 From the SiliconANGLE Media Office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Stu Miniman. Hi, I'm Stu Miniman and welcome to a CUBE conversation here in our new Boston area studio. Happy to welcome back to the program a VIP from our community, Patrick Osborn, who's the Vice President and General Manager for Big Data and Secondary Storage at Hewlett Packard Enterprise. Patrick, great to talk to you. Great to be back, thanks, Stu. All right, we're talking about the big thing, 100th year of the NFL kicking off here. Or maybe we're talking a little bit about the changing role of infrastructure and what we've been talking about at the Wikibon team for a number of years. Data's at the center of the universe today when we talk about IT and businesses and what they're thinking about. And in some ways, everything's changed and in other ways it feels like I go to some of these shows and the people that have even more experience than me are like, oh geez, we've recreated the mainframe. So we're fresh off of VMworld. You skipped the show this year, but I know HPE had a large presence at the show. And let me start there. I guess we've looked at data centers and cloud and the mission VMware has is how do they maintain relevant as customers are changing their applications? They just made billions of dollars of acquisitions to be more in the cloud native environment. So when you look at HPE is very well known in the infrastructure space had some changes as to what pieces are in the company versus partnered with the company. So when you talk to your customers and they're changing what I call the long pole in the tent of modernization, it's the applications. Where are they today? Where are some of the areas that they're doing well and where are the areas that it's challenging and struggling? Yeah, so I'd say from an HPE perspective, we've made a number of investments as well over the last couple of years, both inorganic and organic investments in this space. And I think that even though we've historically been known as an infrastructure company, we're very quickly pivoting towards being known as an enterprise workload company. And so from my perspective, the things that we're trying to do, especially in our division around AI and ML and analytics is being able to provide a platform for customers, especially application developers. I think when we talk about how the world is changing, the buyer personas of people we're selling to now have completely drastically changed. There's no more dedicated backup teams. There's rarely now dedicated storage teams, maybe only in very large organizations. And so now you're catering to a different set of folks. And for instance, over the last two or three years, we've seen the advent of folks like a chief data officer, the CDO, data scientists, data engineers. And so for us, we have a whole new buyer persona and user persona that we not only have to cater to in our UX design, but also present the value, which is a much different conversation we've had in the past. Yeah, I actually had a number of conversations with customers at the VMworld show and they talked about organizationally, they often still have hardware-defined roles, yet they live in a software-defined world. So even groups that are like, I still have some storage headcount and some networking headcount, but virtualization and cloud are slowly eating over pieces of it, but there's still some turf battles, which I was had to hear because I've worked for the last couple of decades to try to eliminate silos and get people working together. So we know those organizational changes often take even longer than the long cycles of technology that we're trying to roll out here. You mentioned some of the big data pieces and HPEs made a number of acquisitions. Most recently, MapR, I wonder if you could help us connect the dots when we covered heavily the big data wave and Dave Vellante would say, look, the people that deploy these technologies, the end users will create way more value than the distributions of Hadoop will. We did our forecast, they were there, but the promise of big data was data was going to go from that burden, how do I keep it? How do I maintain it? How do I back it up to new value for the company, new revenue that we could have along that way and whether or not that happened often, mattered on the deployment, but when you go into the AI space, like what you're doing with blue data, is that a continuation of what we were seeing with the big data space? Is there some new waves that are drastically changing the outcomes and what we're seeing? How does that all fit together in your- Yeah, so I think it's definitely an extension of all these things are creative, right, and incremental at the end of the day. I think some of the things around how people are operationalizing AI and ML are pretty unique and so from our perspective, we made some investments around blue data and we've had some recent product announcements in that area around helping folks operationalize machine learning, which is at this point it's becoming very real and people are putting in a number of different use cases. And then to come along with that, the need to store data, right? So we talk about this often, which nobody talks about storage anymore or talks about data, right? The need to store all this data that's coming in and a persistent data layer is super important, more important than it ever was and it comes in multiple different forms and multiple different factors and also protocols. So to have a data platform that is very scalable, has enterprise resiliency to it, the ability to take data and manifest it in different ways, right? Is important for that entire ecosystem and we felt that MapR was a great platform. They have a great data platform that started with Hadoop, moved into supporting things like streams, Kafka and Spark and then certainly now been shifted into a Kubernetes and container deployment and then mapping their file system and their database and streams to servicing AI and ML workloads. So it's kind of along the same vein and being able to live in that world that you're still separating compute and storage and being able to scale independently but work together from a security perspective I think is really important. Yeah, one of the boundaries that I've always been fascinated with is some of the underlying components that were changing. So when we rolled out virtualization, the whole storage and networking industry had to work to kind of put the pieces back together as we took advantage of that. You mentioned Kubernetes, at the KubeCon show, there's lots of that same plumbing things that need to be understand and work. But on the other hand, we've seen massive transformation in the database market. 