>>Okay, we're back with the hybrid Cloud power panel. I'm Dave Ante, and with me our Eric Lockard, who's the corporate vice president of Microsoft Azure Specialized Thomas Corn's, the senior vice president of products at Nutanix. And Indu Carey, who's the Senior Vice President of engineering, NCI and nnc two at Nutanix. Gentlemen, welcome to the cube. Thanks for coming on. >>It's to be >>Here. Have us, >>Eric, let's, let's start with you. We hear so much about cloud first. What's driving the need for hybrid cloud for organizations today? I mean, I not just ev put everything in the public cloud. >>Yeah, well, I mean the public cloud has a bunch of inherent advantages, right? I mean it's, it has effectively infinite capacity, the ability to, you know, innovate without a lot of upfront costs, you know, regions all over the world. So there is a, a trend towards public cloud, but you know, not everything can go to the cloud, especially right away. There's lots of reasons. Customers want to have assets on premise, you know, data gravity, sovereignty and so on. And so really hybrid is the way to achieve the best of both worlds, really to kind of leverage the assets and investments that customers have on premise, but also take advantage of, of the cloud for bursting or regionality or expansion, especially coming outta the pandemic. We saw a lot of this from work from home and, and video conferencing and so on, driving a lot of cloud adoption. So hybrid is really the way that we see customers achieving the best of both worlds. >>Yeah, it makes sense. I wanna, Thomas, if you could talk a little bit, I don't wanna inundate people with the acronyms, but, but the Nutanix cloud clusters on Azure, what is that? What problems does it solve? Give us some color there please. >>Yeah, there, so, you know, cloud clusters on Azure, which we actually call NC two to make it simple and SONC two on Azure is really our solutions for hybrid cloud, right? And you about hybrid cloud, highly desirable customers want it. They, they know this is the right way to do it for them, given that they wanna have workloads on premises at the edge, any public clouds, but it's complicated. It's hard to do, right? And the first thing that you did with just silos, right? You have different infrastructure that you have to go and deal with. You have different teams, different technologies, different areas of expertise and dealing with different portals, networkings get complicated, security gets complicated. And so you heard me say this already, you know, hybrid can be complex. And so what we've done, we then c to Azure is we make that simple, right? We allow teams to go and basically have a solution that allows you to go and take any application running on premises and move it as is to any Azure region where Ncq is available. Once it's running there, you keep the same operating model, right? And that's, so that's actually super valuable to actually go and do this in a simple fashion, do it faster, and basically do hybrid in a more cost effective fashion, know for all your applications. And that's really what's really special about NC two Azure today. >>So Thomas, just a quick follow up on that. So you're, you're, if I understand you correctly, it's an identical experience. Did I get that right? >>This is, this is the key for us, right? Is when you think you're sending on premises, you are used to way of doing things of how you run your applications, how you operate, how you protect them. And what we do here is we extend the Nutanix operating model two workloads running in Azure using the same core stack that you're running on premises, right? So once you have a cluster deploying C to an Azure, it's gonna look like the same cluster that you might be running at the edge or in your own data center using the same tools you, using the same admin constructs to go protect the workloads, make them highly available, do disaster recovery or secure them. All of that becomes the same. But now you are in Azure, and this is what we've spent a lot of time working with Americanist teams on, is you actually have access now to all of those suites of Azure services in from those workloads. So now you get the best of both world, you know, and we bridge them together and you get seamless access of those services between what you get from Nutanix, what you get from Azure. >>Yeah. And as you alluded to, this is traditionally been non-trivial and people have been looking forward to this for, for quite some time. So Indu, I want to understand from an engineering perspective, your team had to work with the Microsoft team, and I'm sure there was this, this is not just a press releases or a PowerPoint, you had to do some some engineering work. So what specific engineering work did you guys do and what's unique about this relative to other solutions in the marketplace? >>So let me start with what's unique about this, and I think Thomas and Eric both did a really good job of describing that the best way to think about what we are delivering jointly with Microsoft is that it speeds of the journey to the public cloud. You know, one way to think about this is moving to the public cloud is sort of like remodeling your house. And when you start remodeling your house, you know, you find that you start with something and before you know it, you're trying to remodel the entire house. And that's a little bit like what journey to the public cloud sort of starts to look like when you start to refactor applications. Because it wasn't, most of the applications out there today weren't designed for the public cloud to begin with. NC two allows you to flip that on its head and say that take your application as is and then lift and shift it to the public cloud, at which point you start the refactor journey. >>And one of the things that you have done really well with the NC two on Azure is that NC two is not something that sits by Azure side. It's fully integrated into the Azure fabric, especially the software defined network and SDN piece. What that means is that, you know, you don't have to worry about connecting your NC two cluster to Azure to some sort of an net worth pipe. You have direct access to the Azure services from the same application that's now running on an NC two cluster. And that makes your refactoring journey so much easier. Your management plan looks the same, your high performance notes let the NVMe notes, they look the same. And really, I mean, other than the facts that you're doing something in the public cloud, all the nutanix's goodness that you're used to continue to receive that, there is a lot of secret sauce that we have had to develop as part of this journey. >>But if we had to pick one that really stands out, it is how do we take the complexity, the network complexity of a public cloud, in this case Azure, and make it as familiar to Nutanix's customers as the VPC construc, the virtual private cloud construc that allows them to really think of that on-prem networking and the public cloud networking in very similar terms. There's a lot more that's gone on behind the scenes. And by the way, I'll tell you a funny sort of anecdote. My dad used to say when I drew up that, you know, if you really want to grow up, you have to do two things. You have to like build a house and you have to marry your kid off to someone. And I would say our dad a third do a flow development with the public cloud provider of the partner. This has been just an absolute amazing journey with Eric and the Microsoft team, and you're very grateful for their >>Support. I, I need NC two for my house. I live in a house that was built in, it's 1687 and we connect all to new and it's, it is a bolt on, but, but, but, and so, but the secret sauce, I mean there's, there's a lot there, but is it a PAs layer? You didn't just wrap it in a container and shove it into the public cloud, You've done more than that. I'm inferring, >>You know, the, it's actually an infrastructure layer offering on top of fid. You can obviously run various types of platform services. So for example, down the road, if you have a containerized application, you'll actually be able to TA it from OnPrem and run it on C two. But the NC two offer itself, the NCAA offer itself is an infrastructure level offering. And the trick is that the storage that you're used to the high performance storage that you know, define tenants to begin with, the hypervisor that you're used to, the network constructs that you're used to light MI segmentation for security purposes, all of them are available to you on NC two in Azure, the same way that we're used to do on-prem. And furthermore, managing all of that through Prism, which is our management interface and management console also remains the same. That makes your security model easier, that makes your management challenge easier, that makes it much easier for an application person or the IT office to be able to report back to the board that they have started to execute on the cloud mandate and they've done that much faster than they'll be able to otherwise. >>Great. Thank you for helping us understand the plumbing. So now Thomas, maybe we can get to like the customers. What, what are you seeing, what are the use cases that are, that are gonna emerge for the solution? >>Yeah, I mean we've, you know, we've had a solution for a while, you know, this is now new on Azure's gonna extend the reach of the solution and get us closer to the type of use cases that are unique to Azure in terms of those solutions for analytics and so forth. But the kind of key use cases for us, the first one you know, talks about it is a migration. You know, we see customers on that cloud journey. They're looking to go and move applications wholesale from on premises to public cloud. You know, we make this very easy because in the end they take the same concept that are around the application and make them, we make them available Now in the Azure region, you can do this for any applications. There's no change to the application, no networking change. The same IP will work the same whether you're running on premises or in Azure. >>The app stays exactly the same, manage the same way, protected the same way. So that's a big one. And you know, the type of drivers point politically or maybe I wanna go do something different or I wanna go and shut down location on premises, I need to do that with a given timeline. I can now move first and then take care of optimizing the application to take advantage of all that Azure has to offer. So migration and doing that in a simple fashion, in a very fast manner is, is a key use case. Another one, and this is classic for leveraging public cloud force, which are doing on premises, is disaster recovery. And something that we refer to as elastic disaster recovery, being able to go and actually configure a secondary site to protect your on premises workloads. But I think that site sitting in Azure as a small site, just enough to hold the data that you're replicating and then use the fact that you cannot get access to resources on demand in Azure to scale out the environment, feed over workloads, run them with performance, potentially fill them back to on premises and then shrink back the environment in Azure to again, optimize cost and take advantage of elasticity that you get from public cloud models. >>And then the last one, building on top of that is just the fact that you cannot get bursting use cases and maybe running a large environment, typically desktop, you know, VDI environments that we see running on premises and I have, you know, a seasonal requirement to go and actually enable more workers to go get access the same solution. You could do this by sizing for the large burst capacity on premises wasting resources during the rest of the year. What we see customers do is optimize what they're running on premises and get access to resources on demand in Azure and basically move the workload and now basically get combined desktop running on premises desktops running on NC two on Azure, same desktop images, same management, same services, and do that as a burst use case during, say you're a retailer that has to go and take care of your holiday season. You know, great use case that we see over and over again for our customers, right? And pretty much complimenting the notion of, look, I wanna go to desktop as a service, but right now, now I don't want to refactor the entire application stack. I just won't be able to get access to resources on demand in the right place at the right time. >>Makes sense. I mean this is really all about supporting customers', digital transformations. We all talk about how that was accelerated during the pandemic and, but the cloud is a fundamental component of the digital transformations. And Eric, you, you guys have obviously made a commitment between Microsoft and and Nutanix to simplify hybrid cloud and that journey to the cloud. How should customers, you know, measure that? What does success look like? What's the ultimate vision here? >>Well, the ultimate vision is really twofold. I think the one is to, you know, first is really to ease a customer's journey to the cloud to allow them to take advantage of all the benefits to the cloud, but to do so without having to rewrite their applications or retrain their, their administrators and or, or to obviate their investment that they already have in platforms like, like Nutanix. And so the, the work that companies have done together here, you know, first and foremost is really to allow folks to come to the cloud in the way that they want to come to the cloud and take really the best of both worlds, right? Leverage, leverage their investment in the capabilities of