so that you can connect your on-prem workloads to the cloud, to both simplify cloud migration, while at the same time creating an identical experience across your estate? >>Hello, and welcome to this special program, Accelerate Hybrid Cloud with Nutanix and Microsoft Made Possible by By Nutanix and produced by the Cube. I'm Dave Ante, one of your hosts today. Now, in this session, we'll hear how Nutanix is evolving its initial vision of simplifying infrastructure, deployment and management to support modern applications by partnering with Microsoft to enable that consistent experience that we talked about earlier, to extend hybrid cloud to Microsoft Azure and take advantage of cloud native tooling. Now, what's really important to stress here, and you'll hear this in our second segment, substantive engineering work has gone into this partnership. A lot of partnerships are sealed with a press release. We sometimes call it a Barney deal. You know, I love you, you love me. Like Barney, the once popular children's dinosaur character. We dig into the critical engineering aspects that enable that seamless connection between on-prem infrastructure and the public cloud. >>Now, in our first segment, Lisa Martin talks to Alro Salise, who is the vice president of Global ISD Commercial Solutions at Microsoft, and Michael Les Chica, who is the vice president of business development for the cloud and database partner ecosystem at Nutanix. Now, after that, Lisa will kick it back to me in our Boston studios to speak with Eric Lockard, who is the corporate vice president of Microsoft Azure specialized, along with Thomas Cornell, who is the senior vice president of products at Nutanix. And Indu Carey, who's the senior vice president of of engineering for NCI and NNC two at Nutanix. And we'll dig deeper into the announcement and it's salient features. Thanks for being with us. We hope you enjoy the program. Over to Lisa. >>Hi everyone. Welcome to our event Accelerate Hybrid Cloud with Nutanix and Microsoft. I'm your host Lisa Martin, and I've got two great guests here with me to give you some exciting news. Please welcome Alva Salise, the Vice President of Global ISD Commercial Solutions at Microsoft, and Michael Les Chika, VP of Business Development Cloud and database partner ecosystem at Nutanix. Guys, it's great to have you on the program. Thanks so much for joining me today. Great to be here. >>Thank you, Lisa. Looking forward, >>Yeah, so let's go ahead and start with you. Talk to me from your lens, what are you seeing in terms of the importance of the role of the the ISV ecosystem and really helping customers make their business outcomes successful? >>Oh, absolutely. Well, first of all, thank you for the invitation and thank you Michael and the Nutanix team for the partnership. The the ISV ecosystem plays a critical role as we support our customers and enable them in their data transformation journeys to create value, to move at their own pace, and more important to be sure that every one of them, as they transform themselves, have the right set of solutions for the long term with high differentiation, cost effectiveness and resiliency, especially given the times that we're living. >>Yeah, that resiliency is getting more and more critical as each day goes on. Ava was sticking with you. We got Microsoft Ignite going on today. What are some of the key themes that we should expect this year and how do they align to Microsoft's vision and strategy? >>Ah, great question. Thank you. When you think about it, we wanna talk about the topics that are very relevant and our customers have asked us to go deeper and, and share with them. One of them, as you may imagine, is how can we do more with less using Azure, especially given the current times that we're living in the, the business context has changed so much, they have different imperative, different different amount of pressure and priorities. How can we help? How can we combine the platform, the value that Microsoft can bring and our Microsoft ISV partner ecosystem to deliver more value and enable them to have their own journey? Actually, in that frame, if I may, we are making this announcement today with Nutanix. I, the Nutanix cloud clusters are often the fastest way on which customers will be able to do that journey into the cloud because it's very consistent with environments that they already know and use on premise. And once they go into the cloud, then they have all the benefit of scale, agility, resiliency, security, and cost benefits that they're looking for. So that topic and this type of announcements will be a big part of what we doing. Ignite, >>Exciting. Michael, let's bring you into the conversation now. Big milestone of our RDTs that the general availability of Nutanix Cloud clusters on Azure. Talk to us about that from Nutanix's perspective and also gimme a little bit of color, Michael, on the partnership, the relationship. >>Yeah, sure, absolutely. So we actually entered a partnership couple years ago, so we've been working on this solution quite a while, but really our ultimate goal from day one was really to make our customers journeys to hybrid cloud simpler and faster. So really for both companies, I think our goal is really being that trusted partner for our customers in their innovation journey. And as mentioned, you know, in the current macroeconomic conditions, really our customers really care about, but they have to be mindful of their bottom line as well. So they're really looking to leverage their existing investments in technology skill sets and leverage the most out of that. So the things like, for example, cost to operations and keeping those things consistent, cost on premises and the cloud are really important as customers are thinking about growth initiatives that they wanna implement. And of course, going to Azure public cloud is an important one as they think about flexibility, scale and modernizing their apps. >>And of course, as we look at the customer landscape, a lot of customers have an on on footprint, right? Whether that's for regulatory reasons for business or other technical reasons. So hybrid cloud has really become an ideal operating model for a lot of the customers that we see today. So really our partnership with Microsoft is critical because together, I really do see our US together simplifying that journey to the public cloud and making sure that it's not only easy but secure and really seamless. And really, I see our partnership as bringing the strengths of each company together, right? So Nutanix, of course, is known in the past versus hyperconverge infrastructure and really breaking down those silos between networking, compute, storage, and simplifying that infrastructure and operations. And our customers love that for the products and our, our NPS score of 90 over the last seven years. And if you look at Azure, at Microsoft, they're truly best in class cloud infrastructure with cutting edge services and innovation and really global scale. So when you think about those two combinations, right, that's really powerful for customers to be able to take their applications and whether they're on or even, and really combining all those various hybrid scenarios. And I think that's something that's pretty unique that we're to offer customers. >>Let's dig into that uniqueness of our, bringing you back into the conversation. You guys are meeting customers where they are helping them to accelerate their cloud transformations, delivering that consistency, you know, whether they're on-prem in Azure, in in the cloud. Talk to me about, from Microsoft's perspective about the significance of this announcement. I understand that the, the preview was oversubscribed, so the demand from your joint customers is clear. >>Thank you, Lisa. Michael, personally, I'm very proud and at the company we're very proud of the world that we did together with Nutanix. When you see two companies coming together with the mission of empowering customers and with the customer at the center and trying to solve real problems in this case, how to drive hybrid cloud and what is the best approach for them, opening more opportunities is, is, is extremely inspiring. And of course the welcome reception that we have from customer reiterates that we generating that value. Now, when you combine the power of Azure, that is very well known by resiliency, the scale, the performance, the elasticity, and the range of services with the reality of companies that might have hundreds or even thousands of different applications and data sources, those cloud journeys are very different for each and every one of them. So how do we combine our capabilities between Nutanix and Microsoft to be sure that that hybrid cloud journey that every one is gonna take can be simplified, you can take away the risk, the complexity on that transformation creates tones of value. >>And that's what a customers are asking us today. Either because they're trying to move and modernize their environment to Azure, or they're bringing their, you know, a enable ordinate services and cluster and data services on premise to a Nutanix platform, we together can combine and solve for that adding more value for any scenario that customers may have. And this is not once and done, this is not that we building, we forget it. It's a partnership that keeps evolving and also includes work that we do with our solution sales alliances that go to market seems to be sure that the customers have diverse service and support to make, to create the outcomes that they're asking us to deliver. >>Talk to me a little bit about the customers that were in the beta, as we mentioned, Alva, the, the preview was oversubscribed. So as I talked about earlier, the demand is clearly there. Talk to me about some of the customers in beta, you can even anonymize them or maybe talk about them by industry, but what, what were some of the, the key things they came to these two companies looking to, to solve, get to the cloud faster, be able to deliver the same sets of services with familiarity so that from a, they're able to do more with less? >>Maybe I could take that one out of our abital lines. It did. It means, but yeah, so like, like we, like you mentioned Lisa, you know, we've had a great preview oversubscribe, we had lots of, of cu not only customers, but also partners battle testing the solution. And you know, we're obviously very pleased now to have GN offered to everyone else, but one of our customers, Camper J was really looking forward to seeing how do they leverage Ncq and Azure to, like I mentioned, reduce that work workload, my, my migration and a risk for that and making sure, hey, some of the applications, maybe we are going to go and rewrite them, refactor them to take them natively to Azure. But there's others where we wanna lift and shift them to Azure. But like I mentioned, it's not just customers, right? We've been working with partners like PCs and Citrix where they share the same goal as Microsoft and Nutanix provides that superior customer experience where whatever the operating model might be for that customer. So they're going to be leveraging NC two on Azure to really provide those hybrid cloud experiences for their solutions on top of building on top of the, the work that we've done together. >>So this really kind of highlights the power of that Alva, the power of the ISV ecosystem and what you're all able to do together to really help customers achieve the outcomes that they individually need. >>A absolutely, look, I mean, we strongly believe that when you partner properly with an V you get to the, to the magical framework, one plus one equals three or more because you are combining superpowers and you are solving the problem on behalf of the customer so they can focus on their business. And this is a wonderful example, a very inspiring one where when you see the risk, the complexity that all these projects normally have, and Michael did a great job framing some of them, and the difference that they have now by having NC to on Azure, it's night and day. And we are fully committed to keep driving this innovation, this partnership on service of our customers and our partner ecosystem because at the same time, making our partners more successful, generating more value for customers and for all of us. >>Abar, can you comment a little bit on the go to market? Like how, how do your joint customers engage? What does that look like from their perspective? >>You know, when you think about the go to market, a lot of that is we have, you know, teams all over the world that will be aligned and working together in service of the customer. There is marketing and demand generation that will be done, that will be also work on enjoying opportunities that we will manage as well as a very tight connection on projects to be sure that the support experience for customers is well aligned. I don't wanna go into too much detail, but I will like to guarantee that our intent is not only to create an incredible technological experience, which the, the development teams are done, but also a great experience for the customers that are going through these projects, interacting with both teams that will work as one in service to empower the customer to achieve the outcomes that they need. >>Yeah, and just to comment maybe a little bit more on what Albar said, you know, it's not just about the product integration or it's really the full end to end experience for our customers. So when we embarked on this partnership with Microsoft, we really thought about what is the right product integration and with our engineering teams, but also how do we go and talk to customers with value prop together and all the way down through to support. So we actually been worked on how do we have a single joint support for our customers. So it doesn't really matter how the customer engages, they really see this as an end to end single solution across two companies. >>And that's so critical given just the, the natural challenges that that organizations face and the dynamics of the macro economic environment that we're living in. For them, for customers to be able to have that really seamless single point of interaction, they want that consistent experience on-prem to the cloud. But from an engagement perspective that you're, what sounds like what you're doing, Michael and Avaro is, is goes a long way to really giving customers a much more streamlined approach so that they can be laser focused on solving the business problems that they have, being competitive, getting products to market faster and all that good stuff. Michael, I wonder if you could comment on maybe the cultural alignment that Nutanix and Microsoft have. I know Microsoft's partner program has been around for decades and decades. Michael, what does that cultural alignment look like from, you know, the sales and marketing folks down to engineering, down to support? >>Yeah, I think honestly that was, that was something that kind of fit really well and we saw really a long alignment from day one. Of course, you know, Nutanix cares a lot about our customer experience, not just within the products, but again, through the entire life cycle to support and so forth. And Microsoft's no different, right? There's a huge emphasis on making sure that we provide the best customer experience and that we're also focusing on solving real world customer problems, right? And really focusing on the biggest problems that customers have. So really culturally it felt, it felt really natural. It felt like we were a single team, although it's, you know, two bar organizations working together, but I really felt like a single team working day in, day out on, on solving customer problems together. >>Yeah, >>Let, go ahead. >>No, I would say, well say Michael, the, the one element that we complement, the, I think the answer was super complete, is the, the fact that we work together from the outside in, look at it from the customer lenses is extremely powerful and inspire, as I mentioned, because that's what it's all about. And when you put the customer at the center, everything else falls in part on its its own place very, very quickly. And then it's hard work and innovation and, you know, doing what we do best, which is combining over superpowers in service of that customer. So that was the piece that, you know, I, I cannot emphasize enough how inspiring he's been. And again, the, the response for the previous is a great example of the opportunity that we have in there. >>And you've taken a lot of complexity out of the customer environment and I can imagine that the GA of Nutanix cloud clusters on Azure is gonna be a huge benefit for customers in every industry. Last question guys, I wanna get both your perspectives on Michael, we'll start with you and then Lvra will wrap with you. What's next? Obviously a lot of exciting stuff. What's next for the partnership of these, these two superheroes together, Michael? >>Yeah, so I think our goal doesn't change, right? I think our North star is to continue to make it easy for our customers to adopt, migrate and modernize their applications, leveraging Nutanix and Microsoft Azure, right? And I think NC two and Azure is just the start of that. So kind of maybe more immediate, like, you know, we mentioned obviously we have, we announced the ga that's J in Americas, but kind of the next more immediate step over the next few months look for us to continue expanding beyond Americas and making sure that we have support across all the global regions. And then beyond that, you know, again, as of our mentioned, it's working from kind of the s backwards. So we're, we're not, no, we're not waiting for ega. We're already working on the next set of solutions saying what are other problems that customer facing, especially across, they're running their workload cross on premises and public cloud, and what are the next set of solutions that we can deliver to the market to solve those real challenges for. >>It sounds really strongly that, that the partnership here, we're talking about Nutanix and Microsoft, it's really Nutanix and Microsoft with the customer at this center. I think you've both done a great job of articulating that there's laser focus there. Our last word to you, what excites you about the momentum that Microsoft and Nutanix have for the customers? >>Well, thank you Lisa. Michael, I will tell you, when you hear the customer feedback on the impact that you're having, that's the most inspiring part because you know you're generating value, you know, you're making a difference, especially in these complex times when the, the partnership gets tested where the, the right, you know, relationship gets built. We're being there for customers is extremely inspiring. Now, as Michael mentioned, this is all about what customer needs and how do we go even ahead of the game, being sure that we're ready not for what is the problem today, but the opportunities that we have tomorrow to keep working on this. We have a huge TA task ahead to be sure that we bring this value globally in the right way with the right quality. Every word, which is a, is never as small fist as you may imagine. You know, the, the world is a big place, but also the next wave of innovations that will be customer driven to keep and, and raise the bar on how, how much more value can we unlock and how much empowerment can we make for the customer to keep in innovating at their own pace, in their own terms. >>Absolutely that customer empowerment's key. Guys, it's been a pleasure talking to you about the announcement Nutanix cloud clusters on Azure of our Michael, thank you for your time, your inputs and helping us understand the impact that this powerhouse relationship is making. >>Thank you for having Lisa and thank you AAR for joining >>Me. Thank you Lisa, Michael, it's been fantastic. I looking forward and thank you to the audience for being here with us. Yeah, stay >>Tuned. Thanks to the audience. Exactly. And stay tuned. There's more to come. We have coming up next, a deeper conversation on the announcement with Dave and product execs from both Microsoft. You won't wanna.

>>Hi everyone. Welcome to our event Accelerate Hybrid Cloud with Nutanix and Microsoft. I'm your host Lisa Martin, and I've got two great guests here with me to give you some exciting news. Please welcome Alva Salise, the Vice President of Global ISV Commercial Solutions at Microsoft. And Michael Luka, VP of Business Development Cloud and database partner ecosystem at Nutanix. Guys, it's great to have you on the program. Thanks so much for joining me today. Great to be here. >>Thank you, Lisa. Looking forward, >>Yeah, so a, let's go ahead and start with you. Talk to me from your lens, what are you seeing in terms of the importance of the role of the the ISV ecosystem and really helping customers make their business outcomes successful? >>Well, absolutely. Well, first of all, thank you for the invitation and thank you Michael and the Nutanix team for the partnership. So the, the ISV ecosystem plays a critical role as we support our customers and enable them in their data transformation journeys to create value, to move at the own pace, and more important to ensure that every one of them as they transform themselves, have the right set of solutions for the long term with high differentiation, cost effectiveness and resiliency, especially given the times that we're living in. >>Yeah, that resiliency is getting more and more critical as each day goes on. Ava was sticking with you. We got Microsoft Ignite going on today. What are some of the key themes that we should expect this year and how do they align to Microsoft's vision and strategy? >>Ah, great question. Thank you. When you think about it, we wanna talk about the topics that are very relevant and our customers have asked us to go deeper and, and share with them. One of them, as you may imagine, is how can we do more with less using Azure, especially given the current times that we're living in the, the business context has changed so much. They have different imperative, different different amount of pressure and priorities. How can we help, how can we combine the platform, the value that Microsoft can bring and or Microsoft ISV power ecosystem to deliver more value and enable them to have their own journey? Actually, in that frame, if I may, we are making this announcement today with Nutanix. The Nutanix cloud clusters are often the fastest way on which customers will be able to do that journey into the cloud because it's very consistent with environments that they already know and use on premise. And once they go into the cloud, then they have all the benefit of scale, agility, resiliency, security and cost benefits that they're looking for. So that topic and this type of announcements will be a big part of what we doing. Ignite >>Then exciting. Michael, let's bring you into the conversation now. Sure. Big milestone of our RDTs that the general availability of Nutanix Cloud clusters on Azure. Talk to us about that from Nutanix's perspective and also gimme a little bit of color, Michael, on the partnership, the relationship. >>Yeah, sure. Absolutely. So we actually entered a partnership couple years ago, so we've been working on this quite a while. But really our ultimate goal from day one was really to make our customers journeys to hybrid cloud simpler and faster. So really for both companies, I think our goal is really being that trusted partner for our customers in their innovation journey. And as I mentioned, you know, in the current macroeconomic conditions, really our customers really care about growing their top line, but they have to be mindful of their bottom line as well. So they're really looking to leverage their existing investments in technology skill and leverage the most that, So the things like, for example, cost to operations and keeping those things cost on premises and are really important as customers are thinking about growth initiatives that they wanna implement. And of course going to Azure public cloud is an important one as they think about flexibility, scale and modernizing in their apps. >>And of course as we look at the customer landscape, a lot of customers have an footprint, right? Whether that's for regulatory reasons for business or other technic for reasons. So hybrid cloud has really become an ideal operating model for a lot of the customers that we see today. So really our partnership with Microsoft is critical because together, I really do see our US together simplifying that journey to the public cloud and making sure that it's not only easy but secure and really seamless. And really, I see our partnership as bringing the strengths of each company together, right? So Nutanix, of course, is known in the past versus hyperconverge infrastructure and really breaking down those silos between networking, compute, storage, and simplifying that infrastructure and operations. And our customers love that for the products and our, our NPS score of 90 over the last seven years. And if you look at Azure, at Microsoft, they're truly best in class cloud infrastructure with cutting edge services and innovation and really global scale. So when you think about those two combinations, right, that's really powerful for customers to be able to take their applications and whether they're on pre the cloud or even the edge and really combining all those various hybrid scenarios. And I think that's something that's pretty unique that we're able to offer our joint customers. >>Let's into that uniqueness of our, bringing you back into the conversation, you guys are meeting customers where they are helping them to accelerate their cloud transformations, delivering that consistency, you know, whether they're on-prem in Azure, in in the cloud. Talk to me about, from Microsoft's perspective about the significance of this announcement. I understand that the, the preview was oversubscribed, so the demand from your joint customers is clear. >>Thank you, Lisa. Michael, personally, I'm very proud and at the company we're very proud of the world that we did together with Nutanix. When you see two companies coming together with the mission of empowering customers and with the customer at the center and trying to solve real problems in this case, how to drive hybrid cloud and what is the best approach for them, opening more opportunities is, is is extremely inspiring. And of course the welcome reception that we have from customer reiterates that we generating that value. Now, when you combine the power of Azure, that is very well known by resiliency, the scale, the performance, the elasticity, and the range of services with the reality of companies that might have hundreds of even thousands of different applications and data sources, those cloud journeys are very different for each and every one of them. So how do we combine our capabilities between Nutanix and Microsoft to be sure that that hybrid cloud journey that every one is gonna take can be simplified, you can take away the risk, the complexity on that transformation creates tons of value. >>And that's what a customers are asking us today. Either because they're trying to move and modernize their environment to Azure, or they're bringing their, you know, a enable services and cluster and data services on premise to the Nutanix platform, we together can combine and solve for that adding more value for any scenario that customers may have. And this is not once and done, this is not that we building, we forget it, it's a partnership that keeps evolving and also includes work that we do with our solution sales alliances that go to market seems to be sure that the customers have diverse service and support to make, to, to create the outcomes that they're asking us to deliver. >>And can you comment a little bit further, maybe both of you, of our, starting with you and then Michael, what are some of those business outcomes that customers are coming to Microsoft and Nutanix saying, help us, we've gotta be more competitive, we've gotta get, we've gotta be able to get solutions to market faster, et cetera. What are those key outcomes that these two powerhouse companies are helping customers to unlock? >>Yeah, I will say, look, the range of imperative of customers varies greatly depending on the industry, depending on the positioning. I think that the fundamental question is given your imperative, do we have the ability to empower you to achieve the outcome that you want? And these days, of course, the tons of companies, given the the business context that are being very conscious on cost and efficiency, how do you do more with less? How do I keep innovating? Because innovation will be at the heart of the solutions, but I do that on my own pace with my own priorities. That higher level answer is the one that we're enabling through partnership, like the one we're we're sharing today to the market with Nutanix. >>Yeah, I think >>From you, >>Go ahead. I was just gonna comment ON'S pump as well is that absolutely really depends on the customer and what they're trying to achieve, right? As they think about the next set of innovation that they're trying to develop. But for example, we take a, a web, a use case that we've seen with some of the customers is like migration to the cloud, right? And you know, a lot of companies, they embark on that migration. We see there's a lot of data that says basically, you know, it's much harder than it looks, right? And a lot of these projects become years behind schedule and millions and millions of dollars over budget, right? So reducing that risk and saying, Hey, how do I, can I land in Azure? And then bit by bit start thinking, how do I continue to innovate to get, since now I have easy and secure access while I'm in Azure with, and seek with Nutanix Nutanix clusters on Azure to continue my innovation by taking advantage of Azure native services, right? But again, like Aaro said, it's, it really depends on what the customer goals are. >>Talk to me a little bit about the customers that were in the beta, as we mentioned, Alva, the, the preview was oversubscribed. So as I talked about earlier, the demand is clearly there. Talk to me about some of the customers and beta, you can even anonymize them or maybe talk about them by industry, but what, what were some of the, the key things they came to these two companies looking to, to solve, get to the cloud faster, be able to deliver the same sets of services with familiarity so that from a, they're able to do more with less? >>Maybe I could take that one out of our rebuttal lines. It does means, but yeah, so like, like, like you mentioned, Lisa, you know, we've had a great preview oversubscribe, we had lots of CU not only s but also partners battle solution. And you know, we're obviously very pleased now to have offered to everyone else, but one of our customers Camp Day was really looking forward to seeing how do they leverage Nstitute and Azure to, like I mentioned, reduce that work workload, migration and risk for that and making sure, hey, some of the applications maybe we are going to go and rewrite them, refactor them to take them natively to Azure. But there's others where we wanna lift and shift them to Azure. But like I mentioned, it's not just customers, right? We've been working with partners like PCs and Citrix where they share the same goal as Microsoft and Nutanix provides that superior customer experience where whatever the operating model might be for that customer. So they're going to be leveraging NC two on Azure to really provide those hybrid cloud experiences for their solutions on top of building on top of the, the work that we've done together. >>So this really kind of highlights the power of that Ava, the power of the ISB ecosystem and what you're all able to do together to really help customers achieve the outcomes that they individually need. >>A absolutely, look, I mean, we strongly believe that when you partner properly with an isv, you get to the, to the magical framework, one plus one equals three or more because you are combining superpowers and you are solving the problem on behalf of the customer so they can focus on their business. And this is a wonderful example, a very inspiring one where when you see the risk, the complexity that all these projects normally have, and Michael did a great job framing some of them, and the difference that they have now by having NC to on Azure, it's night and day. And we are fully committed to keep driving this innovation, this partnership on service of our customers and our power ecosystem. Because at the same time, making our powers more successful, generating more value for customers and for all of us >>Of, Can you comment a little bit on the go to market? Like how, how do your joint customers engage? What does that look like from their perspective? >>You know, when you think about the go to market, a lot of that is we have, you know, teams all over the world that will be aligned and working together in service of the customer. There's marketing and demand generation that will be done, that will be also work on joy opportunities that we will manage as well as a very tight connection on projects to be sure that the support experience for customers is well aligned. I don't wanna talk, go into too much detail, but I would like to guarantee that our intent is not only to create an incredible technological experience, which the, the development teams are done, but also a great experience for the customers that are going through these projects, interacting with both teams that will work as one in service to empower the customer to achieve the outcomes that they need. >>Yeah, and just to comment maybe a little bit more on what all Borrow said, you know, it's not just about the product integration area, it's really the full end to end experience for our customers. So when we embarked on this partnership with Microsoft, we really thought about what is the right product integration and with our engineering teams, but also how do we go and talk to customers with value prop together and all the way down through to support. So we actually even worked on how do we have a single joint support for our customer. So it doesn't really matter how the customer engages, they really see this as an end to end single solution across two companies. >>And that's so critical given just the, the natural challenges that that organizations face and the dynamics of the macro economic environment that we're living in. For them, for customers to be able to have that really seamless single point of interaction, they want that consistent experience on-prem to the cloud. But from an engagement perspective that you're, what sounds like what you're doing, Michael and Avaro is, is goes a long way to really giving customers a much more streamlined approach so that they can be laser focused on solving the business problems that they have, being competitive, getting products to market faster and all that good stuff. Michael, I wonder if you could comment on maybe the cultural alignment that Nutanix and Microsoft have. I know Microsoft's partner program has been around for decades and decades. Michael, what does that cultural alignment look like from, you know, the sales and marketing folks down to engineering, down to support? >>Yeah, I think honestly that was, that was something that kind of fit really well and we saw really a lot alignment from day one. Of course, you know, Nutanix cares a lot about our customer experience, not just within the products, but again, through the entire life cycle to support and so forth. And Microsoft's no different, right? There's a huge emphasis on making sure that we provide the best customer experience and that we're also focusing on solving real world customer problems, right? And really focus on the biggest problems the customers have. So really culturally it felt, it felt really natural. It felt like we were a single team, although it's, you know, two bar drug organizations working together, but I really felt like a single team working day in, day out on, on solving customer problems together. >>Yeah. >>Let me, Go ahead. >>No, I will say, well say Michael, I think that the, the one element that we complement, I think the answer was super complete, is the, the fact that we work together from the outside in, look at it from the customer lenses is extremely powerful and far as I mentioned, because that's what it's all about. And when you put the customer at the center, everything else falls in part on its its own place very, very quickly. And then it's hard work and innovation and, you know, doing what we do best, which is combining over superpowers in service of that customer. So that was the piece that, you know, I i, I cannot emphasize enough how inspiring he's been. And again, the, the response for the previous is a great example of the opportunity that we have in there. >>Yeah. And, and you know, with every hard problem there's challenges along the way, right? And so I'm actually really proud of both of the teams that stepped up and, you know, figure it out. How do we go solve some of these technical problems? How do we go solve, making sure we continue to provide world class support for sports organizations? And, you know, these weren't easy things to solve and, and you know, everyone really stepped up the challenge >>And you've taken a lot of complexity out of the customer environment and I can imagine that the GA of Nutanix cloud clusters on Azure is gonna be a huge benefit for customers and every industry. Last question guys, I wanna get both your perspectives on Michael, we'll start with you and then Lvra will wrap with you. What's next? Obviously a lot of exciting stuff. What's next for the partnership of these, these two superheroes together, Michael? >>Yeah, so I think our goal doesn't change, right? I think our North star is to continue to make it easy for our customers to adopt, migrate and modernize their applications, leveraging Nutanix and Microsoft Azure, right? And I think NC two and Azure is just the start of that. So kind of maybe more immediate, like, you know, we mentioned obviously we have, we announced the GA that's J in Americas kind of the next more immediate step over the next few months. Look for us to continue expanding beyond Americas and making sure that we have support across all the global regions. And then beyond that, you know, again, as of our mentioned is working from kind of the customers backwards. So we're, we're not, no, we're not waiting for the ga, we're already working on the next set of solutions saying what are other problems that customer facing, especially across as they're running their workloads cross on premises and public cloud, and what are the next set of solutions that we can deliver to the market to solve those real challenges for them. >>It sounds really strongly that, that the partnership here, we're talking about Nutanix and Microsoft. It's really Nutanix and Microsoft with the customer at this center. I think you've do both, done a great job of articulating that there's laser focus there. Of our last word to you, what excites you about the momentum that Microsoft and Nutanix have for the customers? >>Well, thank you Lisa. Michael, I will tell you, when you hear the customer feedback on the impact that you're having, that's the most inspiring part because you know, you're generating value, you know, you're making a difference, especially in this complex times when the, the partnership gets tested where the, the right, you know, relationship gets built. We're being there for customers is extremely inspired. Now, as Michael mentioned, this is all about what customer needs and how do we go even ahead of the game so that we're ready not for what is the problem today, but the opportunities that we have tomorrow to keep working on this. We have a huge task ahead to be sure that we bring this value globally in the right way with the right quality. Every word, which is a, is never a small fist as you may imagine. You know, the, the world is a big place, but also the next wave of innovations that will be customer driven to keep and, and raise the bar on how, how much more value can we unlock and how much empowerment can we make for the customer to keep in innovating at their own pace, in their own terms. >>Absolutely that customer empowerment's key. Guys, it's been a pleasure talking to you about the announcement, Nutanix cloud clusters on Azure of our Michael, thank you for your time, your inputs and helping us understand the impact that this powerhouse relationship is making. >>Thank you for having Lisa and thank you Avara for joining me. >>Thank you, Lisa, Michael, it's been fantastic and looking forward and thank you to the audience for being here with us. Yeah, stay >>Tuned. Exactly. Thanks to the audience. >>Exactly. >>And stay tuned. There's more to come. We have coming up next, a deeper conversation on the announcement with Dave Valante and product execs from both and Microsoft. You won't wanna miss it.

