>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. All right, you're continuing or we're continuing around the clock coverage and around the world coverage off a W s reinvent 2020 virtual conference This year, I'm guessing hundreds of thousands of folks are tuning in for coverage. And we have we have on the other end of the country a cube alarm. Stephen Towel, co founder and CTO of immunity. Stephen, welcome back to the show. >>Great. Great to be here. Thanks for having me again. I hope to match your enthusiasm. >>You know what is, uh, your co founder? I'm sure you could match the enthusiasm. Plus, we're talking about data governance. You You've been on the cute before, and you kind of laid the foundation for us last year. Talking about challenges around data access and data access control. I want to extend this conversation. I had a conversation with a CEO chief data officer a couple of years ago. He shared how his data analysts his the people that actually take the data and make business decisions or create outcomes to make business decisions spent 80% of their time wrangling the data just doing transformations. >>How's the >>Muda helping solve that problem? >>Yeah, great questions. So it's actually interesting. We're seeing a division of roles in these organizations where we have data engineering teams that are actually managing. Ah, lot of the prep work that goes into exposing data and releasing data analysts. Uh, and as part of their day to day job is to ensure that that data that they're released into the analyst is what they're allowed to see. Um and so we kind of see this, this problem of compliance getting in the way of analysts doing their own transformation. So it would be great if we didn't have to have a limited to just this small data engineering team to release the data. What we believe one of the rial issues behind that is that they are the ones that are trusted. They're the only ones that could see all the data in the clear. So it needs to be a very small subset of humans, so to speak, that can do this transformation work and release it. And that means that the data analyst downstream are hamstrung to a certain extent and bottlenecked by requesting these data engineers do some of this transformation work for them. Eso I think because, as you said, that's so critical to being able to analyze data, that bottleneck could could be a back breaker for organization. So we really think that to you need to tie transformation with compliance in order to streamline your analytics in your organization. >>So that has me curious. What does that actually look like? Because Because when I think of a data analyst, they're not always thinking about Well, who should have this data? They're trying to get the answer to the question Thio provide to the data engineer. What does that functionally looked like when that when you want to see that relationship of collaboration? >>Yeah, So we e think the beauty of a Muda and the beauty of governance solutions done right is that they should be invisible to the downstream analysts to a certain extent. So the data engineering team will takes on some requirements from their legal compliance. Seems such as you need a mask p I I or you need Thio. Hi. These kinds of rose from these kinds of analysts, depending on what the users doing. And we've just seen an explosion of different slices or different ways, you should dice up your data and what who's allowed to see what and not just about who they are, but what they're doing on DSO. You can kind of bake all these policies upfront on your data on a tool like Kamuda, and it will dynamically react based on who the analyst is and what they're doing to ensure that the right policies air being enforced. And we could do that in a way that when the analysts I mean, what we also see is just setting your policies on your data. Once up front, that's not the end of the story. Like a lot of people will tap themselves on the back and say, Look, we've got all our data protected appropriately, job done. But that's not really the case, because the analysts will start creating their own data products and they want to share that with other analysts. And so when you think about this, this becomes a very complex problem of okay. Before someone can share their data with anyone else, we need to understand what they were allowed to see eso being able to control the kind of this downstream flow of of transformations and feature engineering to ensure that Onley the right people, are seeing the things that they're allowed to see. But still, enabling analytics is really the challenges that that we saw that in Muda Thio, you know, help the the data teams create those initial policies at scale but also help the analytical teams build driven data products in a way that doesn't introduce data leaks. >>So as I think about the traditional ways in which we do this, we kind of, you know, take a data sad. Let's say, is the databases and we said, security rules etcetera on those data states. That's what you're painting to ISMM or of Dynamic. Has Muto approaching this problem from just a architectural direction? >>Yeah, great question. So I'm sure you've probably heard the term role based access control on, but it's been around forever where you basically aggregate your users in the roles, and then you build rules around those roles on gritty, much every legacy. Already, BMS manages data access this way. Um, what we're seeing now and I call it the private data era that we're now embarking on or have been embarking on for the past three years or so. Where