the Nutanix platform, but do so in conjunction with the advantages and and capabilities of of Azure, you know. Second, it is really to extend some of the cloud capabilities down onto the on-premise infrastructure. And so with investments that we've done together with Azure arc for example, we're really extending the Azure control plane down onto on-premise Nutanix clusters and bringing the capabilities that that provides to the Nutanix customer as well as various Azure services like our data services and Azure SQL server. So it's really kind of coming at the problem from, from two directions. One is from kind of traditional on-prem up into the cloud, and then the second is kind of from the cloud leveraging the investment customers have in in on-premise hci. >>Got it. Thank you. Okay, last question. Maybe each of you could just give us one key takeaway for our audience today. Maybe we start with with with with Thomas and then Indu and then Eric you can bring us home. >>Sure. So the key takeaway is, you know, you takes cloud clusters on Azure is ngi, you know, this is something that we've had tremendous demand from our customers, both from the Microsoft side and the Nutanix side going, going back years literally, right? People have been wanting to go and see this, this is now live GA open for business and you know, we're ready to go and engage and ready to scale, right? This is our first step in a long journey in a very key partnership for us at Nutanix. >>Great Indu >>In our Dave. In a prior life about seven or eight, eight years ago, I was a part of a team that took a popular patch preparation software and moved it to the public cloud. And that was a journey that took us four years and probably several hundred million dollars. And if we had had NC two then it would've saved us half the money, but more importantly would've gotten there in one third the time. And that's really the value of this. >>Okay. Eric, bring us home please. >>Yeah, I'll just point out like this is not something that's just both on or something. We, we, we started yesterday. This is something the teams, both companies have been working on together for, for years really. And it's, it's a way of, of deeply integrating Nutanix into the Azure Cloud and with the ultimate goal of, of again, providing cloud capabilities to the Nutanix customer in a way that they can, you know, take advantage of the cloud and then compliment those applications over time with additional Azure services like storage, for example. So it really is a great on-ramp to the cloud for, for customers who have significant investments in, in Nutanix clusters on premise, >>Love the co-engineering and the ability to take advantage of those cloud native tools and capabilities, real customer value. Thanks gentlemen. Really appreciate your time. >>Thank >>You. Thank you. Thank you. >>Okay, keep it right there. You're watching. Accelerate hybrid cloud, that journey with Nutanix and Microsoft technology on the cube. You're leader in enterprise and emerging tech coverage >>Organizations are increasingly moving towards a hybrid cloud model that contains a mix of on premises public and private clouds. A recent study confirms 83% of businesses agree that hybrid multi-cloud is the ideal operating model. Despite its many benefits, deploying a hybrid cloud can be challenging, complex, slow and expensive require different skills and tool sets and separate siloed management interfaces. In fact, 87% of surveyed enterprises believe that multi-cloud success will require simplified management of mixed infrastructures >>With Nutanix and Microsoft. Your hybrid cloud gets the best of both worlds. The predictable costs, performance control and data sovereignty of a private cloud and the scalability, cloud services, ease of use and fractional economics of the public cloud. Whatever your use case, Nutanix cloud clusters simplifies IT. Operations is faster and lowers risk for migration projects, lowers cloud TCO and provides investment optimization and offers effortless, limitless scale and flexibility. Choose NC two to accelerate your business in the cloud and achieve true hybrid cloud success. Take a free self-guided 30 minute test drive of the solutions provisioning steps and use cases at nutanix.com/azure td. >>Okay, so we're just wrapping up accelerate hybrid cloud with Nutanix and Microsoft made possible by Nutanix where we just heard how Nutanix is partnering with cloud and software leader Microsoft to enable customers to execute on a true hybrid cloud vision with actionable solutions. We pushed and got the answer that with NC two on Azure, you get the same stack, the same performance, the same networking, the same automation, the same workflows across on-prem and Azure Estates. Realizing the goal of simplifying and extending on-prem workloads to any Azure region to move apps without complicated refactoring and to be able to tap the full complement of native services that are available on Azure. Remember, all these videos are available on demand@thecube.net and you can check out silicon angle.com for all the news related to this announcement and all things enterprise tech. Please go to nutanix.com as of course information about this announcement and the partnership, but there's also a ton of resources to better understand the Nutanix product portfolio. There are white papers, videos, and other valuable content, so check that out. This is Dave Ante for Lisa Martin with the Cube, your leader in enterprise and emerging tech coverage. Thanks for watching the program and we'll see you next time.

>>Okay, we're back with the hybrid Cloud power panel. I'm Dave Ante and with me our Eric Lockhart, who's the corporate vice president of Microsoft Azure, Specialized Thomas Corny, the senior vice president of products at Nutanix, and Indu Care, who's the Senior Vice President of engineering, NCI and nnc two at Nutanix. Gentlemen, welcome to the cube. Thanks for coming on. >>It's to >>Be here. Have us, >>Eric, let's, let's start with you. We hear so much about cloud first. What's driving the need for hybrid cloud for organizations today? I mean, I wanna just ev put everything in the public cloud. >>Yeah, well, I mean, the public cloud has a bunch of inherent advantages, right? I mean, it's, it has effectively infinite capacity, the ability to, you know, innovate without a lot of upfront costs, you know, regions all over the world. So there is a, a trend towards public cloud, but you know, not everything can go to the cloud, especially right away. There's lots of reasons. Customers want to have assets on premise, you know, data gravity, sovereignty and so on. And so really hybrid is the way to achieve the best of both worlds, really to kind of leverage the assets and investments that customers have on premise, but also take advantage of, of the cloud for bursting or regionality or expansion, especially coming outta the pandemic. We saw a lot of this from work from home and, and video conferencing and so on, driving a lot of cloud adoption. So hybrid is really the way that we see customers achieving the best of both worlds. >>Yeah, makes sense. I wanna, Thomas, if you could talk a little bit, I don't wanna inundate people with the acronyms, but, but the Nutanix Cloud clusters on Azure, what is that? What problems does it solve? Give us some color there, please. >>That is, so, you know, cloud clusters on Azure, which we actually call NC two to make it simple. And so NC two on Azure is really our solutions for hybrid cloud, right? And you think about the hybrid cloud, highly desirable customers want it. They, they know this is the right way to do for them, given that they wanna have workloads on premises at the edge, any public clouds. But it's complicated. It's hard to do, right? And the first thing that you deal with is just silos, right? You have different infrastructure that you have to go and deal with. You have different teams, different technologies, different areas of expertise and dealing with different portals. Networkings get complicated, security gets complicated. And so you heard me say this already, you know, hybrid can be complex. And so what we've done, we then c to Azure is we make that simple, right? We allow teams to go and basically have a solution that allows you to go and take any application running on premises and move it as is to any Azure region where ncq is available. Once it's running there, you keep the same operating model, right? And that's something actually super valuable to actually go and do this in a simple fashion, do it faster, and basically do, do hybrid in a more cost effective fashion, know for all your applications. And that's really what's really special about NC Azure today. >>So Thomas, just a quick follow up on that. So you're, you're, if I understand you correctly, it's an identical experience. Did I get that right? >>This is, this is the key for us, right? Is when you think you're sending on premises, you are used to way of doing things of how you run your applications, how you operate, how you protect them. And what we do here is we extend the Nutanix operating model two workloads running in Azure using the same core stack that you're running on premises, right? So once you have a cluster deploying C to an Azure, it's gonna look like the same cluster that you might be running at the edge or in your own data center, using the same tools, using, using the same admin constructs to go protect the workloads, make them highly available with disaster recovery or secure them. All of that becomes the same, but now you are in Azure, and this is what we've spent a lot of time working with Americanist teams on, is you actually have access now to all of those suites of Azure services in from those workloads. So now you get the best of both world, you know, and we bridge them together and you get seamless access of those services between what you get from Nutanix, what you get from Azure. >>Yeah. And as you alluded to, this is traditionally been non-trivial and people have been looking forward to this for, for quite some time. So Indu, I want to understand from an engineering perspective, your team had to work with the Microsoft team, and I'm sure there was this, this is not just a press releases or a PowerPoint, you had to do some some engineering work. So what specific engineering work did you guys do and what's unique about this relative to other solutions in the marketplace? >>So let me start with what's unique about this, and I think Thomas and Eric both did a really good job of describing that the best way to think about what we are delivering jointly with Microsoft is that it speeds up the journey to the public cloud. You know, one way to think about this is moving to the public cloud is sort of like remodeling your house. And when you start remodeling your house, you know, you find that you start with something and before you know it, you're trying to remodel the entire house. And that's a little bit like what journey to the public cloud sort of starts to look like when you start to refactor applications. Because it wasn't, most of the applications out there today weren't designed for the public cloud to begin with. NC two allows you to flip that on its head and say that take your application as is and then lift and shift it to the public cloud, at which point you start the refactor journey. >>And one of the things that you have done really well with the NC two on Azure is that NC two is not something that sits by Azure side. It's fully integrated into the Azure fabric, especially the software defined network and SDN piece. What that means is that, you know, you don't have to worry about connecting your NC two cluster to Azure to some sort of a net worth pipe. You have direct access to the Azure services from the same application that's now running on an C2 cluster. And that makes your refactoring journey so much easier. Your management claim looks the same, your high performance notes let the NVMe notes, they look the same. And really, I mean, other than the facts that you're doing something in the public cloud, all the Nutanix goodness that you're used to continue to receive that, there is a lot of secret sauce that we have had to develop as part of this journey. >>But if we had to pick one that really stands out, it is how do we take the complexity, the network complexity, offer public cloud, in this case Azure, and make it as familiar to Nutanix's customers as the VPC construc, the virtual private cloud construct that allows them to really think of their on-prem networking and the public cloud networking in very similar terms. There's a lot more that's gone on behind the scenes. And by the way, I'll tell you a funny sort of anecdote. My dad used to say when I drew up that, you know, if you really want to grow up, you have to do two things. You have to like build a house and you have to marry your kid off to someone. And I would say our dad a third do a code development with the public cloud provider of the partner. This has been just an absolute amazing journey with Eric and the Microsoft team, and you're very grateful for their support. >>I need NC two for my house. I live in a house that was built and it's 1687 and we connect old to new and it's, it is a bolt on, but, but, but, and so, but the secret sauce, I mean there's, there's a lot there, but is it a PAs layer? You didn't just wrap it in a container and shove it into the public cloud, You've done more than that. I'm inferring, >>You know, the, it's actually an infrastructure layer offering on top of fid. You can obviously run various types of platform services. So for example, down the road, if you have a containerized application, you'll