>>The past two and a half years have seen a dramatic change in the security posture of virtually all organizations. By accelerating the digital business mandate, the isolation economy catalyzed a move toward cloud computing to support remote workers. This, we know this had several ripple effects on CISO and CIO strategies that were highly visible at the board of directors level. Now, the first major change was to recognize that the perimeter had suddenly been vaporized protection. As a result moved away from things like perimeter based firewalls toward more distributed endpoints, cloud security, and modern identity management. The second major change was a heightened awareness of the realities of ransomware. Ransomware as a service, for example, emerges a major threat where virtually anyone with access to critical data and criminal intentions could monetize corporate security exposures. The third major change was a much more acute understanding of how data protection needed to become a fundamental component of cybersecurity strategies. >>And more specifically, CIOs quickly realized that their business resilient strategies were too narrowly DR focused that their DR approach was not cost efficient and needed to be modernized. And that new approaches to operational resilience were needed to reflect the architectural and business realities of this new environment. Hello, and welcome to Why Ransomware isn't your Only Problem, a service of the Cube made possible by dva. And in collaboration with idc. I'm your host, Dave Ante, and today we're present a three part program. We'll start with the data. IDC recently conducted a global survey of 500 business technology practitioners across 20 industries to understand the degree to which organizations are aware of and prepared for the threats they face. In today's new world, IDC Research Vice President Phil Goodwin is here to share the highlights of the study and summarize the findings from a recent research report on the topic. >>After that, we're gonna hear from Curtis Preston, who's the Chief Technical Evangelist at Druva. I've known Curtis for decades. He's one of the world's foremost experts on backup and recovery, specifically in data protection. Generally. Curtis will help us understand how the survey data presented by IDC aligns with the real world findings from the field, from his point of view. And he'll discuss why so many organizations have failed to successfully recover from an attack without major pains and big costs, and how to avoid such operational disruptions and disasters. And then finally, we'll hear from the technical experts at dva, Steven Manly and Anja Serenas. Steven is a 10 time cubo and Chief technology officer at dva. And Anjan is vice president and general manager of product management at the company. And these individuals will specifically address how DVA is closing the gaps presented in the IDC survey through their product innovation. Or right now I'm gonna toss it to Lisa Martin, another one of the hosts for today's program. Lisa, over to you. >>Bill Goodwin joins me next, the VP of research at idc. We're gonna be breaking down what's going on in the threat landscape. Phil, welcome to the program. It's great to have you back on the cube. >>Hey, Lisa, it's great to be here with you. >>So talk to me about the state of the global IT landscape as we see cyber attacks massively increasing, the threat landscape changing so much, what is IDC seeing? >>You know, you, you really hit the, the top topic that we find from IT organizations as well as business organizations. And really it's that digital resilience that that ransomware that has everybody's attention, and it has the attention not just of the IT people, but of the business people alike, because it really does have profound effects across the organization. The other thing that we're seeing, Lisa, is really a move towards cloud. And I think part of that is driven by the economics of cloud, which fundamentally changed the way that we can approach disaster recovery, but also is accelerated during the pandemic for all the reasons that people have talked about in terms of work from home and so on. And then really the third thing is the economic uncertainty. And this is relatively new for 2022, but within idc we've been doing a lot of research around what are those impacts going to be. And what we find people doing is they want greater flexibility, they want more cost certainty, and they really want to be able to leverage those cloud economics to be, have the scale, upper scale, down on demand nature of cloud. So those are in a nutshell, kind of the three things that people are looking at. >>You mentioned ransomware, it's a topic we've been talking about a lot. It's a household word these days. It's now Phil, no longer if we're gonna get attacked. It's when it's how often it's the severity. Talk about ransomware as a priority all the way up the stack to the C-suite. And what are they trying to do to become resilient against it? >>Well, what, what some of the research that we did is we found that about 77% of organizations have digital resilience as a, as a top priority within their organization. And so what you're seeing is organizations trying to leverage things to become more, more resilient, more digitally resilient, and to be able to really hone in on those kinds of issues that are keeping keeping them awake at night. Quite honestly, if you think about digital resilience, it really is foundational to the organization, whether it's through digital transformation or whether it's simply data availability, whatever it might happen to be. Digital resilience is really a, a large umbrella term that we use to describe that function that is aimed at avoiding data loss, assuring data availability, and helping the organization to extract value from their data >>And digital resilience, data resilience as every company these days has to be a data company to be competitive, digital resilience, data resilience. Are you using those terms interchangeably or data resilience to find as something a little bit different? >>Well, sometimes yeah, that we do get caught using them when, when one is the other. But data resilience is really a part of digital resilience, if you think about the data itself and the context of of IT computing. So it really is a subset of that, but it is foundational to IT resilience. You, you really, you can't have it resilience about data resilience. So that, that's where we're coming from on it >>Inextricably linked and it's becoming a corporate initiative, but there's some factors that can complicate digital resilience, data resilience for organizations. What are some of those complications that organizations need to be aware of? >>Well, one of the biggest is what, what you mentioned at the, at the top of the segment. And, and that is the, the area of ransomware, the research that we found is about 46% of organizations have been hit within the last three years. You know, it's kind of interesting how it's changed over the years. Originally being hit by ransomware had a real stigma attached to it. Organizations didn't want to admit it, and they really avoided confronting that. Nowadays, so many people have been hit by it, that that stigma has gone. And so really it is becoming more of a community kind of effort as people try to, to defend against these ransoms. The other thing about it is it's really a lot like whackamole. You know, they attack us in one area and and, and we defend against it. They, so they attack us in another area and we defend against it. >>And in fact, I had a, an individual come up to me at a show not long ago and said, You know, one of these days we're gonna get pretty well defended against ransomware and it's gonna go away. And I responded, I don't think so because we're constantly introducing new systems, new software, and introducing new vulnerabilities. And the fact is ransomware is so profitable, the bad guys aren't gonna just fade into the night without giving it a a lot of fight. So I really think that ransomware is one of those things that here is here for the long term and something that we, we have to address and have to get proactive about. >>You mentioned some stats there and, and recently IDC and DVA did a white paper together that really revealed some quite shocking results. Talk to me about some of the things. Let, let's talk a little bit about the demographics of the survey and then talk about what was the biggest finding there, especially where it's concern concerning ransomware. >>Yeah, this, this was a worldwide study. It was sponsored by DVA and conducted by IDC as an independent study. And what we did, we surveyed 500 is a little over 500 different individuals across the globe in North America select countries in in western Europe, as well as several in, in Asia Pacific. And we did it across industries with our 20 different industries represented. They're all evenly represented. We had surveys that included IT practitioners, primarily CIOs, CTOs, VP of of infrastructure, you know, managers of data centers, things like that. And the, and the biggest finding that we had in this, Lisa, was really finding that there is a huge disconnect, I believe, between how people think they are ready and what the actual results are when they, when they get attacked. Some of the, some of the statistics that we learned from this, Lisa, include 83% of organizations believe or tell, told us that they have a, a playbook that, that they have for ransomware. >>I think 93% said that they have a high degree or a high or very high degree of confidence in their recovery tools and, and are fully automated. And yet when you look at the actual results, you know, I told you a moment ago, 46% have been attacked successfully. I can also tell you that in separate research, fewer than a third of organizations were able to fully recover their data without paying the ransom. And some two thirds actually had to pay the ransom. And even when they did, they didn't necessarily achieve their full recovery. You know, the bad guys aren't, aren't necessarily to be trusted. And, and so the software that they provide sometimes is, is fully recovered. Sometimes it's not. So you look at that and you go, Wow. On, on the one hand, people think they're really, really prepared, and on the other hand, the results are, are absolutely horrible. >>You know, two thirds of people having, having to pay their ransom. So you start to ask yourself, well, well, what is, what's going on there? And I believe that a lot of it comes down to, kind of reminds me of the old quote from Mike Tyson. Everybody has a plan until they get punched in the mouth. And I think that's kind of what happens with ransomware. You, you think you know what you're, you're doing, you think you're ready based on the information you have. And these people are smart people and, and they're professionals, but oftentimes you don't know what you don't know. And like I say, the bad guys are always dreaming up new ways to attack us. And so I think for that reason, a lot of these have been successful. So that was kind of the key finding to me in kind of the aha moment really in this whole thing. Lisa, >>That's a massive disconnect with the vast majority saying we have a cyber recovery playbook, yet nearly half being the victims of ransomware in the last three years, and then half of them experiencing data loss. What is it then that organizations in this situation across any industry can do to truly enable cyber resilience data resilience as it's, as we said, this is a matter of this is gonna happen just a matter of when and how often >>It it is a matter, Yeah, as you said, it's not if when or, or how often. It's really how badly. So I think what organizations are really do doing now is starting to turn more to cloud-based services. You know, finding professionals who know what they're doing, who have that breadth of experience and who have seen the kinds of, of necessary steps that it takes to do a recovery. And the fact of the matter is a disaster recovery and a cyber recovery are really not the same thing. And so organizations need to be able to, to plan the kinds of recovery associated with cyber recovery in terms of forensics, in terms of, of scanning, in terms of analysis and so forth. So they're, they're turning to professionals in the cloud much more in order to get that breadth of experience and, and to take advantage of cloud based services that are out there. >>Talk to me about some of the key advantages of cloud-based services for data resilience versus traditional legacy on-prem equipment. What are some of the advantages? Why are is IDC seeing this big shift to cloud where, where data resilience is concerned? >>Well, the first and foremost is the economics of it. You know, you can, you can have on demand resources. And in the old days when we had disaster recoveries where there we had two different data centers and a failover and so forth, you know, you had double the infrastructure. If your financial services, it might even be triple, the infrastructure is very complicated, very difficult by going to the cloud. Organizations can subscribe to disaster recovery as a service. It increasingly what we see is a new market of cyber recovery as a service. So being able to leverage those resources to be able to have the forensic analysis available to them, to be able to have the other resources available that are on demand, and to have that plan in place to have those resources in place. I think what happens in a number of situations, Lisa, is that that organizations think they're ready, but then all of a sudden they get hit and all of a sudden they have to engage with outside consultants or they have to bring in other experts and that, and that extends the time to recover that they have and it also complicates it. >>So if they have those resources in place, then they can simply turn them on, engage them, and get that recover going as quickly as possible. >>So what do you think the big issue here is, is it that these, these I p T practitioners over 500 that you surveyed across 20 industries is a global survey? Do they not know what they don't know? What's the the overlying issue here? >>Yeah, I think that's right. It's, you don't know what you don't know and until you get into a specific attack, you know, there, there are so many different ways that, that organizations can be attacked. And in fact, from this research that we found is that in many cases, data exfiltration exceeds data corruption by about 50%. And when you think about that, the, the issue is, once I have your data, what are you gonna do? I mean, there's no amount of recovery that is gonna help. So organizations are either faced with paying the ransom to keep the data from perhaps being used on the dark web or whatever, or simply saying no and, and taking their chances. So best practice things like encryption, immutability, you know, things like that that organizations can put into place. Certainly air gaps. Having a, a solid backup foundation to, to where data is you have a high recovery, high probability of recovery, things like that. Those are the kinds of things that organizations have to put into place really is a baseline to assure that they can recover as fast as possible and not lose data in the event of a ransomware attack. >>Given some of the, the, the disconnect that you articulated, the, the stats that show so many think we are prepared, we've got a playbook, yet so many are being, are being attacked. The vulnerabilities and the, and the, as the, the landscape threat landscape just gets more and more amorphous. Why, what do you recommend organizations? Do you talk to the IT practitioners, but does this go all the way up to the board level in terms of, hey guys, across every industry, we are vulnerable, this is gonna happen, we've gotta make sure that we are truly resilient and proactive? >>Yes, and in fact, what we found from this research is in more than half of cases, the CEO is directly involved in the recovery. So this is very much a C-suite issue. And if you look at the, the, the consequences of ransom where it's not just the ransom, it's the loss productivity, it's, it's the loss of, of revenue. It's, it's the loss of, of customer faith and, and, and goodwill and organizations that have been attacked have, have suffered those consequences. And, and many of them are permanent. So people at the board level where it's, whether it's the ceo, the cfo, the cio, the c cso, you know, whoever it is, they're extremely concerned about these. And I can tell you they are fully engaged in addressing these issues within their organization. >>So all the way at the top critically important, business critical for any industry. I imagine some industries may be a little bit more vulnerable than others, financial services, healthcare, education, we've just seen big attack in Los Angeles County. But in terms of establishing data resilience, you mentioned ransomware isn't going anywhere, It's a big business business, it's very profitable. But what is IDCs prediction where ransomware is concerned? Do you think that organizations, if they truly adopt cloud and status based technologies, can they get to a place where the C-suite doesn't have to be involved to the point where they're, they really actually have i i functioning playbook? >>I i, I don't know if we'll ever get to the point where the CCC C suite is not involved. It's probably very important to have that, that level of executive sponsorship. But, but what we are seeing is, in fact, we predicted by 20 25, 50 5% of organizations we'll have shifted to a cloud centric strategy for their data resilience. And the reason we say that is, you know, workloads on premises aren't going away. So that's the core. We have an increasing number of workloads in the cloud and, and at the edge, and that's really where the growth is. So being able to take that cloud centric model and take advantage of, of cloud resources like immutable storage, being able to move data from region to region inexpensively and easily and, and to be able to take that cloud centric perspective and apply it on premises as well as in the cloud and at the edge is really where we believe that organizations are shifting their focus. >>Got it. We're just cracking the surface here. Phil, I wish we had more time, but I had a chance to read the Juba sponsored IDC White paper. Fascinating finds. I encourage all of you to download that, Take a read, you're gonna learn some very interesting statistics and recommendations for how you can really truly deploy data resilience in your organization. Phil, it's been a pleasure to have you on the program. Thank you for joining >>Me. No problem. Thank you, Lisa. >>In a moment, John Furrier will be here with his next guest. For right now, I'm Lisa Martin and you are watching the Cube, the leader in live tech coverage. >>We live in a world of infinite data, sprawling, dispersed valuable, but also vulnerable. So how do organizations achieve data resiliency when faced with ever expanding workloads, increasing security threats and intensified regulations? Unfortunately, the answer often boils down to what flavor of complexity do you like best? The common patchwork approaches are expensive, convoluted, and difficult to manage. There's multiple software and hardware vendors to worry about different deployments for workloads running on premises or in the cloud. And an inconsistent security framework resulting in enterprises maintaining four of five copies of the same data, increasing costs and risk building to an incoherent mess of complications. Now imagine a world free from these complexities. Welcome to the dr. A data resiliency cloud where full data protection and beautiful simplicity converge. 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As data grows and becomes more critical to your business advantage, a data resiliency plan is vital, but it shouldn't be complicated. Dr. A makes it simple. >>Welcome back everyone to the cube and the drew of a special presentation of why ransomware isn't your only problem. I'm John Furrier, host of the Cube. We're here with w Curtis Preston. Curtis Preston, he known in the industry Chief Technical Evangelist at Druva. Curtis, great to see you. We're here at why ransomware isn't your only problem. Great to see you. Thanks for coming on. >>Happy to be here. >>So we always see each other events now events are back. So it's great to have you here for this special presentation. The white paper from IDC really talks about this in detail. I to get your thoughts and I'd like you to reflect on the analysis that we've been covering here and the survey data, how it lines up with the real world that you're seeing out there. >>Yeah, I think it's the, the survey results really, I'd like to say, I'd like to say that they surprised me, but unfortunately they didn't. The, the, the, the data protection world has been this way for a while where there's this, this difference in belief or difference between the belief and the reality. And what we see is that there are a number of organizations that have been hit successfully, hit by ransomware, paid the ransom and, and, and or lost data. And yet the same people that were surveyed, they had to high degrees of confidence in their backup system. And I, you know, I, I could, I could probably go on for an hour as to the various reasons why that would be the case, but I, I think that this long running problem that as long as I've been associated with backups, which you know, has been a while, it's that problem of, you know, nobody wants to be the backup person. And, and people often just, they, they, they don't wanna have anything to do with the backup system. And so it sort of exists in this vacuum. And so then management is like, oh, the backup system's great, because the backup person often, you know, might say that it's great because maybe it's their job to say so. But the reality has always been very, very different. >>It's funny, you know, we're good boss, we got this covered. Good, >>It's all good, it's all good, >>You know, and the fingers crossed, right? So again, this is the reality and, and, and as it becomes backup and recovery, which we've talked about many times on the cube, certainly we have with you before, but now with ransomware also, the other thing is people get ransomware hit multiple times. So it's not, not only like they get hit once, so, you know, this is a constant chasing the tail on some ends, but there are some tools out there, You guys have a solution. And so let's get into that. You know, you have had hands on backup experience. What are the points that surprised you the most about what's going on in this world and the realities of how people should be going forward? What's your take? >>Well, I would say that the, the, the one part in the survey that surprised me the most was people that had a huge, you know, that there, there was a huge percentage of people that said that they had a, a, a, you know, a a a ransomware response, you know, in readiness program. And you look at that and you, how could you be, you know, that high percentage of people be comfortable with their ransomware readiness program and a, you know, which includes a number of things, right? There's the cyber attack aspect of responding to a ransomware attack, and then there's the recovery aspect. And so your, you believe that your company was ready for that, and then you go, and I, I think it was 67% of the people in the survey paid the ransom, which as, as a person who, you know, has spent my entire career trying to help people successfully recover their data, that number I think just hurt me the most is that because you, you talked about re infections, the surest way to guarantee that you get rein attacked and reinfected is to pay the ransom. This goes back all the way ransom since the beginning of time, right? Everyone knows if you pay the blackmail, all you're telling people is that you pay blackmail and >>You're in business, you're a good customer arr for ransomware. >>Yeah. So the, the fact that, you know, 60 what two thirds of the people that were attacked by ransomware paid the ransom. That one statistic just, just hurt my heart. >>Yeah. And I think this is the reality. I mean, we go back and even the psychology of the practitioners was, you know, it's super important to get back in recovery and that's been around for a long time, but now that's an attack vector, okay? And there's dollars involved, like I said, the arr joking, but there's recurring revenue for the, for the bad guys if they know you're paying up and if you're stupid enough not to change, you're tooling, right? So, so again, it works both ways. So I gotta ask you, why do you think so many are unable to successfully respond after an attack? Is it because they know it's coming? I mean, I mean, they're not that dumb. I mean, they have to know it's coming. Why aren't they responding and successfully to this? >>I I think it's a, it's a litany of thing starting with the, that aspect that I mentioned before, that nobody wants to have anything to do with the backup system, right? So nobody wants to be the one to raise their hand because if, if you're the one that raises their hand, you know what, that's a good idea, Curtis, why don't you look into that? Right. Nobody, nobody wants to be, Where's >>That guy now? He doesn't work here anymore. Yeah, but I I I hear where you come from exactly. Psychology. >>Yeah. So there, there's that. But then the second is that because of that, no one's looking at the fact that backups are the attack vector. They, they, they become the attack vector. And so because they're the attack vector, they have to be protected as much, if not more than the rest of the environment. The rest of the environment can live off of active directory and, you know, and things like Okta, so that you can have SSO and things like that. The backup environment has to be segregated in a very special way. Backups have to be stored completely separate for from your environment. The login and authentication and authorization system needs to be completely separate from your typical environment. Why? Because if you, if that production environment is compromised now knowing that the attacks or that the backup systems are a significant portion of the attack vector, then you've, if, if the production system is compromised, then the backup system is compromised. So you've got to segregate all of that. And I, and I just don't think that people are thinking about that. Yeah. You know, and they're using the same backup techniques that they've used for many, many years. >>So what you're saying is that the attack vectors and the attackers are getting smarter. They're saying, Hey, we'll just take out the backup first so they can backup. So we got the ransomware it >>Makes Yeah, exactly. The the largest ransomware group out there, the KTI ransomware group, they are specifically targeting specific backup vendors. They know how to recognize the backup servers. They know how to recognize where the backups are stored, and they are exfiltrating the backups first and then deleting them and then letting you know you have ransom. >>Okay, so you guys have a lot of customers, they all kind of have the same this problem. What's the patterns that you're seeing? How are they evolving? What are some of the things that they're implementing? What is the best practice? >>Well, again, you, you've got to fully segregate that data. There are, and, and everything about how that data is stored and everything about how that data's created and accessed. There are ways to do that with other, you know, with other commercial products, you can take a, a, a standard product and put a number of layers of defense on top of it, or you can switch to the, the way Druva does things, which is a SAS offering that stores your data completely in the cloud in our account, right? So your account could be completely compromised. That has nothing to do with our account. And the, the, it's a completely different authentication and authorization system. You've got multiple layers of defense between your computing environment and where we store your backups. So basically what you get by default with the, the way juva stores your backups is the best you can get after doing many, many layers of defense on the other side and having to do all that work with us. You just log in and you get all of that. >>I guess how do, how do you break the laws of physics? I guess that's the question here. >>Well, when, because that's the other thing is that by storing the data in the cloud, we, we do, and I've said this a few times, that you get to break the laws of physics and the, the only way to do that is to, is time travel and what, that's what it, so yeah, so Druva has time travel. What, and this is a criticism by the way. I don't think this is our official position, but Yeah. But the, the idea is that the only way to restore data as fast as possible is to restore it before you actually need it. And that's what kind of what I mean by time travel in that you basically, you configure your dr your disaster recovery environment in, in DVA one time. And then we are pre restoring your data as often as you tell us to do, to bring your DR environment up to the, you know, the, the current environment as quickly as we can so that in a disaster recovery scenario, which is part of your ransomware response, right? Again, there are many different parts, but when you get to actually restoring the data, you should be able to just push a button and go the, the data should already be restored. And that's the, i that's the way that you break the laws of physics is you break the laws of time. >>Well, I, everyone wants to know the next question, and this is the real big question, is, are you from the future? >>Yeah. Very much the future. >>What's it like in the future? Backup recovery as a restore, Is it air gaping? Everything? >>Yeah. It, it, it, Well it's a world where people don't have to worry about their backups. I I like to use the phrase, get outta the backup business. Just get into the ReSTOR business. I I, you know, I'm, I'm a grandfather now and I, and I love having a granddaughter and I often make the joke that if I don't, if I'd have known how great grandkids were, I would've skipped straight to them, right? Not possible. Just like this. Recoveries are great. Backups are really hard. So in the future, if you use a SAS data protection system and data resiliency system, you can just do recoveries and not have to worry about >>Backups. Yeah. And what's great about your background is you've got a lot of historical perspective. You've seen that been in the ways of innovation now it's really is about the recovery and real time. So a lot of good stuff going on. And God think automated thingss gotta be rocking and rolling. >>Absolutely. Yeah. I do remember, again, having worked so hard with many clients over the years, back then, we worked so hard just to get the backup done. There was very little time to work on the recovery. And I really, I kid you not that our customers don't have to do all of those things that all of our competitors have to do to, you know, to, to break, to try to break the laws of physics. I've been fighting the laws of physics my entire career to get the backup done in the first place. Then to secure all the data, right to air gap it and make sure that a ransomware attack isn't going to attack it. Our customers get to get straight to a fully automated disaster recovery environment that they get to test as often as possible and they get to do a full test by simply pressing a single button. And you know, I, I wish that, I wish everybody had that ability. >>Yeah, I mean, security's a big part of it. Data's in the middle of it all. This is now mainstream front lines. Great stuff Chris, great to have you on, bring that perspective and thanks for the insight. Really >>Appreciate it. Always happy to talk about my favorite subject. >>All right, we'll be back in a moment. We'll have Steven Manley, the cto and on John Shva, the GM and VP of Product Manage will join me. You're watching the cube, the leader in high tech enterprise coverage. >>Ransomware is top of mind for everyone. Attacks are becoming more frequent and more sophisticated. It's a problem you can't solve alone anymore. Ransomware is built to exploit weaknesses in your backup solution, destroying data and your last line of defense. With many vendors, it can take a lot of effort and configuration to ensure your backup environment is secure. Criminals also know that it's easy to fall behind on best practices like vulnerability, scans, patches and updates. In fact, 42% of vulnerabilities are exploited after a patch has been released after an attack. Recovery can be a long and manual process that still may not restore clean or complete data. The good news is that you can keep your data safe and recover faster with the DR A data resiliency cloud on your side. The DR A platform functions completely in the cloud with no hardware, software, operating system, or complex configurations, which means there are none of the weaknesses that ransomware commonly uses to attack backups. >>Our software as a service model delivers 24 7 365 fully managed security operations for your backup environment. We handle all the vulnerability scans, patches and upgrades for you. DVA also makes zero trust security easy with builtin multifactor authentication, single sign-on and role-based access controls in the event of an attack. Druva helps you stop the spread of ransomware and quickly understand what went wrong. With builtin access insights and anomaly detection, then you can use industry first tools and services to automate the recovery of clean unencrypted data from the entire timeframe of the attack. Cyber attacks are a major threat, but you can make protection and recovery easy with dva. >>Welcome back everyone to the Cubes special presentation with DVA on why ransomware isn't your only problem. I'm John er, host of the Cube. Our next guest are Steven Manley, Chief Technology Officer of dva and I, John Trini VAs, who is the general manager and vice president of product management and Druva. Gentleman, you got the keys to the kingdom, the technology, ransomware, data resilience. This is the topic, the IDC white paper that you guys put together with IDC really kind of nails it out. I want to get into it right away. Welcome to this segment. I really appreciate it. Thanks for coming on. >>Great to be here John. >>So what's your thoughts on the survey's conclusion? I've obviously the resilience is huge. Ransomware is continues to thunder away at businesses and causes a lot of problems. Disruption, I mean just it's endless ransomware problems. What's your thoughts on the con conclusion? >>So I'll say the, the thing that pops out to me is, is on the one hand, everybody who sees the survey, who reads, it's gonna say, well that's obvious. Of course ransomware continues to be a problem. Cyber resilience is an issue that's plaguing everybody. But, but I think when you dig deeper and there and there's a lot of subtleties to look into, but, but one of the things that, that I hear on a daily basis from the customers is it's because the problem keeps evolving. It, it's not as if the threat was a static thing to just be solved and you're done because the threat keeps evolving. It remains top of mind for everybody because it's so hard to keep up with with what's happening in terms of the attacks. >>And I think the other important thing to note, John, is that people are grappling with this ransomware attack all of a sudden where they were still grappling with a lot of legacy in their own environment. So they were not prepared for the advanced techniques that these ransomware attackers were bringing to market. It's almost like these ransomware attackers had a huge leg up in terms of technology that they had in their favor while keeping the lights on was keeping it away from all the tooling that needed to do. A lot of people are even still wondering when that happens next time, what do I even do? So clearly not very surprising. Clearly I think it's here to stay and I think as long as people don't retool for a modern era of data management, this is going to stay this >>Way. Yeah, I mean I hear this whole time and our cube conversations with practitioners, you know there, it's kind of like the security pro give me more tools, I'll buy anything that comes in the market. I'm desperate. There's definitely attention but it doesn't seem like people are satisfied with the tooling that they have. Can you guys share kind of your insights into what's going on in the product side? Because you know, people claim that they have tools at fine points of, of recovery opportunities but they can't get there. So it seems to be that there's a confidence problem here in the market. What, how do you guys see that? Cuz I think this is where the rubber meets the road with ransomware cuz it's, it is a moving train, it's always changing but it doesn't seem as confidence. Can you guys talk about that? What's your reaction? >>Yeah, let me jump in first and Steven can add to it. What happens is I think this is a panic buying and they have accumulated this tooling now just because somebody said could solve your problem, but they haven't had a chance to take a re-look from a ground up perspective to see where are the bottlenecks, where are the vulnerabilities and which tooling set needs to lie? Where, where does the logic need to recite and what in Drew we are watching people do and people do it successfully, is that as they have adopted through our technology, which is ground up built for the cloud and really built in a way which is, you know, driven at a data insight level where we have people even monitoring our service for anomalies and activities that are suspicious. We know where we need to play a role in really kind of mitigating this ransomware. >>And then there's a whole plethora of ecosystem players that kind of combine to really really finish the story so to say, right? So I think this has been a panic buying situation. This is like, get me any help you can give me. And I think as this settles down and people really understand that longer term as they really build out a true defense mechanism, they need to think really ground up. They will start to really see the value of technologies like Druva and tried to identify the right set of ecosystem to really bring together to solve it meaningfully. >>Steven, >>I was gonna say, I mean one, one of the, one of the really interesting things in the survey for me and, and, and for a moment, little more than a moment, it made me think was that the large number of respondents who said I've got a really efficient well run backup environment, who then on basically the next question said, and I have no confidence that I can recover from a ransomware attack. And you scratch your head and you think, well if your backup environment is so good, why do you have such low confidence? And, and, and I think that's the moment when we, we dug deeper and we realized, you know, if you've got a traditional architecture and let's face the dis base architecture's been around for almost two decades now in terms of dis based backup, you can have that tune to the help that can be running as efficiently, efficiently as you want it, but it was built before the ransomware attacks before, before all these cyber issues, you know, really start hitting companies. And so I have this really well run traditional backup environment that is not at all built for these modern threat vectors. And so that's really why customers are saying I'm doing the best I can, but as Angen pointed out, the architecture, the tooling isn't there to support what, what problems I need to solve today. Yeah, >>Great point. And so yeah, well that's a great point. Before we get into the customer side, I wanna get to in second, you know, I interviewed Jare, the the founder CEO many years ago, even before the pandemic. You mentioned modern, you guys have always had the cloud, which r this is huge. Now that you're past the pandemic, what is that modern cloud edge you guys have? Cuz that's a great point. A lot of stuff was built kind of Beckham recovery bolted on, not really kind of designed into the, the current state of the infrastructure and the cloud native application modern environment we're seeing. Right? Now's a huge issue >>I think. I think it's, it's to me there's, there's three things that come up over and over and over again as, as we talk to people in terms of, you know, being built in cloud, being cloud native, why is an advantage? The first one is, is security and ransomware. And, and, and we can go deeper, but the most obvious one that always comes up is every single backup you do with DVA is air gap offsite managed under a separate administrative domain so that you're not retrofitting any sort of air gap network and buying another appliance or setting up your own cloud environment to manage this. Every backup is ransomware protected, guaranteed. I think the second advantage is the scalability. And you know this, this certainly plays into account as your, your business grows or in some cases as you shrink or repurpose workloads, you're only paying for what you use. >>But it also plays a a big role again when you start thinking of ransomware recoveries because we can scale your recovery in cloud on premises as much or as little as you want. And then I think the third one is we're seeing a basically things evolving new workloads, data sprawl, new threat vectors. And one of the nice parts of being a SA service in the cloud is you're able to roll out new functionality every two weeks and there's no upgrade cycle, there's no waiting, you know, the customer doesn't have to say, Wow, I need it six months in the lab before I upgrade it and it's an 18 month, 24 month cycle before the functionality releases. You're getting it every two weeks and it's backed by Druva to make sure it works. >>That says on John, you know, you got the, the product side, you know, it's challenging job cuz you have so many customers asking for things probably on the roadmap you probably go hour for that one. But I wanna get your thoughts on what you're hearing and seeing from customers. You know, we just reviewed the IDC with Phil. How are you guys responding to your customer's needs? Because it seems that it's highly accelerated on the, probably on the feature request, but also structurally as as ransomware continues to evolve. What are you hearing, what's the key customer need? How are you guys responding? >>Yeah, actually I have two things that I hear very clearly when I talk to customers. One, I think after listening to their security problems and their vulnerability challenges because we see customers and help customers who are getting challenge by ransomware on a weekly basis. And what I find that this problem is not just a technology problem, it's an operating model problem. So in order to really secure themselves, they need a security operating model and a lot of them haven't figured out that security operating model in totality. Now where we come in as rua is that we are providing them the cloud operating model and a data protection operating model combined with a data insights operating model which all fit into their overall security operating model that they are really owning and they need to manage and operate because this is just not about a piece of technology. >>On top of that, I think our customers are getting challenged by all the same challenges of not just spending time on keeping the lights on but innovating faster with faster, with less. And that has been this age old problem, do more with less. But in this, in this whole, they're like trying to innovate in the middle of the war so to say, right, the war is happening, they're getting attacked, but there's also net new shadow IT challenges that's forcing them to make sure that they can manage all the new applications that are getting developed in the cloud. There is thousands of SaaS applications that they're consuming not knowing which data is critical to their success and which ones to protect and govern and secure. So all of these things are coming at them at a hundred miles per hour while they're just, you know, trying to live one day at a time. >>And unless they really develop this overall security operating model helped by cloud native technologies like Druva that really providing them a true cloud native model of really giving like a touchless and an invisible protection infrastructure. Not just beyond backups, beyond just the data protection that we all know of into this kind of this mindset of kind of being able to look at where each of those functionalities need to lie. That's where I think they're grappling with now. Drew is clearly helping them with keep up to pace with the public cloud innovations that they need to do and how to protect data. We just launched our EC two offering to protect EC two virtual machines back in aws and we are gonna be continuing to evolve that to further many services that public cloud software cuz our customers are really kind of consuming them at breakneck speed. >>So the new workloads, the new security capabilities. Love that. Good, good call out there. Steven, this still the issue of the disruption side of it, you guys have a guarantee there's a cost of ownership as you get more tools. Can you talk about that angle of it? Because this is, you got new workloads, you got the new security needs, what's the disruption impact? Cause you know, you won't avoid that. How much is it gonna cost you? And you guys have this guarantee, can you explain that? >>Yeah, absolutely. So, so Dr launched our 10 million data resiliency guarantee. And, and for us, you know, there were, there were really two key parts to this. The first obviously is 10 million means that, you know, again we're, we're we're willing to put our money where our mouth is and, and that's a big deal, right? That that, that we're willing to back this with the guarantee. But then the second part, and, and, and this is the part that I think reflects that, that sort of model that Angen was talking about, we, we sort of look at this and we say the goal of DVA is to do the job of protecting and securing your data for you so that you as a customer don't have to do it anymore. And so the guarantee actually protects you against multiple types of risks all with SLAs. So everything from, you know, your data's gonna be recoverable in the case of a ransomware attack. >>Okay, that's good. Of course for it to be recoverable, we're also guaranteeing, you know, your backup, your backup success rate. We're also guaranteeing the availability of the service. You know, we're, we're guaranteeing that the data that we're storing for you can't be compromised or leaked externally and you know, we're guaranteeing the long term durability of the data so that if you back up with us today and you need to recover 30 years from now, that data's gonna be recovered. So we wanted to really attack the end to end, you know, risks that, that, that affect our customers. Cybersecurity is a big deal, but it is not the only problem out there and the only way for this to work is to have a service that can provide you SLAs across all of the risks because that means, again, as a SAS vendor, we're doing the job for you so you're buying results as opposed to technology. >>That's great. Great point. Ransomware isn't the only problem that's the title of this presentation, but is a big one. People concerned about it. So great stuff. In the last five minutes guys, if you don't mind, I'd love to have you share what's on the horizon for dva. You mentioned the new workloads on John, you mentioned this new security hearing shift left DevOps is now the developer model, they're running it get data and security teams now stepping in and trying to be as vo high velocity as possible for the developers and enterprises. What's on the horizon, Ava? What trends is the company watching and how are you guys putting that together to stay ahead in the marketplace and the competition? >>Yeah, I think listening to our customers, what we realize is they need help with the public cloud. Number one. I think that's a big wave of consumption. People are consolidating their data centers, moving to the public cloud. They need help in expanding data protection, which becomes the basis of a lot of the security operating model that I talked about. They need that first from before they can start to get into much more advanced level of insights and analytics on that data to protect themselves and secure themselves and do interesting things with that data. So we are expanding our coverage on multiple fronts there. The second key thing is to really bring together a very insightful presentation layer, which I think is very unique to thwa because only we can look at multiple tenants, multiple customers because we are a SAS vendor and look at insights and give them best practices and guidances and analytics that nobody else can give. >>There's no silo anymore because we are able to take a good big vision view and now help our customers with insights that otherwise that information map is completely missing. So we are able to guide them down a path where they can optimize which workloads need, what kind of protection, and then how to secure them. So that is the second level of insights and analytics that we are building. And there's a whole plethora of security offerings that we are gonna build all the way from a feature level where we have things like recycle bin that's already available to our customers today to prevent any anomalous behavior and attacks that would delete their backups and then they still have a way to recover from it, but also things to curate and get back to that point in time where it is safe to recover and help them with a sandbox which they can recover confidently knowing it's not going to jeopardize them again and reinfect the whole environment again. So there's a whole bunch of things coming, but the key themes are public cloud, data insights and security and that's where my focus is to go and get those features delivered and Steven can add a few more things around services that Steven is looking to build in launch. >>Sure. So, so yeah, so, so John, I think one of the other areas that we see just an enormous groundswell of interest. So, so public cloud is important, but there are more and more organizations that are running hundreds if not thousands of SaaS applications and a lot of those SaaS applications have data. So there's the obvious things like Microsoft 365 Google workspace, but we're also seeing a lot of interest in protecting Salesforce because if you think about it, you know, if you, if if someone you know deletes some really important records in Salesforce, that's, that's actually actually kind of the record of your business. And so, you know, we're looking at more and more SaaS application protection and, and really getting deep in that application awareness. It's not just about backup and recovery. When you look at something like, like a sales force or something like Microsoft 365, you do wanna look into sandboxing, you wanna, you wanna look into long term archival because again, this is the new record of the business, what used to be in your on premises databases that all lives in cloud and SaaS applications now. >>So that's a really big area of investment for us. The second one, just to echo what, what engine said is, you know, one of the great things of being a SaaS provider is I have metadata that spans across thousands of customers and tens of billions of backups a year. And I'm tracking all sorts of interesting information that is going to enable us to do things like make backups more autonomous so that customers, again, I want to do the job for them, will do all the tuning, we'll do all the management for them to be able to better detect ransomware attacks, better respond to ransomware attacks because we're seeing across the globe. And then of course being able to give them more insight into what's happening in their data environment so they can get a better security posture before any attack happens. Because let's face it, if you can set your, your data up more cleanly, you're gonna be a lot less worried and a lot less exposed from that attack happens. So we want to be able to again, cover those SaaS applications in addition to the public cloud. And then we want to be able to use our metadata and use our analytics and use this massive pipeline. We've got to deliver value to our customers, not just charts and graphs, but actual services that enable them to focus their attention on other parts of the business. >>That's great stuff. Run John. >>And remember John, I think all this while keeping things really easy to consume consumer grade UI APIs and the, the really, the power of SaaS as a service simplicity to kind of continue on amongst kind of keeping these complex technologies together. >>Aj, that's a great call out. I was gonna mention ease of use is and self-service, big part of the developer and IT experience expected, it's the table stakes, love the analytic angle. I think that brings the scale to the table and faster time to value to get to learn best practices. But the end of the day automation, cross cloud protection and security to protect and recover. This is huge and this is big part of not only just protecting against ransomware and other things, but really being fast and being agile. So really appreciate the insights. Thanks for sharing on this segment, really under the hood and really kind of the value of of the product. Thanks for coming on. Appreciate it. >>Thank you very much. >>Okay, there it is. You got the experts talking about under the hood, the product, the value, the future of what's going on with Druva and the future of cloud native protecting and recovering. This is what it's all about. It's not just ransomware they have to worry about. In a moment, Dave Ante will give you some closing thoughts on the subject here you're watching the cube, the leader in high tech enterprise coverage. >>As organizations migrate their business processes to multi-cloud environments, they still face numerous threats and risks of data loss. With a growing number of cloud platforms and fragmented applications, it leads to an increase in data silos, sprawl, and management complexity. As workloads become more diverse, it's challenging to effectively manage data growth infrastructure, and resource costs across multiple cloud deployments. Using numerous backup vendor solutions for multiple cloud platforms can lead to management complexity. More importantly, the lack of centralized visibility and control can leave you exposed to security vulnerabilities, including ransomware that can cripple your business. The dr. A Data Resiliency Cloud is the only 100% SAS data resiliency platform that provides centralized, secure air gapped and immutable backup and recovery. With dva, your data is safe with multiple layers of protection and is ready for fast recovery from cyber attack, data corruption, or accidental data loss. Through a simple, easy to manage platform, you can seamlessly protect fragmented, diverse data at scale, across public clouds and your business critical SaaS applications. Druva is the only 100% SAS fender that can manage, govern, and protect data across multiple clouds and business critical SAS applications. It supports not just backup and recovery, but also data resiliency across high value use cases such as e-discovery, sensitive data governance, ransomware, and security. No other vendor can match Druva for customer experience, infinite scale storage optimization, data immutability and ransomware protection. The DVA data resiliency cloud your data always safe, always ready. Visit druva.com today to schedule a free demo. >>One of the big takeaways from today's program is that in the scramble to keep business flowing over the past two plus years, a lot of good technology practices have been put into place, but there's much more work to be done specifically because the frequency of attacks is on the rise and the severity of lost, stolen, or inaccessible data is so much higher. Today, business resilience must be designed into architectures and solutions from the start. It cannot be an afterthought. Well, actually it can be, but you won't be happy with the results. Now, part of the answer is finding the right partners, of course, but it also means taking a systems' view of your business, understanding the vulnerabilities and deploying solutions that can balance cost efficiency with appropriately high levels of protection, flexibility, and speed slash accuracy of recovery. You know, we hope you found today's program useful and informative. Remember, this session is available on demand in both its full format and the individual guest segments. All you gotta do is go to the cube.net and you'll see all the content, or you can go to druva.com. There are tons of resources available, including analyst reports, customer stories. There's this cool TCO calculator. You can find out what pricing looks like and lots more. Thanks for watching why Ransomware isn't your only problem Made possible by dva, a collaboration with IDC and presented by the Cube, your leader in enterprise and emerging tech coverage.