consumers are more aware of their data, privacy and the needs they had their there's, you know, data regulations coming fast and furious with no end in sight. Um, we believe that this role based access control paradigm is just broken. We've got customers with thousands of roles that they're trying to manage Thio to, you know, slice up the data all the different ways that they need Thio. So instead, we we offer an accurate based access control solution and also policy based access control solution. We're. Instead, it's really about How do you dynamically enforced policy by separating who the user is from the policy that needs to be enforced and and having that execute at runtime? A good analogy to this is role based. Access control is like writing code without being able to use variables. You're writing the same block a code over and over again with slight changes based on the roll where actually based access control is, you're able to use variables and basically the policy gets decided at runtime based on who the user is and what they're doing. So >>that dynamic nature kind of lends itself to the public cloud. Were you seeing this applied in the world off a ws were here Reinvent so our customers using this with a W s >>So it all comes down to scalability so that the same reasons that used to separate storage from compute. You know, you get your storage in one place you could ephemera, lee, spin up, compute like EMR if you want. Um, you can use Athena against your storage in a server lis way that that kind of, um, freedom to choose whatever compute you want. Um, the same kind of concepts of apply with policy enforcement. You wanna separate your policy from your platform on that This private data era has has, you know, created this need just like you have to separate your compute from storage in the big data era. And this allows you to have a single plane of glass to enforce policy consistently, no matter what compute you're using or what a U s resource is you're using, um and so this gives our customers power to not only, um, you know, build the rules that they need to build and not have to do it uniquely her service in the U. S. But also proved to their legal and compliance teams that they're doing it correctly because, um, when when you do it this way, it really simplifies everything. And you have one place to go toe, understand how policies being enforced. And this really gives you the auditing and reporting around, um, be enforcement that you've been doing to put every one of these, that everything is being done correctly and that your data consumers can understand You know how your data is being protected. Their data is being protected. Um, and you could actually answer those questions when they come at you. >>So let's put this idea to the test a little bit. So I have the data engineer who kind of designs the security policy around the data or implements that policy using Kamuda Aziz dictated by the security and chief data officer of the organization. Then I have the analyst, and the analyst is just using the tools at their disposal. Let's say that one analyst wants to use AWS Lambda and another analysts wants to use our type database or analysis tools. You're telling me that Muda allows the flexibility for that analyst to use either tool within a W S. >>That's right, because we enforce policy at the data layer. Eso If you think about a Muda, it's really three layers policy authoring, which you touched on where those requirements get turned into real policies. Policy decision ing. So at query time we see who the user is, what they're doing on what policy has been defined to dynamically build that policy at run time and then enforcement, which is what you're getting at. The enforcement happens at the data layer, for example, we can enforce policies, natively and spark. So no matter how you're connecting to spark, that policy is going to get enforced appropriately. So we don't really care about what the clients Liz, because the enforcement is happening at the data or the compute layer is is a more accurate way todo to say it >>so. A practical reality off collaboration, especially around large data sets, is the ability to share data across organizations. How is immune hoping thio just make that barrier? Ah, little lower but ensuring security so that when I'm sharing data with, uh, analysts with within another firm. They're only seeing the data that they need to see, but we can effectively collaborate on those pieces of content. >>Yeah, I'm glad you asked this. I mean, this is like the, you know, the big finale, right? Like, this is what you get when you have this granularity on your own data ecosystem. It enables you to have that granularity now, when you want to share outside of your internal ecosystem. And so I think an important part about this is that when you think about governance, you can't necessarily have one God users so to speak, that has control over all tables and all policies. You really need segmentation of duty, where different parts of the organ hooking their own data build their own policies in a way where people can't step on each other and then this can expand this out. The third party data sharing where you can set different anonymous ation levels on your data when you're sharing an external the organization verse, if it's internal users and then someone else in your ord could share their data with you and then that also do that Third party. So it really enables and freeze these organizations Thio share with each other in ways that weren't possibly before. Because it happens