actually be able to tat it from OnPrem and run it on C two. But the NC two offer itself, the NCAA often itself is an infrastructure level offering. And the trick is that the storage that you're used to the high performance storage that you know, define Nutanix to begin with, the hypervisor that you're used to, the network constructs that you're used to light MI segmentation for security purposes, all of them are available to you on NC two in Azure, the same way that we're used to do on-prem. And furthermore, managing all of that through Prism, which is our management interface and management console also remains the same. That makes your security model easier, that makes your management challenge easier, that makes it much easier for an accusation person or the IT office to be able to report back to the board that they have started to execute on the cloud mandate and they have done that much faster than they'll be able to otherwise. >>Great. Thank you for helping us understand the plumbing. So now Thomas, maybe we can get to like the customers. What, what are you seeing, what are the use cases that are, that are gonna emerge for this solution? >>Yeah, I mean we've, you know, we've had a solution for a while and you know, this is now new on Azure is gonna extend the reach of the solution and get us closer to the type of use cases that are unique to Azure in terms of those solutions for analytics and so forth. But the kind of key use cases for us, the first one you know, talks about it is a migration. You know, we see customers on the cloud journey, they're looking to go and move applications wholesale from on premises to public cloud. You know, we make this very easy because in the end they take the same culture that are around the application and make them, we make them available Now in the Azure region, you can do this for any applications. There's no change to the application, no networking change. The same IP will work the same whether you're running on premises or in Azure. >>The app stays exactly the same, manage the same way, protected the same way. So that's a big one. And you know, the type of drivers point to politically or maybe I wanna go do something different or I wanna go and shut down education on premises, I need to do that with a given timeline. I can now move first and then take care of optimizing the application to take advantage of all that Azure has to offer. So migration and doing that in a simple fashion, in a very fast manner is, is a key use case. Another one, and this is classic for leveraging public cloud force, which are doing on premises IT disaster recovery and something that we refer to as elastic disaster recovery, being able to go and actually configure a secondary site to protect your on premises workloads, but I that site sitting in Azure as a small site, just enough to hold the data that you're replicating and then use the fact that you cannot get access to resources on demand in Azure to scale out the environment, feed over workloads, run them with performance, potentially feed them back to on premises and then shrink back the environment in Azure to again, optimize cost and take advantage of elasticity that you get from public cloud models. >>Then the last one, building on top of that is just the fact that you cannot get boosting use cases and maybe running a large environment, typically desktop, you know, VDI environments that we see running on premises and I have, you know, a seasonal requirement to go and actually enable more workers to go get access the same solution. You could do this by sizing for the large burst capacity on premises wasting resources during the rest of the year. What we see customers do is optimize what they're running on premises and get access to resources on demand in Azure and basically move the workload and now basically get combined desktops running on premises desktops running on NC two on Azure, same desktop images, same management, same services, and do that as a burst use case during, say you're a retailer that has to go and take care of your holiday season. You know, great use case that we see over and over again for our customers, right? And pretty much complimenting the notion of, look, I wanna go to desktop as a service, but right now I don't want to refactor the entire application stack. I just wanna be able to get access to resources on demand in the right place at the right time. >>Makes sense. I mean this is really all about supporting customers', digital transformations. We all talk about how that was accelerated during the pandemic and, but the cloud is a fundamental component of the digital transformation generic. You, you guys have obviously made a commitment between Microsoft and and Nutanix to simplify hybrid cloud and that journey to the cloud. How should customers, you know, measure that? What does success look like? What's the ultimate vision here? >>Well, the ultimate vision is really twofold. I think the one is to, you know, first is really to ease a customer's journey to the cloud to allow them to take advantage of all the benefits to the cloud, but to do so without having to rewrite their applications or retrain their, their administrators and or or to obviate their investment that they already have and platforms like, like Nutanix. And so the, the work that companies have done together here, you know, first and foremost is really to allow folks to come to the cloud in the way that they want to come to the cloud and take really the best of both worlds, right? Leverage, leverage their investment in the capabilities of the Nutanix platform, but do so in conjunction with the advantages and and capabilities of, of Azure. You know, Second is really to extend some of the cloud capabilities down onto the on-premise infrastructure. And so with investments that we've done together with Azure arc for example, we're really extending the Azure control plane down onto on premise Nutanix clusters and bringing the capabilities that that provides to the, the Nutanix customer as well as various Azure services like our data services and Azure SQL server. So it's really kind of coming at the problem from, from two directions. One is from kind of traditional on-premise up into the cloud and then the second is kind of from the cloud leveraging the investment customers have in in on-premise hci. >>Got it. Thank you. Okay, last question. Maybe each of you can just give us one key takeaway for our audience today. Maybe we start with with with with Thomas and then Indu and then Eric you can bring us home. >>Sure. So the key takeaway is, you know, Nutanix Cloud clusters on Azure is now ga you know, this is something that we've had tremendous demand from our customers, both from the Microsoft side and the Nutanix side going, going back years literally, right? People have been wanting to go and see this, this is now live GA open for business and you know, we're ready to go and engage and ready to scale, right? This is our first step in a long journey in a very key partnership for us at Nutanix. >>Great Indu >>In our Dave. In a prior life about seven or eight, eight years ago, I was a part of a team that took a popular cat's preparation software and moved it to the public cloud. And that was a journey that took us four years and probably several hundred million. And if we had had NC two then it would've saved us half the money, but more importantly would've gotten there in one third the time. And that's really the value of this. >>Okay. Eric, bring us home please. >>Yeah, I'll just point out like this is not something that's just both on or something. We, we, we started yesterday. This is something the teams, both companies have been working on together for, for years, really. And it's, it's a way of, of deeply integrating Nutanix into the Azure Cloud and with the ultimate goal of, of again, providing cloud capabilities to the Nutanix customer in a way that they can, you know, take advantage of the cloud and then compliment those applications over time with additional Azure services like storage, for example. So it really is a great on-ramp to the cloud for, for customers who have significant investments in, in Nutanix clusters on premise, >>Love the co-engineering and the ability to take advantage of those cloud native tools and capabilities, real customer value. Thanks gentlemen. Really appreciate your time. >>Thank >>You. Thank you. >>Okay. Keep it right there. You're watching Accelerate Hybrid Cloud, that journey with Nutanix and Microsoft technology on the cube. You're a leader in enterprise and emerging tech coverage.

>> From the heart of Silicon Valley, it's theCUBE! Covering Comcast Innovation Day. Brought to you by Comcast. >> Hey welcome back, get ready, Jeff Frick here with theCUBE, we're at the Comcast Silicon Valley Innovation Center, here in Sunnyvale, California. They had a really cool thing today, it was a customer experience day, brought a bunch of Comcast executives and a bunch of thought leaders in the customer experience base. We're excited to come down and sit in and talk to some of the guests, and really excited about our next guest, 'cause he's an anthropologist, he's Brian Solis, digital analyst, author, analyst, anthropologist, futurist, Brian, you've got it all going on, thanks for taking a few minutes of your day. >> Course, this is a really great conversation, so, I'm happy to be here. >> So first off, just kind of impressions of the conversation earlier today, talking about customer experience, the expectation, consumerization of IT is something we talk a lot about, where people's expectations of the way this stuff is supposed to work, change, all the time, and what was magical and almost impossible, like talking on a cell phone in your car, suddenly becomes expected and the norm, so how do you think of this, as you look at these big, sweeping changes that we're going through? >> Well today's conversation I think has been sort of, a spotlight on what's most important, which is innovation not for the sake of innovation, but innovation for the sake of pushing the customer experience forward, changing customer behaviors in a way that's going to create a new standard for experiences, and that way you become the leader in engagement. Everybody else has to catch up to you, and what was so important is that we're here at a company with all the love that wasn't the best in customer experience several years ago, and now they're sort of one of the pioneers in what customer experience needs to be, from a technological standpoint, a customer service standpoint, and an overall experience standpoint, right? >> I want to jump into the voice capability specifically, because I don't think there's really enough accolades as to what Comcast has achieved with the voice remote, I think if you don't have it you don't know it's there, and the ability to migrate across hundreds or thousands of channels, multiple services, to find the show that you want with just the ask of your voice is amazing. What's even more amazing is trying to teach people to actually navigate that way, so changing people's behavior in the way they interact with devices is not a simple thing. >> So, it's come up, and it's an expression shared in many UI and UX circles, which is the best interface is no interface, and in many ways, voice was the next frontier, that's a frontier that was pioneered, I think at a mass level by Amazon and Alexa, Apple and Siri, Google and Ok Google, we're really starting to see that voice as a UI is much more natural, what makes it so complex is all of the back end, I think Comcast has done a really nice job in the simplistic linguistic engagement of saying the name of a TV show or a genre of shows or movies, and then the back end to be reimagined in order to bring you something that's not just this long list of stuff, that is much more intuitive and helps you get to what they call time to joy, much faster. That's game changing, right, but that isn't just something that Comcast looked, for example, to just Alexa, or anything specifically, it looked, and also, especially not to other cable companies. They looked to the best-in-class experiences in every area, to pick those parts and build something altogether new that becomes the new standard, and I think voice, one of the things that you and I were talking about, Jeff, earlier, was kids, there was a time when they would walk up to a screen and they still do to some regard, where they want to do this, but I have a three year old at home who has a toy remote control, and I had to record video from afar of just watching her talk into her toy remote, "Mickey Mouse Club, Mickey Mouse Club," and just sitting there, with all the patience in the world, nothing was happening but expecting that something was going to happen. And it's just a new standard. The other thing, though, is that we're not done, we now live in an era of AI, machine learning, automation, so personalization now is really going to start to build upon voice experiences where it's just simply turning on the TV is going to give you instant options of all of the things you're most likely going to want to watch all on one nav. >> Right, it's just, we say that and yet we still have qwerty keyboards, right, which were specifically designed to slow people down and yet now we're not using arm typewriters anymore, and we still have qwerty keyboards, so changing people's behavior is not easy, and it's interesting to see kind of these generational shifts based on the devices in which they grew up using, kind of define the way in which they expect everything else to work. But it's, I still get the email, maybe, or even, they talked about here at Comcast, where instead of just