>>We're back at the red hat summit, 2022, the Cube's continuous coverage. This is day one. We're here all day tomorrow as well. My name is Dave LAN. I'm here with Paul Gillon. Matt Hicks is here. He's executive vice president of products and technologies at red hat. Matt. Good to see you. Thanks for coming on. Nice to see you face to >>Face. Thanks. Thanks Dave. Thanks fall. It's uh, good to be here. >>So you took a different tack with your, uh, keynote today, had a homage to ate a love lace and Serena VA Ramian, which was kind of cool. And your, your point was they weren't noted at their time and nobody was there to build on their early ideas. I mean, ate a lovely, I think it was a century before, right. Ram illusion was a, you know, decade plus, but, and you tied that to open source. You can give us your kind of bumper sticker of your premise there. >>Yeah. You know, I think I have a unique seat in this from red hat where we see, we see new engineers that come in that sort of compete on a world stage and open source and the, the best, which is easy to track just in contributions are not necessarily from the background you would expect them from. And, and it, for me, it's always really inspiring. Like you have this potential in, in people and open source is a great model for getting that out. We told the history story, cuz it, I think when you look over history, just some of that potential that's been ignored before. Um, sure. It's happening right now. But getting that tied into open source models, we think can hopefully let us tap into a little more than, than we have in the past. So >>Greatly. So when you're thinking about innovation and specific to open source, is it a case where I wonder, I really know the history here of open source. Maybe you can educate me. Is it the case where open source observes, uh, a de factacto standard let's say, or some other proprietary approach and says, Hey, we can build that in open and that's so the, the inspiration, or is it an innovation flywheel that just invents? >>I think it's both at this stage. So in the, in the early days, if you take something like Linux, it was a little more of, you know, there was the famous memo of like, this is gonna be a hobbyist project. We're just gonna light up X 86 hardware and have an operating system we can work with. That was a little more of like this standards were there, but it was, can we just build a better operating system with it, be >>Better than Unix cuz would live up to the promise of units. >>That's right. Where in Unix you had some standardization to models, but it wasn't open in that same sense. Uh, Linux has gone well beyond a hobbyist project at this point. Uh, but that was maybe that clone model, um, to units these days though, if you take something like Kubernetes or take something like Ansible, that's just more pure innovation, you didn't necessarily have a Kubernetes model that you're building a better version of it was distributed computing and how can we really make that tick and, um, bring a lot of great minds into that to build it. Um, so I think you see both of 'em, which is it's one of the things that makes open source fun. Like it, it has a broad reach at this point. >>There's one major area of software that opensource has not penetrated yet. And that is applications. I mean, we, there have been, you know, sugar CRM there have been open E R P applications and, and such, none of them really taken off and in fact tend to be drawn back to being proprietary. Why do you suppose opensource has been limited to infrastructure and has hasn't branched out further? >>Yeah, I think part of it is, uh, where can you find a, a model where lots of different companies are, are comfortable contributing into, if you have one solution and one domain from one company you're gonna struggle more getting a real vibrant community built around that. When you pick an area like infrastructure or core platforms, you have a lot of hardware providers, the use cases span from traditional apps to AI. You have a lot of places to run that it's a massive companies. So >>Volume really, it, >>It really is. You just have an interest that spans beyond companies and that's where we've seen open source projects really pick up and build critical mass. How about crypto >>Dows? I mean, that's right. Isn't that the, a form of open source? I mean, is it, isn't that the application really what exactly what you're talking about? It is true or >>It, well, if you look at cryptography encryption algorithms even go to, um, quantum going forward, I think a lot of quantum access will be driven in an open source model. The machines themselves, uh, will be machines, but things like kids kit, uh, that is how most people will access that. So it is a powerful model for getting into areas that are, um, pretty bleeding edge on it as well. >>We were talking, go ahead. We were talking before Andy mentioned that hardware and software increasingly intersecting. That was the theme we heard at the, at the keynote this morning. Yeah. Why do you believe that's happening and how do you see that? How does that affect what you do? >>Uh, I, I think the reason that's happening is there is a push to make decisions closer and closer to users on it because on one side, like law of physics and then on the other of it's just a better experience for it. And so whether that is in transportation or it's in telecommunications, so you see this push outside of data centers to be able to get at that data locally for it. Uh, but if that's the draw, I think also we're seeing hardware architectures are changing. There are, um, standards like arm that are lower power that lets you run pretty powerful compute at the edge as well. And I think it's that combination saying we can do a lot at the edge now and that actually benefits us building user experiences in a lot of different domains is, is making this pull to the edge, uh, really quickly. But it's, it's a, it's an exciting time to be seeing that happening >>And, and, and pretty powerful is almost an understatement. When you think about what the innovations that are going on. Right. I mean, in, in, in, in particular, at the edge mm-hmm, <affirmative>, I mean, you're seeing Moore's law be blown. Everybody says Moore's law is dead, but you're seeing the performance of when you combine the GPU and the CPU and the NPU and the Excel. I mean, it blows away anything we've historically known. Yeah. So you think about the innovations in software that occurred as a result of Moore's law. What are the new beachheads that we could potentially see in open source? >>I think when you start taking the, um, AI patterns on this and AI is a broad space, but if you go even to like machine learning of optimization type use cases, you start, uh, leveraging how you're gonna train those models, which gets you into, you know, CPUs and GPU and TPUs in that world. And then you also have the, how am I gonna take that train model, put it on a really lightweight device and efficiently ask that model questions. And that gets you into a different architecture design. Uh, but that combination, I think we're gonna see these domains build differently where you have mass compute training type capabilities, and then push that as close to the user, as you can, to make decisions that are more dynamic than traditional codes. >>So a lot of the AI that's done today is modeling that's done in the cloud. Yep. And what you're talking about at the edge, and you think about, you know, vehicles is real time influencing. Yep. And that's, that's massive amounts of data. It's a different architecture. Right. And requires different hardware presumably and different software. So, and you guys, well, Linux is obviously there. Yeah. >>That's, that is the, where we get excited about things like the GM announcement you are in the square, in that, um, aspect of running compute right at the end user and actually dealing with sensor and data, that's changing there to help, you know, in this case, like driver's assistance capabilities with it. But I think that the innovation we'll see in that space will be limitless on it. So it's, it's a nice combination of it too. And you'll still have traditional applications that are gonna use those models. I think of it almost as it's like the new middleware, we have our traditional middleware techniques that we know and patterns. Um, they will actually be augmented with things like, um, machine learning models and those capabilities to just be more dynamic. So it's a fun time right now seeing >>That conversion a lot of data too. And again, I wonder how much of that is even gonna be persisted prob probably enough, cuz there's gonna be so much of it, how much it'll come back to the cloud a lot, but maybe not most of it, but it's still massive amounts relative to what we've seen before >>It is. And this is, you know, you've heard our announcement around OpenShift streams in those capabilities. So in red hat, what we do, we will always focus on hybrid with it because a lot of that data it'll be dropped at the edge cuz you won't need it, but the data you act on and the data you need, you will probably need at your indice and in your cloud. And maybe even on premise and capabilities like Kafka and the ability to pick and stream and stay consistent. We think there's a set of really exciting services to be able to enable that class of development where, um, hopefully we'll be at the center of, of that. >>You, you announced, uh, today an agreement with GM, uh, to, to build on their all to five platform, uh, auto industry, very proprietary historically, uh, with their technology. Do you think that this is an opportunity to crank that open? >>A absolutely. I think in, I've been involved with opensource for, for a while, but I think all of them started in a very proprietary model. And then you get to a tipping point where open source models can just unlock more innovation than proprietary models and you see 'em tip and flip. And I think in the automotive industry and actually in a lot of other industries, the capabilities of being able to combine hardware and software fast with the latest capabilities, it'll drive more innovation than just sticking to proprietary models. So yeah, I believe it will be one of many things to come there. >>You've been involved in open surf for a while. Like how long of a while people must joke about when they look at you, Matt, they must say, oh, did you start when you were five? Yeah. >>It's >>Uh, you get that a lot. >>I, I do, uh, it's my, my children, I think aged me a bit, but uh, but yeah, for me it was the mid nineties. That's when I started with, uh, with open source. >>It was uh, wow. So >>It's been a long, long >>Run. You made the statement in your keynote, that software development is, is, is messy. I presumably part of your job is to make it less messy. But now we talk about all this, these new beachheads, this new new innovations, a lot of it's unknown. Yeah. And it could be really messy. So who are the, who is there a new breed of developer that's emerging? Are they gonna come over from the cloud developers or is it the, is it the OT crowd and the, and the OT crowd? That's gonna be the new developers. >>I, I wish I knew, but I would say, I think you, I do think you'll get to almost like a laws of physics type challenge where you won't learn everything. You're not gonna know, uh, the depths of 5g implementation and Kubernetes and Linux on that. And so for us, this is where ecosystem providers are really, really critical where you have to know your intersection points, but you also have to partner really well to actually drive innovation in some of these spaces cuz uh, the domains themselves are massive on it. So our areas we're gonna know hybrid, we're gonna know, you know, open source based platforms to enable hybrid. And then we're gonna partner with companies that know their domains and industries really well to bring solutions to customers. So >>I'm curious about partnering, uh, cuz Paul cor may mentioned that as well as, as being critical, do you have sort of a template for partnering or is each partnership unique? >>Um, >>I think at this point, uh, the market's changing so fast that, uh, we do have templates of, uh, who are you going to embed solutions with? Who are you going to co-sell with? And co-create uh, the challenge in technology though, is it shifts so quickly. If you go back five years, maybe even 10 years, public cloud probably wasn't as dominant. Um, as it is now, now we're starting to see the uptick of edge solutions, probably being, having as much draw as public cloud. And so I think for us, the partnership follows the innovation on those curves and finding the right model where that works for customers is the key thing for us. But I wish there was more of a pattern. We could say it stays stable for decades, but I think it changes with the market on, we do that. >>But you know, it's funny cuz you you've, you see every 15 years or so the industry gets disrupted. I mean we certainly saw it with mainframes and PC and then the internet and then the cloud, uh, you guys have kind of been there. Well Linux throughout, I mean, okay. It built the, built the internet, built the cloud, it's building the edge. So it's almost, I don't wanna say your disruption proof cause that's just, that's gonna jinx you, but, but in, but you've architected the products in a way that they're compatible with these new errors. Mm-hmm <affirmative> of industry, >>Everything needs an operating >>System. Everything needs an operating system, but you've seen operating systems come and go, you know, and, and Linux has survived so many different waves. Why, how >>You know, I, I think for us, when you see open source projects, they definitely get to a critical mass where you have so much contribution, so much innovation there that they're gonna be able to follow the trends pretty well. If you look at a Linux, whatever the next hardware innovation that comes out is Linux has enough gravity that, um, it's open, it's successful, you're gonna design to it. The capability will be there. I think you're seeing similar things in Kubernetes now where if you're going to try to drive application innovation, it is a model that gives you a ton of reach. You have thousands of contributors. That's been our model though is find those projects be influential in, 'em be able to drive value in life cycles. But I think it's that open source model that gives us the durability where it can keep changing and tracking to new patterns. So, so >>Yeah, there's been a lot of open source that wasn't able to sustain. So I think you guys obviously have a magic formula. That's true. >>We, there is a, there is some art to picking, I think millions of projects. Uh, but you've gotta watch for that. >>Yeah. Open source is also a place place where failed products go to die. Yeah. <laugh> so you have to be sure you're not, you're not in that corner. >>Yeah. Well >>Look at Kubernetes. I mean the fact that that actually happened is it's astounding to me when you think about it, I mean even red hat was ready to go on a different path. What if that had happened? Who knows? Maybe it never would've maybe to your point about Ava Lovelace, maybe it would've taken a decade to, or run revolution. >>You know, I think in some of these you have to, you have to watch really closely. We obviously have a lot of signals of what will make good long term health. And I, I don't think everyone looks at those the same. We look at 'em from trademark controls and how foundations are structured and um, who the contributors are and the spread of that. And it's not perfect. But I think for us, you have to have those that longevity built in there where you will have a spike of popularity that has the tendency to just, um, fall apart on it. So we've been yeah. Doing that pretty >>Well conditions for a long life is something that's a that's maybe it's an art form. I don't know if it's a data form. It's a culture. Maybe, maybe it's >>Cultural. Yeah. Probably a combination some days I think I'm like this could part art, part science. Yeah. But, uh, but it's certainly a fun space to be in and see that happen. It, um, yeah, it's inspiring to me. Yeah. >>Matt Hicks. Great to have you back on the cube and uh, good job on the keynote really, um, interesting angle that you took. So >>Congratulations. Thanks for having me. >>Yeah. You're very welcome. All right. Keep it right there. Dave ante for Paul Gillon red hat summit, 2022 from Boston. You're watching the cube.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hello, everyone. Welcome back to the cubes. Virtual coverage of AWS reinvent 2020 it's virtual this year because of the pandemic. We're not there in person and in real life, we're remote. I'm John for a year hosting the cube or the cube virtual. Um, as we continue to cover the three weeks of AWS reinvent and analyze the keynotes, we bring it in, uh, from our Cuban alumni, uh, network experts. And we have here great guest, Steve Malaney, CEO of Ava Trex, industry executive legend, former entrepreneur had done startups, um, been very, very successful with luminary and Silicon Valley, um, Palo Alto networks and the Sierra Cisco, I me, all the companies you've worked for. Um, Steve, great to see you again. >>Oh yeah. Hey awesome. Even if it's just virtual, John's great to be back in the cube. >>Okay, Steve, what's up? Am I muted? I got you. Okay. >>Gotcha. Oh, okay. I just said it's great. They're great to be back in the cube. >>I had to shut up my volume, got to love live cube TV. Um, I wanted to bring you on, because one, we've been talking with you guys and your company that you're now heading. You came off the board to take the helm of Ava tricks. You really saw the vision early on before the pandemic. We were actually, we did a hybrid event with you guys, a digital hybrid and your vision of multi-cloud and hybrid was pretty much in line with what Andy Jassy. And Amazon's now rolling out, except they're not calling it. Multi-cloud, they're just saying hybrid. But when you factor in the edge, the complexity there, you're really talking multiple environments. So I want to get your take, as you look at what Amazon has done in their announcements, they're continuing to power long. What's your analysis. What's your industry take? >>Yeah, I, I think it's, uh, you know, I think it's great. I think, you know, when we were a year ago, it was just a little over a year ago, we were at a multi-cloud conference and I think people kind of thought, wow, is multicloud something that the vendors are wanting to happen because they don't want to be killed by AWS. And you know, I mean, I saw this two years ago, I call it the Cambridge and explosion to cloud where every enterprise to we are now going to move to cloud. And they had been talking about it for six or seven years, but they didn't really mean it. And two years ago I saw they meant it and I knew what was going to happen. It was going to go multi-cloud they we're going to care about day two operations, visibility, control, security, all the things that enterprises care about. And I think, um, you know, what we've seen really over the last year is AWS and all the other cloud providers recognizing this, that the world is going multicloud. Um, and day two operations matter. You've gotta be able to operationalize this and enterprises. Can't just, it's not just about wiring it and building it up. You got do, you can operate it. And so that's, I think the thing that's really interesting is the maturity of the messaging. I would say from AWS to recognize, um, where enterprises are in their journey. >>You know, Steve, I want to just reflect on something. When I was 19 years old in my first job, uh, in New York, it was on a prime mini computer, my first exposure to the enterprise office and then went and worked for IBM and HP and others. I've been in the, around the enterprise. Let me just go back 10 years in Silicon Valley, you could literally count on one or two hands. The number of enterprise experts out there that you knew of that were out circulating that weren't retired. Um, because it went through this kind of commodity stage of outsource everything kind of down to the bone, you know, just keeping the lights on there. Wasn't really a lot of innovation in the enterprise. Now it's the hottest thing in the world. And you, and you look at what's happening with cloud. They're redefining the enterprise in Andy Jassy said to me, and I'm going to interview him, uh, later this week. And you know, he said, we're done with eyes and pads. We checked that's anything. I say anyone, but he's kind of implying that we did. I, as in pass, we're targeting global it. >>Yeah. Well, you know, >>Now enterprise is super hot and you know, it's, it's a whole nother ball game to restructuring on G >>Yeah, I mean, so I, uh, the AWS is marketing slogan, Mark. My words I'll bet you a hundred bucks within the next year is going to change. They are not going to say go build anymore. Right? Because that's what they're going to say. Go consume because no enterprise wants to build and Oh, by the way, here's the other thing that they're now also figuring out. Cause I know Andy Jassy analysis, there's a skills shortage of cloud, so they don't have the skills at the aptitude, but there's also a people shortage. It's not just the skills, it's the amount of people. They don't have the ability to go deploy this. And they're going to, you're going to need solutions like ABA tricks, abstract the way a lot of the complexities of the underlying clouds and deliver this architecture for people to be able to actually deploy. >>Where is the skill gaps in your opinion, where do you see them? >>You know, I was just talking to a customer yesterday and he said most of my, most of my team are CLI jockeys. And so for networking, that means the CLI the command line interface that a human manipulates to control the Cisco router. That's the old operational model. The model of this, these days are Terraform. You're going to infrastructure is code everything. You need scriptures. You need, you need developers that are going to be driving your infrastructure. And, and, but I can't, I can't fire all these people that I've had in my enterprise for the last 30 years. I got to bring them along. I got to bring them along and the tools and the platforms to be able to go, to go do that. >>Andy's argument and Amazon's position is we eliminate the undifferentiated heavy lifting and we have all this training and content to bring everyone along. Okay. By that. >>Well, I mean, here's, here's the thing that I think AWS and all the, all the cloud providers are figuring out is the enterprise is a different beast. You know, when you go to a company as AWS and say, Hey, you can get it as long as it's any color you want, as long as it's black. And so guess what, I'm a service. And the beautiful thing is you don't need to know anything about how we do anything and just trust me, it's all going to work that does not go over well with an enterprise because they say, I'm the guy that needs to know I will get fired. If this infrastructure goes down, you know, you saw us East one go down two weeks ago, Google had a outage to two days ago or whatever it was, shit happens. I don't know if I can say that on the cube. >>We're not going to actually see regulated at this point, but who's going to know. >>Um, and you know what? I've got to have that visibility in controls and enterprise, and I need the granular controls and the visibility to troubleshoot and the security controls and the performance controls that I used to have on prem, because I'm a regulated enterprise. I need that visibility and control. And the cloud providers just say, look, I deliver a service and I deliver it to everybody. And it's the same service. And you don't need to know that does not fly with the >>Well, certainly you're seeing more regulated industries. It used to be just public sector. I just talked with Teresa Carlson. She now took over all the industries. So FinTech is regulated. Energy is regulated. Telecom's regulated. The only thing that's not regulated is a VC and startup sectors, right? So there's a >>Well, and, and, and every, every good CIO of an enterprise knows nothing good comes from your, from your infrastructure that gets outsourced. We tried that it doesn't work. Now, maybe in 20 years, I can outsource my infrastructure if I'm the CIO of a major enterprise corporation. But right now I am not outsourcing that I have to have control. Now, am I going to leverage services and basic infrastructure from the cloud providers? Absolutely. I'm not going to build it on my own data centers. That world is over, but what I'm going to maintain is the visibility and control. >>Yeah. And that's what we heard from Verner. Vogel's around observability systems, thinking control versus observability, um, evolvable systems, things like reasoning, um, you know, these are, these are innovations, right? So, so let's get back to that builders thing, because you mentioned that earlier, I think there might be an opportunity. And I think this is where I think Jassy will either look brilliant or it might not pan out. So go big or go home moment. Can Amazon create a market for companies to say, instead of bringing along everybody, I'm going to bring along some people and hire more builders because there's rewards as spoils to be had for those builders. At this point in time, given the pandemic, it's kind of put everything on full display in terms of what to do. What's your thoughts on that? >>I think, I think outside in meaning I, I look at the customer and I, and I sit at the same side of the table as a customer. I think, what did they want? And every enterprise customer right now is building out their PRI it's just like in 1992, when they built out their private infrastructures, global infrastructure, and they did it with on-prem and data centers. I bought my stories, my compute, my networking, my MPLS, and I built my infrastructure. And it was my infrastructure. They're doing the same thing. It's just, they're architecting on top of cloud and they're doing it in a multi-cloud world because they're not going to be locked in to just one cloud. And they're going to have some applications that run better on GCP. Some have better in AWS and some on Oracle, and all of our customers are doing this. And what they want though, is a common infrastructure. That's their architecture and their infrastructure, not an AWS architecture and a Google architecture and an Azure architecture. What architecture, abstracted away above the clouds. That's my architecture. And it's common for my global network that that's what enterprises want to do. And I think each of the individual clouds are going to have to understand that they are a piece of the puzzle. They are not the puzzle. And I think you're going to have to come to that realization. >>I appreciate your expertise and insight into the commentary real quick, last 30 seconds, give a quick plug for Ava tricks. What are you guys doing? What's new cause the quick update. >>I mean, it's, it's, it's crazy just since, uh, I've been the CEO for two years and you know, the, the logos of large enterprise that we're getting right now. My, my Cambrian explosion that I saw two years ago is real, um, more executing on that strategy. It's a, who's who of logos right now. We've got 450 customers now we're, uh, exploding and more importantly, enterprises are now getting that deployment phase. They have, they're done with the architecture phase of, Hey, let me check this whole thing out in cloud. And now they're pushing the button and they're, they're accelerating, which my guess is it's not a coincidence that AWS is now talking about operations. And what Aviatrix does is, is, is, does gives that visibility and control cloud networking, but in a very cloud native way with Terraform simplicity, agility, because agility is part of mission critical infrastructure. Now can't be like it was in 1994 with a Cisco infrastructure where it said, what year do you want your, your, your infrastructure, Mr. Customer? >>Great. And the biggest thing people should pay attention to this year, uh, for around the enterprise dynamics with cloud and scale what's what should people be watching >>In your opinion? Just the continued movement of big enterprises, uh, all into cloud. The center of gravity is now into cloud and, uh, they're going to be completely running away from everything on prem. >>All right. Steven Landy, CEO of VBA tricks, a proven success entrepreneur CEO, back in the two years of the helm, the VBA tricks. Great to see you. I wish we were in person. One of our last events was your altitude event. It's on YouTube. If anyone was interested in watching, we had a great time. Steve, thank you so much for your candid commentary. Yeah. Thanks, John. Okay. I'm Jennifer with the cube. You're watching the cube virtual here on the cube. Thanks for watching..