in the day. The layer, um, these organizations can choose their own compute and still have the same policies being forced again. Going back to that consistency piece, um, it provides. Think of it is almost a authoritative way to share data in your organization. It doesn't have to be ad hoc. Oh, I have to share with this group over here. How should I do it? What policies should enforce. There's a single authoritative way to set policy and share your data. >>So the first thing that comes to my mind, especially when we give more power to the users, is when the auditors come and they say, You know what, Keith? I understand this is the policy, but prove it. How do we provide auditors with the evidence that you know, the we're implementing the policy that we designed and then two were ableto audit that policy? >>Yeah. Good question. So, um, I briefly spoke about this a little bit, but the when you author and define the policies in the Muda there immediately being enforced. So when you write something in our platform, um, it's not a glorified Wikipedia, right? It's actually turning those policies on and enforcing it at the data later. And because of that, any query that's coming through a Muda is going to be audited. But I think even more importantly, to be honest, we keep a history of how policy changes happening over time, too. So you could understand, you know, so and so changed the policy on this table versus other table, you know, got newly added, these people got dropped from it. So you get this rich history of not only who's touching what data and what data is important, but you're also getting a rich history off. Okay, how have we been treating this data from a policy perspective over time? How is it like what were my risk levels over the past year? With B six tables on? You can answer those kinds of questions as well. >>And then we're in the era of cloud. We expect to be able to consume these services via AP I via pay as you go type of thing. How is your relationship with AWS and how in the cutting. Ultimately, the customer. How do I consume a music? >>Yeah, so in Munich can pretty much be deployed anywhere. So obviously we're talking to us here. We have a SAS offering where you can spin up Muda pretrial and just be often running building policies and hooking up hooking our policy enforcement engine into your compute. Um, that runs in our, um you know, infrastructure. There's also a deployment model where you deploy immune it into your VPC s so it can run on your infrastructure. Behind your firewalls on DWI do not require any public Internet access at all for that to run. We don't do any kind of phone homing because, obviously, privacy company, we take this very seriously internally as well. We also have on premise deployments, um, again with zero connectivity air gapped environments. Eso. So we offer that kind of flexibility to our customers wherever they want immediate toe to be deployed. An important thing to remember their two is immediate. Does not actually store any data. We just store metadata and policy information. Um, so it's that also provides the customers some flexibility where if they want to use our SAS, they can simply go policy in there, and then the data still lives in their account. We're just kind of pushing policy down into that. Dynamically. >>So Stephen Towel co founder c t o of immunity. I don't think you have to worry about matching my energy level. I through some pretty tough questions at at you and you were ready there with all the answers. You wanna see more interesting conversations from around the world with founders, builders, AWS reinvent is all about builders and we're talking to the builders throughout this show. Visit us on the web. The Cube. You can engage with us on Twitter. Talk to you next episode off the Cube from AWS reinvent 2020.

>> From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Welcome to this CUBE Conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We are here with Arpit Joshipura, GM of Networking, Edge, IoT for the Linux Foundation. Arpit, great to see you again, welcome back to theCUBE, thanks for joining us. >> Thank you, thank you. Happy to be here. >> So obviously, we love the Linux Foundation. We've been following all the events; we've chatted in the past about networking. Computer storage and networking just doesn't seem to go away with cloud and on-premise hybrid cloud, multicloud, but open-source software continues to surpass expectations, growth, geographies outside the United States and North America, just overall, just greatness in software. Everything's an abstraction layer now; you've got Kubernetes, Cloud Native- so many good things going on with software, so congratulations. >> Well thank you. No, I think we're excited too. >> So you guys got a big event coming up in China: OSS, Open Source Summit, plus KubeCon. >> Yep. >> A lot of exciting things, I want to talk about that in a second. But I want to get your take on a couple key things. Edge and IoT, deep learning and AI, and networking. I want to kind of drill down with you. Tell us what's the updates on the projects around Linux Foundation. >> Okay. >> The exciting ones. I mean, we know Cloud Native CNCF is going to take up more logos, more members, keeps growing. >> Yep. >> Cloud Native clearly has a lot of opportunity. But the classic in the set, certainly, networking and computer storage is still kicking butt. >> Yeah. So, let me start off by Edge. And the fundamental assumption here is that what happened in the cloud and core is going to move to the Edge. And it's