saying NCAA Football, it knows I like to watch Stanford football, it suggests, maybe you should just say Stanford football, so there's still kind of a lot of education, surprising amount of education that has to happen. >> Yes and no, if you think about the conversation, I often talk about it in terms of iteration and innovation, iteration is doing the same things better, innovation is creating new value, and if you look at the evolution of the remote control, I mean just go back 50 years, it has gotten progressively worse over time, in fact on average, today's remote control has 70 buttons on it, and if you think about iteration in that regard, we've completely started to fail in the user interface, I don't know that anybody has mastered their relationship with the remote control except for some geeks, so I think if anything, voice is going to change the game for the better. >> Yeah, I was in the business for a long time, and now I know what killed the VCR, right, was the flashing 12, nobody could ever get their flashing 12, and for all the young people, look it up on the internet, you'll figure out what a VCR and a flashing 12 is. So you talk about something called Generation C, what is Generation C, why should we be paying attention? >> Look, I think voice is a good example of Generation C, so anybody who uses, you mentioned qwerty, right, I don't know that I've actually even used qwerty in a sentence in a really long time, but I'm old enough to, I trained on a manual typewriter back in the day, so it doesn't mean that I don't get it, it means that my behaviors and my expectations as a human being have changed, because of my relationship, my personal relationship, so for example, in consumerization of technology and IT, my personal relationship has changed with technology, and so what I had found in my research over the years was especially when it comes to customer experience, if you study a customer journey, and you look at demographics of these personas that we've created, you can see specifically that people who live a mobile-first lifestyle, regardless of age, will make decisions the same way, they're increasingly impatient, they're demanding, they're self-centered, I call 'em accidental narcissists, they, time, convenience are really important, they want personalization, their standards are much different than the personas that we've developed in the past, and so I gave it a name, which is Generation C, because it wasn't one, where C stood for connected, it wasn't one bound by age, or traditional demographics, education, income, it was defined by shared interests, behaviors, and shared outcomes, and it was a game changer for all things, if you're going to point innovation or customer experience or whatever it is, and you're going to aim at that growing customer segment, then they're going to have a different set of needs than your traditional customer, right? >> But it's so bizarre, again, how quickly the novel becomes expected baseline, and how the great search algorithm that we get out of Google, which is based on lots and lots and lots and lots of data, and a bunch of smart people and a whole bunch of hardware and software, suddenly now we expect that same search result if we're searching on, pick some random retailer or some other random website, when in fact, that is special, but we have this crazy sliding scale of what's expected and how can companies stay out in front of that, at least chase close behind, 'cause it's a very different world in how fast the expectations change. >> I'm sorry, I totally spaced out 'cause my attention span went away. I'm just kidding, I'm kidding. >> Well I didn't even get to the attention economy question yet. >> It's, you're competing at a much different level today, and I think that's what so disruptive for companies, is that they're still thinking that momentum and progress and experience and performance and success, I have to say that success is the worse teacher when it comes to innovation because you're basing your decisions on the future based on things that you did in the past. So what do companies need to get, is that the customers change, I'll give you an example. I think in many ways, companies compete against Uber, right, because Uber has changed the game for what it takes to get a service brought to you, and to give it to you and take you where you need to go, where time and convenience are big factors of that. So for example, one of the things I studied was how long is too long to wait for an Uber before you open Lyft in certain markets, and the reason that I wanted to do that was I wanted to show that the number went down every single year. Now, for example, Uber will advertise in Sydney that the average pickup time is three minutes and 39 seconds, because it knows it adds a competitive advantage over everybody else, because it's important, because once that experience happens to you and you get something your way fast, you're not going to suddenly realize, when you're at the Department of Motor Vehicles, that "Well, I understand that this isn't Uber, "and therefore I shouldn't expect "to have things done at a much more efficient "and personal manner." You take that mindset subconsciously to everything you do, so while it's a threat, it's also an opportunity, but you got to break that executive mindset to say, "How can we take "best-in-class experiences across the board, "and how can we apply it to what we do?" >> Yeah, again, an interesting concept in the conversation earlier today, where there was a question about ROI, and you threw it back as ROE, return on experience, so how should people start to adjust their thinking, because the thing on, return on investment implies almost a very small kind of direct impact, kind of one to one benefit, where really, return on experience implies a much broader, kind of accidental benefits, benefits across a lot of parameters that you may or may not necessarily be measuring, it's a very, a much better way to measure your investment. >> Look, it's almost impossible to get away from the ROI conversation, it's important, executives have to make decisions based on what they know the outcomes are going to be, a lot of this is, you don't know what you don't know, and so if you can tie some types of rudimentary metrics that are going to show progress and also return, it helps, but at the same time, I always say, what happens in the ROI equation if I equals ignorance, what's the return of ignorance? What's the return of not doing something, and so what I tried to demonstrate in a book I wrote about experience design, which was called X, it was, let's break it down to what we're actually trying to do, the word experience actually means an emotional reaction to a moment, and so for example, in a high sales pitch situation like a dealership for an automobile, that's not a good experience. If you have to call customer service, you've probably not had a good experience, and all of those things are emotional, so if you can design for emotional outcomes, where people are going to feel great in the moment and feel great afterwards, that is a metric that you can have a before and after state. The likelihood of attaching that emotion to things like loyalty, customer lifetime value, growth, then you can get to your ROI in a different way, but you have to first do it with intention. >> Yeah, Brian, fascinating conversation, we could go all day, but unfortunately, we're going to have to leave it there, but thanks for joining today, and thanks for spending a few minutes with us. >> Thank you, thank you, it was a pleasure. >> Absolutely, he's Brian, I'm Jeff, you're watching theCUBE, we're at the Comcast Innovation Center in Sunnyvale, thanks for watching, we'll see you next time. (techno music)

>> Live from San Francisco, it's theCUBE. Covering Google Cloud Next 19. Brought to you by Google Cloud and its eco system partners. >> Hello everyone welcome back here to theCUBE live coverage here in San Francisco, California. We're in the Moscone Center on the ground floor here. Day three of three days of coverage for Google Cloud Next 2019. I'm John Furrier, my co-host, Dave Vellante, Stew Miniman out there getting stories out there He's also been hosting. Dave, great to see you! Evren, Director of Product Management at Google Cloud, doing all the data streaming the data. We're streaming data right now. >> Absolutely, this is it. This is it. >> So let's stream some data. So streaming data has certainly been around for awhile. Dave and I when we first started theCUBE ten years ago, it was part of Silk and Angle Media hadoop was just a small little project. That really kind of was the catalyst moment for around big data that's now evolved to it's own position. Now you have streaming data, you have cloud scale, the Cloud has really changed the game on big data. Changed the nature and dynamics of it and one of the things is streaming data, streaming analytics as a core value proposition for enterprises, and this is fairly new. >> Very true. >> What's your take on it and how does it relate to what's going on with Google Cloud? >> I am glad we're talking about that. This is an exciting time for us. Streaming like you said is growing. Batch is not going away, but streaming is actually overtaking a lot of the applications that we're seeing. Today we're seeing more streaming applications taking place than batch. One of the things that we're seeing is everybody is gathering data from all over the place from your websites, from your mobile phones, from your IoT devices, just like we're doing right now. There's data coming in and people want to make decisions real time whether it's in the banking industry, in your healthcare, retail, it doesn't matter which word cycle you're working with and we're seeing how those messages how those events are coming in and where the decisions are being made real time, milliseconds we're talking about. >> Why is it happening, what's the real catalyst here? Just tsunami of data, nature of the value, all of the above, what's the? >> We believe one of the things is like you mentioned Cloud really changed the game. Where people actually can reach globally data and messages at scale. We're talking about billions of messages coming in and processing capacity is available now we can actually process it and make a decision within milliseconds and get to the results. To me, that was the biggest catalyst. And we're seeing many of us have grown up using batch data, making decisions now everybody is talking about M.L. and A.I. You need that data coming in real time and we can actual process it and make the decision. To me, that's the catalyst. >> First of all we love streaming data, this topic. One we believe streaming where shooting video but data, real time, has been one of the keys you see self driving cars monging of data, mixing and matching of data to get better signal and better machine learning and I got to ask you, because batch is certainly the role for batch is kind of old school it's some old techniques it's been around for awhile, >> It's not going to go away though. >> It's not going to go away it's established it's place but the knee jerk reaction of existing old school people who haven't migrated to the new modern version they go to the batch kind of mind set. I want to get you're reaction. Data lakes, there's nothing flowing in a lake. Okay, so there is a role for a data lake streaming gives me the impression of like an ocean or a river or something moving fast. Talk about the differences because it's not just the data lake okay that's a batch kind of reaction. >> It is a complementary. Actually it's not going away because all of that data that we had in the back is something we're relying on to really augment and see what's changing. So if you're in a retail house you're buying something, you're going to make a decision and your support is actually behind it. OK here's Evren, he's actually shopping around this and he wants this for his son. That's what the models built around it is looking at what is my behavior and in the moment making a decision for me. So that's not going away. The other thing is batch users are able to take advantage of the technology today. If you look at our data flow, same set of codes, same set of capability can be used by the same folks that are used to batch. You don't have to change anything so that actually we help folks to be up skilled using the same set of tools and become much more experienced and experts in the streaming too. That's not going away we help both of the worlds. >> So, complementary. >> Very complementary. >> So data lakes are good for kind of setting the table if you have to store it somewhere but that's not the end game though. >> No. >> Okay. >> I wonder if we could talk about the evolution from batch to real time streaming. And my favorite example, because I think people can relate to it, is fraud detection. Ten years ago, it was up to the user to go through his or her bill, right? And then you started to get inundated with false positives, and now lately, last couple of years it's getting better and better. Fewer false positives, usually when you usually no news is good news. News is usually bad news now, so take that example and use that to describe how things have evolved. >> I am a student of AI I did my Master's and PhD in that and I went through that change in my career because we had to collect the data, batch it and analyze it, and actually make a decision about it and we had a lot of false positives and in some cases some negative misses too which you don't want that either. And what happened is our modeling capabilities became much better. With this rich data, and you actually tap