>> Narrator: From around the globe, It's the CUBE with digital coverage of next level network experience event. (upbeat music) Brought to you by Infoblox. >> Everyone welcome back to the CUBE's coverage and co-hosting of the Infoblox next level networking experience virtual event. With a pop up event, only a few hours, but four great segments. Officer Stu Miniman helped me kick it off this morning, and Stu, I want to bring you in, Stu Miniman who's the... He host for the CUBE, covering networking with me Stu we do all the cloud native shows. We can, we can smell what's relevant, and I want to get your take on this, because, Infoblox putting out some pretty good content with some great guests. But, next level networking, let's just unpack that, next level networking and next level networking experience. The word experience changes the context of that definition, because going the next level with networking is one thing, having an experience is another, just what's your take, you seen, we talk about this all the time, what's your take? >> Yeah, so John, one of the words that we've talked about so much is, how do we simplify this environment? Networking is known for its complexity. Too often, it's, stuck down in protocols and just the arcane arts that I don't want to think about. Networking at its best, is just going to work. And I don't want to think about it, so, if I'm adopting SaaS models, if I'm going cloud native, it should, tie into everything else we're doing. What I was hearing, the themes, John, and the interviews you discussed, they're talking about SaaS, they're talking about cloud native, things like visibility, moving real time, really changes so much of these environments, so, IP addresses used to be a lot more static. We know now, things just change constantly and that's one of the big challenges. How do I monitor that environment? How do I keep them secure? And that's where modern environments need to go to the next level to be able to keep up with all of those changes. >> The word experience means something to me in a sense, I think contemporary, right? I think something new, relevant and cool, and still we're old enough to remember the '80s and '90s, and I was coming out of college late '80s, and I remember I never had a punch, I never did any program with a punch card. I was kind of the young gun, coming into the workforce with a technical degree, and I remember looking at the mainframe guys going, "who are those old relics?" And they, those guys hung onto their job as long as they could, and the smart ones moved and said, "Hey, I'm going to jump on this mini computer bandwagon, Oh, there's inter networking and local area networking that the PC toys are attaching to, that's interesting." And so you had a migration of systems talent move to the new, the new way. Some didn't, and I look at that and I say, hmm, that's similar to what's going on in networking, if you're the old networking guy or gal, and you're hugging onto the router, or you're hugging onto that old way, you could be extinct, because there is a new experience coming. It's programmable, it's automation, it's different. It's not, the big, old way, similar to the mainframe. So, a lot of psychology in this networking industry right now is, and the young people come in. It's like, why we do it that way? This to me is about next level networking, experience. Your reaction to that. >> Yeah, well, John, it's been interesting here in 2020, you talk about the acceleration of things moving, people that were dipping their toe in cloud and have to move in a matter of weeks, if not, hours and days to get things up and running. So, leveraging software, open source is a big component of what a lot of companies are doing, and of course, cloud and that cloud experience means in the public cloud and edge environments, you talked a bit about IOT in some of these cases, the order of magnitude of networking challenges that are out there are such that I have to have automation, it needs to be simpler because I could not do things the manual old way. John, I lived through so many generations, you work with people in the networking, it's manually done. It was done via CLI, because I knew how to do it. Maybe I did some scripting, but in today's day and era, things change too fast and the amount of work that needs to be done is so much so that that's why automation needs to be front and center. And you see Infoblox, as some of their new solutions, especially leveraging SnapRoute take advantage of the modern way that people need to do things. >> Well, we actually did a deep dive on SnapRoute and it was super impressive, again, I thought it was way too early, but they were doing some stuff with Kubernetes thinking, just thinking like Linux kernel, low level thinking. And I think Stu, this is what I want to get your thoughts on, because in the industry we cover Cisco aggressively. We saw them by open DNS, manage services versus low level, we got automation, you got Amazon out there, I mean, hell I can just have a screen that goes in and manages my DNS in the cloud, I can start thinking differently about how I wire my services together, if I think about Amazon, for instance, or hybrid and multicloud, this a whole new level of thinking. And, these are going to be new solutions, and this is the theme that came up and it's come up across every single major vendor, whether we're talking the Google cause they have a pretty damn good network. You got Cisco, you've got, all these people out there, they got to reinvent themselves. And, new expectations require new solutions. This has been something that's clearly coming out of the COVID, that, you know what I like working from home, I'm more productive. We don't need the real estate costs, wait, why do we even need a VPN? Why we over-provisioned? What are we paying for? Let's just build and secure. So again, all these projects are going to come out of the woodwork, I think that they're going to create a new vendor, a new brand or new opportunity because, these new solutions need to come because of the demand has been highlighted by COVID and other cloud scale. What's your thoughts on that, because this may not be your grandfather's networking company that comes out of the woodwork, It might be a cloud app. >> Yeah, well John, first of all, I think you nailed it. You look at a company like Infoblox, founded back in the .com era, back in 1999 and dominant in their space. So, they're not here saying, oh, we're the tried and trusted company that you work with, and you shouldn't try that new Fangled, Kubernetes piece or anything like that. It's not ready for prime time. As you said, they're getting, they're looking to skate where, to where the pack is going, they're aggressively going after these environments to make sure that they maintain their leadership in this environment. And, you're absolutely right, for the longest time, generally in networking, you were talking about, it was Cisco and everybody else out there, but now the cloud is such a big piece of what's going on, we've seen chip acquisitions by the big Hyperscalers, we've seen how they build their environments, and in many ways there's been consolidation, but there's also been dis-aggregation. So, the fundamental layer, but like what Infoblox has with their DDI stack, is something that customers need, I need to make sure my identity and my IP is something that I can manage wherever I am in all of these environment. >> It's funny Stu, we joke about SD-WAN, and now that's the internet and you think about the internet, one constant in all of it is you got to move packets from point a to point B and store a packet in a storage device, and ultimately you need to have to resolve addresses. And DNS, as old as it is, is fundamentally the standard, and a lot of people take it for granted, so to me, DNS has survived. It's a low level building block, but as things evolve, new abstraction layers come up, and I think we'll see more. I mean, I think there'll be a new naming system on how to deal with different scale across multicloud. And I think, Amazon is talking about it. We hear Ava Trix talking about it, we hear, things going on within Google talking about it, so, I think you're going to start to see new levels of innovation because, that's where the packets are moving, that's what the bad guys are, and you can't cover your footprints if you're trying to get in there. So, huge change is coming will be on it, And the CUBE we'll be monitoring it, as always, we can see the waves coming, Stu, what do you see? What's your future ball, tell you, as we come out of COVID, networking world, cloud collision, multicloud, apps, microservices, all this massive wave, what's your take, What's going to happen? >> Well yeah John, we've talked so much, It's those builders out there, how do I make sure that I can build my application, allow my users to access things wherever they are. The shift we hear for post COVID, it goes from work from home to work from anywhere. So, we were not going to see everybody just go back to the pre COVID era, this will have a lasting impact, and especially from a networking standpoint, we were starting to look at how does 5G and IOT change the way we think of networking? This just accelerates what we Needed to look at. Some networking technologies, take a long time to go through their maturation and standards, but being able to manage my entire environment, be able to spin up my new applications, and as you said John, DNS, like identity is something that is a fundamental piece that I need to make sure is rock solid so that I can get my employees access to the information while still keep things secure. >> Well, when you click on a link, that's malware, that's DNS, so this is where the action is, and people got to preserve it. Stu, We're going to be covering it, we're going to be watching all the waves, and again, this the CUBE on top of the big wave of networking and as networking evolves, I just still, I just still think, it's one big IOT world now, and it's an internet of things. They're all connected, there's no perimeter, it's borderless. This is going to change the game. I think in the next 18 months, we're going to see really different connected experiences and whoever can deliver them, will be the winner. Of course, we'll be watching it, go to siliconangle.com. We have a special report on next gen networking, Rob hope from Paul Gillin are constantly reporting, Stu has been getting a ton of great interviews, and again, we're getting the stories out, during COVID-19, with our remote interviews. Thanks for watching the CUBE, for the special next level networking experience event by Infoblox. (upbeat music)

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Okay. Welcome back everyone. Cube coverage, Las Vegas live action ADA was reinvent 2019 third day of a massive show where our seventh year of the eight years of Ava when documenting the history and the rise and the changing landscape of the business. I'm Jon Favreau, Stu Miniman, my cohost, our next guest, bill McGee, senior vice president, general manager of the hybrid cloud security group within trend micro sold this company, those guys now lead executive of the cloud and hybrid hybrid cloud security. You've got hybrid in there looking through the queue and I've been to every re-invent every single one. Congratulations. Welcome to the cube. Nice to be here. So eight years. What's changed in your mind real quick? >>Ah, wow. The um, yeah, certainly the amount of adop uh, the amount of adoption is now massive mainstream. You don't have the question, should I go to the cloud? It's all about how and how much. Probably the biggest change we've seen is how it's really being embraced all around the world. We're a global company. We saw initially a U S on Australia type focused UK. Now it's all over the place and so really relevant everywhere. Oh Phil. I, you know, at least from my standpoint, and I have enough friends of mine in the security industry when we first started coming to the show, I mean security was here, security is not only is so front and center in the discussion of cloud that they had a whole show for it here. So, you know, give us the 2019 view of security inside that the, the broader hybrid cloud discussion here at Reinventure. >>Let me tell you a couple of things. Kind of what we're seeing within our customer base and then what matters from a security perspective. So we see some organizations doing cloud migration, moving workloads to the cloud. A various farms had a couple of meetings yesterday. One was call it evacuating their data center. The other one was celebrating that two weeks ago they closed their data center. So that's a big step. Windows and Linux workloads moving to the cloud and really changing existing security controls to work better in the cloud. But certainly what a lot of these cloud builders are here for is, uh, you know, developing cloud native applications. And originally, you know, back seven, eight years ago, that was on top of what's now seemed like pretty simple services like S three. Now you've got containers and serverless and other platforms that people are using. >>And then the last thing, a lot of companies are establishing a cloud center of excellence and they're trying to optimize their use of the cloud. They still have compliance requirements that they need to achieve. So these are what we see happening and really the challenge for the customer, okay, how do we secure all this? How do we secure the aggressive, aggressive cloud native application development? How do we help a customer achieve compliance easily from a cloud center of excellence? So that's where we see fitting. And we made a big announcement a couple of weeks ago about a new platform that we've created and you know, I'd love to talk to. >>Yeah, let's dig into that. Let's dig into that. But first when we were at was Amazon's first security conference, Dave latte and I were talking about wow, cloud security versus on prem security. And then what's happening here is I had a conversation with someone who was close to the CIA, can't say his or her name and that, and they said cloud has changed the game for them because their cost line was pretty much flat, but the demand for missions, which we're growing scaling. So we're seeing that same dynamic you were referring to it earlier, that cost in data centers is kind of flat, but the demand for application new stuff's happened. So there's a real increased her demand for apps. This is the real driver of how people are flexing and deploying technology. So the security becomes really the built in conversation. Correct. Comment on that dynamic. And what do you recommend while, so here's a couple of things >>as we've seen really. Uh, you know, again, we've been doing cloud security for about a decade and really it was primarily focused on one service of AWS, which is. Now that's a pretty darn big service. And, uh, you know, widely used within their customer base. There's now 170 services I think is the, you know, the most recent number. Um, so developers are embracing all these new services. We acquired a new capability in October company called cloud conformity based in Sydney, Australia. Very focused on AWS analyzes implementations against the AWS well architected framework. So the first step we see for customers is you got to get visibility into your use of the cloud for the security team. What services are being used? Then can you set up a set of security guard rails to allow those services to be used in a secure manner? Then we help our customers turn to more detailed specialized protection of or containers or serverless. So that's what we've recognized ourselves. We had to create a very modest version of what Amazon has created themselves, which is a platform that allows builders to connect to and choose what security services they want >>to help. Lota how broad is your service base? Is it all the services? Are you guys now pick and choose? I can't. It's hard to do all, but yeah, there's the main ones. What are the highlights? >>Yeah, I'll give you the ones where we provide, uh, a very large breadth of protection. So in the, what we're calling cloud one conformity service, so that's this, uh, technology we acquired a couple months ago. Um, it cuts across about 70 services right now and gives you visibility of potential security configuration errors that you have in your environment. Now, if it's in a dev team, maybe not such a big deal, but if it's in production, it is a big deal. Even better, you can scan your cloud formation templates on the way to, to, to being live. Then we have a set of specialized protection that will, you know, will run on a workload and protect it, protect a containerized environment, a library that can sit within a serverless application. So that's kinda how we look at it. >>They'll want, one of the things of going to the more and more cloud for customers is that there's that shared responsibility model. We know that security is everyone's responsibility. It needs to be built in from the ground up. How are your customers doing with that shift and how are they understanding what they need to do? There've been some pretty visible like, Oh wait, I really had to configure that. I'm not about that. And Amazon's trying to close the gap on some, bring us through some of those. >>We've seen a big positive change over the years. Initially I would say that there was what I would call a naive perception that the cloud was magic and it was perfectly secure and that I don't have to worry about it. Right. Amazon did a, did the industry a real favor by establishing the shared responsibility model and making crystal clear what they've got covered that you don't need to worry about anymore as a customer and then what are the capabilities you still need to worry about? They've delivered a set of security tools that help their customers and then they rely on partners like us to deliver a set of more in depth tools to a, you know, specialized markets. >>You actually used a word that we've been talking about a lot this week. Naive. So we said there's, you know, the one letter difference between being cloud native, I mean cloud naive there. What does it mean to be cloud native in the security world? >>Well, I would say what allows you to be so first the most important thing in every customer's mind. I don't care how good the security capabilities you're helping me with. If you're going to slow down the improvements that I've just made to my development life cycle, I'm not interested. So that is the most important thing is are you able to inject your security technology and allow the customer to deliver at the rate that they're currently or continuing to improve? That is by far the most important thing. Then it's are your controls fitting into an environment in a way that that are as easy as possible for the customer? One part that's been very critical for us. We've been a lead adopter of the AWS marketplace allowing customers to procure security technology easily. They don't actually have to talk to us to buy our product. That's pretty revolutionary. >>Talking about the number of breaches that have gone on and what's changed with you guys over the year because new vectors are coming out, there's more surface area. Obviously it's been been discussed what's changed most in years? I'll tell you what we're worried about and what we expect to see. Although I would say the evidence, it's early. Uh, the reality in our traditional data centers, they were so porous at runtime in terms of the infrastructure and vulnerabilities that it was relatively easy for attackers to get in. The cloud has actually improved the level of security because of automation, less configuration errors. Unfortunately, what we expect as attackers to move to the developers move to the dev pipeline, injecting code, not at runtime, but injecting it earlier in the life cycle. We've seen evidence of container images, uh, up on Docker hub getting infected and then developers just pulling in without thinking about it. >>That's where attackers are going to move to the dev pipeline and we need to move some of our security technology to the dev pipeline to help customers defend themselves. What about international geo geo issues around compliance? How is that changing the game or slowing it down or I'd say doubling it or can you talk about that dynamic? Because I'm sure with regions, I'm sure you know, the U S is the most innovative market and the most risk taking market and therefore people move to the cloud quite bravely. Uh, you know, over this over this decade. Um, and some of the markets, so for example, we're Japanese headquartered company, um, in general Japanese companies, you know, really, uh, take into a lot of considerations before they make that type of big bet. But now we're seeing it, we're seeing auto manufacturers, uh, embrace the cloud. So I think those, it was a struggle for us in the early days, how regional the adoption of cloud was. >>That's not the case anymore. It's really a relevant conversation in every one of our markets. Bill, thank you for coming on the Cuban sharing your insights on hybrid cloud security. I got to ask you to end the segment. Yeah. What is going on for you this year? I see hybrids in your title. That's obviously the, the operating model is clouds and are gravity clouds going to the edge or data center and just operating model. What's on your mind this year? What are you trying to do and accomplish? What's, what are you excited about? What we're really excited about was this a product announcement that we made called cloud one and what cloud one is, is a set of security services which customers can access through, you know, common, uh, common access, common billing infrastructure, common cloud account management, and choose what to use. You know, Andy put it pretty well in his keynote where, you know, he talked about, he doesn't think of AWS as, as a Swiss army knife. >>He thinks of it as a specialized set of tools that builders get to adopt. We want to create a set of security tools in a similar way where customers can choose which of these specialized security services that they want to adopt. Bill, great pleasure to meet you and have this conversation pro and then security area entrepreneur sold this company to trend micro. This is the hybrid world is all about the cloud operating model. So all about agility and getting things done with application developers, just cube bringing you all the data from re-invent. Stay with us for more coverage after this short break.

>> [Disembodied Voice] From New York, it's the cube. Covering escape 19. >> Welcome back to the Cube's coverage here in New York City for the inaugural multi-cloud conference called Escape 2019. This is a conference dedicated, first conference dedicated to conversations and content and people around the trend of multi-cloud, which I've been critical of, I've called BS in the past. But it is multi-vendor it's developing and the architecture for true multi-cloud is on the horizon. Where well, we will be relevant. Our next guest is Daniel Wall, senior Distinguished Engineer at Red Hat, working on a lot of the technology around what Kubernetes and containers is create a lot of buzz around and that is the abstraction layer around working across clouds. Daniel, welcome to The Cube. >> Thank you for having me. >> I'm sure I butchered what you do, but I know you make a lot of tools. You make containers work. Talk about your role at Red Hat real quick. >> So my role at Red Hat is I am the technical lead of container technology, everything basically underneath Kubernetes main projects over the last few years is to look at what Docker did and then split them into individual tasks. I believe that Docker should be broken into four different main tasks move and container images around playing with containers, building container images, and running containers in production. And then we can run different security realms around each one of them. So we have tools to do that a Scopio, Podman build, and CRI-O is the one we use for Kubernetes. >> And how's it going? Good? >> It's going great. Yeah, we're getting great up. A lot of community support. A lot of people are really excited about some of the security features for so you can run full containers where you traditionally would do a darker you can run a totally non routes and much more secure. >> Well, I'm really interested in your talk you're giving here because the folks that follow The Cube know some of my tirades I've been on the past. I've been saying for a long time that there's been a very non-selection of clouds outside the big three, >> Right. >> And you had a power law that has, know who the big guys and a long tail of people creating their own little niche services. But income, the essentials and the global size and channel part is people using cloud. We have a video cloud, we're building more and more clouds for our media business. This, I was talking about the rise of these new clouds right?. >> Right. >> You're actually put some structure around this around, You're talking about Walmart clouds and niche clouds. Could you explain so this is really important. I want you to take some time to explain that. >> Okay, so my talk today I call it the Walmart clouds, although the analogy is when Walmart first started showing up in the United States and different areas, all of the department stores basically went out of business because Walmart was able to out-commoditize everything in the universe. And so, all these major vendors, district department stores went out of business. The one thing that didn't go out of business was sort of like specialty stores. So, I always kid around and say my wife has me every weekend sitting out front of these specialty stores that she loves to stop shop at. So if I look at the way the clouds have happened is basically most people say there's three major clouds, although I think they ignore one. So you look at Amazon, you have Google and you have Microsoft Azure, although I think Alibaba is going to become important in the future. And I call those the Walmart clouds, because basically, their whole goal is to commoditize and get rid of all the other, >> And scale up and provide more and more departments more services, >> But basically it they will always be rushing to get to the cheapest price. But there were a bunch of other cloud vendors out of the specialty cloud vendors like you talking about the Cube one, you this might be the best one to do in video. So I might want to put part of my workload in the Microsoft cloud or the Amazon cloud to get the cheapest price but I want to run certain certain workloads inside the U.S. We look at another example that is Nvidia right? and there's the Nvidia cloud and they might put the best GPUs in there. And you might want to do your machine learning your AI technologies might want to go in there because they might work better. IBM, who obviously bought Red Hat, but but IBM, you look at what they're doing in their cloud, they can have power series, they can have, mainframe workload z series. They might even have, some of their future, super duper computers type things in it. And then you have Oracle and Oracle would have database, they're probably going to do databases, they've massive database technologies inside of their cloud. So when you really >> Well, they think they're one of the big clouds. But they're not. >> They do. >> But their specialty Database Cloud. >> Basically, I believe that they are. I believe IBM, I think all of them are niche, especially clouds. But the bottom line is you need an API to move between these clouds so you can put workloads in different instances. And I believe that the Walmart clouds the the AWS and Microsoft and Google, their whole goal is to get you into their cloud. They might talk a little bit about on prem cloud and supporting your data centers. But their real goal is to get you off your data centers into their cloud, so they can start making money. They won't have no interest in supporting these, sort of the specialty cloud vendors. But if you look at open shift, which Kubernetes, from Red Hat, our goal is to basically make moving your cloud instances around and keep commodity and stability and move to clouds around. >> Let's take it through a working example. So there's couples and use cases that I see happening, I want to get your reaction. One is our cloud. We're (mutters intelligibly) which we do have. It's coming out. It's on Amazon. So we were small, self funded, company growing having fun. We're building on Amazon. We don't do any work in Azure our solutions on Amazon. Another use case might be a vendor that says hey I have proprietary software, I'm going to stand up my own cloud infrastructure and do all that and build it from the ground up. Is there a difference between the two? Because one is co-locating essentially on Amazon. leveraging the cheap commodity, but building differentiated niche on top of it, versus the standing up a full cloud? >> Well, what I would argue, first of all is is I would want you are your cloud, the one that you're saying is in say, Amazon, it what is the chance of you guys basically getting an offer from Azure to a nickel less per hour. >> Pretty high!(laughs amusingly) >> To be able to move your cloud over, >> It might be high. >> And the problem I would see is if your cloud inside of Amazon cloud starts to take advantage of Amazon's features, then all of a sudden it gets harder and harder for you. the cost of moving off is going to get harder and harder. If you use open source solutions, pure open source and not tied to individual cloud vendors. Then it would become much easier for you to move around. So you could take advantage of, commodity, right? And that you mean, another analogy I use with the big cloud vendors is Hansel and Gretel right. They all want you to come on in and come on in have some of our candy, have some our candy. And next thing you know, you're inside the cage. And, you know, but if you stick to open source, right, this is in a lot of ways the major cloud vendors is a major threat to open source, and that they're trying to lock everybody in right there. We lost it. >> Okay so what's the path? So I told it, by the way, I'm getting what your saying. So I say great I'll take advantage of the cheap I as the infrastructure service layer. But then what an open source toy usually open shift, I still got to build my app, I got to still host it. >> Right. So you build you build your app on top of it. So let me define what open shift is. And so open shift is basically Red Hat's enterprise version of Kubernetes. So if you look at Kubernetes, Kubernetes in some ways is just a higher level distribution of software. So when when Red Hats got into Linux business, there were lots and lots of Linux distributions. And what Red Hat did is they picked a whole kernel and a whole bunch of packages and joined them together and created a distribution that everybody could agree on and build on. So with open shift we're doing is we're taking Kubernetes, but there's a whole bunch of CNCF projects, and we're joining them all together and then testing them and making enterprise so that ready. But really Kubernetes is the key factor here in that if you build everything Kubernetes you CNCF open source projects, for your, save your storage, put it on staff, so Gloucester, one of the network based file systems in the open source world, instead of diving directly into Amazon, now you have the flexibility to be able to get out of the- >> So here's an Architectural question. So I got to ask you as multi-cloud conversation starts to heat up, and by the way, I think people have multiple clouds. It's just not multi-clouding. Right, right, right. Yeah, but it's coming. So architecturally what do someone have to think about architecturally for multi cloud? What's in the mind of the technical architect out there? >> What's on them? What are they should be thinking about from an architecture because you don't want to forclose the future. But I also want to get the best what I can get today from the clouds. >> I mean, I keep keep on hammering on it, but stick to the open source projects to do this as the CNCF projects just to allow you flexibility. A lot of it, the real problem with a lot of this technology right now is it's developing so fast. I mean, I think we have a Kubernetes version every two weeks, it seems at least in my team and see it feels like it. >> So you think Red Hat's of good vendor for the supplier for that person. >> Obviously >> Yeah you know some stuff is hard to deal with so it (mutters) look I'm so busy, these guys, I'm trying to get the transformation going. I don't have time to keep track of what's going on in CNCF. >> Yeah, well, we're a co worker of mine talks about your Red Hat and open shift is a plumbing tool or an electric we're building the foundation of your house and we put the electrical systems and the plumbing empties into your house, but we still need applications to run so we need you you need a toast or you need a toilet, you need a sink. And the applications and one of the one of the differences between Red hat and sort of the cloud vendors is we try not to get into the product, the lab and product business. So we want to support open source projects and other products running in our environment. If you compare that to running inside a cloud, you know if you become incredibly successful inside of Amazon, your video cloud business wants to prevent Amazon to say, oh, we'll just do video will steal everything they're doing and all a sudden we'll do the video inside of Amazon and then put your your cloud out of business. And, your only option then is now you're competing Amazon in Amazon against Amazon. How do you get out of Amazon >> That's called 3D chess.I think. Or maybe 4D chess. >> So if you you know My point being if you have an opportunity to get out and compete against Amazon say on Microsoft compete on your local compete on one of the niche clouds So any vendor that basically ties totally to Amazon, >> This is this is absolutely why I'm here because I believe multi-vendor, that was the buzzword in the 80s and 90s. Is everyone wants to they want to homogeneous they want a heterogeneous network. So multi-vendor will be around multi-cloud has to survive, it will survive. But right now we are in the foundational stages. The second interview, he has talked about plumbing and streets, and that's what we're at. So I guess the final question for you is, as we're setting the foundational infrastructure for multi-cloud, what's the big takeaway that you see that you could share? You mentioned get involved in open source what specifically architecturally should should folks think about in terms of foundational. >> I think, look at what the CNCF that cloud cloud native foundation is doing for open source projects, depends on what level you want to come in. And the bottom line is, built on top of Kubernetes use open standards to do it. Don't fall for the Hansel and Gretel effect of eating the candy because you will find yourself in a cage. >> Well, multi cloud is arrived and it's being thought through by industry leaders from entrepreneurs. We just had the former CEO of Sierra on, now running AVA trace, industry veteran, lot of tech chops in here, laying down the lines, if you will. A lot of good stuff Kubernetes is a key part of the containers. >> Okay, huge part of it. Thanks for coming on. >> Thanks for having me. >> And thanks for sharing the insights here on The Cube. We're in New York City for the inaugural multi-cloud conference Escape 19. I'm John Furrier back with more after this short break (pulsating music) (pulsating music)

>> from Santa Clara, California It's the Cube covering techno ovation World Pitch Summit 2019 Brought to you by Silicon Angle Media Now here's Sonia to Gari >> Hi and welcome to the Cube. I'm your host, Sonia to Gari. And we're here at Oracle's Agnew's campus in Santa Clara, California covering techno vacations World Pitch Summit 2019. Ah, pitch competition in which girls from around the world developed mobile lapse in order to create positive change in the world with us. Today we have Team Coco from Kazakhstan. Welcome. The members are, um Dilma as camel Over and Mallika Bree by Ava Uh, Donna Ulanova and Lube of do Chen Kuo Welcome. And congratulations on being finalists. Thank you. So your app is called tech Go. Can you tell us more about it? >> Yes. Uh so so techo in three d mobile application, which has a minute reality and as connected to the hardware which has dedicated for the behavioral change of people for so that they can become more conscious and like a friendly. >> And can you tell us more about how it works? Yes, >> of course there is. Luba, who can explain this? Okay. S >> o r application is about an astronaut who needs to save the planet. S O Firstly is there is a game in which a person needs to save your hair. Virtual airs by selling some ecological problems in it so that he or she wrote, be educated to both real life scenarios. And I also have a step counter which tracks your carbon footprint and encourages people to trust Morgan Friend the transportation options And that's a rare make really impact is that we connect our application with a special trash boxes in our city. All those locations are shown on the map, and coming to this place is user received trash box. And since Rosa Garbage and then because he has restaurants carriage here, she will get some points and your impact will be realized in the eventuality. Yeah, >> So what impact in society do you hope that this app will help change >> Rapids three t mobile application and it's a game. That is why Gamification and theater magic reality, which is a r which is inside this game a cz more visually in psychological attractive to people and those challenges that we provide a game are intensified so that most of the people. When they accomplish their goals, they might get, like, have a certain profit out of it so that they can become worker friendly and gain benefits. This is how we want to make sure that people might gain my changed a behavior for the sake of ecology. >> That's awesome. So you're using essentially a game incentivize people to make better choices in their everyday lives. That's great. And so how >> did you >> come up with this idea? >> So look, I will explain >> this. Actually, there were before some eco trash boxes in our school because like the thing off, ecological problems and recycling is one of the most talked about topics in Kazakhstan nowadays. And like in our school, the students try. Thio make this echo charge boxes, but they were always empty because students wasn't incent ified to recycle the garbage. And we tested our up in our school and we already launched it in our school and this ups incentivize our students. And now this I could trash boxes with our hard way always full. So >> that's awesome. See, you already found some success with your app. Thank you. Do you think that that this is a problem in the bigger community. >> Oh, maybe Donna Comptel. >> So we're saying that we started locally, but we got to go globally within that, uh, a pollution, like a pollution global problem and we trying to solve all over the world. So in our game, we have the whole world that you become an astronaut. So you should be aware for hold the problem that was happening in the earth. So we are trying to engage and educate people to be more global on to be more responsible for our final for our home. >> It sounds like everyone in the world should download that app. Yes, I do hope Thio uh, expand if you get the funding. >> Yes, um, we plan to expand not only in our country, Kazakhstan on only locally, but also globally. And we would like to create the eco friendly community across Central Asia since we want to make sure that consciousness is global in our area. >> And what struggles have you faced trying to create this app? >> Um, probably there were some struggles and off course in the realization and, uh, the realization of technical part of this project and creating a business model, since we are not very experienced in this kind of things. But since we have participated in techno vacation and we were immersed in this protest and were modified Thio motivated. Yeah, and we're motivated to learn all this things and acquire those skills. And this is why we became more experienced in this stuff. So right now, uh, those struggles that we face before not longer problem for us. So yeah, this what we faced? >> So techno vacation has definitely helped. Do you improve your app and yes, right houses. Tech innovation Helped you? >> Yeah, Um, probably someone else wants to ask you this question. >> How is SECNAV ation help? You were What skills have you learned from this journey? For >> example, one of the most important skills, I guess iss a teamwork. Like after we started to work on the one project, we started to listen each other excavation actually helped us too. Um, I understand the opinions off other people and like to understand the problems in our society. We start to dream bigger to think bigger, wider kind of that >> That's amazing. And also take Novation helping us >> to explore new companies to be more like open a person to come to The company's asked about the help on not like B just like see the problems and trying to solve trying to find a solution and be the people of the world and be responsible for our planet for what's happening in our local community on be aware of everything. >> And, um So I heard you guys had an amazing week. Um, you you went to whoever You went some other places. So can you tell us more about your week >> you want? So we went to amazing places in a Silicon Valley in a San Francisco San Jose and we so, like it'd, for example, Golden Gate Bridge. And also the Alcatraz so were so impressed by their architecture by the people by the nature on DDE. We just expected a lot of Onda. We just got this old expectations come to the reality on dhe. We hope that that kind of dream will come true in our future, and we gonna to work in a one of the big companies that were located here. I know all the universities. So >> how is it like going to the different tech companies and seeing it in real life. >> So we >> visited Uber Company and Google Ventures, and both we I have seen people who work is there, and we're really impressive on. And we really like it. It? Yeah. And, uh, I think so. Before, like in my childhood, I dreaming to be to be in Silicon Valley, to goes there and, like, meet people who are work already working you And now, like my dream came through. >> That's awesome. And you get to see California And you you might be able to win today. So thank you so much for being on. I wish you all the best. And I hope you haven't amazing pitch tonight. Thank you. This has been Team Coco from Kazakhstan. I'm your host, Sonia to Garey. This is the Cube. Stay tuned for more