going to be 50, 100, 200 times larger in terms of opportunity, applications, spending, et cetera. And so what LF did was we announced a very exciting project called Linux Foundation Edge, as an umbrella, earlier in January. And it was announced with over 60 founding members, right. It's the largest founding member announcement we've had in quite some time. And the reason for that is very simple- the project aims at unifying the fragmented edge in IoT markets. So today, edge is completely fragmented. If you talk to clouds, they have a view of edge. Azure, Amazon, Baidu, Tencent, you name it. If you talk to the enterprise, they have a view of what edge needs to be. If you talk to the telcos, they are bringing the telecom stack close to the edge. And then if you talk to the IoT vendors, they have a perception of edge. So each of them are solving the edge problems differently. What LF Edge is doing, is it is unifying a framework and set of frameworks, that allow you to create a common life cycle management framework for edge computing. >> Yeah. >> Now the best part of it is, it's built on five exciting technologies. So people ask, "You know, why now?" So, there are five technologies that are converging at the same time. 5G, low latency. NFV, network function virtualization, so on demand. AI, so predictive analytics for machine learning. Container and microservices app development, so you can really write apps really fast. And then, hardware development: TPU, GPU, NPU. Lots of exciting different size and shapes. All five converging; put it close to the apps, and you have a whole new market. >> This is, first of all, complicated in the sense of... cluttered, fragmented, shifting grounds, so it's an opportunity. >> It's an opportunity. >> So, I get that- fragmented, you've got the clouds, you've got the enterprises, and you've got the telcos all doing their own thing. >> Yep. >> So, multiple technologies exploding. 5G, Wi-Fi 6, a bunch of other things you laid out, >> Mhmm. >> all happening. But also, you have all those suppliers, right? >> Yes. >> And, so you have different manufacturers-- >> And different layers. >> So it's multiple dimensions to the complexity. >> Correct, correct. >> What are you guys seeing, in terms of, as a solution, what's motivating the founding members; when you say unifying, what specifically does that mean? >> What that means is, the entire ecosystem from those markets are coming together to solve common problems. And I always sort of joke around, but it's true- the common problems are really the plumbing, right? It's the common life cycle management, how do you start, stop, boot, load, log, you know, things like that. How do you abstract? Now in the Edge, you've 400, 500 interfaces that comes into an IoT or an edge device. You know, Zigbee, Bluetooth, you've got protocols like M2T; things that are legacy and new. Then you have connectivity to the clouds. Devices of various forms and shapes. So there's a lot of end by end problems, as we call it. So, the cloud players. So for LF Edge for example, Tencent and Baidu and the cloud leaders are coming together and saying, "Let's solve it once." The industrial IoT player, like Dynamic, OSIsoft, they're coming in saying, "Let's solve it once." The telcos- AT&T, NTT, they're saying "Let's solve it once. And let's solve this problem in open-source. Because we all don't need to do it, and we'll differentiate on top." And then of course, the classic system vendors that support these markets are all joining hands. >> Talk about the business pressure real quick. I know, you look at, say, Alibaba for instance, and the folks you mentioned, Tencent, in China. They're perfecting the edge. You've got videos at the edge; all kinds of edge devices; people. >> Correct. >> So there's business pressures, as well. >> The business pressure is very simple. The innovation has to speed up. The cost has to go down. And new apps are coming up, so extra revenue, right? So because of these five technologies I mentioned, you've got the top killer apps in edge are anything that is, kind of, video but not YouTube. So, anything that the video comes from 360 venues, or drones, things like that. Plus, anything that moves, but that's not a phone. So things like connected cars, vehicles. All of those are edge applications. So in LF Edge, we are defining edge as an application that requires 20 milliseconds or less latency. >> I can't wait for someone to define- software define- "edge". Or, it probably is defined. A great example- I interviewed an R&D engineer at VMware yesterday in San Francisco, it was at the RADIO event- and we were just riffing on 5G, and talking about software at the edge. And one of the advances >> Yes. >> that's coming is splicing the frequency so that you can put software in the radios at the antennas, >> Correct. Yeah. >> so you can essentially provision, in real time. >> Correct, and that's a telco use case, >> Yeah. >> so our projects at the LF Edge are EdgeX Foundry, Akraino, Edge Virtualization Engine, Open Glossary, Home Edge. There's five and growing. And all of these software projects can allow you to put edge blueprints. And blueprints are really reference solutions for smart cities, manufacturing, telcos, industrial gateways, et cetera et cetera. So, lots of-- >> It's kind of your fertile ground for entrepreneurship, too, if you think about it, >> Correct; startups are huge. >> because, just the radio software that splices the radio spectrum is going to potentially maybe enable a service provider market, and towers, right? >> Correct, correct. >> Own my own land, I can own the tower and rent it out, one radio. >> Yep. >> So, business model innovations also an opportunity, >> It's a huge-- >> not just the business pressure to have an edge, but-- >> Correct. So technology, business, and market pressures. All three are colliding. >> Yeah, perfect storm. >> So edge is very exciting for us, and we had some new announcements come out in May, and more exciting news to come out in June, as well. >> And so, going back to Linux Foundation. If I want to learn more. >> LFEdge.org. >> That's kind of the CNCF of edge, if you will, right? Kind of thing. >> Yeah. It's an umbrella with all the projects, and that's equivalent to the CNCF, right. >> Yeah. >> And of course it's a huge group. >> So it's kind of momentum. 