into that data lake, you can go in there the data is there, and this is spread data we can pull in data from different sources and actually remove the outliers and make our decision real time right there. We didn't have the processing capability we didn't have a place like PostUp where globic can scan and bring in data at hundreds of gigabytes of data. That's messaging you want to deal with at scale no matter where it is and process that, that wasn't available for us. Now it's available it's like a candy shelf for technologists, all the technology is in our hands and we wanted all these things. >> You were talking about I think the simplicity of, I'm able to use my batch processes and apply them. One of the complaints I hear from developers sometimes is that the data pipeline is getting so complicated. You were talking about you're grabbing stuff from websites, from financial databases, and so depending on what data store you're using and what streaming tools you're using or other A.I. tools, the pipeline gets very complicated the A.P.Is start to get complicated but I'm hearing a story of simplicity. Can you elaborate on that and add some color? >> Yeah I'm glad you're asking that question you may have heard, yesterday we announced a whole bunch of new things and ease of use is the top of the line for us. Really are trying to make it easy. If you look at this eco pipeline we're building with data flow, it helps you end to end. Data engineered no matter which angle their coming in should be able to use their known skill sets and be able to build their pipelines end to end so that you can achieve your goals around streaming. We aren't really having to go through a lot of the clusters of the pipelines we are going to continue to push that ease of use over and over, we're not going to let it go because make it easier, everyone will adapt it faster. >> You mentioned you got a PhD in A.I., Master's in A.I., A.I. has been around for awhile. A lot of people have been saying that but machine learning certainly has changed the game. Machine learning plus cloud has been a real accelerant in the academic and now commercial aspects of A.I. So I want to get your thoughts on the notion of scale which you talk about, plus the diversity of data. So if you can bring in data at scale get more signaling points more access to data signaling the diversity of data becomes very key. But cleanliness, data cleaning, used to be an old practice of you get a bunch of data, stack it up, put it in a pile corpus, and you kind of go clean it. With streaming, if it's always flowing there's kind of a behavioral characteristic of data cleanliness, data monitoring, talk about that diversity of data clean data and how that feeds machine learning and makes better A.I. >> Good one, so that's where we actually are able to, if you look at PostUp, you're building joint your table set of datas with streaming set of datas you can actually put it into data filter it and make those analyses. And within both, we provide enough of a window for you to be able to go back, hey are there things that I should be looking at, up to seven days we can provide a snapshot because you will always find something you can go back, you know what I'm going to remove this outlier. All worrying about all the processing we do before we bring in the data so there's a lot of cleanliness that takes place but we have the built in tools we have the built in capabilities for everyone to get going. It's ready to scale for you from the moment you open it up. That's the beauty of it, that's the beauty of when you start from PostUp to data flow to streaming engine it's ready for you to run. >> Talk about what's changed though when people hear diversity of data they get scared, oh my god I work, heavy lifting. Now it's a benefit. What's easier now to deal with all of these diverse data sets, what's the easy revolution? >> So do you remember the big V's of big data right? Volume, velocity, variety. People were scared about the variety. Now I can actually bring in my data from different places. Again, let's go back to the shopping example. Where I shop, what I shop for, that actually defines my behavior around it. Those data sit somewhere else. We bring those in to make a decision about okay everyone wants to go buy a scooter or whatever else, that's the diversity of the data. We're now able to deal to with this at scale. That was not available we could actually bring in and render this, now everything is going to do this much more sequential. We're now able to bring all of them together process it at the same time and make the decision. >> What's the key products that will make all of those happen, take us through the portfolio if I want that would you just said which is a great value. It sounds like not a heavy lift all I have to do is point the data sources into this engine, what are the products that make up that capability? >> So if I look at the overall portfolio on Google Cloud from our data analysts point of view, so you actually can bring in your data through PostUp, lots of messaging capability globally and you can actually do it regionally because we have a lot of regional requirements coming from various countries and data flow is where we actually transfer the data. That's where you do the processing. And you use all of these advance analytics capabilities through your streaming engine that we released and you have your B query, you have your OMLs, you have all kinds of things that you can bring in you're big tables and what have you. That's all easily integrated end to end for any analyst to be able to use. >> What is beam? >> Beam ah that's great I'm so glad you asked that question I almost forgot! Beam is one of our open sources we donated the same set, just like we did with Koppernes few years ago, we donated to the open source it's growing. This year actually it won The Technology Awards. So the source is open the community really took it upon, they use that toolkit to build their pipelines you can use any kind of a code that you want Java, Gold, whatever you want to do it and they contribute. We use it internally and externally. It's one of those things that's going to grow. We have a lot of community events coming up this year. We might, and I've seen the increase, I'm really really proud of that community. >> Evren, I love the A.I. can't get my mind off your background and academic because I studied A.I. as well in the 80s and 90s all that good stuff. Young kids are flocking to computer science now because A.I. is very sexy, it's very intoxicating and it's so easy to deal with now. You guys had a hack-a-thon here with NCAA using data really kind of real time and kind of cool things are happening. So it's a moment now for A.I. this is the moment. What's your advice, you've been through the wars you've done your chore duty all those years now it's actually happening. What's your advice for young people who want to come in, get their hands dirty, build things, use A.I., what's your advice, how they should tackle that? >> I am living it, both of my sons one is finishing junior high, the other one is a senior in high school, their both in it. So when I hear my young kids come and say, "hey bubba we just built this using transfer flow." Like it is making me really proud. At the middle school level they were doing it. So the good news is we have all of this publicly available data for them. I encourage every one of them. If you look at what we provide from Google Cloud, you come in there, we have the data for them, we have the tools for them, it's all ready for them to play so schools get free access to it too. >> It's a major culture but how do they get someone who's interested but never coded before, how do they jump right in and get ingratiated and immersed into the code, what do they do? >> We have some community reaches that we're actually doing as Google. We go out to them and we're actually establishing centers to really build community events for them to really learn some new skills. And we're making this easy for them. And I'm happy to hear more and do it, but I'm an advocate I go to middle schools, I go to high schools, I go to colleges. Colleges are a different story. We provide school classes and we provide our technologies at the universities because enterprises need that talent, need that skill, when they graduate, their going to hire them just like I'm going to hire them into my organization. >> So my number one complaint my kids have about school, they're talking about kids that, oh school's going to be a waste it's so linear I can learn everything on YouTube and Google.com. All the stuff I learned in school I'm never going to use in the real world. So the question is, what skill should kids learn that could be applied to machine learning, thinking, the kind of constructs, data structures, or methodologies, what are some of the skills and classes that can tease out and be natural lead into computer science and machine learning A.I.? >> You know, actually their going to build up the skills. The languages will evolve and so forth. As long as they have that inner curiosity asking new questions, how can I find the answer a little faster, that will push them towards different sets of tools, different sets of areas. If you go to Berkeley in here, you will see a whole bunch of high school kids working side by side with graduate students asking those questions, developing those skill sets, but it's all coming down to their curiosity. >> And I think that applies for business too. I mean there's a big gap between the A.I. haves and have-nots I always say. And the good news here that my take away is, you're going to buy A.I, you're going to buy it from people like Google and you're going to build it and apply it, you're going to spend time applying it, and that's how these incumbents can close the gap and that's the good news here. >> Very true if you look at it, look at all the A.P.Is that we have. From text recognition to image recognition to whatever it is, those are all built models and I've seen some customers build some fantastic applications starting from there and they use their own data, bring it in, they update their model for their own businesses cases. >> It's composition it's composing. It's not coding it's composing. >> Exactly, it's composing. We are taking it to the next level. That abstraction is going to actually help others come into the field because they know their field of expertise, they can ask direct questions. You and I may not know it but, they will ask direct questions. And they will go with the tools available for them for the curiosity that they reach. >> Okay what's the coolest thing you're working on right now? >> Coolest thing, I just y'know streaming is my baby. We are working on, I want to solve all the streaming challenges, whatever the industry is. I really want to welcome everyone, bring you to us. I think, if I look at it, one of the things we discussed today was Antos was fantastic right? I mean we're really going to change the game for all enterprises to be able to provide those capabilities at the infrastructure. But imagine what we can do with all the data analytics capabilities we have on top of it. I think this is the next five years is going to be fantastic for us. >> What's the coolest use case thing you see emerging out of streaming? >> Ah you know, yesterday I actually had one of my clients with me onstage, AB Tasty. They had a fantastic capability that they built. They tried everything. And we were not their first choice, I'll be very open. They said the same thing to everybody, you guys were not our first choice. They went around, they looked at all the tool kits, everything. They came they used PostUp, they used data flow, they used engine, streaming engine. And they AB testing for marketing. And they do that at scale, billions of messages every minute, and they do it within seconds, milliseconds, 32 milliseconds at most. Because they have to make the decision. That was awesome, go check. I don't know if you're familiar with that. One of our customers, they provide these real time delivery. In India, imagine where things are. In global leaders, you can actually ask for a food to be delivered and they have to optimize, depending on what the traffic is and go with their scooters, and provide you this delivery. They aren't doing it as well. Okato, they believe, provide food in UK 70% of the population use our technologies for real time delivery. Those are some great examples. >> Evren, great insight, great to have you on. Just a final word here, next couple years, how do you see the trajectory of machine learning A.I. Analytics feeding into the value of making life easier society better, and businesses more productive? >> We are seeing really good pull from enterprises from every archival that you can think of. Regulated, retail, what have you. And we're going to solve some really hard problems whether it's in health care industry, financial industry, retail industry, we're going to make lives of people much easier. And their going to benefit from it at scale. And I believe we're just scratching the tip of it and you're seeing this energy in here. Year over year this has gotten better and better. I can't wait to see what's going to happen next year. >> Evren Eryurek great energy, expert at A.Is, streaming analytics, again this is early days of a brand new shift that's happening. You get on the right side of history it's A.I. machine learning, streaming analysts. Thanks for coming, I appreciate it. >> Thank you so much, take care guys. >> More live coverage here in theCUBE in San Francisco at Google next Cloud 2019. We'll be back after this short break.