>> So as folks make their way over from Javits I'm going to give you the least interesting part of the evening and that's my segment in which I welcome you here, introduce myself, lay out what what we're going to do for the next couple of hours. So first off, thank you very much for coming. As all of you know Wikibon is a part of SiliconANGLE which also includes theCUBE, so if you look around, this is what we have been doing for the past couple of days here in the TheCUBE. We've been inviting some significant thought leaders from over on the show and in incredibly expensive limousines driven them up the street to come on to TheCUBE and spend time with us and talk about some of the things that are happening in the industry today that are especially important. We tore it down, and we're having this party tonight. So we want to thank you very much for coming and look forward to having more conversations with all of you. Now what are we going to talk about? Well Wikibon is the research arm of SiliconANGLE. So we take data that comes out of TheCUBE and other places and we incorporated it into our research. And work very closely with large end users and large technology companies regarding how to make better decisions in this incredibly complex, incredibly important transformative world of digital business. What we're going to talk about tonight, and I've got a couple of my analysts assembled, and we're also going to have a panel, is this notion of software is eating the Edge. Now most of you have probably heard Marc Andreessen, the venture capitalist and developer, original developer of Netscape many years ago, talk about how software's eating the world. Well, if software is truly going to eat the world, it's going to eat at, it's going to take the big chunks, big bites at the Edge. That's where the actual action's going to be. And what we want to talk about specifically is the entangling of the internet or the industrial internet of things and IoT with analytics. So that's what we're going to talk about over the course of the next couple of hours. To do that we're going to, I've already blown the schedule, that's on me. But to do that I'm going to spend a couple minutes talking about what we regard as the essential digital business capabilities which includes analytics and Big Data, and includes IIoT and we'll explain at least in our position why those two things come together the way that they do. But I'm going to ask the august and revered Neil Raden, Wikibon analyst to come on up and talk about harvesting value at the Edge. 'Cause there are some, not now Neil, when we're done, when I'm done. So I'm going to ask Neil to come on up and we'll talk, he's going to talk about harvesting value at the Edge. And then Jim Kobielus will follow up with him, another Wikibon analyst, he'll talk specifically about how we're going to take that combination of analytics and Edge and turn it into the new types of systems and software that are going to sustain this significant transformation that's going on. And then after that, I'm going to ask Neil and Jim to come, going to invite some other folks up and we're going to run a panel to talk about some of these issues and do a real question and answer. So the goal here is before we break for drinks is to create a community feeling within the room. That includes smart people here, smart people in the audience having a conversation ultimately about some of these significant changes so please participate and we look forward to talking about the rest of it. All right, let's get going! What is digital business? One of the nice things about being an analyst is that you can reach back on people who were significantly smarter than you and build your points of view on the shoulders of those giants including Peter Drucker. Many years ago Peter Drucker made the observation that the purpose of business is to create and keep a customer. Not better shareholder value, not anything else. It is about creating and keeping your customer. Now you can argue with that, at the end of the day, if you don't have customers, you don't have a business. Now the observation that we've made, what we've added to that is that we've made the observation that the difference between business and digital business essentially is one thing. That's data. A digital business uses data to differentially create and keep customers. That's the only difference. If you think about the difference between taxi cab companies here in New York City, every cab that I've been in in the last three days has bothered me about Uber. The reason, the difference between Uber and a taxi cab company is data. That's the primary difference. Uber uses data as an asset. And we think this is the fundamental feature of digital business that everybody has to pay attention to. How is a business going to use data as an asset? Is the business using data as an asset? Is a business driving its engagement with customers, the role of its product et cetera using data? And if they are, they are becoming a more digital business. Now when you think about that, what we're really talking about is how are they going to put data to work? How are they going to take their customer data and their operational data and their financial data and any other kind of data and ultimately turn that into superior engagement or improved customer experience or more agile operations or increased automation? Those are the kinds of outcomes that we're talking about. But it is about putting data to work. That's fundamentally what we're trying to do within a digital business. Now that leads to an observation about the crucial strategic business capabilities that every business that aspires to be more digital or to be digital has to put in place. And I want to be clear. When I say strategic capabilities I mean something specific. When you talk about, for example technology architecture or information architecture there is this notion of what capabilities does your business need? Your business needs capabilities to pursue and achieve its mission. And in the digital business these are the capabilities that are now additive to this core question, ultimately of whether or not the company is a digital business. What are the three capabilities? One, you have to capture data. Not just do a good job of it, but better than your competition. You have to capture data better than your competition. In a way that is ultimately less intrusive on your markets and on your customers. That's in many respects, one of the first priorities of the internet of things and people. The idea of using sensors and related technologies to capture more data. Once you capture that data you have to turn it into value. You have to do something with it that creates business value so you can do a better job of engaging your markets and serving your customers. And that essentially is what we regard as the basis of Big Data. Including operations, including financial performance and everything else, but ultimately it's taking the data that's being captured and turning it into value within the business. The last point here is that once you have generated a model, or an insight or some other resource that you can act upon, you then have to act upon it in the real world. We call that systems of agency, the ability to enact based on data. Now I want to spend just a second talking about systems of agency 'cause we think it's an interesting concept and it's something Jim Kobielus is going to talk about a little bit later. When we say systems of agency, what we're saying is increasingly machines are acting on behalf of a brand. Or systems, combinations of machines and people are acting on behalf of the brand. And this whole notion of agency is the idea that ultimately these systems are now acting as the business's agent. They are at the front line of engaging customers. It's an extremely rich proposition that has subtle but crucial implications. For example I was talking to a senior decision maker at a business today and they made a quick observation, they talked about they, on their way here to New York City they had followed a woman who was going through security, opened up her suitcase and took out a bird. And then went through security with the bird. And the reason why I bring this up now is as TSA was trying to figure out how exactly to deal with this, the bird started talking and repeating things that the woman had said and many of those things, in fact, might have put her in jail. Now in this case the bird is not an agent of that woman. You can't put the woman in jail because of what the bird said. But increasingly we have to ask ourselves as we ask machines to do more on our behalf, digital instrumentation and elements to do more on our behalf, it's going to have blow back and an impact on our brand if we don't do it well. I want to draw that forward a little bit because I suggest there's going to be a new lifecycle for data. And the way that we think about it is we have the internet or the Edge which is comprised of things and crucially people, using sensors, whether they be smaller processors in control towers or whether they be phones that are tracking where we go, and this crucial element here is something that we call information transducers. Now a transducer in a traditional sense is something that takes energy from one form to another so that it can perform new types of work. By information transducer I essentially mean it takes information from one form to another so it can perform another type of work. This is a crucial feature of data. One of the beauties of data is that it can be used in multiple places at multiple times and not engender significant net new costs. It's one of the few assets that you can say about that. So the concept of an information transducer's really important because it's the basis for a lot of transformations of data as data flies through organizations. So we end up with the transducers storing data in the form of analytics, machine learning, business operations, other types of things, and then it goes back and it's transduced, back into to the real world as we program the real world and turning into these systems of agency. So that's the new lifecycle. And increasingly, that's how we have to think about data flows. Capturing it, turning it into value and having it act on our behalf in front of markets. That could have enormous implications for how ultimately money is spent over the next few years. So Wikibon does a significant amount of market research in addition to advising our large user customers. And that includes doing studies on cloud, public cloud, but also studies on what's happening within the analytics world. And if you take a look at it, what we basically see happening over the course of the next few years is significant investments in software and also services to get the word out. But we also expect there's going to be a lot of hardware. A significant amount of hardware that's ultimately sold within this space. And that's because of something that we call true private cloud. This concept of ultimately a business increasingly being designed and architected around the idea of data assets means that the reality, the physical realities of how data operates, how much it costs to store it or move it, the issues of latency, the issues of intellectual property protection as well as things like the regulatory regimes that are being put in place to govern how data gets used in between locations. All of those factors are going to drive increased utilization of what we call true private cloud. On premise technologies that provide the cloud experience but act where the data naturally needs to be processed. I'll come a little bit more to that in a second. So we think that it's going to be a relatively balanced market, a lot of stuff is going to end up in the cloud, but as Neil and Jim will talk about, there's going to be an enormous amount of analytics that pulls an enormous amount of data out to the Edge 'cause that's where the action's going to be. Now one of the things I want to also reveal to you is we've done a fair amount of data, we've done a fair amount of research around this question of where or how will data guide decisions about infrastructure? And in particular the Edge is driving these conversations. So here is a piece of research that one of our cohorts at Wikibon did, David Floyer. Taking a look at IoT Edge cost comparisons over a three year period. And it showed on the left hand side, an example where the sensor towers and other types of devices were streaming data back into a central location in a wind farm, stylized wind farm example. Very very expensive. Significant amounts of money end up being consumed, significant resources end up being consumed by the cost of moving the data from one place to another. Now this is even assuming that latency does not become a problem. The second example that we looked at is if we kept more of that data at the Edge and processed at the Edge. And literally it is a 85 plus percent cost reduction to keep more of the data at the Edge. Now that has enormous implications, how we think about big data, how we think about next generation architectures, et cetera. But it's these costs that are going to be so crucial to shaping the decisions that we make over the next two years about where we put hardware, where we put resources, what type of automation is possible, and what types of technology management has to be put in place. Ultimately we think it's going to lead to a structure, an architecture in the infrastructure as well as applications that is informed more by moving cloud to the data than moving the data to the cloud. That's kind of our fundamental proposition is that the norm in the industry has been to think about moving all data up to the cloud because who wants to do IT? It's so much cheaper, look what Amazon can do. Or what AWS can do. All true statements. Very very important in many respects. But most businesses today are starting to rethink that simple proposition and asking themselves do we have to move our business to the cloud, or can we move the cloud to the business? And increasingly what we see happening as we talk to our large customers about this, is that the cloud is being extended out to the Edge, we're moving the cloud and cloud services out to the business. Because of economic reasons, intellectual property control reasons, regulatory reasons, security reasons, any number of other reasons. It's just a more natural way to deal with it. And of course, the most important reason is latency. So with that as a quick backdrop, if I may quickly summarize, we believe fundamentally that the difference today is that businesses are trying to understand how to use data as an asset. And that requires an investment in new sets of technology capabilities that are not cheap, not simple and require significant thought, a lot of planning, lot of change within an IT and business organizations. How we capture data, how we turn it into value, and how we translate that into real world action through software. That's going to lead to a rethinking, ultimately, based on cost and other factors about how we deploy infrastructure. How we use the cloud so that the data guides the activity and not the choice of cloud supplier determines or limits what we can do with our data. And that's going to lead to this notion of true private cloud and elevate the role the Edge plays in analytics and all other architectures. So I hope that was perfectly clear. And now what I want to do is I want to bring up Neil Raden. Yes, now's the time Neil! So let me invite Neil up to spend some time talking about harvesting value at the Edge. Can you see his, all right. Got it. >> Oh boy. Hi everybody. Yeah, this is a really, this is a really big and complicated topic so I decided to just concentrate on something fairly simple, but I know that Peter mentioned customers. And he also had a picture of Peter Drucker. I had the pleasure in 1998 of interviewing Peter and photographing him. Peter Drucker, not this Peter. Because I'd started a magazine called Hired Brains. It was for consultants. And Peter said, Peter said a number of really interesting things to me, but one of them was his definition of a customer was someone who wrote you a check that didn't bounce. He was kind of a wag. He was! So anyway, he had to leave to do a video conference with Jack Welch and so I said to him, how do you charge Jack Welch to spend an hour on a video conference? And he said, you know I have this theory that you should always charge your client enough that it hurts a little bit or they don't take you seriously. Well, I had the chance to talk to Jack's wife, Suzie Welch recently and I told her that story and she said, "Oh he's full of it, Jack never paid "a dime for those conferences!" (laughs) So anyway, all right, so let's talk about this. To me, things about, engineered things like the hardware and network and all these other standards and so forth, we haven't fully developed those yet, but they're coming. As far as I'm concerned, they're not the most interesting thing. The most interesting thing to me in Edge Analytics is what you're going to get out of it, what the result is going to be. Making sense of this data that's coming. And while we're on data, something I've been thinking a lot lately because everybody I've talked to for the last three days just keeps talking to me about data. I have this feeling that data isn't actually quite real. That any data that we deal with is the result of some process that's captured it from something else that's actually real. In other words it's proxy. So it's not exactly perfect. And that's why we've always had these problems about customer A, customer A, customer A, what's their definition? What's the definition of this, that and the other thing? And with sensor data, I really have the feeling, when companies get, not you know, not companies, organizations get instrumented and start dealing with this kind of data what they're going to find is that this is the first time, and I've been involved in analytics, I don't want to date myself, 'cause I know I look young, but the first, I've been dealing with analytics since 1975. And everything we've ever done in analytics has involved pulling data from some other system that was not designed for analytics. But if you think about sensor data, this is data that we're actually going to catch the first time. It's going to be ours! We're not going to get it from some other source. It's going to be the real deal, to the extent that it's the real deal. Now you may say, ya know Neil, a sensor that's sending us information about oil pressure or temperature or something like that, how can you quarrel with that? Well, I can quarrel with it because I don't know if the sensor's doing it right. So we still don't know, even with that data, if it's right, but that's what we have to work with. Now, what does that really mean? Is that we have to be really careful with this data. It's ours, we have to take care of it. We don't get to reload it from source some other day. If we munge it up it's gone forever. So that has, that has very serious implications, but let me, let me roll you back a little bit. The way I look at analytics is it's come in three different eras. And we're entering into the third now. The first era was business intelligence. It was basically built and governed by IT, it was system of record kind of reporting. And as far as I can recall, it probably started around 1988 or at least that's the year that Howard Dresner claims to have invented the term. I'm not sure it's true. And things happened before 1988 that was sort of like BI, but 88 was when they really started coming out, that's when we saw BusinessObjects and Cognos and MicroStrategy and those kinds of things. The second generation just popped out on everybody else. We're all looking around at BI and we were saying why isn't this working? Why are only five people in the organization using this? Why are we not getting value out of this massive license we bought? And along comes companies like Tableau doing data discovery, visualization, data prep and Line of Business people are using this now. But it's still the same kind of data sources. It's moved out a little bit, but it still hasn't really hit the Big Data thing. Now we're in third generation, so we not only had Big Data, which has come and hit us like a tsunami, but we're looking at smart discovery, we're looking at machine learning. We're looking at AI induced analytics workflows. And then all the natural language cousins. You know, natural language processing, natural language, what's? Oh Q, natural language query. Natural language generation. Anybody here know what natural language generation is? Yeah, so what you see now is you do some sort of analysis and that tool comes up and says this chart is about the following and it used the following data, and it's blah blah blah blah blah. I think it's kind of wordy and it's going to refined some, but it's an interesting, it's an interesting thing to do. Now, the problem I see with Edge Analytics and IoT in general is that most of the canonical examples we talk about are pretty thin. I know we talk about autonomous cars, I hope to God we never have them, 'cause I'm a car guy. Fleet Management, I think Qualcomm started Fleet Management in 1988, that is not a new application. Industrial controls. I seem to remember, I seem to remember Honeywell doing industrial controls at least in the 70s and before that I wasn't, I don't want to talk about what I was doing, but I definitely wasn't in this industry. So my feeling is we all need to sit down and think about this and get creative. Because the real value in Edge Analytics or IoT, whatever you want to call it, the real value is going to be figuring out something that's new or different. Creating a brand new business. Changing the way an operation happens in a company, right? And I think there's a lot of smart people out there and I think there's a million apps that we haven't even talked about so, if you as a vendor come to me and tell me how great your product is, please don't talk to me about autonomous cars or Fleet Managing, 'cause I've heard about that, okay? Now, hardware and architecture are really not the most interesting thing. We fell into that trap with data warehousing. We've fallen into that trap with Big Data. We talk about speeds and feeds. Somebody said to me the other day, what's the narrative of this company? This is a technology provider. And I said as far as I can tell, they don't have a narrative they have some products and they compete in a space. And when they go to clients and the clients say, what's the value of your product? They don't have an answer for that. So we don't want to fall into this trap, okay? Because IoT is going to inform you in ways you've never even dreamed about. Unfortunately some of them are going to be really stinky, you know, they're going to be really bad. You're going to lose more of your privacy, it's going to get harder to get, I dunno, mortgage for example, I dunno, maybe it'll be easier, but in any case, it's not going to all be good. So let's really think about what you want to do with this technology to do something that's really valuable. Cost takeout is not the place to justify an IoT project. Because number one, it's very expensive, and number two, it's a waste of the technology because you should be looking at, you know the old numerator denominator thing? You should be looking at the numerators and forget about the denominators because that's not what you do with IoT. And the other thing is you don't want to get over confident. Actually this is good advice about anything, right? But in this case, I love this quote by Derek Sivers He's a pretty funny guy. He said, "If more information was the answer, "then we'd all be billionaires with perfect abs." I'm not sure what's on his wishlist, but you know, I would, those aren't necessarily the two things I would think of, okay. Now, what I said about the data, I want to explain some more. Big Data Analytics, if you look at this graphic, it depicts it perfectly. It's a bunch of different stuff falling into the funnel. All right? It comes from other places, it's not original material. And when it comes in, it's always used as second hand data. Now what does that mean? That means that you have to figure out the semantics of this information and you have to find a way to put it together in a way that's useful to you, okay. That's Big Data. That's where we are. How is that different from IoT data? It's like I said, IoT is original. You can put it together any way you want because no one else has ever done that before. It's yours to construct, okay. You don't even have to transform it into a schema because you're creating the new application. But the most important thing is you have to take care of it 'cause if you lose it, it's gone. It's the original data. It's the same way, in operational systems for a long long time we've always been concerned about backup and security and everything else. You better believe this is a problem. I know a lot of people think about streaming data, that we're going to look at it for a minute, and we're going to throw most of it away. Personally I don't think that's going to happen. I think it's all going to be saved, at least for a while. Now, the governance and security, oh, by the way, I don't know where you're going to find a presentation where somebody uses a newspaper clipping about Vladimir Lenin, but here it is, enjoy yourselves. I believe that when people think about governance and security today they're still thinking along the same grids that we thought about it all along. But this is very very different and again, I'm sorry I keep thrashing this around, but this is treasured data that has to be carefully taken care of. Now when I say governance, my experience has been over the years that governance is something that IT does to make everybody's lives miserable. But that's not what I mean by governance today. It means a comprehensive program to really secure the value of the data as an asset. And you need to think about this differently. Now the other thing is you may not get to think about it differently, because some of the stuff may end up being subject to regulation. And if the regulators start regulating some of this, then that'll take some of the degrees of freedom away from you in how you put this together, but you know, that's the way it works. Now, machine learning, I think I told somebody the other day that claims about machine learning in software products are as common as twisters in trail parks. And a lot of it is not really what I'd call machine learning. But there's a lot of it around. And I think all of the open source machine learning and artificial intelligence that's popped up, it's great because all those math PhDs who work at Home Depot now have something to do when they go home at night and they construct this stuff. But if you're going to have machine learning at the Edge, here's the question, what kind of machine learning would you have at the Edge? As opposed to developing your models back at say, the cloud, when you transmit the data there. The devices at the Edge are not very powerful. And they don't have a lot of memory. So you're only going to be able to do things that have been modeled or constructed somewhere else. But that's okay. Because machine learning algorithm development is actually slow and painful. So you really want the people who know how to do this working with gobs of data creating models and testing them offline. And when you have something that works, you can put it there. Now there's one thing I want to talk about before I finish, and I think I'm almost finished. I wrote a book about 10 years ago about automated decision making and the conclusion that I came up with was that little decisions add up, and that's good. But it also means you don't have to get them all right. But you don't want computers or software making decisions unattended if it involves human life, or frankly any life. Or the environment. So when you think about the applications that you can build using this architecture and this technology, think about the fact that you're not going to be doing air traffic control, you're not going to be monitoring crossing guards at the elementary school. You're going to be doing things that may seem fairly mundane. Managing machinery on the factory floor, I mean that may sound great, but really isn't that interesting. Managing well heads, drilling for oil, well I mean, it's great to the extent that it doesn't cause wells to explode, but they don't usually explode. What it's usually used for is to drive the cost out of preventative maintenance. Not very interesting. So use your heads. Come up with really cool stuff. And any of you who are involved in Edge Analytics, the next time I talk to you I don't want to hear about the same five applications that everybody talks about. Let's hear about some new ones. So, in conclusion, I don't really have anything in conclusion except that Peter mentioned something about limousines bringing people up here. On Monday I was slogging up and down Park Avenue and Madison Avenue with my client and we were visiting all the hedge funds there because we were doing a project with them. And in the miserable weather I looked at him and I said, for godsake Paul, where's the black car? And he said, that was the 90s. (laughs) Thank you. So, Jim, up to you. (audience applauding) This is terrible, go that way, this was terrible coming that way. >> Woo, don't want to trip! And let's move to, there we go. Hi everybody, how ya doing? Thanks Neil, thanks Peter, those were great discussions. So I'm the third leg in this relay race here, talking about of course how software is eating the world. And focusing on the value of Edge Analytics in a lot of real world scenarios. Programming the real world for, to make the world a better place. So I will talk, I'll break it out analytically in terms of the research that Wikibon is doing in the area of the IoT, but specifically how AI intelligence is being embedded really to all material reality potentially at the Edge. But mobile applications and industrial IoT and the smart appliances and self driving vehicles. I will break it out in terms of a reference architecture for understanding what functions are being pushed to the Edge to hardware, to our phones and so forth to drive various scenarios in terms of real world results. So I'll move a pace here. So basically AI software or AI microservices are being infused into Edge hardware as we speak. What we see is more vendors of smart phones and other, real world appliances and things like smart driving, self driving vehicles. What they're doing is they're instrumenting their products with computer vision and natural language processing, environmental awareness based on sensing and actuation and those capabilities and inferences that these devices just do to both provide human support for human users of these devices as well as to enable varying degrees of autonomous operation. So what I'll be talking about is how AI is a foundation for data driven systems of agency of the sort that Peter is talking about. Infusing data driven intelligence into everything or potentially so. As more of this capability, all these algorithms for things like, ya know for doing real time predictions and classifications, anomaly detection and so forth, as this functionality gets diffused widely and becomes more commoditized, you'll see it burned into an ever-wider variety of hardware architecture, neuro synaptic chips, GPUs and so forth. So what I've got here in front of you is a sort of a high level reference architecture that we're building up in our research at Wikibon. So AI, artificial intelligence is a big term, a big paradigm, I'm not going to unpack it completely. Of course we don't have oodles of time so I'm going to take you fairly quickly through the high points. It's a driver for systems of agency. Programming the real world. Transducing digital inputs, the data, to analog real world results. Through the embedding of this capability in the IoT, but pushing more and more of it out to the Edge with points of decision and action in real time. And there are four capabilities that we're seeing in terms of AI enabled, enabling capabilities that are absolutely critical to software being pushed to the Edge are sensing, actuation, inference and Learning. Sensing and actuation like Peter was describing, it's about capturing data from the environment within which a device or users is operating or moving. And then actuation is the fancy term for doing stuff, ya know like industrial IoT, it's obviously machine controlled, but clearly, you know self driving vehicles is steering a vehicle and avoiding crashing and so forth. Inference is the meat and potatoes as it were of AI. Analytics does inferences. It infers from the data, the logic of the application. Predictive logic, correlations, classification, abstractions, differentiation, anomaly detection, recognizing faces and voices. We see that now with Apple and the latest version of the iPhone is embedding face recognition as a core, as the core multifactor authentication technique. Clearly that's a harbinger of what's going to be universal fairly soon which is that depends on AI. That depends on convolutional neural networks, that is some heavy hitting processing power that's necessary and it's processing the data that's coming from your face. So that's critically important. So what we're looking at then is the AI software is taking root in hardware to power continuous agency. Getting stuff done. Powered decision support by human beings who have to take varying degrees of action in various environments. We don't necessarily want to let the car steer itself in all scenarios, we want some degree of override, for lots of good reasons. They want to protect life and limb including their own. And just more data driven automation across the internet of things in the broadest sense. So unpacking this reference framework, what's happening is that AI driven intelligence is powering real time decisioning at the Edge. Real time local sensing from the data that it's capturing there, it's ingesting the data. Some, not all of that data, may be persistent at the Edge. Some, perhaps most of it, will be pushed into the cloud for other processing. When you have these highly complex algorithms that are doing AI deep learning, multilayer, to do a variety of anti-fraud and higher level like narrative, auto-narrative roll-ups from various scenes that are unfolding. A lot of this processing is going to begin to happen in the cloud, but a fair amount of the more narrowly scoped inferences that drive real time decision support at the point of action will be done on the device itself. Contextual actuation, so it's the sensor data that's captured by the device along with other data that may be coming down in real time streams through the cloud will provide the broader contextual envelope of data needed to drive actuation, to drive various models and rules and so forth that are making stuff happen at the point of action, at the Edge. Continuous inference. What it all comes down to is that inference is what's going on inside the chips at the Edge device. And what we're seeing is a growing range of hardware architectures, GPUs, CPUs, FPGAs, ASIC, Neuro synaptic chips of all sorts playing in various combinations that are automating more and more very complex inference scenarios at the Edge. And not just individual devices, swarms of devices, like drones and so forth are essentially an Edge unto themselves. You'll see these tiered hierarchies of Edge swarms that are playing and doing inferences of ever more complex dynamic nature. And much of this will be, this capability, the fundamental capabilities that is powering them all will be burned into the hardware that powers them. And then adaptive learning. Now I use the term learning rather than training here, training is at the core of it. Training means everything in terms of the predictive fitness or the fitness of your AI services for whatever task, predictions, classifications, face recognition that you, you've built them for. But I use the term learning in a broader sense. It's what's make your inferences get better and better, more accurate over time is that you're training them with fresh data in a supervised learning environment. But you can have reinforcement learning if you're doing like say robotics and you don't have ground truth against which to train the data set. You know there's maximize a reward function versus minimize a loss function, you know, the standard approach, the latter for supervised learning. There's also, of course, the issue, or not the issue, the approach of unsupervised learning with cluster analysis critically important in a lot of real world scenarios. So Edge AI Algorithms, clearly, deep learning which is multilayered machine learning models that can do abstractions at higher and higher levels. Face recognition is a high level abstraction. Faces in a social environment is an even higher level of abstraction in terms of groups. Faces over time and bodies and gestures, doing various things in various environments is an even higher level abstraction in terms of narratives that can be rolled up, are being rolled up by deep learning capabilities of great sophistication. Convolutional neural networks for processing images, recurrent neural networks for processing time series. Generative adversarial networks for doing essentially what's called generative applications of all sort, composing music, and a lot of it's being used for auto programming. These are all deep learning. There's a variety of other algorithm approaches I'm not going to bore you with here. Deep learning is essentially the enabler of the five senses of the IoT. Your phone's going to have, has a camera, it has a microphone, it has the ability to of course, has geolocation and navigation capabilities. It's environmentally aware, it's got an accelerometer and so forth embedded therein. The reason that your phone and all of the devices are getting scary sentient is that they have the sensory modalities and the AI, the deep learning that enables them to make environmentally correct decisions in the wider range of scenarios. So machine learning is the foundation of all of this, but there are other, I mean of deep learning, artificial neural networks is the foundation of that. But there are other approaches for machine learning I want to make you aware of because support vector machines and these other established approaches for machine learning are not going away but really what's driving the show now is deep learning, because it's scary effective. And so that's where most of the investment in AI is going into these days for deep learning. AI Edge platforms, tools and frameworks are just coming along like gangbusters. Much development of AI, of deep learning happens in the context of your data lake. This is where you're storing your training data. This is the data that you use to build and test to validate in your models. So we're seeing a deepening stack of Hadoop and there's Kafka, and Spark and so forth that are driving the training (coughs) excuse me, of AI models that are power all these Edge Analytic applications so that that lake will continue to broaden in terms, and deepen in terms of a scope and the range of data sets and the range of modeling, AI modeling supports. Data science is critically important in this scenario because the data scientist, the data science teams, the tools and techniques and flows of data science are the fundamental development paradigm or discipline or capability that's being leveraged to build and to train and to deploy and iterate all this AI that's being pushed to the Edge. So clearly data science is at the center, data scientists of an increasingly specialized nature are necessary to the realization to this value at the Edge. AI frameworks are coming along like you know, a mile a minute. TensorFlow has achieved a, is an open source, most of these are open source, has achieved sort of almost like a defacto standard, status, I'm using the word defacto in air quotes. There's Theano and Keras and xNet and CNTK and a variety of other ones. We're seeing range of AI frameworks come to market, most open source. Most are supported by most of the major tool vendors as well. So at Wikibon we're definitely tracking that, we plan to go deeper in our coverage of that space. And then next best action, powers recommendation engines. I mean next best action decision automation of the sort of thing Neil's covered in a variety of contexts in his career is fundamentally important to Edge Analytics to systems of agency 'cause it's driving the process automation, decision automation, sort of the targeted recommendations that are made at the Edge to individual users as well as to process that automation. That's absolutely necessary for self driving vehicles to do their jobs and industrial IoT. So what we're seeing is more and more recommendation engine or recommender capabilities powered by ML and DL are going to the Edge, are already at the Edge for a variety of applications. Edge AI capabilities, like I said, there's sensing. And sensing at the Edge is becoming ever more rich, mixed reality Edge modalities of all sort are for augmented reality and so forth. We're just seeing a growth in certain, the range of sensory modalities that are enabled or filtered and analyzed through AI that are being pushed to the Edge, into the chip sets. Actuation, that's where robotics comes in. Robotics is coming into all aspects of our lives. And you know, it's brainless without AI, without deep learning and these capabilities. Inference, autonomous edge decisioning. Like I said, it's, a growing range of inferences that are being done at the Edge. And that's where it has to happen 'cause that's the point of decision. Learning, training, much training, most training will continue to be done in the cloud because it's very data intensive. It's a grind to train and optimize an AI algorithm to do its job. It's not something that you necessarily want to do or can do at the Edge at Edge devices so, the models that are built and trained in the cloud are pushed down through a dev ops process down to the Edge and that's the way it will work pretty much in most AI environments, Edge analytics environments. You centralize the modeling, you decentralize the execution of the inference models. The training engines will be in the cloud. Edge AI applications. I'll just run you through sort of a core list of the ones that are coming into, already come into the mainstream at the Edge. Multifactor authentication, clearly the Apple announcement of face recognition is just a harbinger of the fact that that's coming to every device. Computer vision speech recognition, NLP, digital assistance and chat bots powered by natural language processing and understanding, it's all AI powered. And it's becoming very mainstream. Emotion detection, face recognition, you know I could go on and on but these are like the core things that everybody has access to or will by 2020 and they're core devices, mass market devices. Developers, designers and hardware engineers are coming together to pool their expertise to build and train not just the AI, but also the entire package of hardware in UX and the orchestration of real world business scenarios or life scenarios that all this intelligence, the submitted intelligence enables and most, much of what they build in terms of AI will be containerized as micro services through Docker and orchestrated through Kubernetes as full cloud services in an increasingly distributed fabric. That's coming along very rapidly. We can see a fair amount of that already on display at Strata in terms of what the vendors are doing or announcing or who they're working with. The hardware itself, the Edge, you know at the Edge, some data will be persistent, needs to be persistent to drive inference. That's, and you know to drive a variety of different application scenarios that need some degree of historical data related to what that device in question happens to be sensing or has sensed in the immediate past or you know, whatever. The hardware itself is geared towards both sensing and increasingly persistence and Edge driven actuation of real world results. The whole notion of drones and robotics being embedded into everything that we do. That's where that comes in. That has to be powered by low cost, low power commodity chip sets of various sorts. What we see right now in terms of chip sets is it's a GPUs, Nvidia has gone real far and GPUs have come along very fast in terms of power inference engines, you know like the Tesla cars and so forth. But GPUs are in many ways the core hardware sub straight for in inference engines in DL so far. But to become a mass market phenomenon, it's got to get cheaper and lower powered and more commoditized, and so we see a fair number of CPUs being used as the hardware for Edge Analytic applications. Some vendors are fairly big on FPGAs, I believe Microsoft has gone fairly far with FPGAs inside DL strategy. ASIC, I mean, there's neuro synaptic chips like IBM's got one. There's at least a few dozen vendors of neuro synaptic chips on the market so at Wikibon we're going to track that market as it develops. And what we're seeing is a fair number of scenarios where it's a mixed environment where you use one chip set architecture at the inference side of the Edge, and other chip set architectures that are driving the DL as processed in the cloud, playing together within a common architecture. And we see some, a fair number of DL environments where the actual training is done in the cloud on Spark using CPUs and parallelized in memory, but pushing Tensorflow models that might be trained through Spark down to the Edge where the inferences are done in FPGAs and GPUs. Those kinds of mixed hardware scenarios are very, very, likely to be standard going forward in lots of areas. So analytics at the Edge power continuous results is what it's all about. The whole point is really not moving the data, it's putting the inference at the Edge and working from the data that's already captured and persistent there for the duration of whatever action or decision or result needs to be powered from the Edge. Like Neil said cost takeout alone is not worth doing. Cost takeout alone is not the rationale for putting AI at the Edge. It's getting new stuff done, new kinds of things done in an automated consistent, intelligent, contextualized way to make our lives better and more productive. Security and governance are becoming more important. Governance of the models, governance of the data, governance in a dev ops context in terms of version controls over all those DL models that are built, that are trained, that are containerized and deployed. Continuous iteration and improvement of those to help them learn to do, make our lives better and easier. With that said, I'm going to hand it over now. It's five minutes after the hour. We're going to get going with the Influencer Panel so what we'd like to do is I call Peter, and Peter's going to call our influencers. >> All right, am I live yet? Can you hear me? All right so, we've got, let me jump back in control here. We've got, again, the objective here is to have community take on some things. And so what we want to do is I want to invite five other people up, Neil why don't you come on up as well. Start with Neil. You can sit here. On the far right hand side, Judith, Judith Hurwitz. >> Neil: I'm glad I'm on the left side. >> From the Hurwitz Group. >> From the Hurwitz Group. Jennifer Shin who's affiliated with UC Berkeley. Jennifer are you here? >> She's here, Jennifer where are you? >> She was here a second ago. >> Neil: I saw her walk out she may have, >> Peter: All right, she'll be back in a second. >> Here's Jennifer! >> Here's Jennifer! >> Neil: With 8 Path Solutions, right? >> Yep. >> Yeah 8 Path Solutions. >> Just get my mic. >> Take your time Jen. >> Peter: All right, Stephanie McReynolds. Far left. And finally Joe Caserta, Joe come on up. >> Stephie's with Elysian >> And to the left. So what I want to do is I want to start by having everybody just go around introduce yourself quickly. Judith, why don't we start there. >> I'm Judith Hurwitz, I'm president of Hurwitz and Associates. We're an analyst research and fault leadership firm. I'm the co-author of eight books. Most recent is Cognitive Computing and Big Data Analytics. I've been in the market for a couple years now. >> Jennifer. >> Hi, my name's Jennifer Shin. I'm the founder and Chief Data Scientist 8 Path Solutions LLC. We do data science analytics and technology. We're actually about to do a big launch next month, with Box actually. >> We're apparent, are we having a, sorry Jennifer, are we having a problem with Jennifer's microphone? >> Man: Just turn it back on? >> Oh you have to turn it back on. >> It was on, oh sorry, can you hear me now? >> Yes! We can hear you now. >> Okay, I don't know how that turned back off, but okay. >> So you got to redo all that Jen. >> Okay, so my name's Jennifer Shin, I'm founder of 8 Path Solutions LLC, it's a data science analytics and technology company. I founded it about six years ago. So we've been developing some really cool technology that we're going to be launching with Box next month. It's really exciting. And I have, I've been developing a lot of patents and some technology as well as teaching at UC Berkeley as a lecturer in data science. >> You know Jim, you know Neil, Joe, you ready to go? >> Joe: Just broke my microphone. >> Joe's microphone is broken. >> Joe: Now it should be all right. >> Jim: Speak into Neil's. >> Joe: Hello, hello? >> I just feel not worthy in the presence of Joe Caserta. (several laughing) >> That's right, master of mics. If you can hear me, Joe Caserta, so yeah, I've been doing data technology solutions since 1986, almost as old as Neil here, but been doing specifically