64 founding members-- >> Huge momentum. Yeah, now we are at 70 founding members, and growing. >> And how long has it been around? >> The umbrella has been around for about five months; some of the projects have been around for a couple of years, as they incubate. >> Well let us know when the events start kicking in. We'll get theCUBE down there to cover it. >> Absolutely. >> Super exciting. Again, multiple dimensions of innovation. Alright, next topic, one of my favorites, is AI and deep learning. AI's great. If you don't have data you can't really make AI work; deep learning requires data. So this is a data conversation. What's going on in the Linux Foundation around AI and deep learning? >> Yeah. So we have a foundation called LF Deep Learning, as you know. It was launched last year, and since then we have significantly moved it forward by adding more members, and obviously the key here is adding more projects, right. So our goal in the LF Deep Learning Foundation is to bring the community of data scientists, researchers, entrepreneurs, academia, and users to collaborate. And create frameworks and platforms that don't require a PhD to use. >> So a lot of data ingestion, managing data, so not a lot of coding, >> Platforms. >> more data analyst, and/or applications? >> It's more, I would say, platforms for use, right? >> Yeah. >> So frameworks that you can actually use to get business outcomes. So projects include Acumos, which is a machine learning framework and a marketplace which allows you to, sort of, use a lot of use cases that can be commonly put. And this is across all verticals. But I'll give you a telecom example. For example, there is a use case, which is drones inspecting base stations-- >> Yeah. >> And doing analytics for maintenance. That can be fed into a marketplace, used by other operators worldwide. You don't have to repeat that. And you don't need to understand the details of machine learning algorithms. >> Yeah. >> So we are trying to do that. There are projects that have been contributed from Tencent, Baidu, Uber, et cetera. Angel, Elastic Deep Learning, Pyro. >> Yeah. >> It's a huge investment for us. >> And everybody wins when there's contribution, because data's one of those things where if there's available, it just gets smarter. >> Correct. And if you look at deep learning, and machine learning, right. I mean obviously there's the classic definition; I won't go into that. But from our perspective, we look at data and how you can share the data, and so from an LF perspective, we have something called a CDLA license. So, think of an Apache for data. How do you share data? Because it's a big issue. >> Big deal. >> And we have solved that problem. Then you can say, "Hey, there's all these machine learning algorithms," you know, TensorFlow, and others, right. How can you use it? And have plugins to this framework? Then there's the infrastructure. Where do you run these machine learning? Like if you run it on edge, you can run predictive maintenance before a machine breaks down. If you run it in the core, you can do a lot more, right? So we've done that level of integration. >> So you're treating data like code. You can bring data to the table-- >> And then-- >> Apply some licensing best practices like Apache. >> Yes, and then integrate it with the machine learning, deep learning models, and create platforms and frameworks. Whether it's for cloud services, for sharing across clouds, elastic searching-- >> And Amazon does that in terms of they vertically integrate SageMaker, for instance. >> That's exactly right. >> So it's a similar-- >> And this is the open-source version of it. >> Got it- oh, that's awesome. So, how does someone get involved here, obviously developers are going to love this, but-- >> LF Deep Learning is the place to go, under Linux Foundation, similar to LF Edge, and CNCF. >> So it's not just developers. It's also people who have data, who might want to expose it in. >> Data scientists, databases, algorithmists, machine learning, and obviously, a whole bunch of startups. >> A new kind of developer, data developer. >> Right. Exactly. And a lot of verticals, like the security vertical, telecom vertical, enterprise verticals, finance, et cetera. >> You know, I've always said- you and I talked about this before, and I always rant on theCUBE about this- I believe that there's going to be a data development environment where data is code, kind of like what DevOps did with-- >> It's the new currency, yeah. >> It's the new currency. >> Yeah. Alright, so final area I want to chat with you before we get into the OSS China thing: networking. >> Yeah. >> Near and dear to your heart. >> Near and dear to my-- >> Networking's hot now, because if you bring IoT, edge, AI, networking, you've got to move things