>> Announcer: Live from San Francisco, it's theCUBE. Covering Google Cloud Next '19. Brought to you by Google Cloud and its ecosystem partners. >> Hey welcome back everyone. It's theCUBE's live coverage here in San Francisco in Moscone South. We're on the ground floor here at Google Next, Google's Cloud conference. I'm chatting with Stu Miniman; Dave Vellante's also hosting. He's out there getting stories. Our next two guests: Dana Berg, Chief Operating Officer of SADA and Chris Lehman, Head of Engineering for SADA. Guys, welcome to theCUBE. Thanks for joining us. We're here on the ground floor. >> Thank you. >> Thank you. >> This is exciting. I feel like a movie star right here. >> It's game day here. All the tech athletes are out, Dave. If you look at the show, look at the demographics, hardcore developers, lot of IT, leaders also here, cloud architects, a lot of people trying to figure it out. We heard the keynote. Google is bringing a lot to the table. So what's new with you guys? You guys recently sold your Microsoft business, going all-in on Google. Talk about that relationship. >> We are. This is a brand new day for SADA. The energy around this place, where we are in the market, and where we are with the expanded attendance here has actually reaffirmed our business strategy to go all-in with Google. I don't know if you are aware but SADA has been around for almost 20 years. Historically have always been leaders in bringing people to the cloud even before there was really much of a cloud. We were a you know a pilot partner within Microsoft and Google and had a great thriving Microsoft business but an even bigger Google business and you know, we looked at the tea leaves, we looked at where we wanted to be, and aligned with a company that shared our mission and values and it was a clear choice. We chose Google. We made a very specific and deliberate act to sell off our Microsoft business so that we could take the horsepower of all of our engineering staff and apply them to Google. >> It's interesting you know, we've been around for 10 years doing theCUBE, go to a lot of events, I mean Dave Vellante, Stu, and I have been around for 30 years covering the IT, you guys 20 years. You guys have seen many ways of innovation come and go. Now you're going all in on Google. What is it about this wave right now that made that decision? What do you guys see? You're seeing something early here. Expand on that. Give us some color commentary because there's a wave here, right? A lot of people try. It's a combination of things. I mean, we saw the client-server thing. We saw that movement. Also the internet, we saw the web, mobile, now it's cloud. What's the big wave? What are you guys riding? >> I think there's a couple of things and I think it's unique to, philosophically, how we think of our real special relationship with Google. There is a momentum, right, and not to quote like a Bernie Sanders, but, seems like there's a revolution going on here, right, and, you know, I think, you know, what we see when we look around and we hear conversations and even with our customers, the way that we're all winning together is because we're winning the hearts and minds of the people inside of our customer base that are actually the ones responsible for inventing and the ones responsible for building, so when we're in board rooms and we're selling and along with Google, we're talking with developers, we're talking with designers, we're talking about people that are actually driving the vision for these business applications. We're not always talking to the CIO down like some of our other competitors seems to have only been able to sell that way. We're talking about the people responsible for not only constructing it but maintaining it. So that revolution is there. These folks are bubbling that up and they're seeing the real value inside of Google and what is that value from our point of view, and why did we make such a bold statement just to stick with Google is, and we saw Thomas today echo this, I think there's very few cloud providers that are bold enough to actually lead with the fact that we want our customers to have full choice whether you're using GCP or not. We want to build, architect, and manufacture a product offering that allows you to keep your stuff in your data centers, move your stuff to AWS. That power of choice is really not like what we've never heard anywhere else. >> And then on top of that, too, you got an application renaissance, right? A whole new way of coding, infrastructure that's programmable and going away, I mean if you think about what that does to the existing infrastructures, they can now mix and match and rearchitect everything from scratch and accelerate the app movement. >> Well, that's absolutely true, and a lot of that has to do with the fact that there are managed services in the cloud which makes it dramatically easier to build applications of course, so there's no question about that. Some of the offerings on GCP are particularly attractive for our clients, particularly the managed Kubernetes service. That's where we're seeing perhaps most of the interest that we're seeing, like that's a very common theme. Also the ML stack is an area that our customers are very interested in. >> Chris, can you bring us in some of those customer environments, you know, one of the things you hear, you know, most customers, it's, "I've got my application portfolio." Modernizing that is pretty challenging. There are some things that are kind of easy, some things that take a lot more work, but, you know, migration is one of those things that makes most people that have been in IT a while cringe because there's always the devil in the details and something goes wrong once you've got 95 percent done. What are you seeing, what's working, what's not working, how's the role of data changing, and all of that? >> I think migrations are usually more complex than they at first appear and so even with best intentions thinking that customers can just move their workloads seamlessly to the cloud have actually in practice been more challenging. So some of the areas that we find challenges are around data migration, especially in the context of zero downtime. That's always more difficult than with applications. So that's definitely an area that were we're spending a lot of time working with our customers to deliver. >> Just to add to that, I have to keep reminding myself of the name, but obviously the Anthos announcement today sounds incredibly intriguing as a lower barrier of effort to actually migrate. Our customers have been trying to really absorb and take a hold of Kubernetes and can it containerize methods for a long time. Some are having a harder time doing it than others. I think Anthos promises to make that endeavor much, much easier, and I think about as we leave here this week and we go back and we reeducate our own engineering teams as well as our customers, I think we might see some highly accelerated project timelines go from here down to here. >> And the demo that Jennifer Lynn did was pretty impressive. I mean, running inside of containers, whether it's VMs, and then having service patches on the horizon coming to the table is going to change the implementation delivery piece too in a massive way. I mean, you've got-- >> Oh, absolutely. >> Code, build, run on the cloud side, but this this kind of changes the equation on your end. Can you guys share the insight into that equation, because Google's clearly posturing to be partner friendly. You guys are a big partner now. You're going all-in. This is an interesting dynamic because you can focus on solving customers' problems. All this heavy lifting kind of goes away. Talk about the impact to you as a partner when you look at Anthem, Anthem migrate in particular, some of these migration challenges with containers and Kubernetes seems like it's a perfect storm right now to kind of jump in and do more, faster. >> Yeah. >> Well, it's certainly very interesting. Well, we'll want to take a really hard look at it. I mean, a very, very cool announcement. Moving to containers in the source prior to the migration obviously solves a lot of challenges so for that reason, it's definitely a move forward. >> And I think... You know, we always talk about, in this industry, the acceleration for consumption, but really that's a poor way of saying... Probably what we should be saying is an acceleration of value. So we're constantly in this battle to try and deliver value to our customers faster. That's what our customers want, right, and in essence we see Anthos as being potentially a big game-changer there so that, you know, our CIOs that we're talking with can show to their various stakeholders that they are making very good proactive moves into the cloud at lower-caught barriers of entry, right? >> Yeah. So, you brought up the the ML piece of Google. Wondering if you could help share a little bit on that. When I think back two years ago, you know, data was really at the core of what a lot of what Google was talking about. I was actually surprised not to hear a lot of it on the main stage this morning, but you know, AI, ML, what are you doing, what are your customers doing, does Google have leadership in the space? >> Google certainly has leadership in the space. Our customers, I think, relatively universally, think that their ML stack is the strongest among the competitors, but I think in practice what we're finding is there's a lot more urgency as far as just literal data migrations off of their data centers into the cloud, and I foresee a lot more AI and ML work as more move in. >> John: Yeah. >> So you might, in our booth here, not to give a plug, but we've got a booth down at the end with a full-fledged racing car, just to talk about the art of the possible with AI and ML. Our engineering teams in the race teams that we sponsor, they're there, the driver's there, you should go down and talk to 'em. We've taken all the race telemetry data for the last six months and all of his races and practices, we've aggregated that data all into GCP, run AI and ML algorithms on it to provide his racing team some very predictive ways that he can get better and that team can get better, and so I'd invite just anybody that wants to go there and take a look at, even if you're in banking, or if you're in retail, or if you're in health care, take a look at some of how that was done, because it's a very, very powerful way, to answer your question, head and shoulders down why Google is actually accelerating and exceeding in AI. >> And one of the things that Thomas Kurian showed onstage was the recent Hack-a-Thon they had with the college students with the NCAA data of the game that just finished, and throughout that experience, this is a core theme of GCP, and now Anthos, which is getting data in and using it easily, and scaling at a scale level that seems unprecedented. So this team seems to be the application... The new differentiator. >> I think it is. I think that announcement, obviously the big three takeaways for us, certainly, scale, unmatched. Certainly speed and migration with Anthos. If I could highlight one other, I was incredibly pleased with, well I've been pleased since Thomas' arrival in general by bringing an enterprise class strategy within sight of Google that I think are going to respond well to our enterprise customers, and part of enterprise class is also making sure that their partner community has amazing enhancement programs that really incentivize those partners that are actually in the full managed services space from cradle to grave, lifetime customer value. So we're very excited about even further announcements this week that no doubt have been inspired by Thomas to try and really take advantage of their partner community that are in the business of cradle to grave support of customers. >> You feel comfortable with Thomas. He's taught a lot of customers, he knows the enterprise. >> We've had an opportunity to meet with him. We've had some shared customers that have had a great privilege of getting to know him and support us and collectively them. >> John: He knows the partner equation pretty well, and the enterprise. >> Without a doubt. >> It's about partnering, because there's a monetization, the shared go to markets together. Talk about the importance of that and what's it like to be a partner. >> Yeah, without a doubt, again, you know, his embrace of the open-source community that you saw today, really taking advantage of highlighting partner value is wonderful, but I think Thomas, above anything else, knows that Google needs to scale. They need to scale, and then they have to have breadth and they have to have depth, and, you know, to get to where Google needs to be over the course of the next two, three years, it's wonderful, it's refreshing, it's 100% accurate that Google knows and Thomas knows that the path to do that is via partners; partners that share in Google's vision, that are 100% aligned to the same things that Google is aligned with, and I think that's why I'm so thankful to be at SADA, large in part, because all of the things that we care about in terms of our customer success as well as Google's success, we all share that, so it's a great trifecta. >> It's a ground-floor opportunity. Congratulations. Guys, talk about your business. What's going on? You've got some new offices I heard you opened up. What's going on in the state of the business? Obviously the Google focus you're excited about obviously. >> Yeah, yeah, yeah. >> There, at the beginning, I called Google the dark horse. I think with the tech that they have and the renewed focus on the enterprise, building on what Diane Greene had put foundationally, Thomas is meeting with hundreds of customers. He's so busy he doesn't have time to come