like BI, data warehousing, business intelligence type of work since 1996. And been doing, wholly dedicated to Big Data solutions and modern data engineering since 2009. Where should I be looking? >> Yeah I don't know where is the camera? >> Yeah, and that's basically it. So my company was formed in 2001, it's called Caserta Concepts. We recently rebranded to only Caserta 'cause what we do is way more than just concepts. So we conceptualize the stuff, we envision what the future brings and we actually build it. And we help clients large and small who are just, want to be leaders in innovation using data specifically to advance their business. >> Peter: And finally Stephanie McReynolds. >> I'm Stephanie McReynolds, I had product marketing as well as corporate marketing for a company called Elysian. And we are a data catalog so we help bring together not only a technical understanding of your data, but we curate that data with human knowledge and use automated intelligence internally within the system to make recommendations about what data to use for decision making. And some of our customers like City of San Diego, a large automotive manufacturer working on self driving cars and General Electric use Elysian to help power their solutions for IoT at the Edge. >> All right so let's jump right into it. And again if you have a question, raise your hand, and we'll do our best to get it to the floor. But what I want to do is I want to get seven questions in front of this group and have you guys discuss, slog, disagree, agree. Let's start here. What is the relationship between Big Data AI and IoT? Now Wikibon's put forward its observation that data's being generated at the Edge, that action is being taken at the Edge and then increasingly the software and other infrastructure architectures need to accommodate the realities of how data is going to work in these very complex systems. That's our perspective. Anybody, Judith, you want to start? >> Yeah, so I think that if you look at AI machine learning, all these different areas, you have to be able to have the data learned. Now when it comes to IoT, I think one of the issues we have to be careful about is not all data will be at the Edge. Not all data needs to be analyzed at the Edge. For example if the light is green and that's good and it's supposed to be green, do you really have to constantly analyze the fact that the light is green? You actually only really want to be able to analyze and take action when there's an anomaly. Well if it goes purple, that's actually a sign that something might explode, so that's where you want to make sure that you have the analytics at the edge. Not for everything, but for the things where there is an anomaly and a change. >> Joe, how about from your perspective? >> For me I think the evolution of data is really becoming, eventually oxygen is just, I mean data's going to be the oxygen we breathe. It used to be very very reactive and there used to be like a latency. You do something, there's a behavior, there's an event, there's a transaction, and then you go record it and then you collect it, and then you can analyze it. And it was very very waterfallish, right? And then eventually we figured out to put it back into the system. Or at least human beings interpret it to try to make the system better and that is really completely turned on it's head, we don't do that anymore. Right now it's very very, it's synchronous, where as we're actually making these transactions, the machines, we don't really need, I mean human beings are involved a bit, but less and less and less. And it's just a reality, it may not be politically correct to say but it's a reality that my phone in my pocket is following my behavior, and it knows without telling a human being what I'm doing. And it can actually help me do things like get to where I want to go faster depending on my preference if I want to save money or save time or visit things along the way. And I think that's all integration of big data, streaming data, artificial intelligence and I think the next thing that we're going to start seeing is the culmination of all of that. I actually, hopefully it'll be published soon, I just wrote an article for Forbes with the term of ARBI and ARBI is the integration of Augmented Reality and Business Intelligence. Where I think essentially we're going to see, you know, hold your phone up to Jim's face and it's going to recognize-- >> Peter: It's going to break. >> And it's going to say exactly you know, what are the key metrics that we want to know about Jim. If he works on my sales force, what's his attainment of goal, what is-- >> Jim: Can it read my mind? >> Potentially based on behavior patterns. >> Now I'm scared. >> I don't think Jim's buying it. >> It will, without a doubt be able to predict what you've done in the past, you may, with some certain level of confidence you may do again in the future, right? And is that mind reading? It's pretty close, right? >> Well, sometimes, I mean, mind reading is in the eye of the individual who wants to know. And if the machine appears to approximate what's going on in the person's head, sometimes you can't tell. So I guess, I guess we could call that the Turing machine test of the paranormal. >> Well, face recognition, micro gesture recognition, I mean facial gestures, people can do it. Maybe not better than a coin toss, but if it can be seen visually and captured and analyzed, conceivably some degree of mind reading can be built in. I can see when somebody's angry looking at me so, that's a possibility. That's kind of a scary possibility in a surveillance society, potentially. >> Neil: Right, absolutely. >> Peter: Stephanie, what do you think? >> Well, I hear a world of it's the bots versus the humans being painted here and I think that, you know at Elysian we have a very strong perspective on this and that is that the greatest impact, or the greatest results is going to be when humans figure out how to collaborate with the machines. And so yes, you want to get to the location more quickly, but the machine as in the bot isn't able to tell you exactly what to do and you're just going to blindly follow it. You need to train that machine, you need to have a partnership with that machine. So, a lot of the power, and I think this goes back to Judith's story is then what is the human decision making that can be augmented with data from the machine, but then the humans are actually training the training side and driving machines in the right direction. I think that's when we get true power out of some of these solutions so it's not just all about the technology. It's not all about the data or the AI, or the IoT, it's about how that empowers human systems to become smarter and more effective and more efficient. And I think we're playing that out in our technology in a certain way and I think organizations that are thinking along those lines with IoT are seeing more benefits immediately from those projects. >> So I think we have a general agreement of what kind of some of the things you talked about, IoT, crucial capturing information, and then having action being taken, AI being crucial to defining and refining the nature of the actions that are being taken Big Data ultimately powering how a lot of that changes. Let's go to the next one. >> So actually I have something to add to that. So I think it makes sense, right, with IoT, why we have Big Data associated with it. If you think about what data is collected by IoT. We're talking about a serial information, right? It's over time, it's going to grow exponentially just by definition, right, so every minute you collect a piece of information that means over time, it's going to keep growing, growing, growing as it accumulates. So that's one of the reasons why the IoT is so strongly associated with Big Data. And also why you need AI to be able to differentiate between one minute versus next minute, right? Trying to find a better way rather than looking at all that information and manually picking out patterns. To have some automated process for being able to filter through that much data that's being collected. >> I want to point out though based on what you just said Jennifer, I want to bring Neil in at this point, that this question of IoT now generating unprecedented levels of data does introduce this idea of the primary source. Historically what we've done within technology, or within IT certainly is we've taken stylized data. There is no such thing as a real world accounting thing. It is a human contrivance. And we stylize data and therefore it's relatively easy to be very precise on it. But when we start, as you noted, when we start measuring things with a tolerance down to thousandths of a millimeter, whatever that is, metric system, now we're still sometimes dealing with errors that we have to attend to. So, the reality is we're not just dealing with stylized data, we're dealing with real data, and it's more, more frequent, but it also has special cases that we have to attend to as in terms of how we use it. What do you think Neil? >> Well, I mean, I agree with that, I think I already said that, right. >> Yes you did, okay let's move on to the next one. >> Well it's a doppelganger, the digital twin doppelganger that's automatically created by your very fact that you're living and interacting and so forth and so on. It's going to accumulate regardless. Now that doppelganger may not be your agent, or might not be the foundation for your agent unless there's some other piece of logic like an interest graph that you build, a human being saying this is my broad set of interests, and so all of my agents out there in the IoT, you all need to be aware that when you make a decision on my behalf as my agent, this is what Jim would do. You know I mean there needs to be that kind of logic somewhere in this fabric to enable true agency. >> All right, so I'm going to start with you. Oh go ahead. >> I have a real short answer to this though. I think that Big Data provides the data and compute platform to make AI possible. For those of us who dipped our toes in the water in the 80s, we got clobbered because we didn't have the, we didn't have the facilities, we didn't have the resources to really do AI, we just kind of played around with it. And I think that the other thing about it is if you combine Big Data and AI and IoT, what you're going to see is people, a lot of the applications we develop now are very inward looking, we look at our organization, we look at our customers. We try to figure out how to sell more shoes to fashionable ladies, right? But with this technology, I think people can really expand what they're thinking about and what they model and come up with applications that are much more external. >> Actually what I would add to that is also it actually introduces being able to use engineering, right? Having engineers interested in the data. Because it's actually technical data that's collected not just say preferences or information about people, but actual measurements that are being collected with IoT. So it's really interesting in the engineering space because it opens up a whole new world for the engineers to actually look at data and to actually combine both that hardware side as well as the data that's being collected from it. >> Well, Neil, you and I have talked about something, 'cause it's not just engineers. We have in the healthcare industry for example, which you know a fair amount about, there's this notion of empirical based management. And the idea that increasingly we have to be driven by data as a way of improving the way that managers do things, the way the managers collect or collaborate and ultimately collectively how they take action. So it's not just engineers, it's supposed to also inform business, what's actually happening in the healthcare world when we start thinking about some of this empirical based management, is it working? What are some of the barriers? >> It's not a function of technology. What happens in medicine and healthcare research is, I guess you can say it borders on fraud. (people chuckling) No, I'm not kidding. I know the New England Journal of Medicine a couple of years ago released a study and said that at least half their articles that they published turned out to be written, ghost written by pharmaceutical companies. (man chuckling) Right, so I think the problem is that when you do a clinical study, the one that really killed me about 10 years ago was the women's health initiative. They spent $700 million gathering this data over 20 years. And when they released it they looked at all the wrong things deliberately, right? So I think that's a systemic-- >> I think you're bringing up a really important point that we haven't brought up yet, and that is is can you use Big Data and machine learning to begin to take the biases out? So if you let the, if you divorce your preconceived notions and your biases from the data and let the data lead you to the logic, you start to, I think get better over time, but it's going to take a while to get there because we do tend to gravitate towards our biases. >> I will share an anecdote. So I had some arm pain, and I had numbness in my thumb and pointer finger and I went to, excruciating pain, went to the hospital. So the doctor examined me, and he said you probably have a pinched nerve, he said, but I'm not exactly sure which nerve it would be, I'll be right back. And I kid you not, he went to a computer and he Googled it. (Neil laughs) And he came back because this little bit of information was something that could easily be looked up, right? Every nerve in your spine is connected to your different fingers so the pointer and the thumb just happens to be your C6, so he came back and said, it's your C6. (Neil mumbles) >> You know an interesting, I mean that's a good example. One of the issues with healthcare data is that the data set is not always shared across the entire research community, so by making Big Data accessible to everyone, you actually start a more rational conversation or debate on well what are the true insights-- >> If that conversation includes what Judith talked about, the actual model that you use to set priorities and make decisions about what's actually important. So it's not just about improving, this is the test. It's not just about improving your understanding of the wrong thing, it's also testing whether it's the right or wrong thing as well. >> That's right, to be able to test that you need to have humans in dialog with one another bringing different biases to the table to work through okay is there truth in this data? >> It's context and it's correlation and you can have a great correlation that's garbage. You know if you don't have the right context. >> Peter: So I want to, hold on Jim, I want to, >> It's exploratory. >> Hold on Jim, I want to take it to the next question 'cause I want to build off of what you talked about Stephanie and that is that this says something about what is the Edge. And our perspective is that the Edge is not just devices. That when we talk about the Edge, we're talking about human beings and the role that human beings are going to play both as sensors or carrying things with them, but also as actuators, actually taking action which is not a simple thing. So what do you guys think? What does the Edge mean to you? Joe, why don't you start? >> Well, I think it could be a combination of the two. And specifically when we talk about healthcare. So I believe in 2017 when we eat we don't know why we're eating, like I think we should absolutely by now be able to know exactly what is my protein level, what is my calcium level, what is my potassium level? And then find the foods to meet that. What have I depleted versus what I should have, and eat very very purposely and not by taste-- >> And it's amazing that red wine is always the answer. >> It is. (people laughing) And tequila, that helps too. >> Jim: You're a precision foodie is what you are. (several chuckle) >> There's no reason why we should not be able to know that right now, right? And when it comes to healthcare is, the biggest problem or challenge with healthcare is no matter how great of a technology you have, you can't, you can't, you can't manage what you can't measure. And you're really not allowed to use a lot of this data so you can't measure it, right? You can't do things very very scientifically right, in the healthcare world and I think regulation in the healthcare world is really burdening advancement in science. >> Peter: Any thoughts Jennifer? >> Yes, I teach statistics for data scientists, right, so you know we talk about a lot of these concepts. I think what makes these questions so difficult is you have to find a balance, right, a middle ground. For instance, in the case of are you being too biased through data, well you could say like we want to look at data only objectively, but then there are certain relationships that your data models might show that aren't actually a causal relationship. For instance, if there's an alien that came from space and saw earth, saw the people, everyone's carrying umbrellas right, and then it started to rain. That alien might think well, it's because they're carrying umbrellas that it's raining. Now we know from real world that that's actually not the way these things work. So if you look only at the data, that's the potential risk. That you'll start making associations or saying something's causal when it's actually not, right? So that's one of the, one of the I think big challenges. I think when it comes to looking also at things like healthcare data, right? Do you collect data about anything and everything? Does it mean that A, we need to collect all that data for the question we're looking at? Or that it's actually the best, more optimal way to be able to get to the answer? Meaning sometimes you can take some shortcuts in terms of what data you collect and still get the right answer and not have maybe that level of specificity that's going to cost you millions extra to be able to get. >> So Jennifer as a data scientist, I want to build upon what you just said. And that is, are we going to start to see methods and models emerge for how we actually solve some of these problems? So for example, we know how to build a system for stylized process like accounting or some elements of accounting. We have methods and models that lead to technology and actions and whatnot all the way down to that that system can be generated. We don't have the same notion to the same degree when we start talking about AI and some of these Big Datas. We have algorithms, we have technology. But are we going to start seeing, as a data scientist, repeatability and learning and how to think the problems through that's going to lead us to a more likely best or at least good result? >> So I think that's a bit of a tough question, right? Because part of it is, it's going to depend on how many of these researchers actually get exposed to real world scenarios, right? Research looks into all these papers, and you come up with all these models, but if it's never tested in a real world scenario, well, I mean we really can't validate that it works, right? So I think it is dependent on how much of this integration there's going to be between the research community and industry and how much investment there is. Funding is going to matter in this case. If there's no funding in the research side, then you'll see a lot of industry folk who feel very confident about their models that, but again on the other side of course, if researchers don't validate those models then you really can't say for sure that it's actually more accurate, or it's more efficient. >> It's the issue of real world testing and experimentation, A B testing, that's standard practice in many operationalized ML and AI implementations in the business world, but real world experimentation in the Edge analytics, what you're actually transducing are touching people's actual lives. Problem there is, like in healthcare and so forth, when you're experimenting with people's lives, somebody's going to die. I mean, in other words, that's a critical, in terms of causal analysis, you've got to tread lightly on doing operationalizing that kind of testing in the IoT when people's lives and health are at stake. >> We still give 'em placebos. So we still test 'em. All right so let's go to the next question. What are the hottest innovations in AI? Stephanie I want to start with you as a company, someone at a company that's got kind of an interesting little thing happening. We start thinking about how do we better catalog data and represent it to a large number of people. What are some of the hottest innovations in AI as you see it? >> I think it's a little counter intuitive about what the hottest innovations are in AI, because we're at a spot in the industry where the most successful companies that are working with AI are actually incorporating them into solutions. So the best AI solutions are actually the products that you don't know there's AI operating underneath. But they're having a significant impact on business decision making or bringing a different type of application to the market and you know, I think there's a lot of investment that's going into AI tooling and tool sets for data scientists or researchers, but the more innovative companies are thinking through how do we really take AI and make it have an impact on business decision making and that means kind of hiding the AI to the business user. Because if you think a bot is making a decision instead of you, you're not going to partner with that bot very easily or very readily. I worked at, way at the start of my career, I worked in CRM when recommendation engines were all the rage online and also in call centers. And the hardest thing was to get a call center agent to actually read the script that the algorithm was presenting to them, that algorithm was 99% correct most of the time, but there was this human resistance to letting a computer tell you what to tell that customer on the other side even if it was more successful in the end. And so I think that the innovation in AI that's really going to push us forward is when humans feel like they can partner with these bots and they don't think of it as a bot, but they think about as assisting their work and getting to a better result-- >> Hence the augmentation point you made earlier. >> Absolutely, absolutely. >> Joe how 'about you? What do you look at? What are you excited about? >> I think the coolest thing at the moment right now is chat bots. Like to be able, like to have voice be able to speak with you in natural language, to do that, I think that's pretty innovative, right? And I do think that eventually, for the average user, not for techies like me, but for the average user, I think keyboards are going to be a thing of the past. I think we're going to communicate with computers through voice and I think this is the very very beginning of that and it's an incredible innovation. >> Neil? >> Well, I think we all have myopia here. We're all thinking about commercial applications. Big, big things are happening with AI in the intelligence community, in military, the defense industry, in all sorts of things. Meteorology. And that's where, well, hopefully not on an every day basis with military, you really see the effect of this. But I was involved in a project a couple of years ago where we were developing AI software to detect artillery pieces in terrain from satellite imagery. I don't have to tell you what country that was. I think you can probably figure that one out right? But there are legions of people in many many companies that are involved in that industry. So if you're talking about the dollars spent on AI, I think the stuff that we do in our industries is probably fairly small. >> Well it reminds me of an application I actually thought was interesting about AI related to that, AI being applied to removing mines from war zones. >> Why not? >> Which is not a bad thing for a whole lot of people. Judith what do you look at? >> So I'm looking at things like being able to have pre-trained data sets in specific solution areas. I think that that's something that's coming. Also the ability to, to really be able to have a machine assist you in selecting the right algorithms based on what your data looks like and the problems you're trying to solve. Some of the things that data scientists still spend a lot of their time on, but can be augmented with some, basically we have to move to levels of abstraction before this becomes truly ubiquitous across many different areas. >> Peter: Jennifer? >> So I'm going to say computer vision. >> Computer vision? >> Computer vision. So computer vision ranges from image recognition to be able to say what content is in the image. Is it a dog, is it a cat, is it a blueberry muffin? Like a sort of popular post out there where it's like a blueberry muffin versus like I think a chihuahua and then it compares the two. And can the AI really actually detect difference, right? So I think that's really where a lot of people who are in this space of being in both the AI space as well as data science are looking to for the new innovations. I think, for instance, cloud vision I think that's what Google still calls it. The vision API we've they've released on beta allows you to actually use an API to send your image and then have it be recognized right, by their API. There's another startup in New York called Clarify that also does a similar thing as well as you know Amazon has their recognition platform as well. So I think in a, from images being able to detect what's in the content as well as from videos, being able to say things like how many people are entering a frame? How many people enter the store? Not having to actually go look at it and count it, but having a computer actually tally that information for you, right? >> There's actually an extra piece to that. So if I have a picture of a stop sign, and I'm an automated car, and is it a picture on the back of a bus of a stop sign, or is it a real stop sign? So that's going to be one of the complications. >> Doesn't matter to a New York City cab driver. How 'about you Jim? >> Probably not. (laughs) >> Hottest thing in AI is General Adversarial Networks, GANT, what's hot about that, well, I'll be very quick, most AI, most deep learning, machine learning is analytical, it's distilling or inferring insights from the data. Generative takes that same algorithmic basis but to build stuff. In other words, to create realistic looking photographs, to compose music, to build CAD CAM models essentially that can be constructed on 3D printers. So GANT, it's a huge research focus all around the world are used for, often increasingly used for natural language generation. In other words it's institutionalizing or having a foundation for nailing the Turing test every single time, building something with machines that looks like it was constructed by a human and doing it over and over again to fool humans. I mean you can imagine the fraud potential. But you can also imagine just the sheer, like it's going to shape the world, GANT. >> All right so I'm going to say one thing, and then we're going to ask if anybody in the audience has an idea. So the thing that I find interesting is traditional programs, or when you tell a machine to do something you don't need incentives. When you tell a human being something, you have to provide incentives. Like how do you get someone to actually read the text. And this whole question of elements within AI that incorporate incentives as a way of trying to guide human behavior is absolutely fascinating to me. Whether it's gamification, or even some things we're thinking about with block chain and bitcoins and related types of stuff. To my mind that's going to have an enormous impact, some good, some bad. Anybody in the audience? I don't want to lose everybody here. What do you think sir? And I'll try to do my best to repeat it. Oh we have a mic. >> So my question's about, Okay, so the question's pretty much about what Stephanie's talking about which is human and loop training right? I come from a computer vision background. That's the problem, we need millions of images trained, we need humans to do that. And that's like you know, the workforce is essentially people that aren't necessarily part of the AI community, they're people that are just able to use that data and analyze the data and label that data. That's something that I think is a big problem everyone in the computer vision industry at least faces. I was wondering-- >> So again, but the problem is that is the difficulty of methodologically bringing together people who understand it and people who, people who have domain expertise people who have algorithm expertise and working together? >> I think the expertise issue comes in healthcare, right? In healthcare you need experts to be labeling your images. With contextual information where essentially augmented reality applications coming in, you have the AR kit and everything coming out, but there is a lack of context based intelligence. And all of that comes through training images, and all of that requires people to do it. And that's kind of like the foundational basis of AI coming forward is not necessarily an algorithm, right? It's how well are datas labeled? Who's doing the labeling and how do we ensure that it happens? >> Great question. So for the panel. So if you think about it, a consultant talks about being on the bench. How much time are they going to have to spend on trying to develop additional business? How much time should we set aside for executives to help train some of the assistants? >> I think that the key is not, to think of the problem a different way is that you would have people manually label data and that's one way to solve the problem. But you can also look at what is the natural workflow of that executive, or that individual? And is there a way to gather that context automatically using AI, right? And if you can do that, it's similar to what we do in our product, we observe how someone is analyzing the data and from those observations we can actually create the metadata that then trains the system in a particular direction. But you have to think about solving the problem differently of finding the workflow that then you can feed into to make this labeling easy without the human really realizing that they're labeling the data. >> Peter: Anybody else? >> I'll just add to what Stephanie said, so in the IoT applications, all those sensory modalities, the computer vision, the speech recognition, all that, that's all potential training data. So it cross checks against all the other models that are processing all the other data coming from that device. So that the natural language process of understanding can be reality checked against the images that the person happens to be commenting upon, or the scene in which they're embedded, so yeah, the data's embedded-- >> I don't think we're, we're not at the stage yet where this is easy. It's going to take time before we do start doing the pre-training of some of these details so that it goes faster, but right now, there're not that many shortcuts. >> Go ahead Joe. >> Sorry so a couple things. So one is like, I was just caught up on your incentivizing programs to be more efficient like humans. You know in Ethereum that has this notion, which is bot chain, has this theory, this concept of gas. Where like as the process becomes more efficient it costs less to actually run, right? It costs less ether, right? So it actually is kind of, the machine is actually incentivized and you don't really know what it's going to cost until the machine processes it, right? So there is like some notion of that there. But as far as like vision, like training the machine for computer vision, I think it's through adoption and crowdsourcing, so as people start using it more they're going to be adding more pictures. Very very organically. And then the machines will be trained and right now is a very small handful doing it, and it's very proactive by the Googles and the Facebooks and all of that. But as we start using it, as they start looking at my images and Jim's and Jen's images, it's going to keep getting smarter and smarter through adoption and through very organic process. >> So Neil, let me ask you a question. Who owns the value that's generated as a consequence of all these people ultimately contributing their insight and intelligence into these systems? >> Well, to a certain extent the people who are contributing the insight own nothing because the systems collect their actions and the things they do and then that data doesn't belong to them, it belongs to whoever collected it or whoever's going to do something with it. But the other thing, getting back to the medical stuff. It's not enough to say that the systems, people will do the right thing, because a lot of them are not motivated to do the right thing. The whole grant thing, the whole oh my god I'm not going to go against the senior professor. A lot of these, I knew a guy who was a doctor at University of Pittsburgh and they were doing a clinical study on the tubes that they put in little kids' ears who have ear infections, right? And-- >> Google it! Who helps out? >> Anyway, I forget the exact thing, but he came out and said that the principle investigator lied when he made the presentation, that it should be this, I forget which way it went. He was fired from his position at Pittsburgh and he has never worked as a doctor again. 'Cause he went against the senior line of authority. He was-- >> Another question back here? >> Man: Yes, Mark Turner has a question. >> Not a question, just want to piggyback what you're saying about the transfixation of maybe in healthcare of black and white images and color images in the case of sonograms and ultrasound and mammograms, you see that happening using AI? You see that being, I mean it's already happening, do you see it moving forward in that kind of way? I mean, talk more about that, about you know, AI and black and white images being used and they can be transfixed, they can be made to color images so you can see things better, doctors can perform better operations. >> So I'm sorry, but could you summarize down? What's the question? Summarize it just, >> I had a lot of students, they're interested in the cross pollenization between AI and say the medical community as far as things like ultrasound and sonograms and mammograms and how you can literally take a black and white image and it can, using algorithms and stuff be made to color images that can help doctors better do the work that they've already been doing, just do it better. You touched on it like 30 seconds. >> So how AI can be used to actually add information in a way that's not necessarily invasive but is ultimately improves how someone might respond to it or use it, yes? Related? I've also got something say about medical images in a second, any of you guys want to, go ahead Jennifer. >> Yeah, so for one thing, you know and it kind of goes back to what we were talking about before. When we look at for instance scans, like at some point I was looking at CT scans, right, for lung cancer nodules. In order for me, who I don't have a medical background, to identify where the nodule is, of course, a doctor actually had to go in and specify which slice of the scan had the nodule and where exactly it is, so it's on both the slice level as well as, within that 2D image, where it's located and the size of it. So the beauty of things like AI is that ultimately right now a radiologist has to look at every slice and actually identify this manually, right? The goal of course would be that one day we wouldn't have to have someone look at every slice to like 300 usually slices and be able to identify it much more automated. And I think the reality is we're not going to get something where it's going to be 100%. And with anything we do in the real world it's always like a 95% chance of it being accurate. So I think it's finding that in between of where, what's the threshold that we want to use to be able to say that this is, definitively say a lung cancer nodule or not. I think the other thing to think about is in terms of how their using other information, what they might use is a for instance, to say like you know, based on other characteristics of the person's health, they might use that as sort of a grading right? So you know, how dark or how light something is, identify maybe in that region, the prevalence of that specific variable. So that's usually how they integrate that information into something that's already existing in the computer vision sense. I think that's, the difficulty with this of course, is being able to identify which variables were introduced into data that does exist. >> So I'll make two quick observations on this then I'll go to the next question. One is radiologists have historically been some of the highest paid physicians within the medical community partly because they don't have to be particularly clinical. They don't have to spend a lot of time with patients. They tend to spend time with doctors which means they can do a lot of work in a little bit of time, and charge a fair amount of money. As we start to introduce some of these technologies that allow us to from a machine standpoint actually make diagnoses based on those images, I find it fascinating that you now see television ads promoting the role that the radiologist plays in clinical medicine. It's kind of an interesting response. >> It's also disruptive as I'm seeing more and more studies showing that deep learning models processing images, ultrasounds and so forth are getting as accurate as many of the best radiologists. >> That's the point! >> Detecting cancer >> Now radiologists are saying oh look, we do this great thing in terms of interacting with the patients, never have because they're being dis-intermediated. The second thing that I'll note is one of my favorite examples of that if I got it right, is looking at the images, the deep space images that come out of Hubble. Where they're taking data from thousands, maybe even millions of images and combining it together in interesting ways you can actually see depth. You can actually move through to a very very small scale a system that's 150, well maybe that, can't be that much, maybe six billion light years away. Fascinating stuff. All right so let me go to the last question here, and then I'm going to close it down, then we can have something to drink. What are the hottest, oh I'm sorry, question? >> Yes, hi, my name's George, I'm with Blue Talon. You asked earlier there the question what's the hottest thing in the Edge and AI, I would say that it's security. It seems to me that before you can empower agency you need to be able to authorize what they can act on, how they can act on, who they can act on. So it seems if you're going to move from very distributed data at the Edge and analytics at the Edge, there has to be security similarly done at the Edge. And I saw (speaking faintly) slides that called out security as a key prerequisite and maybe Judith can comment, but I'm curious how security's going to evolve to meet this analytics at the Edge. >> Well, let me do that and I'll ask Jen to comment. The notion of agency is crucially important, slightly different from security, just so we're clear. And the basic idea here is historically folks have thought about moving data or they thought about moving application function, now we are thinking about moving authority. So as you said. That's not necessarily, that's not really a security question, but this has been a problem that's been in, of concern in a number of different domains. How do we move authority with the resources? And that's really what informs the whole agency process. But with that said, Jim. >> Yeah actually I'll, yeah, thank you for bringing up security so identity is the foundation of security. Strong identity, multifactor, face recognition, biometrics and so forth. Clearly AI, machine learning, deep learning are powering a new era of biometrics and you know it's behavioral metrics and so forth that's organic to people's use of devices and so forth. You know getting to the point that Peter was raising is important, agency! Systems of agency. Your agent, you have to, you as a human being should be vouching in a secure, tamper proof way, your identity should be vouching for the identity of some agent, physical or virtual that does stuff on your behalf. How can that, how should that be managed within this increasingly distributed IoT fabric? Well a lot of that's been worked. It all ran through webs of trust, public key infrastructure, formats and you know SAML for single sign and so forth. It's all about assertion, strong assertions and vouching. I mean there's the whole workflows of things. Back in the ancient days when I was actually a PKI analyst three analyst firms ago, I got deep into all the guts of all those federation agreements, something like that has to be IoT scalable to enable systems agency to be truly fluid. So we can vouch for our agents wherever they happen to be. We're going to keep on having as human beings agents all over creation, we're not even going to be aware of everywhere that our agents are, but our identity-- >> It's not just-- >> Our identity has to follow. >> But it's not just identity, it's also authorization and context. >> Permissioning, of course. >> So I may be the right person to do something yesterday, but I'm not authorized to do it in another context in another application. >> Role based permissioning, yeah. Or persona based. >> That's right. >> I agree. >> And obviously it's going to be interesting to see the role that block chain or its follow on to the technology is going to play here. Okay so let me throw one more questions out. What are the hottest applications of AI at the Edge? We've talked about a number of them, does anybody want to add something that hasn't been talked about? Or do you want to get a beer? (people laughing) Stephanie, you raised your hand first. >> I was going to go, I bring something mundane to the table actually because I think one of the most exciting innovations with IoT and AI are actually simple things like City of San Diego is rolling out 3200 automated street lights that will actually help you find a parking space, reduce the amount of emissions into the atmosphere, so has some environmental change, positive environmental change impact. I mean, it's street lights, it's not like a, it's not medical industry, it doesn't look like a life changing innovation, and yet if we automate streetlights and we manage our energy better, and maybe they can flicker on and off if there's a parking space there for you, that's a significant impact on everyone's life. >> And dramatically suppress the impact of backseat driving! >> (laughs) Exactly. >> Joe what were you saying? >> I was just going to say you know there's already the technology out there where you can put a camera on a drone with machine learning within an artificial intelligence within it, and it can look at buildings and determine whether there's rusty pipes and cracks in cement and leaky roofs and all of those things. And that's all based on artificial intelligence. And I think if you can do that, to be able to look at an x-ray and determine if there's a tumor there is not out of the realm of possibility, right? >> Neil? >> I agree with both of them, that's what I meant about external kind of applications. Instead of figuring out what to sell our customers. Which is most what we hear. I just, I think all of those things are imminently doable. And boy street lights that help you find a parking place, that's brilliant, right? >> Simple! >> It improves your life more than, I dunno. Something I use on the internet recently, but I think it's great! That's, I'd like to see a thousand things like that. >> Peter: Jim? >> Yeah, building on what Stephanie and Neil were saying, it's ambient intelligence built into everything to enable fine grain microclimate awareness of all of us as human beings moving through the world. And enable reading of every microclimate in buildings. In other words, you know you have sensors on your body that are always detecting the heat, the humidity, the level of pollution or whatever in every environment that you're in or that you might be likely to move into fairly soon and either A can help give you guidance in real time about where to avoid, or give that environment guidance about how to adjust itself to your, like the lighting or whatever it might be to your specific requirements. And you know when you have a room like this, full of other human beings, there has to be some negotiated settlement. Some will find it too hot, some will find it too cold or whatever but I think that is fundamental in terms of reshaping the sheer quality of experience of most of our lived habitats on the planet potentially. That's really the Edge analytics application that depends on everybody having, being fully equipped with a personal area network of sensors that's communicating into the cloud. >> Jennifer? >> So I think, what's really interesting about it is being able to utilize the technology we do have, it's a lot cheaper now to have a lot of these ways of measuring that we didn't have before. And whether or not engineers can then leverage what we have as ways to measure things and then of course then you need people like data scientists to build the right model. So you can collect all this data, if you don't build the right model that identifies these patterns then all that data's just collected and it's just made a repository. So without having the models that supports patterns that are actually in the data, you're not going to find a better way of being able to find insights in the data itself. So I think what will be really interesting is to see how existing technology is leveraged, to collect data and then how that's actually modeled as well as to be able to see how technology's going to now develop from where it is now, to being able to either collect things more sensitively or in the case of say for instance if you're dealing with like how people move, whether we can build things that we can then use to measure how we move, right? Like how we move every day and then being able to model that in a way that is actually going to give us better insights in things like healthcare and just maybe even just our behaviors. >> Peter: Judith? >> So, I think we also have to look at it from a peer to peer perspective. So I may be able to get some data from one thing at the Edge, but then all those Edge devices, sensors or whatever, they all have to interact with each other because we don't live, we may, in our business lives, act in silos, but in the real world when you look at things like sensors and devices it's how they react with each other on a peer to peer basis. >> All right, before I invite John up, I want to say, I'll say what my thing is, and it's not the hottest. It's the one I hate the most. I hate AI generated music. (people laughing) Hate it. All right, I want to thank all the panelists, every single person, some great commentary, great observations. I want to thank you very much. I want to thank everybody that joined. John in a second you'll kind of announce who's the big winner. But the one thing I want to do is, is I was listening, I learned a lot from everybody, but I want to call out the one comment that I think we all need to remember, and I'm going to give you the award Stephanie. And that is increasing we have to remember that the best AI is probably AI that we don't even know is working on our behalf. The same flip side of that is all of us have to be very cognizant of the idea that AI is acting on our behalf and we may not know it. So, John why don't you come on up. Who won the, whatever it's called, the raffle? >> You won. >> Thank you! >> How 'about a round of applause for the great panel. (audience applauding) Okay we have a put the business cards in the basket, we're going to have that brought up. We're going to have two raffle gifts, some nice Bose headsets and speaker, Bluetooth speaker. Got to wait for that. I just want to say thank you for coming and for the folks watching, this is our fifth year doing our own event called Big Data NYC which is really an extension of the landscape beyond the Big Data world that's Cloud and AI and IoT and other great things happen and great experts and influencers and analysts here. Thanks for sharing your opinion. Really appreciate you taking the time to come out and share your data and your knowledge, appreciate it. Thank you. Where's the? >> Sam's right in front of you. >> There's the thing, okay. Got to be present to win. We saw some people sneaking out the back door to go to a dinner. >> First prize first. >> Okay first prize is the Bose headset. >> Bluetooth and noise canceling. >> I won't look, Sam you got to hold it down, I can see the cards. >> All right. >> Stephanie you won! (Stephanie laughing) Okay, Sawny Cox, Sawny Allie Cox? (audience applauding) Yay look at that! He's here! The bar's open so help yourself, but we got one more. >> Congratulations. Picture right here. >> Hold that I saw you. Wake up a little bit. Okay, all right. Next one is, my kids love this. This is great, great for the beach, great for everything portable speaker, great gift. >> What is it? >> Portable speaker. >> It is a portable speaker, it's pretty awesome. >> Oh you grabbed mine. >> Oh that's one of our guys. >> (lauging) But who was it? >> Can't be related! Ava, Ava, Ava. Okay Gene Penesko (audience applauding) Hey! He came in! All right look at that, the timing's great. >> Another one? (people laughing) >> Hey thanks everybody, enjoy the night, thank Peter Burris, head of research for SiliconANGLE, Wikibon and he great guests and influencers and friends. And you guys for coming in the community. Thanks for watching and thanks for coming. Enjoy the party and some drinks and that's out, that's it for the influencer panel and analyst discussion. Thank you. (logo music)