10 years ago everybody had one database to rule them all and now most companies we talk to is like, oh, well, I've got lots of little databases and now pulling them together differently. But that boundary between what's happening at the infrastructure layer and what's happening at the application layer. On the one hand, they seem to be pulling apart. I should just be able to use cloud or serverless and it makes it easy. But on the same hot time, you're talking everybody's like, I've got the best infrastructure for your AI deployment. So can you talk a little bit about some of the hard challenges that HPE's looking at solving? What do you look to actually create? Whether that be a box or a service or some offering because I know HPE has lots of different areas that you look at those solutions. We're trying to, when we go and have a successful deployment at our customers and we've got, we have deployments in most verticals in the Fortune 500, Global 2000, whether it's financial services, automotive, manufacturing, you can name it healthcare. I think what we've seen is that the successful deployments are the ones that bring together the application owners, line of business, even the data sciences engineers, along with the infrastructure folks. I think sometimes they're at odds. And so when you can bring together a very platform that the end of the day is going to provide something as a service. It's either an analytics sandbox, big data and analytics as a service, AI as a service. There's a set of folks that are trying to service a number of application developers and data sciences internally. That's a platform that can have a uniform data structure where you can grab all this data and have access to it securely and be able to deploy your workflow on top of that in a virtualized, multi-tenant way, deployed in containers with the tool sets and the applications that they want to have access to, but not have to deal with the infrastructure. And then that can be the provenance of the CIO and the data center team, the infrastructure folks working with those teams. That's where we've seen the magic happen for successful deployments. And those are the ones that they end up growing and scaling very quickly. And they can be deployed on-prem. They can be deployed. So we have some of our pilots and POCs that start completely in the cloud and then come back on-prem for different reasons, security, data locality, governance, what have you, but it provides the flexibility. But I think what we found is that taking an outcome and a services-based approach that bring everyone to the table is where we see the projects really get a big business benefit for our customers. Yeah, I was having a conversation earlier today. And when we watched the adoption of virtualization, it's been almost 20 years now since most people are doing it. When we'd reached about 10 years in, we felt that most people were doing it and were on their journey. But things like converged and hyper-converged infrastructure really helped accelerate us past that kind of early majority into the late majority because it was the simplicity of that offering. We wonder, are we reaching some of that same point when we look at cloud? And when I say cloud, not just public cloud but what we're doing in private where the hybrid multi-cloud mix up that we have because while cloud is definitely real and here to say, I don't think anybody would really say that cloud circa 2019 is easy. So how does HPE and its partners, how do we make it even easier so that customers can move down that journey to modernize themselves even more and get out of what we call that undifferentiated heavy lifting? Yeah, so I definitely want to avoid the undifferentiated heavy lifting because that's certainly a weight on many organizations. And so what we are trying to provide is a platform that increases customers time to value. And by providing, by abstracting a lot of difficult things. I mean, there's a lot of data gravity in this space. You're talking about, we have projects right now for autonomous cars where they ingest two, five, 10 petabytes a day, for example. It's very difficult to migrate and move that data, right? So you want to be able to bring that data in, tap into it securely. There's a lot of networking that goes on that's very difficult from a security perspective as well as multi-tenancy and making sure that that model is set up correctly. So for us, it's all about providing a platform that can service multiple tenants and multiple organizations that are all using sort of similar tool sets at the end of the day. But you can have your specific data scientists and data engineers operating on a platform that they don't have to worry about infrastructure, right? Because at the end of the day, when we go visit those folks who own those applications, oftentimes they don't want to deal with, I need to go request an VM. I need to go request a block of IP addresses. I need some lones for my storage. I need a server deployment to run bare metal, some bare metal tooling. They really want to establish a service, just like we saw with virtualization, right? And so right now it's sort of the fight for how can I make my infrastructure as invisible as possible and fight for the eyeballs of the developers. Great. Want to just give you the final word, Patrick, what's exciting you kind of second half of the year, things you're looking forward to? Yeah, so the things that excite me is certainly customer acquisition, right? And we've been marching along that very quickly with some of these new acquisitions and some of the net new development we've done within HPE. I think that we've got a lot of stuff cooking with Kubernetes in that area and so we'll make some big announcements at Kubicon and that's always very exciting to talk in these new ecosystems. And speaking of ecosystems, we're establishing, I think there's new ecosystems that are forming in the market, especially around AI and ML. It's still a very nascent market and so we're bringing on new partners every week from an application development perspective and so for me it's really exciting to talk with all these new apps, these new tool chains, new tool sets, libraries, algorithms and I think it's really exciting to kind of move up stack and be in this really cool world of application development. Yeah, I know when I see the market landscape of some of the AI space, you need to have a big monitor or be able to zoom in because there's a lot of players, there's a lot of pieces. We always worry about things like API sprawls and the like but absolutely super exciting space. Patrick Osborn, thanks so much for giving us an update on what's happening, especially how AI is driving a lot of new innovation in the area. Absolutely, yeah, very exciting, thanks for having me. All right, Patrick Osborn with HPE and I'm Stu Miniman, thanks as always for joining theCUBE.