on theCUBE, but he'll come on soon, but he's focused. This is now a great opportunity. Talk about your business. What's the state of the union there? Give an update. >> I can take that one if you don't mind. >> Go ahead. >> You can add poetic color if you want. (laughing) Yeah, so as I said, we're entering a new journey for SADA in light of renewed focus, renewed conviction to Google. We are investing more than we ever have into the common belief that Google is the one to beat in terms of momentum, drive, and ultimately winning the hearts and the minds of who we've talked about. So, over the last four months, we've opened five new offices in New York, Austin, Chicago, Denver. Our headquarters is in Los Angeles, and just recently, we just opened a brand new office in Toronto, so we can really help our Canadian customers really see the the same type of white-glove treatment we provide those customers in the States and so that's why, well, I wasn't earlier, but I'm walking around with a Canadian flag. We're very excited about the presence that we're going to have in Canada >> Its "Toronno." I always blow and I call it "Toron-to," being the American that I am. It's "Toronno." >> Dana: Glad you said it right. Good. >> Now, on the engineering side, so you guys are on the front lines as also a sales, development, there's also customer relationship, engineering side, so I'm sure you guys are hiring. There's some hard problems to solve out there. Can you guys share some color commentary on the type of solutions you guys are doing? What's the heavy? What solutions are you solving, problems that you're solving for customers, what are the key things that you got going on? >> Yeah. >> Well, a lot of cloud migrations, a lot of web and application development, custom development, and data pipelines. I'd say those are really the three key focus areas that we're working on at the moment. >> One other thing, too: so... we believe that we want 100% customer retention, always, and that goes above and beyond an implementation. So the other big area of investments that we're making is in a whole revamped technical account management team, so for those of our GCP customers that have had the privilege, we've had the privilege of working with and for, we are building out a team of individuals that will, well beyond the project, stay with that customer, work with them weekly, monthly, quarterly, and try to always find ways to expand and move workloads into the cloud. We think that provides stickiness. We think that provides ultimate value to try and help our customers identify where else they can take full advantage of the cloud, and it's a fairly new program, and large in part I just want to thank Thomas and the partner team for new programs that are coming out to help us so that we can actually reinvest in things that go you know throughout the lifecycle of the customer. So, very, very good stuff. >> Dana, Chris, thanks for coming on. Appreciate it. We'll check out your booth, the car's there, with the data. Bring that data exhaust to the table, pun intended. >> Yes. >> Analyzing with Google Cloud, Anthos. Good commentary. Thanks for sharing. >> Really appreciate being on board. Thanks for having us. >> Alright, great. CUBE coverage here live on the floor in San Francisco. Google Next 2019. This is Google's cloud conference. Customers are here. A lot of developers. More action, live on the day one of three days of coverage after this short break. Stay with us. (theCUBE Theme)

>>Hi buddy, we're back. This is Dave Volante with the cube goes out to the shows. We extract the signal from the noise. Nate Silver's here. Nate, we've been saying that since 2010, rip you off. Hey Marcus feeder. Oh, you have that trademarks. Okay. So anyway, welcome to the cube. You man who needs no introduction, but in case you don't know Nate, uh, he's a very famous author, five 30 eight.com. Statistician influence, influential individual predictor of a lot of things including presidential elections. And uh, great to have you here. Great to be here. So we listened to your keynote this morning. We asked earlier if some of our audience, can you tweet it and you know, what would you ask Nate silver? So of course we got the predictable, how the red Sox going to do this year? Who's going to be in the world series? Are we going to attack Syria? >>Uh, will the fed E's or tightened? Of course we're down here. Who'd you vote for? Or they, you know, they all want to know. And of course, a lot of these questions you can't answer because it's too far out. But, uh, but anyway, again, welcome, welcome to the cube. Um, so I want to start by, uh, picking up on some of the themes in your keynote. Uh, you're here at the Tableau conference. Obviously it's all about about data. Uh, and you, your basic, one of your basic premises was that, um, people will misinterpret data, they'll just use data for their own own biases. You have been a controversial figure, right? A lot of people have accused you of, of bias. Um, how, what do you F how do you feel about that as a person who's, uh, you know, statistician, somebody who loves data? >>I think everyone has bias in the sense that we all have one relatively narrow perspective as compared to a big set of problems that we all are trying to analyze or solve or understand together. Um, you know, but I do think some of this actually comes down to, uh, not just bias, but kind of personal morality and ethics really. It seems weird to talk about it that way, but there are a lot of people involved in the political world who are operating to manipulate public opinion, um, and that don't really place a lot of value on the truth. Right. And I consider that kind of immoral. Um, but people like that I think don't really understand that someone else might act morally by actually just trying to discover the way the objective world is and trying to use science and research to, to uncover things. >>And so I think it's hard people to, because if they were in your shoes, they would try and manipulate the forecast and they would cheat and put their finger on their scale. They assume that anyone else would do the same thing cause they, they don't own any. Yeah. So will you, you've made some incredibly accurate predictions, uh, in the face of, of, of others that clearly had bias that, that, that, you know mispredicted um, so how did you feel when you got those, those attacks? Were you flabbergasted? Were you pissed? Were you hurt? I mean, all of the above having you move houses for, for you? I mean you get used to them with a lot of bullshit, right? You're not too surprised. Um, I guess it surprised me how, but how much the people who you know are pretty intelligent are willing to, to fool themselves and how specious arguments where meet and by the way, people are always constructing arguments for, for outcomes they happen to be rooting for. >>Right? It'd be one thing if you said, well I'm a Republican, but boy I think Obama's going to crush Romney electoral college or vice versa. But you should have an extra layer of scrutiny when you have a view that diverges from the consensus or what kind of the markets are saying. And by the way, you can go and they're betting Margaret's, you can go and you could have bet on the outcome of election bookies in the UK, other countries. Right. And they kind of had forecast similar to ours. We were actually putting their money where their mouth was. Agree that Obama was a. Not a lot, but a pretty heavy favorite route. Most of the last two months in the election. I wanted to ask you about prediction markets cause as you probably know, I mean the betting public are actually very efficient. Handicappers right over. >>So I'll throw a two to one shot is going to be to three to one is going to be a four to one, you know, more often than not. But what are your thoughts on, on prediction markets? I mean you just sort of betting markets, you'd just alluded it to them just recently or is that a, is that a good, well there a lot there then then I think the punditry right. I mean, you know, so with, with prediction markets you have a couple of issues. Number one is do you have enough, uh, liquidity, um, and my volume in the markets for them to be, uh, uh, optimal. Right. And I think the answer right now is maybe not exactly. And like these in trade type markets, knowing trade has been, has been shut down. In fact, it was pretty light trading volumes. It might've had people who stood to gain or lose, um, you know, thousands of dollars. >>Whereas in quote, unquote real markets, uh, the stakes are, are several orders of magnitude higher. If you look at what happened to, for example, just prices of common stocks a day after the election last year, um, oil and gas stocks lost billions of dollars of market capitalization after Romney lost. Uh, conversely, some, you know, green tech stocks or certain types of healthcare socks at benefit from Obamacare going into play gain hundreds of millions, billions of dollars in market capitalization. So real investors have to price in these political risks. Um, anyway, I would love to have see fully legal, uh, trading markets in the U S people can get bet kind of proper sums of money where you have, um, a lot of real capital going in and people can kind of hedge their economic risk a little bit more. But you know, they're, they're bigger and it's very hard to beat markets. They're not flawless. And there's a whole chapter in the book about how, you know, the minute you assume that markets are, are clairvoyant and perfect, then that's when they start to fail. >>Ironically enough. But they're very good. They're very tough to beat and they certainly provide a reality check in terms of providing people with, with real incentives to actually, you know, make a bet on, on their beliefs and people when they have financial incentives, uh, uh, to be accurate then a lot of bullshit. There's a tax on bullshit is one way. That's okay. I've got to ask him for anyway that you're still a baseball fan, right? Is that an in Detroit fan? Right. I'm a tiger. There's my bias. You remember the bird? It's too young to remember a little too. I, so I grew up, I was born in 78, so 84, the Kirk Gibson, Alan Trammell teams are kind of my, my earliest. So you definitely don't remember Mickey Lola cha. I used to be a big guy. That's right fan as well. But so, but Sony, right when Moneyball came out, we just were at the Vertica conference. >>We saw Billy being there and, and uh, when, when, when, when, when that book came out, I said Billy Bean's out of his mind for releasing all these secrets. And you alluded to in your talk today that other teams like the rays and like the red Sox have sort of started to adopt those techniques. At the same time, I feel like culturally when another one of your V and your Venn diagram, I don't want you vectors, uh, that, that Oakland's done a better job of that, that others may S they still culturally so pushing back, even the red Sox themselves, it can be argued, you know, went out and sort of violated the, the principles were of course Oakland A's can't cause they don't have a, have a, have a budget to do. So what's your take on Moneyball? Is the, is the strategy that he put forth sustainable or is it all going to be sort of level playing field eventually? >>I mean, you know, the strategy in terms of Oh fine guys that take a lot of walks, right? Um, I mean everyone realizes that now it's a fairly basic conclusion and it was kind of the sign of, of how far behind how many biases there were in the market for that, you know, use LBP instead of day. And I actually like, but that, that was arbitrage, you know, five or 10 years ago now, um, put butts in the seat, right? Man, if they win, I guess it does, but even the red Sox are winning and nobody goes to the games anymore. The red Sox, tons of empty seats, even for Yankees games. Well, it's, I mean they're also charging 200 bucks a ticket or something. you can get a ticket for 20, 30 bucks. But, but you know, but I, you know, I, I, I mean, first of all, the most emotional connection to baseball is that if your team is in pennant races, wins world series, right then that produces multimillion dollar increases in ticket sales and, and TV contracts down the road. >>So, um, in fact, you know, I think one thing is, is looking at the financial side, like modeling the martial impact of a win, but also kind of modeling. If you do kind of sign a free agent, then, uh, that signaling effect, how much does that matter for season ticket sales? So you could do some more kind of high finance stuff in baseball. But, but some of the low hanging fruit, I mean, you know, almost every team now has a Cisco analyst on their payroll or increasingly the distinctions aren't even as relevant anymore. Right? Where someone who's first in analytics is also listening to what the Scouts say. And you have organizations that you know, aren't making these kind of distinctions between stat heads and Scouts at all. They all kind of get along and it's all, you know, finding better ways, more responsible ways to, to analyze data. >>And basically you have the advantage of a very clear way of measure, measure success where, you know, do you win? That's the bottom line. Or do you make money or, or both. You can isolate guys Marshall contribution. I mean, you know, I am in the process now of hiring a bunch of uh, writers and editors and developers for five 38 right? So someone has a column and they do really well. How much of that is on the, the writer versus the ed or versus the brand of the site versus the guy at ESPN who promoted it or whatever else. Right. That's hard to say. But in baseball, everyone kind of takes their turn. It's very easy to measure each player's kind of marginal contribution to sort of balance and equilibrium