>> Announcer: Live from Washington D.C., it's theCube, covering .Next conference. Brought to you by Nutanix. >> We're back, this is Dave Vellante with Stu Miniman, and this the wrap of .Next, Nutanix's customer event, #NEXTConf and this is theCube, the leader in the live tech coverage for enterprise technology. Stu, second day. I got to say, Nutanix has always done a good job, innovative venues, they do funky, fun stuff with marketing, we haven't seen the end of it. We have another keynote today, there's a keynote tomorrow morning, big names, Bill McDermott's here, we just saw Peter MacKay, Chad Sakac is here. Who am I missing? >> Stu: Diane Greene >> Diane Gree was up yesterday. >> Y'know, thought leaders, had the CEO of NASDAQ on this morning Dave, y'know really good customers, thought leaders, Nutanix always makes me think a little bit, which I really enjoy. My fourth one of these Dave, usually by the fourth show I've gotten to, it's like I've seen it. Have we made progress, where are we going? >> I thought Sunil Podi's comment was really interesting, he said, "Look, we saw the trends, "we knew that hardware was going down." I mean, they're essentially admitting that they were a hardware oriented company, infrastructure company, we saw what was happening to infrastructure and hyper-converge, and we could just packed it up then, sold the company for a bunch of money, there were rumors floating around, you know they were pre-IPO, they easily could have sold this thing for a billion plus, all could have cashed out and made a buncha dough, and they said, "Y'know what, we're going to do something "different, we're going to go for it." You got to love the ambition, and so many companies today just can't weather that independent storm. I mean, you've seen it over and over and over again. The last billion dollar storage company that remained independent was NetApp, that was 14 years ago, now Nutanix isn't a storage company, but look around here, look at the analysts, a buncha storage guys that have grown up, and it's to me, Stu, it's a representation of what's happening in the marketplace. Storage as we know it is going away, and it always has transformed, y'know it used to be spinning disc drives, then it was subsystems, then it was the SAN, now it's evolving, these guys call it invisible infrastructure, call it whatever you want, but it's moving toward infrastructure as code, which is just a stepping stone to cloud. So your thoughts on the event, the ecosystem, and their position in the marketplace. >> Right, they reach a certain point, they've gone public, can they keep innovating? Look at a number of announcements there, we spent a lot of time talking about the new CloudZi service out there. >> Si? >> Zi. >> Zi, zi, sorry, you got it. (chuckles) >> Pronunciation of some of these, "it's Nutanix, right?" >> Nutonix, Nutanicks, (chuckles) >> They made jokes about the company last year, but this year, that's product, we're talking vision. The ink is still drying on the relationship with Google, doesn't mean they haven't been working for a while, but where this deal goes, interesting to see where it is six months from now, a year from now, because also Google, small player, I mean it wasn't to be honest, I was at the Red Hat Summit and they had a video of Andy Jassy saying, "We've extending AWS with OpenShift." And you're like wow. Red Hat has a position in a lot of clouds, but for Andy Jassy to make an appearance, Amazon, the behemoth in the cloud, that's good. Look, getting Diane Greene here, I said number one, it gives Nutanix credibility, number two it really pokes at VMware a little bit, she's like, "Oh, I did this before." And everybody's like, "Well, she's here now at Nutanix." Nutanix wants to be, that they've compared themselves to both Amazon, I think we hear it was Sunil or Dheeraj in an analyst session said they "want to be like the A Block." Not the V Block that EMC did, but the Amazon Block for the enterprise, or the next VMware, they talked about the new operating system. It's funny, in a lot of my circles, we've been trying to kill the operating system for a while, I need just enough operating system, I want to serverless and containerize all of these things because we need to modernize, and the old general-purpose processor and general-purpose operating system has come and gone, it's seen its day, but Nutanix has a play there. When I look at some of the things going on, we're talking about microsegmentation Dave, we're talking about multi-cloud and some interesting pieces. I like the ecosystem, I like that balance of how do you keep growing and expand where they can go into, leading the customers, but they're delivering today, they've got real products, they've got real growth, sure they have some challenges as to that competitive back and forth, but you asked Chad Sakac if this reminded him of Dell EMC, and kind of that partnership that they had for years, reminded me a little bit of kind of EMC and VMware too, once EMC bought VMware, VMware, the relationship they had, HP, and IBM, and other companies that they needed to treat as good or better than EMC. They're some of those tough relationships, and Dell with Nutanix, their partner, not only do they do Dell XC, but now they're doing like Pivotal on top of it, they can do Hyper-V deployments, Lenovo's another partner, Nutanix is broadening their approach, there's a lot of options out there and a lot of things to dig into, interesting, they keep growing their customers, keep delighting their customers, it reminds me of other shows we go to, Dave, like Amazon re:Invent, customers are super excited, You tell me about the Splunk conference and the ServiceNow conference where those customers are in there, they're excited, and Nutanix is another one of those, that every year you come, there's good solid content, there's a customer base that is growing and exciting and sharing, and that's a fun one to be part of. >> So, I want to ask you about VMware, it's kind of a good reference model. EMC paid out, I don't know, $630 million for VMware, which was the greatest acquisition in enterprise IT history, no question about it in terms of return. A couple questions for you, you were there at the time, you signed the original NDA between EMC and VMware, kind of sniffed em out. Would VMware's ascendancy been as fast and as successful, or even more successful, without EMC? Would VMware have got there on its own? >> I don't think so Dave, because my information that I had, and some of it's piecing together after the fact is VMware was really looking for that company to help them get to the next state. The fundraising was a little bit different back in 2003 than it was later, but rumors were Semantic was going to buy them. Everybody I talked to, you'd know better than me Dave, if Semantic had bought them, they would have integrated into all their pieces, they would have squashed it, the original talent probably would have fled much sooner. EMC didn't really know what they had, I had worked on some of the due diligence for some of the product integration, which took years and years to deliver, and it was mostly we're going to buy them. Diane had a bit of a tense relationship with Joe Tucci kind of from day one, and it was like okay, you're out there in Palo Alto, we're on the other coast, you go and do your thing, and you grow, and by the time EMC had gotten into VMware a little bit more, they were much bigger. So I think as you said, they're one of the great success stories, EMC did best in a lot of its acquisitions where it either let it ran a division and go, or let it kind of sit on its own and just funded it more, so I think that was a-- >> Well, and the story was always that Diane was pissed because she sold out at such a low price, but that's sort of ancient history. The reason I brought that up is I want to try to draw the parallel with Nutanix today, and come back to what you were saying about the A Block. When you look at Amazon, we agree, they have a lead, whether that lead is five years, seven years, four years, probably more like five to seven, but whatever, whatever it is, it's a lead, it's substantive. Beyond the infrastructure, the storage and the compute, they're building out just all kinds of services, I mean just look at their website, whether it's messaging, on and on and on, there's database, there's AI, there's their version of VDI, there's all this big data stuff, with things like Kinesis, and on and on and on, so many services that are much, much larger than the entire Nutanix ecosystem. So the reason for all this background is does Nutanix need a bigger, can Nutanix become it's ambition, which is essentially to be the next VMware, without some kind of white knight? >> So my answer, Dave, is if you look at Nutanix's ambition, one of the challenges for every infrastructure company today, if you think okay, we've talked about True Private Cloud, Dave, what services can I run on that? How can I leverage that? Look at Amazon, y'know a thousand new services coming every year, look at Google, they've got TensorFlow, really cool stuff, they've got those brilliant people coming up with the next stuff, how do I get that in my environment? Well, Nutanix's answer, coming at the show was we're going to partner with Google, we're going to have that partnership, you're going to be able to plug in, and you want to do your analytics and everything, use GCP, they're great at that, we're not, we know that you need to be able to leverage Google services to do that. The Red Hat announcement that I mentioned before, another way how I can take OpenShift and bridge from my data center and my environment and get access to those services. The promise of VMware on Amazon, yeah we're going to have a similar stack that I can go there, but I want to be able to access those VMware servers. Now, could it suck them eventually into all of Amazon and leave VMware behind? Absolutely, it's tough to partner with Amazon. So, the thing I've been looking at at almost every show this year is how are you tying into and working with those public clouds, we talked about it at VMON, Dave, they have Microsoft up on stage, they have partnerships with the public cloud-- >> David: HPE was up there. >> But the public cloud players, if you're not allowing your customers and the infrastructure that you're building to find ways to leverage and access those public cloud services, which not only are they spending $10 billion a year for each one of the big guys on infrastructure to get all around the globe, but it's all of those new services ahead, moving up the stack. To stitch together that in your own environment is going to be really challenging, how many different software pieces, how do I license it? How do I get it on, as opposed to oh, I'm in the public cloud, it's a checkbox, okay I want to access that, and I consume it as I need it, that consumption model needs to change, so I think Nutanix understands that's directionally where they want to go, I look at the Calm software that they launched and say hey, you want to use TensorFlow? Oh, it's just a choice here, absolutely, go. Where is it and how do I use it? Well, some of these details need to be worked out, as Detu said, "it's not like it's one click for every application, any cloud, anywhere." But that's directionally where they're going to make it easy, so all that cool analytic stuff that we cover a lot on theCube, a lot of that is now happening in the cloud, and I should be able to access it whether I'm in my private cloud or public cloud, and it's just going to be consumption model, whether I have certain characteristics that make it that I'm going to want to have that infrastructure for whether that's governance or locality, we talked to Scholastic yesterday, and they said, "Well when you've got manufacturing "in books, I need things close "to where they're coming off the production line, "otherwise there's things that I'm doing "in the public cloud." So that's there we see, when I talk to companies like I do here, at the Vienna show last year, when I talk to Christian Reilly with Citrix, who had been at Bechtel for many years, there's reasons why things need to live close to what's happening, y'know we've talked a lot about Edge, and therefore public cloud doesn't win it all, I know we had one guest on this week that said, "Right, depending on what industry you're is, "is it a 30/70 mix or a 70/30 mix?" There's a lot of nuance to sort this out, and this is long game, Dave, there's this change of the way we do things is a journey, and Nutanix has positioned themselves to continue to grow, continue to expand, some good ambition to expand on, like the five vectors of support that they have, so I've liked what I've heard this week. >> So in thinking about what we're talking about VMware, the imperative for virtualization was so high in the early 2000's because we were coming out of the dot com bust, IT was out of favor, VMware was really the only game in town, there really wasn't a strong alternative, had by far the best product, Microsoft Hyper-V was sort of in-concept, and KVM and others were just really not there, so there really was no choice, it appealed to 100% of the IT shops, I mean essentially. So I wonder though, today, is the imperative for multi-cloud the same? The fundamental is yes, everybody has multiple clouds. But this industry has lived in stovepipes forever, and has figured out how to manage stovepipes, it manages them by fencing things off. So I wonder is the imperative as high, you could maybe make an argument that it's higher, but I'm still not quite getting it yet, as it was in the early 2000's, where the aspirin of virtualization to soothe the pain of do more with less was such an obvious and game changing paradigm shift. I don't see it as much here, I see people still trying to figure out okay, what is our cloud strategy? Number one, number two is the competition seems to be much more wide open, it's unclear at this time that any one company has a fast-track to multi-cloud. >> I think you've got some really good points there, Dave. A thing that I've pointed out a few times is that one of the things that bothered me from the early days with VMware is from an application standpoint, it tended to freeze my application. I didn't have a reason to kind of move forward and modernize my application. Back in 2002 it was like oh, I'm running Windows NT with a really old application, my operating system going to end of life, well maybe it's time to uplift. Oh wait, there's this great virtualization stuff, my hardware's going end of life too. No, shove it in a VM, let's keep it for another five years. Oh my god, that application sucked then, it's going to suck even more in five years, and workforce productivity was way down. So, the vision for Nutanix is they're going to be a platform that are going to be able to help you modernize your environment and how do we get beyond, is it virtualization, is it containerization, is it a lot of the cloud-native pieces, how does that fit in? Starting to hear a little bit more of it, a critique I'd have on HCI about two years ago was it was the same applications that were in my VMware SAN, not VSAN, but my just traditional storage area network was what was running on Nutanix. We're starting to see more interesting applications going on there, and look, Nutanix has a bullseye on them, there are all the HCI direct replacements, there is the threat of the cloud, and I haven't heard as many SAAS applications living on Nutanix as I do when we talk to all flash-array companies, Dave, every single on of them can roll out, here's all these SAAS deployments on our environment, just scalable environments that build that for the future. I haven't heard it as much from Nutanix. >> So VMware was aspirin , Nutanix originally started as aspirin, and now they're pivoting to vitamin. Who are they up against? Who do you like? Who are the horses on the track? Let's analyze the race and then wrap. >> Yeah, so when Nutanix got into this business, it was well, they're helping VMware environments, it was 100% VMware when they first started that relationship with VMware was really tough, they've lowered that too, they've now got what, 28% is running HV, they've got a little bit on Hyper-V, but they've still got about 60% of their customers are VMware. So VMware, y'know, huge challenge, VSAN has more customers than anyone in the hyper-convergent infrastructure space, easy, number of customers, but virtualization admin has taken that. Microsoft, huge potential threat, Azure Stack's coming this year, it's been coming, it's been coming, it's really close there, all the server guys are lining up. Microsoft's a huge player, Microsoft owns applications, they're pulling applications into their SAAS offerings, they're pulling applications into Azure, when they launch Azure Stack, even if the 1.0, if you looked at it on paper and say Nutanix is better, well, Microsoft's a huge threat to both VMware, which uses a lot of Microsoft apps, as well as Nutanix. So those are the two biggest threats, then of course, there's just the general trend of push to SAAS and push to public cloud where Nutanix is starting to play in the multi-cloud, as we talked about, and COM and the DR cloud services are good, but can Nutanix continue to stay ahead of their customers? They're ahead of the vast majority of enterprises, but can they convince them to come on board to them, rather than some of these big guys? Nutanix is a public company now, they're doing great, but yeah, it's a big TAM that they're going after, but that means they're going to have a tax from every side of the market. >> I see HCI as one where you got a leader, and that leader can make some good money. I don't see multi-cloud as a winner-take-all market because I think IBM's going to have its play in multi-cloud, HPE has its play in multi-cloud, Dell EMC is going to have its play in multi-cloud. You got guys coming out of different places like ServiceNow, who's got an IT operations management practice, builds business big, hundreds of millions of dollars of business there, coming at multi-cloud, so a lot of different competitors that are going to be going for it, and some of them with very large service organizations that I think are going to get there fair share, so I would predict, Stu, that this is going to continue to be, multi-cloud is going to be a multi-stovepipe cloud for a long, long time. Now, if Nutanix can come in and solve that control plane problem, and demonstrate substantial business value, and deliver competitive advantage, y'know that might change the game. It's difficult at this point in 2017 to see that Nutanix, over those other guys that I just mentioned, has an advantage, clear advantage, maybe from a product standpoint, maybe. But from a resource standpoint, a distribution channel, services organization, ecosystem, all those other things, they seem to me to be counterbalancing. Alright, I'll give you last thought. >> Yeah, so it's great to see Nutanix, they're aiming high, they're expanding into a couple of areas, and they keep listening, so I hope they keep listening to their customers, expand their partnerships, SAAS customers would be really interesting, service provider is something that they've gotten into little bit, but plenty more opportunity for them to go there. Dave, personally for me, to it have been a company I've watched since the earliest days, it's been a pleasure to watch, y'know I think back, right, VMware you said, I think it was a hundred person company when I first started talking to them and Diane Greene, and I look at where VMware went. I've been tracking VMware for now five years, and reminds me a lot of some of those trends, for a 20 person company, I said to hear almost 3000 boggles the mind, I've been to their headquarters a bunch. So it's been fun to watch the Newton army, and they've been loving watching it from our angles. >> Well and these events are very good events, and so there's a lot of passion here, and that's a great fundamental for this company. So I'm a fan, I think it may be undervalued, I think it very well may be undervalued. >> Wall Street definitely doesn't understand this stuff. >> Alright Stu, great working with you this year, (chuckles) this month, this quarter, this month, certainly this show, so great job. I really appreciate it >> Stu: Thanks, Dave. >> There's a big crew behind what Stu and I, and John Ferrier, and Jeff Frick, and others do here. Here today with us Ava, Patrick, Alex, Jay, you guys have had an awesome spring. Brendan is somewhere, I guess Brendan is doing the keynote right now. So, fantastic job, as always, Kristen Nicole and her team, writing up the articles. Jay Johanson back at the controls, Bert with the crowd shots. Everybody, really appreciate all your support, thanks for watching everybody. We'll see you, we got a little break, I think, in the action, cause it's July Fourth, well it's Canada year, or Canada week-- >> Canada Day and Independence Day next week. >> And Independence Day in the United States, and then we'll be at Infor Inforum, second week of July, I'll be there with Rebecca Knight and the crew, so watch for that, check out SiliconAngle.com for all the news, Wikibon.com for all the research, and theCube.net to find all these videos, Youtube.com/SiliconAngle, it's everywhere, if you can't find it, you're not on Twitter, you're not on social. Thanks for watching, everybody. This is Dave Vellante with Stu Miniman, we're out. (lo-fi synthesizer music)

>> Live from Boston, Massachusetts. It's theCUBE covering Red Hat Summit 2017. Brought to you by Red Hat. {Electronic music} >> Welcome to the session wrap of the Red Hat Summit. I am your host, Rebecca Knight, along with my co-host Stu Miniman. Wrapping up three great days of open source talk. Where are we, Stu? Tell us the state of Red Hat, the state of open source. What have we learned? >> You mean, beyond we're in the seaport district of Boston, Massachusetts, you know, a couple blocks away from >> or the heart of open source the new open innovation lab coming from Red Hat. So, Rebecca it's been a lot of fun with you these last couple of days. >> I feel the same way. >> Did over thirty interviews: executives from Jim Whitehurst you know on down to many of the product teams many people participating greatly in open source, open innovation award winners, the women of open source award winners, open invest in lab participants. A lot of topics but okay Red Hat itself. I've worked with Red Hat in various roles in my career for quite a long time. We didn't talk a lot about Linux this week. >> Stu, Stu, Stu I've got to stop you Linux is containers, containers is Linux. So we're hearing so much about containers it's the same diff. >> Yeah, well I got the t-shirt "Linux is containers, containers are Linux" however, if I even look at Red Hat's messaging Red Hat Enterprise Linux is like the first platform what they built around and it's a little surprising that they didn't at least in the conversation we had, it was very much about some of the newer things coming into the show I said What's the progress that they've made around some of the cloud offerings, some of the management offerings, Ansible, weaving its way into a lot of the products. OpenShift really maturing and expanding the portfolio with things like the OpenShift io to be able to really help with application modernization. Middleware progressing even heard a little bit of the future where they are doing things like server lists. So Red Hat's making good progress. We love when we do these shows multiple years is they talk about it, they deliver on it, and in the way a couple guests talked about there's a little more transparency in open source and being part of all of these communities you have some visibility as to where you're going it doesn't mean that things don't slip every now and again and not every piece makes it into the product lease that they're expecting, but they've made great progress. Linux still is just a mainstay. It's a piece of lots of environments. The ecosystem reminds me of the same way I talk about OpenStack which we'll go into next week. We had a great session with Radhesh towards the end here talking about OpenStack in many ways is like that it's weaving its way into lots of infrastructure pieces some we'll dig into more this week, but let's focus on this week for now. >> Right, so you said we didn't talk a lot about Linux. I set you straight there. But what else did we, what else did you not hear? What do you remain skeptical of? As you said, Red Hat seems to be going from strength to strength. It had two point four billion in revenue this year. >> Yeah it did. For 2016, two point four billion in revenue and three billion in bookings >> Right And there was, I read a financial report that Jim Whithurst said Golda Company is five billion within five years. And you look at it and you say okay from two point four to five well, you know >> yeah actually if it was three billion in bookings and I think back to three years ago when we first started it was around two billion dollars that was almost a 50% growth rate in three years. So, if three years from now we do 50% growth rate we're going to have three to four point five. Of course the math is not linear, there's scaling of the company, there's lots of products in here, but they've got a big tam. >> Ambitious but achievable. >> Ambitious but achievable. The question we've had for a bunch of years is when I look at the cloud. Public cloud is affecting a lot of the traditional infrastructure companies. Red Hat is a software company. They're an open source company. We heard the cloud messaging. Microsoft and Google up on stage. Andy Jassy on video. That was a big question coming in. What about Amazon? How close will Red Hat do? Amazon actually has their own AMI for Linux which means I can get a package for Linux from Amazon not only that I could take that package outside of Amazon and put it in a data center so I could use the same type of Linux for AWS to work with Red Hat to take RedShift make what's deeper integration in the public cloud with AWS and if I put that on premises I'm going to have access to the AWS services so that tighter application integration for what they're laying out really the open hybrid cloud. Red Hat terminology, we'll see if other people take that up. But really it's a multi-cloud world and Red Hat has a good position to live in lots of those environments and provide and really help solutionize and give really almost that almost adult supervision that the enterprise wants for all of these open packages. So I was heartened to see the progress made. Strong ecosystem. As always, you know passionate customers, developers, and really just heartwarming stories of you know making the world a better place. What was your take on those pieces? >> Yes, absolutely. Those are really what you come away remembering. It is the story of a woman saving a man's life in a park in Singapore. It is the story of an emergency room doing a better job of serving its patients. It is scaling up technology use in the developing world. This is what you come away. And you say that is open source. >> Maybe next year that apple you get at the grocery store won't have been sitting there 18 months. >> Well maybe. But in a code climate. Boston going to be beautiful year round. No, but so I really do agree and that is I think what Red Hat did so brilliantly at this summit. Is really showcasing the ways in which this technology is having an impact at transforming industries obviously, helping businesses make more money, but also really doing a lot of good. >> Yeah, absolutely. And Rebecca I want a big shout out to the community here. This is a community show. Red Hat is a great participant of the community. We talked to Jim Whitehurst they want to help raise up the community it's not about Red Hat leadership. We don't hear number one at a show like this, we hear where they're participating and when they get involved they go deep. We heard about OpenPOWER. How excited they are that Red Hat you know getting involved and working in some of these pieces. So, we could not be here without Red Hat support. It's our fourth year doing the show. We had a blast with it. We see Red Hat at a lot of shows. They bring us great customers, their ecosystem partners and their executives. And it's been a pleasure to cover it. >> Yeah. No, I couldn't agree more and I do think, just in terms of what your talking about, the humility of the Red Hat folks is that they aren't going banging drums of we're number one in this and number one in that and you sort of think, "okay, blah, blah." No, they don't at all. They really are saying, "No we're about making our partners and our customers shine." >> Yeah, yeah. What's going to happen with the future of jobs? You know where are people going to work these days in the future? >> How will they work? >> Rebecca: What kind of processes will they work with? >> We've all said it's very much a global ecosystem here. Got to interview quite a few international guests here and hear how technology is spreading, how people are interacting, how innovation happens in a global environment. I'm sure ties back to a lot of the things that you write about. >> Absolutely. And I think, that Radhesh some of his words of wisdom was technology is the easy part what we need to be fundamentally rethinking is how we write these applications, how we develop these applications, how we design them, and how we deliver them. And, also really bearing in mind the end user. And, that is what we learned in a lot of our other sessions. Is really thinking about that. We heard from another person you know your competitor is maybe not necessarily the competitor you're thinking of it's the last app you opened or the last application that that company was using and what is drawing them toward that application or that technology or that infrastructure and not yours? [Stu]- Right. >> And so it's really thinking much more broadly about technology and who you're competing with and how you're working. >> Yeah, that was it was a bank. I loved that. They're like we're not competing against other banks it's like where's that other attention span that you have. >> Rebecca: Right, where are your eyeballs. >> One of my favorite lines is you know what you, Michelangelo, and Einstein have in common? You only have 24 hours in the day so you need to make sure you take advantage of that. That's the kind of thing that >> That's depressing Stu, when you leverage >> I don't know. the community. I thought it's inspiring. >> Okay. You know we can do >> Good great things when we work together and do that. So, we're always like oh I'm too busy or I don't have time it's like hogwash. >> Right. >> That's not the case. I'm inspired and fired up after all the conversations we had especially some of these great users here and looking forward to the next one. >> You're looking forward to the next one, you're looking forward to OpenStack coming up. >> Yeah, oh my gosh so right. >> Got to plug it. >> So Rebecca next week we're both going to be on theCUBE but in two different locales. Our team is in the midst of the sprint that is the spring tour. So we had the Micron event and we're here. Next week our team is at Service Now Knowledge, we're also at DELL EMC World in Vegas, we're at OpenStack Summit back in Boston. We've got some of our teams going to Microsoft Build. I'm sure we'll have analyst reports follow up from there. Boy do we have more shows than I can mention through the rest of May and June and beyond. Check out siliconangle.tv to catch all of them. Rebecca I'm going to let you do the close, but I have to say a big thanks to our team here and remote. >> Yes, yes. Leonard, Chuck, Alex, Ava. >> We love you all. Jeff and the team back there. You know we were doing some cool things playing with Facebook Live as part of this event, we always love playing around with some of the new technologies finding more ways that we can help reach you. We always appreciate your feedback. And Rebecca take us on home. >> Thank you so much for joining us here at theCUBE Red Hat Summit, Boston, Massachusetts. I'm Rebecca Knight for Stu Miniman, Thanks so much. {Electronic music}

>> Voiceover: Live from Austin, Texas, it's the CUBE. Covering DockerCon 2017. Brought to you by Docker in support from its ecosystem partners. >> Hi I'm Stu Miniman here with the final wrap with Jim Kobielus at DockerCon 2017. The CUBE's really excited that we were here for the third year. Have to have a big shout out to our partners and our sponsors that allow us to be here. Of course, Docker's been a great partnership. They talk a lot about ecosystem, really bringing some media people like ourselves giving us some of the great speakers from their company, the partner ecosystem and their customers, and the sponsors for the show, for ourselves, App Lariat, CISCO, Iguazio, Skelety, Cononical, and Red Hat. Without them we couldn't bring you this programming. Really excited to be able to be here. They're starting to tear down the show here, so not a lot of time, so many things to dock to. >> The show itself is containerized. >> We're not even going to be able to talk about the Franklin's barbeque. >> You just did. >> But Jim ... Absolutely. Jim, you've gotten to be on the CUBE here, see some of the show. Give us your quick hits. >> Sure. >> on your takeaways from the show. >> First of all, my first takeaway is this is a vibrant developer ecosystem, clearly. This show is much larger than the year before, and the year before that. It'll probably be twice as large next year. That's my prediction based on the sheer amount of developers migrating into the Docker ecosystem because so many organizations are Dockerizing their applications, containerizing their applications. That's a huge focus for me and Wikibon, as an analyst, is the containerization of application development with microservices and all that, for cloud deployment and multi-clouds, hot, hot, hot across all niches. So, vibrant ecosystem. Docker as the core solution-provider and the centerpiece of this community. Amazing show. The Enterprise Edition, of course, that preceded the announcement of that and the release preceded this show. That's critically important in getting Docker into new accounts that, with a full stack. Clearly it's enterprise ready. Developers, more developers will be exposed to Docker through the EE. Docker, at this show, had a couple of really important announcements for developers. Moby. Project Moby, for customization of container images and so forth, clearly that's going to be a multiplier effect on the ecosystem of developers, ISVs and so forth, Building applications, and customizing containerized Docker applications and images for a wide range of opportunities. >> Yeah, Jim, just want to comment on the Moby piece here 'cause it was really interesting. I think the last couple of years, it's been that pull and tug as to what was the open-source piece, what is the company itself doing, and I think it's clarifying. Kubernetes is a big rising tide in the environment, and all they cared about is they've got the open-source pieces that they need to be able to do Kubernetes. So, with Moby Project it's like okay, now I understand what's out and open. I understand what Docker's doing. I saw some humility from Solomon Hykes, talking, it's like we're listening. We're working, you know, ecosystem, ecosystem, ecosystem. So it was good to see that maturity. I mean, there were some people that I talk to, and they're like, "Oh, will this be the last DockerCon?" I'm like, "I don't think anybody watching this show would say that coming out." As you said, I expect the show to grow; it's doing really well. >> Solomon's totally partner-focused. Look at him. >> Kudos to what they're doing. The partners are excited. It's not just lip service. "Oh yeah. We did some little announcement on the side." No. We're excited. This is there. I know you've got a bunch of pieces, but I want to ask you, are developers excited about taking this legacy ... >> There's lots of news I'm going to analyze. >> Legacy applications, and like helping to move those in, or they only want to work on the cool new stuff? >> Oh, that's a huge theme. MTA. I forget what exactly the acronym stands for, but it's wrapping legacy applications, containerizing them in the Docker ecosystem. That is so important so all of these legacy applications will be Dockerized before long, and refactored, in addition to all the Greenfield development of containerized applications. So the MTA announcement, just as critical as the Moby announcement and so forth in terms, and EE as part of the show, of getting Docker, getting their ecosystem, getting developers working in this environment, more and more developers. This entire Docker, this entire ecosystem has a magnetic force on the developer community, or will. Those are very important. Also I thought the announcements with Microsoft, in terms of containers are going into Windows in a larger way, Linux containers and so forth, that also, 'cause Microsoft has a huge presence obviously in not only enterprise but small to midsize businesses. We're going to see Docker in ever-smaller deployments, hosts and so forth, across the board. More buyers, in other words, more companies will be Dockerizing more applications thanks to, in part, Microsoft as clearly a forerunner. >> Jim, absolutely. I say it at almost every cloud show. I want to follow the data and I want to follow the applications, and you had Microsoft and you had Oracle. You had two of the big players from an application standpoint, Oracle's now in the Docker store. >> Oracle's in the Docker store. That is huge. >> Yeah. >> That has validated containers and Docker for ... >> How about you? From the data standpoint, I heard, we talked to Iguazio about some of the analytics and things ... >> Jim: I'm a data guy, yeah. >> Yeah, you're a data guy. What's a data guy think at a show like this? Is it too infrastructure-focused, or did you see some of the data future here? >> No. It's infrastructure-focused in the sense that it needs to be to harden this technology for enterprise deployment, but it's really dev-ops focused, you know, Kubernetes and everything, and Swarm and whatnot. Look at all these vendors. Here are these tools for the dev-ops life cycle, Kubernetes and everything. That's really, really important. It's all about developers and speeding of development, and putting containerized Docker applications and images into production, and managing them and securing them and so forth. Just the sheer range of dev-ops tools on this show floor that's packing up now was amazing. I'm just uncracking my research here. Very important. So I'm going to wrap up. So, the adoption is amazing. I mean, all these industries, including like Visa. We had a swap-meet, who have adopted Docker into core applications that they're running major businesses on. That's some serious validation in its own right. >> Jim, one of the feedback I got from, it was actually John White from Expedient. >> Okay. >> talked about, and he said he deals with kind of small to mid, to little bit large enterprises, and he said, all that this keynote reminds us of AWS Re:Invent a couple of years ago. >> Oh yeah. >> Big global names. I mean, it's, you know, Visa. You know. Around the globe. Northern Trust. These are not, you know, your regional companies that did a little initiative. It's virtualization started in a lot of small environments. Containerization really started in the likes of Google. I remember the first DockerCon. It was Google and Facebook, and they're the ones that have been doing these projects pre-Docker, and it's slowly moving down. Part of the things I look at is where's the watermark >> Jim: Yeah. >> Where below this you're probably not going to do containers because you're going to go live on a platform that leverages container. The service writers I talked to ... >> Jim: They're going to live in a public cloud like Microsoft, or you know. >> Stu: The cloud guys. I'm going to go to, right, I'm going to go to Microsoft. I'm going to go to Oracle. >> Jim: AWS or IBM. >> Stu: I'm going to go AWS. >> Jim: Whoever it might be. >> Right. Any of them because they're going to just take care of that, and I won't care that it was containerized, so at the end of the day, it's not that tool, it's the wave of that modernization. >> Oh. Yeah, I want to end on a data note because we were talking about data. Okay. I thought Iguazio, I thought Yaron was very, that was very good to have him. There's a lot of storage foundations like Veritas and so forth, so storage in a Docker environment and persistent storage and data protection, pretty important, but also containerizing the new wave of applications that are machine-learning and deep learning and artificial intelligence. We got a fair look at some of that from Solomon yesterday because Solomon mentioned that the open AI consortium is based in their internal test bed training network on Docker, on Swarm and so forth. I, in my prior life, I just joined Wikibon a few weeks ago, I've focused on data science, which is a key development theme, by the way, I'll focus on for Wikibon. I saw a lot of containerization. I saw a fair amount of Docker and a lot of the data science oriented app dev that was going on in the business world. That's going to be a huge theme for me under Wikibon, but also, I mean Solomon sort of alluded to a lot, and so did Yaron, a lot of the work that's going on in the AI community Dockerized their application. Tenser flowing, all that. Huge theme we'll probably see much more of at next year's DockerCon I predict containerizing AI. >> All right. Well. >> For deployment into autonomous vehicles. Whatever. >> Jim, you've long been a CUBE alumn, but now you are a veteran of doing the CUBE. I really appreciate you coming on. >> I'm on this side of the table now. It's amazing. >> Stu: I want to give a shout out to the whole team here. John Furrier, I know, was really disappointed. He loves this show. Usually my co-host. A lot of these open-sourced shows. John, you better be down here in Austin for CUBECon at the end of the year with me. So many shows now through July 4th. The CUBE has so many activities going on. If you go to theCUBE.net, you can see all of our upcoming shows. Always watch us live. If we're not at the show that you think we should be at, go ahead and Ping us. Reach out to us through Twitter or through the website. Jim's research, a lot of it's going to be on Wikibon.com. Siliconangle.com is also where we have some research corner, some of the other pieces there, so check out the whole SiliconANGLE Media for Jim, myself, Ava, Leonard, Brandon, Jay, Sam, who's already heading to the airport. Thank you so much for watching The CUBE. Hope to see you at lots of shows coming around and thank you for sharing.