and, and, and it's potentially achieved. But, and again, from your talk this morning modeling or volume of data doesn't Trump modeling, right? >>You need both. And you need culture. You need, you need, you know, you need volume of data, you need high quality data. You need, uh, a culture that actually has the right incentives align where you really do want to find a way to build a better product to make more money. Right? And again, they'll seem like, Oh, you know, how difficult should it be for a company to want to make more money and build better products. But, um, when you have large organizations, you have a lot of people who are, uh, who are thinking very short term or only about only about their P and L and not how the whole company as a whole is doing or have, you know, hangups or personality conflicts or, or whatever else. So, you know, a lot of success I think in business. Um, and certainly when it comes to use of analytics, it's just stripping away the things that, that get in the way from understanding and distract you. >>It's not some wave a magic wand and have some formula where you uncover all the secrets in the world. It's more like if you can strip away the noise there and you're going to have a much clearer understanding of, of what's really there. Uh, Nate, again, thanks so much for joining us. So kind of wanna expand on that a little bit. So when people think of Nate silver, sometimes they, you know, they think Nate silver analytics big data, but you're actually a S some of your positions are kind of, you take issue with some of the core notions of big data really around the, the, the importance of causality versus correlation. So, um, so we had Kenneth kookier on from, uh, the economist who wrote a book about big data a while back, the strata conference. And you know, he, in that book, they talk a lot about it really doesn't matter how valid anymore, if you know that your customers are gonna buy more products based on this dataset or this correlation that it doesn't really matter why. >>You just try to try to try to exploit that. Uh, but in your book you talk about, well and in the keynote today you talked about, well actually hypothesis testing coming in with some questions and actually looking for that causality is also important. Um, so, so what is your, what is your opinion of kind of, you know, all this hype around big data? Um, you know, you mentioned volume is important, but it's not the only thing. I mean, like, I mean, I'll tell you I'm, I'm kind of an empiricist about anything, right? So, you know, if it's true that merely finding a lot of correlations and kind of very high volume data sets will improve productivity. And how come we've had, you know, kind of such slow economic growth over the past 10 years, where is the tangible increase in patent growth or, or different measures of progress. >>And obviously there's a lot of noise in that data set as well. But you know, partly why both in the presentation today and in the book I kind of opened up with the, with the history is saying, you know, let's really look at the history of technology. It's a kind of fascinating, an understudied feel, the link between technology and progress and growth. But, um, it doesn't always go as planned. And I certainly don't think we've seen any kind of paradigm shift as far as, you know, technological, economic productivity in the world today. I mean, the thing to remember too is that, uh, uh, technology is always growing in and developing and that if you have roughly 3% economic growth per year exponential, that's a lot of growth, right? It's not even a straight line growth. It's like exponential growth. And to have 3% exponential growth compounding over how many years is a lot. >>So you're always going to have new technologies developing. Um, but what I, I'm suspicious that as people will say this one technology is, is a game changer relative to the whole history of civilization up until now. Um, and also, you know, again, a lot of technologies you look at kind of economic models where you have different factors or productivity. It's not usually an additive relationship. It's more a multiplicative relationships. So if you have a lot of data, but people who aren't very good at analyzing it, you have a lot of data but it's unstructured and unscrutinised you know, you're not going to get particularly good results by and large. Um, so I just want to talk a little bit about the, the kind of the, the cultural issue of adopting kind of analytics and, and becoming a data driven organization. And you talk a lot about, um, you know, really what you do is, is setting, um, you know, try to predict the probabilities of something happening, not really predicting what's going to happen necessarily. >>And you talked to New York, you know, today about, you know, knowledging where, you know, you're not, you're not 100% sure acknowledging that this is, you know, this is our best estimate based on the data. Um, but of course in business, you know, a lot of people, a lot of, um, importance is put on kind of, you know, putting on that front that you're, you know, what you're talking about. It's, you know, you be confident, you go in, this is gonna happen. And, and sometimes that can actually move markets and move decision-making. Um, how do you balance that in a, in a business environment where, you know, you want to keep, be realistic, but you want to, you know, put forth a confident, uh, persona. Well, you know, I mean, first of all, everyone, I think the answer is that you have to, uh, uh, kind of take a long time to build the narrative correctly and kind of get back to the first principles. >>And so at five 38, it's kind of a case where you have a dialogue with the readers of the site every day, right? But it's not that you can solve in one conversation. If you come in to a boss who you never talked to you before, you have to present some PowerPoint and you're like, actually this initiative has a, you know, 57% chance of succeeding and the baseline is 50% and it's really good cause the upside's high, right? Like you know, that's going to be tricky if you don't have a good and open dialogue. And it's another barrier by the way to success is that uh, you know, none of this big data stuff is going to be a solution for companies that have poor corporate cultures where you have trouble communicating ideas where you don't everyone on the same page. Um, you know, you need buy in from, from all throughout the organization, which means both you need senior level people who, uh, who understand the value of analytics. >>You also need analysts or junior level people who understand what business problems the company is trying to solve, what organizational goals are. Um, so I mean, how do you communicate? It's tricky, you know, maybe if you can't communicate it, then you find another firm or go, uh, go trade stocks and, and uh, and short that company if you're not violating like insider trading rules of, of various kinds. Um, you know, I mean, the one thing that seems to work better is if you can, uh, depict things visually. People intuitively grasp uncertainty. If you kind of portray it to them in a graphic environment, especially with interactive graphics, uh, more than they might've just kind of put numbers on a page. You know, one thing we're thinking about doing with the new 580 ESPN, we're hiring a lot of designers and developers is in case where there is uncertainty, then you can press a button, kind of like a slot, Michigan and simulate and outcome many times, then it'll make sense to people. Right? And they do that already for, you know, NCAA tournament stuff or NFL playoffs. Um, but that can help. >>So Nate, I asked you my, my partner John furry, who's often or normally the cohost of this show, uh, just just tweeted me asking about crowd spotting. So he's got this notion that there's all this exhaust out there, the social exhaustive social data. How do you, or do you, or do you see the potential to use that exhaust that's thrown off from the connected consumer to actually make predictions? Um, so I'm >>a, I guess probably mildly pessimistic about this for the reason being that, uh, a lot of this data is very new and so we don't really have a way to kind of calibrate a model based on it. So you can look and say, well, you know, let's say Twitter during the Republican primaries in 2016 that, Oh, Paul Ryan is getting five times as much favorable Twitter sentiment as Rick Santorum or whatever among Republicans. But, but what's that mean? You know, to put something into a model, you have to have enough history generally, um, where you can translate X into Y by means of some function or some formula. And a lot of data is so new where you don't have enough history to do that. And the other thing too is that, um, um, the demographics of who is using social media is changing a lot. Where we are right now you come to conference like this and everyone has you know, all their different accounts but, but we're not quite there yet in terms of the broader population. >>Um, you have a lot of kind of thought leaders now a lot of, you know, kind of young, smart urban tech geeks and they're not necessarily as representative of the population as a whole. That will over time the data will become more valuable. But if you're kind of calibrating expectations based on the way that at Twitter or Facebook were used in 2013 to expect that to be reliable when you want a high degree of precision three years from now, even six months from now is, is I think a little optimistic. Some sentiment though, we would agree with that. I mean sentiment is this concept of how many people are talking about a thumbs up, thumbs down. But to the extent that you can get metadata and make it more stable, longer term, you would see potential there is, I mean, there are environments where the terrain is shifting so fast that by the time you know, the forecast that you'd be interested in, right? >>Like things have already changed enough where like it's hard to do, to make good forecast. Right? And I think one of the kind of fundamental themes here, one of my critiques is some of the, uh, of, uh, the more optimistic interpretations of big data is that fundamentally people are, are, most people want a shortcut, right? Most people are, are fairly lazy like labor. What's the hot stock? Yeah. Right. Um, and so I'm worried whenever people talk about, you know, biased interpretations of, of the data or information, right? Whenever people say, Oh, this is going to solve my problems, I don't have to work very hard. You know, not usually true. Even if you look at sports, even steroids, performance enhancing drugs, the guys who really get the benefits of the steroids, they have to work their butts off, right? And then you have a synergy which hell. >>So they are very free free meal tickets in life when they are going to be gobbled up in competitive environments. So you know, uh, bigger datasets, faster data sets are going to be very powerful for people who have the right expertise and the right partners. But, but it's not going to make, uh, you know anyone to be able to kind of quit their job and go on the beach and sip my ties. So ne what are you working on these days as it relates to data? What's exciting you? Um, so with the, with the move to ESPN, I'm thinking more about, uh, you know, working with them on sports type projects, which is something having mostly cover politics. The past four or five years I've, I've kind of a lot of pent up ideas. So you know, looking at things in basketball for example, you have a team of five players and solving the problem of, of who takes the shot, when is the guy taking a good shot? >>Cause the shot clock's running out. When does a guy stealing a better opportunity from, from one of his teammates. Question. We want to look at, um, you know, we have the world cup the summer, so soccer is an interest of mine and we worked in 2010 with ESPN on something called the soccer power index. So continuing to improve that and roll that out. Um, you know, obviously baseball is very analytics rich as well, but you know, my near term focus might be on some of these sports projects. Yeah. So that the, I have to ask you a followup on the, on the soccer question. Is that an individual level? Is that a team level of both? So what we do is kind of uh, uh, one problem you have with the national teams, the Italian national team or Brazilian or the U S team is that they shift their personnel a lot. >>So they'll use certain guys for unimportant friendly matches for training matches that weren't actually playing in Brazil next year. So the system soccer power next we developed for ESPN actually it looks at the rosters and tries to make inferences about who is the a team so to speak and how much quality improvement do you have with them versus versus, uh, guys that are playing only in the marginal and important games. Okay. So you're able to mix and match teams and sort of predict on your flow state also from club league play to make inferences about how the national teams will come together. Um, but soccer is a case where, where we're going into here where we had a lot more data than we used to. Basically you had goals and bookings, I mean, and yellow cards and red cards and now you've collected a lot more data on how guys are moving throughout the field and how many passes there are, how much territory they're covering, uh, tackles and everything else. So that's becoming a lot smarter. Excellent. All right, Nate, I know you've got to go. I really appreciate the time. Thanks for coming on. The cube was a pleasure to meet you. Great. Thank you guys. All right. Keep it right there, everybody. We'll be back with our next guest. Dave Volante and Jeff Kelly. We're live at the Tableau user conference. This is the cube.