(lively music) >> [Announcer] Live from Boston, Massachusetts, this is the Cube, covering Sparks Summit East 2017. Brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> Welcome back to Sparks Summit in Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. We've been here two days, wall-to-wall coverage of Sparks Summit. George Gilbert, my cohost this week, and I are going to review part two of the Wikibon Big Data Forecast. Now, it's very preliminary. We're only going to show you a small subset of what we're doing here. And so, well, let me just set it up. So, these are preliminary estimates, and we're going to look at different ways to triangulate the market. So, at Wikibon, what we try to do is focus on disruptive markets, and try to forecast those over the long term. What we try to do is identify where the traditional market research estimates really, we feel, might be missing some of the big trends. So, we're trying to figure out, what's the impact, for example, of real time. And, what's the impact of this new workload that we've been talking about around continuous streaming. So, we're beginning to put together ways to triangulate that, and we're going to show you, give you a glimpse today of what we're doing. So, if you bring up the first slide, we showed this yesterday in part one. This is our last year's big data forecast. And, what we're going to do today, is we're going to focus in on that line, that S-curve. That really represents the real time component of the market. The Spark would be in there. The Streaming analytics would be in there. Add some color to that, George, if you would. >> [George] Okay, for 60 years, since the dawn of computing, we have two ways of interacting with computers. You put your punch cards in, or whatever else and you come back and you get your answer later. That's batch. Then, starting in the early 60's, we had interactive, where you're at a terminal. And then, the big revolution in the 80's was you had a PC, but you still were either interactive either with terminal or batch, typically for reporting and things like that. What's happening is the rise of a new interaction mode. Which is continuous processing. Streaming is one way of looking at it but it might be more effective to call it continuous processing because you're not going to get rid of batch or interactive but your apps are going to have a little of each. So, what we're trying to do, since this is early, early in its life cycle, we're going to try and look at that streaming component from a couple of different angles. >> Okay, as I say, that's represented by this Ogive curve, or the S-curve. On the next slide, we're at the beginning when you think about these continuous workloads. We're at the early part of that S-curve, and of course, most of you or many of you know how the S-curve works. It's slow, slow, slow. For a lot of effort, you don't get much in return. Then you hit the steep part of that S-curve. And that's really when things start to take off. So, the challenge is, things are complex right now. That's really what this slide shows. And Spark is designed, really, to reduce some of that complexity. We've heard a lot about that, but take us through this. Look at this data flow from ingest, to explore, to process, to serve. We talked a lot about that yesterday, but this underscores the complexity in the marketplace. >> [George] Right, and while we're just looking mostly at numbers today, the point of the forecast is to estimate when the barriers, representing complexities, start to fall. And then, when we can put all these pieces together, in just explore, process, serve. When that becomes an end-to-end pipeline. When you can start taking the data in on one end, get a scientist to turn it into a model, inject it into an application, and that process becomes automated. That's when it's mature enough for the knee in the curve to start. >> And that's when we think the market's going to explode. But now so, how do you bound this. Okay, when we do forecasts, we always try to bound things. Because if they're not bounded, then you get no foundation. So, if you look at the next slide, we're trying to get a sense of real-time analytics. How big can it actually get? That's what this slide is really trying to-- >> [George] So this one was one firm's take on real-time analytics, where by 2027, they see it peaking just under-- >> [Dave] When you say one firm, you mean somebody from the technology district? >> [George] Publicly available data. And we take it as as a, since they didn't have a lot of assumptions published, we took it as, okay one data point. And then, we're going to come at it with some bottoms-up end top-down data points, and compare. >> [Dave] Okay, so the next slide we want to drill into the DBMS market and when you think about DBMS, you think about the traditional RDBMS and what we know, or the Oracle, SQL Server, IBMDB2's, etc. And then, you have this emergent NewSQL, and noSQL entrance, which are, obviously, we talked today to a number of folks. The number of suppliers is exploding. The revenue's still relatively small. Certainly small relative to the RDBMS marketplace. But, take us through what your expectations is here, and what some of the assumptions are behind this. >> [George] Okay, so the first thing to understand is the DBMS market, overall, is about $40 billion of which 30 billion goes to online transaction processing supporting real operational apps. 10 billion goes to Orlap or business intelligence type stuff. The Orlap one is shrinking materially. The online transaction processing one, new sales is shrinking materially but there's a huge maintenance stream. >> [Dave] Yeah which companies like Oracle and IBM and Microsoft are living off of that trying to fund new development. >> We modeled that declining gently and beginning to accelerate more going out into the latter years of the tenure period. >> What's driving that decline? Obviously, you've got the big sucking sound of a dup in part, is driving that. But really, increasingly it's people shifting their resources to some of these new emergent applications and workloads and new types of databases to support them right? But these are still, those new databases, you can see here, the NewSQL and noSQL still, relatively, small. A lot of it's open source. But then it starts to take off. What's your assumption there? >> So here, what's going on is, if you look at dollars today, it's, actually, interesting. If you take the noSQL databases, you take DynamoDB, you take Cassandra, Hadoop, HBase, Couchbase, Mongo, Kudu and you add all those up, it's about, with DynamoDB, it's, probably, about 1.55 billion out of a $40 billion market today. >> [Dave] Okay but it's starting to get meaningful. We were approaching two billion. >> But where it's meaningful is the unit share. If that were translated into Oracle pricing. The market would be much, much bigger. So the point it. >> Ten X? >> At least, at least. >> Okay, so in terms of work being done. If there's a measure of work being done. >> [George] We're looking at dollars here. >> Operations per second or etcetera, it would be enormous. >> Yes, but that's reflective of the fact that the data volumes are exploding but the prices are dropping precipitously. >> So do you have a metric to demonstrate that. We're, obviously, not going to show it today but. >> [George] Yes. >> Okay great, so-- >> On the business intelligence side, without naming names, the data warehouse appliance vendors are charging anywhere from 25,000 per terabyte up to, when you include running costs, as high as 100,000 a terabyte. That their customers are estimating. That's not the selling cost but that's the cost of ownership per terabyte. Whereas, if you look at, let's say Hadoop, which is comparable for the off loading some of the data warehouse work loads. That's down to the 5K per terabyte range. >> Okay great, so you expect that these platforms will have a bigger and bigger impact? What's your pricing assumption? Is prices going to go up or is it just volume's going to go through the roof? >> I'm, actually, expecting pricing. It's difficult because we're going to add more and more functionality. Volumes go up and if you add sufficient functionality, you can maintain pricing. But as volumes go up, typically, prices go down. So it's a matter of how much do these noSQL and NewSQL databases add in terms of functionality and I distinguish between them because NewSQL databases are scaled out version of Oracle or Teradata but they are based on the more open source pricing model. >> Okay and NoSQL, don't forget, stands for not only SQL, not not SQL. >> If you look at the slides, big existing markets never fall off a cliff when they're in the climb. They just slowly fade. And, eventually, that accelerates. But what's interesting here is, the data volumes could explode but the revenue associated with the NoSQL which is the dark gray and the NewSQL which is the blue. Those don't explode. You could take, what's the DBMS cost of supporting YouTube? It would be in the many, many, many billions of dollars. It would support 1/2 of an Oracle itself probably. But it's all open source there so. >> Right, so that's minimizing the opportunity is what you're saying? >> Right. >> You can see the database market is flat, certainly flattish and even declining but you do expect some growth in the out years as part of that evasion, that volume, presumably-- >> And that's the next slide which is where we've seen that growth come from. >> Okay so let's talk about that. So the next slide, again, I should have set this up better. The X-axis year is worldwide dollars and the horizontal axis is time. And we're talking here about these continuous application work loads. This new work load that you talked about earlier. So take us through the three. >> [George] There's three types of workloads that, in large part, are going to be driving most of this revenue. Now, these aren't completely, they are completely comparable to the DBMS market because some of these don't use traditional databases. Or if they do, they're Torry databases and I'll explain that. >> [Dave] Sure but if I look at the IoT Edge, the Cloud and the micro services and streaming, that's a tail wind to the database forecast in the previous slide, is that right? >> [George] It's, actually, interesting but the application and infrastructure telemetry, this is what Splunk pioneered. Which is all the torrents of data coming out of your data center and your applications and you're trying to manage what's going on. That is a database application. And we know Splunk, for 2016, was 400 million. In software revenue Hadoop was 750 million. And the various other management vendors, New Relic, AppDynamics, start ups and 5% of Azure and AWS revenue. If you add all that up, it comes out to $1.7 billion for 2016. And so, we can put a growth rate on that. And we talked to several vendors to say, okay, how much will that work load be compared to IoT Edge Cloud. And the IoT Edge Cloud is the smart devices at the Edge and the analytics are in the fog but not counting the database revenue up in the Cloud. So it's everything surrounding the Cloud. And that, actually, if you look out five years, that's, maybe, 20% larger than the app and infrastructure telemetry but growing much, much faster. Then the third one where you were talking about was this a tail wind to the database. Micro server systems streaming are very different ways of building applications from what we do now. Now, people build their logic for the application and everyone then, stores their data in this centralized external database. In micro services, you build a little piece of the app and whatever data you need, you store within that little piece of the app. And so the database requirements are, rather, primitive. And so that piece will not drive a lot of database revenue. >> So if you could go back to the previous slide, Patrick. What's driving database growth in the out years? Why wouldn't database continue to get eaten away and decline? >> [George] In broad terms, the overall database market, it staying flat. Because as prices collapse but the data volumes go up. >> [Dave] But there's an assumption in here that the NoSQL space, actually, grows in the out years. What's driving that growth? >> [George] Both the NoSQL and the NewSQL. The NoSQL, probably, is best serving capturing the IoT data because you don't need lots of fancy query capabilities for concurrency. >> [Dave] So it is a tail wind in a sense in that-- >> [George] IoT but that's different. >> [Dave] Yeah sure but you've got the overall market growing. And that's because the new stuff, NewSQL and NoSQL is growing faster than the decline of the old stuff. And it's not in the 2020 to 2022 time frame. It's not enough to offset that decline. And then they have it start growing again. You're saying that's going to be driven by IoT and other Edge use cases? >> Yes, IoT Edge and the NewSQL, actually, is where when they mature, you start to substitute them for the traditional operational apps. For people who want to write database apps not who want to write micro service based apps. >> Okay, alright good. Thank you, George, for setting it up for us. Now, we're going to be at Big Data SV in mid March? Is that right? Middle of March. And George is going to be releasing the actual final forecast there. We do it every year. We use Spark Summit to look at our preliminary numbers, some of the Spark related forecasts like continuous work loads. And then we harden those forecasts going into Big Data SV. We publish our big data report like we've done for the past, five, six, seven years. So check us out at Big Data SV. We do that in conjunction with the Strada events. So we'll be there again this year at the Fairmont Hotel. We got a bunch of stuff going on all week there. Some really good programs going on. So check out siliconangle.tv for all that action. Check out Wikibon.com. Look for new research coming out. You're going to be publishing this quarter, correct? And of course, check out siliconangle.com for all the news. And, really, we appreciate everybody watching. George, been a pleasure co-hosting with you. As always, really enjoyable. >> Alright, thanks Dave. >> Alright, to that's a rap from Sparks. We're going to try to get out of here, hit the snow storm and work our way home. Thanks everybody for watching. A great job everyone here. Seth, Ava, Patrick and Alex. And thanks to our audience. This is the Cube. We're out, see you next time. (lively music)

>> live from Boston, Massachusetts. It's the Cube covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now here are your hosts. Day villain Day >> and stew Minimum. We're back. Welcome to Boston, Everybody. This is the IBM Chief Data Officer Summit. This is the Cube, the worldwide leader in live tech coverage. Inderpal. Bhandari is here. He's the newly appointed chief data officer at IBM. He's joined, but joined by Bob Picciano who is the senior vice president of IBM Analytics Group. Bob. Great to see again Inderpal. Welcome. Thank you. Thank you. So good event, Bob, Let's start with you. Um, you guys have been on the chief data officer kicked for several years now. You ahead of the curve. What, are you trying to achieve it? That this event? Yes. So, >> Dave, thanks again for having us here. And thanks for being here is well, tto help your audience share in what we're doing here. We've always appreciated that your commitment to help in the the masses understand all the important pulses that are going on the industry. What we're doing here is we're really moderating form between chief date officers on. We started this really on the curve. As you said 2014, where the conference was pretty small, there were some people who were actually examining the role, thinking about becoming a chief did officer. We probably had a few formal cheap date officers we're talking about, you know, maybe 100 or so people who are participating in the very 1st 1 Now you can see it's not, You know, it's it's grown much larger. We have hundreds of people, and we're doing it multiple times a year in multiple cities. But what we're really doing is bringing together a moderated form, Um, and it's a privilege to be able to do this. Uh, this is not about selling anything to anybody. This is about exchanging ideas, understanding. You know what, the challenges of the role of the opportunities which changing about the role, what's changing about the market and the landscape, what new risks might be on the horizon? What new opportunities might be on the horizon on we you know, we really liketo listen very closely to what's going on so we can, you know, maybe build better approach is to help their mother. That's through the services we provide or whether that's through the cloud capabilities were offering or whether that's new products and services that need to be developed. And so it gives us a great understanding. And we're really fortunate to have our chief data officer here, Interpol, who's doing a great job in IBM and in helping us on our mission around really becoming a cognitive enterprise and making analytics and insight on data really be central to that transformation. >> So, Dr Bhandari, new, uh, new to the chief date officer role, not nude. IBM. You worked here and came back. I was first exposed to roll maybe 45 years ago with the chief Data officer event. OK, so you come in is the chief data officer in December. Where do you start? >> So, you know, I've had the fortune of being in this role for a long time. I was one of the earliest created, the role for healthcare in two thousand six. Then I have honed that roll over three different Steve Data officer appointments at health care companies. And now I'm at IBM. So I do have, you know, I do view with the job as a craft. So it's a practitioner job and there's a craft to it. And do I answer your question? There are five things that you have to do to get moving on the job, and three of those have to be non sequentially and to must be done and powerful but everything else. So the five alarm. The first thing is you've got to develop a data strategy and data strategy is around, is focused around having an understanding ofthe how the company monetize is or plans to monetize itself. You know, what is the strategic monetization part of the company? Not so much how it monetize is data. But what is it trying to do? How is it going to make money in the future? So in the case of IBM, it's all around cognition. It's around enabling customers to become cognitive businesses. So my data strategy or our data strategy, I should say, is focused on enabling cognition becoming a cauldron of enterprise. You know, we've now realized that impacto prerequisite for cognition. So that's the data strategy piece. And that's the very first thing that needs to be done because once you understand that, then you understand what data is critical for the company, so you don't boil the ocean instead, what you do is you begin to govern exactly what's necessary and make sure it's fit for purpose. And then you can also create trusted data sources around those critical data assets that are critical for the for the monetization strategy of the company's. Those three have to go in sequence because if you don't know what you can do to adequately kind of three, and they're also significant pitfalls if you don't follow that sequence because you can end up pointing the ocean and the other two activities that must be done concurrently. One is in terms ofthe establishing deep partnerships with the other areas of the company the key business units, the key functional units because that's how you end up understanding what that data strategy ought to be. You know, if you don't have that knowledge of the company by making that effort that due diligence, that it's very difficult to get the data strategy right, so you've got to establish those partnerships and then the 5th 1 is because this is a space where you do require very significant talent. You have to start developing that talent and that all the organizational capability right from day one. >> So, Bob, you said that, uh, data is the new middle manager. You can't have an effective middle manager come unless you at least have some framework that was just described. >> Yeah, absolutely. So, you know, when Interpol talks about that fourth initiative about the engagement with the business units and making sure that we're in alignment on how the company's monetizing its value to its clients, his involvement with our team goes way beyond how he thinks about what date it is that we're collecting in the products that you're offering and what we might understand about our customers or about the marketplace. His involvement goes also into how we're curating the right user experience for who we want to win power with our products and offerings. Sometimes that's the role of the chief date officer. Sometimes that's the role of a data engineer. Sometimes it's the role of a data scientist. You mentioned data becoming the new middle management middle manager. We think the citizen analyst is ushering in that from from their seat, But we also need to be able to, from a perspective, to help them eliminate the long tail and and get transparency, the information. And sometimes it's the application developer. So we, uh, we collaborate on a very frequent basis, where, when we think about offering new capabilities to those roles, well, what's the data implication of that? What's the governance implication of that? How do we make it a seamless experience? So as people start to move down the path of igniting all of the innovation across those roles, there is a continuum to the information to using To be able to do that, how it's serving the enterprise, how it leads to that transformation to be a cognitive enterprise on DH. That's a very, very close collaboration >> we're moving from. You said you talked the process era to what I just inserted to an insight era. Yeah, um, and I have a question around that I'm not sure exactly how to formulate it, but maybe you can help. In the process, era technology was unknown. The process was very well, Don't know. Well known, but technology was mysterious. But with IBM and said help today it seems as though process is unknown. The technology's pretty known look at what uber airbnb you're doing the grabbing different technologies and putting them together. But the process is his new first of all, is that a reasonable observation? And if so, what does that mean for chief data officers? >> So the process is, you know, is new in the sense that in terms ofthe making it a cognitive process, it's going to end up being new, right? So the memorization that you >> never done it before, but it's never been done before, right >> in that sense. But it's different from process automation in the past. This is much more about knowledge, being able to scale knowledge, not just, you know, across one process, but across all the process cities that make up a company. And so in there. That goes also to the comment about data being the middle manager. I mean, if you've essentially got the ability to scale and manage knowledge, not just data but knowledge in terms of the insights that the people who are working these processes are coming up in conjunction with these data and intelligent capabilities, that that that that that of the hub right, it's the intelligence system that's had the Hubble this that's enabling all that so that That's really what leads Teo leads to the so called civilization >> way had dates to another >> important aspect of this is the process is dramatically different in the sense that it's ongoing. It's it's continuous, right, the process and your intimacy with uber and the trust that you're developing. A brand doesn't start and stop with one transaction and actually, you know branches into many different things. So your expectations, a CZ that relationships have all changed. So what they need to understand about you, what they need to protect about you, how they need to protect you in their transformation, the richness of their service needs to continue to evolve. So how they perform that task on the abundance of information they have available to perform that task. But the difficulty of being able to really consume it and make use of it is is a change. The other thing is, it's a lot more conversational, right? So the process isn't a deterministic set of steps that someone at a desk can really formulate in a business rule or a static process. It's conversationally changes. It needs to be dis ambiguity, and it needs to introduce new information during the process of disintegration. And that really, really calls upon the capabilities of a cognitive system that is rich and its ability to understand and interact with natural language to potentially introduce other sources of rich information. Because you might take a picture about what you're experiencing and all those things change that that notion from process to the conversational element. >> Dr. Bhandari, you've got an interesting role. Companies like IBM I think about the Theo with the CDO. Not only do you have your internal role, but you're also you know, a model for people going out there. You come too. Events like this. You're trying to help people in the role you've been a CDO. It's, um, health care organization to tell Yu know what's different about being kind of internal role of IBM. What kind of things? IBM Obviously, you know, strong technology culture, But tell us a little bit inside. You've learned what anything surprise you. You know, in your time that you've been doing it. >> Oh, you know, over the course ofthe time that I've been doing the roll across four different organizations, >> I guess specifically at IBM. But what's different there? >> You know, I mean IBM, for one thing, is a the The environment has tremendous scale. And if you're essentially talking about taking cognition to the enterprise, that gives us a tremendous A desperate to try out all the capabilities that were basically offering to our to our customers and to home that in the context of our own enterprise, you know, to build our own cognitive enterprise. And that's the journey that way, sharing with our with our customers and so forth. So that's that's different in in in in it. That wasn't the case in the previous previous rules that I had. And I think the other aspect that's different is the complexity of the organisation. This is a large global organization that wasn't true off the previous roles as well. They were Muchmore, not America century, you know, organizations. And so there's a There's an aspect there that also then that's complexity of the role in terms ofthe having to deal with different countries, different languages, different regulations, it just becomes much more complex. >> You first became a CDO in two thousand six, You said two thousand six, which was the same year as the Federal Rules of Civil Procedure came out and the emails became smoking guns. And then it was data viewed as a liability, and now it's completely viewed as an asset. But traditionally the CDO role was financial services and health care and government and highly regulated businesses. And it's clearly now seeping into new industries. What's driving that? Is that that value? >> Well, it is. I mean, it's, I think, that understanding that. You know, there's a tremendous natural resource in in the information in the data. But there is, you know, very much you know, union Yang around that notion of being responsible. I mean, one of the things that we're very proud of is the type of trust that we established over 105 year journey with our clients in the types of interactions we have with one another, the level of intimacy that we have in their business and very foundation away, that we serve them on. So we can never, ever do anything to compromise that you know. So the focus on really providing the ability to do the necessary governance and to do the necessary data providence and lineage in cyber security while not stifling innovation and being able to push into the next horizon. Interpol mentioned the fact that IBM, in and of itself, we think of ourselves as a laboratory, a laboratory for cognitive information innovation, a laboratory for design and innovation, which is so necessary in the digital era. And I think we've done a really good job in the spaces, but we're constantly pushing the envelope. A good example of that is blockchain, a technology that you know sometimes people think about and nefarious circumstances about, You know, what it meant to the ability to launch a Silk Road or something of that nature. We looked at the innovation understanding quite a lot about it being one of the core interview innovators around it, and saw great promise in being able to transform the way people thought about, you know, clearing multiparty transactions and applied it to our own IBM credit organization To think about a very transparent hyper ledger, we could bring those multiple parties together. People could have transparency and the transactions have a great deal of access into that space, and in a very, very rapid amount of time, we're able to take our very sizable IBM credit organization and implement that hyper ledger. Also, while thinking about the data regulation, the data government's implications. I think that's a really >> That's absolutely right. I mean, I think you know, Bob mentioned the example about the IBM credit organizer Asian, but there is. There are implications far beyond that. Their applications far beyond that in the data space. You know, it affords us now the opportunity to bring together identity management. You know, the profiles that people create from data of security aspects and essentially combined all of these aspects into what will then really become a trusted source ofthe data. You know, by trusted by me, I don't mean internally, but trusted by the consumers off the data. The subject's off the data because you'll be able to do that much in a way that's absolutely appropriate, not just fit for business purpose, but also very, very respectful of the consent on DH. Those aspects the privacy aspect ofthe data. So Blockchain really is a critical technology. >> Hype alleges a great example. We're IBM edge this week. >> You're gonna be a world of Watson. >> We will be a world Watson. We had the CEO of ever ledger on and they basically brought 1,000,000 diamonds and bringing transparency for the diamond industry. It's it's fraught with, with fraud and theft and counterfeiting and >> helping preserve integrity, the industry and eliminating the blood diamonds. And they right. >> It's fascinating to see how you know this bitcoin. You know, when so many people disparaged it is a currency, but not just the currency. You know, you guys IBM saw that early on and obviously participated in the open source. Be, You know, the old saying follow the money with us is like follow the data. So if I understand correctly, your job, a CDO is to sort of super charge of the business lines with the data strategy. And then, Bob, you're job is the line of business managers the supercharge your customers, businesses with the data strategy. Is that right? Is that the right value >> chain? I think you nailed it. Yeah, that's >> one of the things people are struggling with these days is, you know, if they can get their own data in house, then they've also gotta deal with third party. That industry did everything like that. IBM's role in that data chain is really interesting. You talked this morning about kind of the Weather Channel and kind of the data play there. Yeah, you know what? What's IBM is rolling. They're going forward. >> It's one of the most exciting things. I think about how we've evolved our strategy. And, you know, we're very fortunate to have Jimmy at the helm. Who really understands, You know, that transformational landscape on DH, how partnerships really change the ability to innovate for the companies we serve on? It was very obvious in understanding our client's problems that while they had a wealth of information that we were dealing with internally, there was great promise and being able to introduce these outside signals. If you will insights from other sources of data, Sometimes I call them vectors of information that could really transform the way they were thinking about solving their customer problem. So, you know, why wouldn't you ever want to understand that customers sentiment about your brand or about the product or service? And as a consequence to that, you know, capabilities that are there on Twitter or we chat or line are essential to that, depending on where your brand is operating in your branch, probably operating in a multinational space anyway, so you have to listen to all those signals and they're all in multiple language and sentiment is very, very bespoke. It's a different language, so you have to apply sophisticated machine learning. We've invented new algorithms to understand how to glean the signal at all that white noise. You use the weather example as well. You know, we think about the economic impact of climate atmosphere, whether on business and its profound. It's 1/2 trillion dollars, you know, in each calendar year that are, you know, lost information, lost assets, lost opportunity, misplaced inventory, you know, un delivered inventory. And we think we can do a better job of helping our clients take the weather excuses out of business in a variety of different industries. And so we've focused our initiatives on that information integration, governance, understanding new analytics toe to introduce those outside signals directly in the heart and want to place it on the desk of the chief data officer of those who are innovating around information and data. >> My my joke last Columbus. If they was Dell's buying DMC, IBM is buying the weather company. What does What does that say? My question is Interpol. When when Emma happens. And Bob, when you go out and purchase companies that are data driven, what role does the chief data officer play in both em in a pre and post. >> So, you know, I think the one that there being a cop, just gonna touch on a couple of points that Bob Major and I'll address your question directly as well. Uh, in terms of the role of the chief data officer, I think you're giving me that question before how that's he walled. The one very interesting thing that's happening now with what IBM is doing is previously the chief data officer. All at least with regard to the data, Not so much the strategy, but the data itself was internal focused. You know, you kind of worried about the data you had in house or the data you're bringing in now you've gotta worry as much about the exogenous status and because, you know, that's so That's one way that that role has changed considerably and is changing and evolving, and it's creating new opportunities for us. The other is again. In the past, the chief state officer all was around creating a warehouse for analytics and separated out from the operational processes. That's changing, too, because now we've got to transform these processes themselves. So that's, you know, that's that's another expanded role to come back to. Acquisitions emanate. I mean, I view that as essentially another process that, you know, company has. And so the chief data officer role is pretty key in terms of enabling that world in terms ofthe data, but also in terms ofthe giving, you know, guidance and advice. If, for instance, the acquisition isn't that problem itself, then you know, then we would be more closely involved. But if it's beyond that in terms of being able to get the right data, do that process as well as then once you've acquired the company in being able to integrate back the critical data assets those out of the key aspect, it's an ongoing role. >> So you've got the simplest level. You've got data sources and all the things associated with that. And then you've got your algorithms and your machine learning, and we're moving beyond sort of do tow cut costs into this new era. But so hot Oh cos adjudicate. And I guess you got to do both. You've got to get new data sources and you've got to improve this continuous process. By that you talked about how do you guide your customers as to where they put their resource? No. And that's >> really Davis. You have, you know, touching out again. That's really the benefit of this sort of a forum. In this sort of a conference, it's sharing the best practices of how the top experts in the world are really wrestling with that and identifying. I think you know Interpol's framework. What do you do sequentially to build the disciplines, to build a solid corn foundation, to make the connections that are lined with the business strategy? And then what do you do concurrently along that model to continue to operate? And how do you How do you manage and make sure your stakeholders understand what's being done? What they need to continue to do to evolve the innovation and come join us here and we'll go through that in detail. But, you know, he deposited a greatjob sharing his framers of success, and I think in the other room, other CEOs are doing that now. >> Yeah, I just wanted to quickly add to Bob's comment. The framework that I described right? It has a check and balance built into it because if you are all about governance, then the Sirio role becomes very defensive in nature. It's all about making sure you within the hour, you know, within the guard rails and so forth. But you're not really moving forward in a strategic way to help the company. And and that's why you know, setting it up by driving it from the strategy don't just makes it easier to strike that plus >> clerical and more about innovation here. We talked about the D and CDO today meaning data, but really, I think about it is being a great crucible for for disruption in information because you've disruption off. I called the Chief Disruption Office under Sheriff you >> incident in Data's digitalis data. So there's that piece of Ava's Well, we have to go. I don't want to go. So that way one last question for each of you. So Interpol, uh, thinking about and you just kind of just touched on it. He's not just playing defense, you know, thinking more offense this role. Where do you want to take it. What do your you know, sort of mid term, long term goals with this role? >> It's the specific role in IBM or just in general specifically. Well, I think in the case of I B M, we have the data strategy pretty well defined. Now it's all about being able to enable a cognitive enterprise. And so in, You know, in my mind and 2 to 3 years, we'll have completely established how that ought to be done, you know, as a prescription. And we'll also have our clients essentially sharing in that in that journey so that they can go off and create cognitive enterprises themselves. So that's pretty well set. You know, I have a pretty short window to three years to make that make that happen, And I think it's it's doable. And I think it will be, you know, just just a tremendous transformation. >> Well, we're excited to be to be watching and documenting that Bob, I have to ask you a world of washing coming up. New name for new conference. We're trying to get Pepper on, trying to get Jimmy on. Say, what should we expect? Maybe could. Although it was >> coming, and I think this year we're sort of blowing the roof off on literally were getting so big that we had to move the venue. It is very much still in its core that multiple practitioner, that multiple industry event that you experienced with insight, right? So whether or not you're thinking about this and the auspices of managing your traditional environments and what you need to do to bring them into the future and how you tie these things together, that's there for you. All those great industry tracks around the product agendas and what's coming out are are there. But the level of inspiration and involvement around this cognitive innovation space is going to be front and center. We're joined by Ginny Rometty herself, who's going to be very special. Key note. We have, I think, an unprecedented lineup of industry leaders who were going to come and talk about disruption and about disruption in the cognitive era on then. And as always, the most valuable thing is the journeys that our clients are partners sharing with us about how we're leading this inflection point transformation, the industry. So I'm very much excited to see their and I hope that your audience joins us as well. >> Great. We'll Interpol. Congratulations on the new roll. Thank you. Get a couple could plug, block post out of your comments today, so I really appreciate that, Bob. Always a pleasure. Thanks so much for having us here. Really? Appreciate. >> Thanks for having us. >> Alright. Keep right, everybody, this is the Cube will be back. This is the IBM Chief Data Officer Summit. We're live from Boston. You're back. My name is Dave Volante on DH. I'm along.