around-- >> Move things around, (laughs) right, so-- >> And you still need networking. >> So we're in the second year of the LF Networking journey, and we are really excited at the progress that has happened. So, projects like ONAP, OpenDaylight, Tungsten Fabric, OPNFV, FDio, I mean these are now, I wouldn't say household names, but business enterprise names. And if you've seen, pretty much all the telecom providers- almost 70% of the subscribers covered, enabled by the service providers, are now participating. Vendors are completely behind it. So we are moving into a phase which is really the deployment phase. And we are starting to see, not just PoCs [Proofs of Concept], but real deployments happening, some of the major carriers now. Very excited, you know, Dublin, ONAP's Dublin release is coming up, OPNFV just released the Hunter release. Lots of exciting work in Fido, to sort of connect-- >> Yeah. >> multiple projects together. So, we're looking at it, the big news there is the launch of what's called OVP. It's a compliance and verification program that cuts down the deployment time of a VNF by half. >> You know, it's interesting, Stu and I always talk about this- Stu Miniman, CUBE cohost with me- about networking, you know, virtualization came out and it was like, "Oh networking is going to change." It's actually helped networking. >> It helped networking. >> Now you're seeing programmable networks come out, you see Cisco >> And it's helped. >> doing a lot of things, Juniper as well, and you've got containers in Kubernetes right around the corner, so again, this is not going to change the need, it's going to- It's not going to change >> It's just a-- >> the desire and need of networking, it's going to change what networking is. How do you describe that to people? Someone saying, "Yeah, but tell me what's going on in networking? Virtualization, we got through that wave, now I've got the container, Kubernetes, service mesh wave, how does networking change? >> Yeah, so it's a four step process, right? The first step, as you rightly said, virtualization, moved into VMs. Then came disaggregation, which was enabled by the technology SDN, as we all know. Then came orchestration, which was last year. And that was enabled by projects like ONAP and automation. So now, all of the networks are automated, fully running, self healing, feedback closed control, all that stuff. And networks have to be automated before 5G and IoT and all of these things hit, because you're no longer talking about phones. You're talking about things that get connected, right. So that's where we are today. And that journey continues for another two years, and beyond. But very heavy focused on deployment. And while that's happening, we're looking at the hybrid version of VMs and containers running in the network. How do you make that happen? How do you translate one from the other? So, you know, VNFs, CNFs, everything going at the same time in your network. >> You know what's exciting is with the software abstractions emerging, the hard problems are starting to emerge because as it gets more complicated, end by end problems, as you said, there's a lot of new costs and complexities, for instance, the big conversation at the Edge is, you don't want to move data around. >> No, no. >> So you want to move compute to the edge, >> You can, yeah-- >> But it's still a networking problem, you've still got edge, so edge, AI, deep learning, networking all tied together-- >> They're all tied together, right, and this is where Linux Foundation, by developing these projects, in umbrellas, but then allowing working groups to collaborate between these projects, is a very simple governance mechanism we use. So for example, we have edge working groups in Kubernetes that work with LF Edge. We have Hyperledger syncs that work for telecoms. So LFN and Hyperledger, right? Then we have automotive-grade Linux, that have connected cars working on the edge. Massive collaboration. But, that's how it is. >> Yeah, you connect the dots but you don't, kind of, force any kind of semantic, or syntax >> No. >> into what people can build. >> Each project is autonomous, >> Yeah. >> and independent, but related. >> Yeah, it's smart. You guys have a good view, I'm a big fan of what you guys are doing. Okay, let's talk about the Open Source Summit and KubeCon, happening in China, the week of the 24th of June. >> Correct. >> What's going on, there's a lot of stuff going on beyond Cloud Native and Linux, what are some of the hot areas in China that you guys are going to be talking about? I know you're going over. >> Yeah, so, we're really excited to be there, and this is, again, life beyond Linux and Cloud Native; there's a whole dimension of projects there. Everything from the edge, and the excitement of Iot, cloud edge. We have keynotes from Tencent, and VMware, and all the Chinese- China Mobile and others, that are all focusing on the explosive growth of open-source in China, right. >> Yeah, and they have a lot of use cases; they've been very aggressive on mobility, Netdata, >> Very aggressive on mobility, data, right, and they have been a big contributor to open-source. >> Yeah. >> So all of that is going to happen there. A lot of tracks on AI and deep learning, as a lot more algorithms come out of the Tencents and the Baidus and the Alibabas of the world. So we have tracks there. We have huge tracks on networking, because 5G and implementation of ONAP and network automation is all part of the umbrella. So we're looking at a cross-section of projects in Open Source Summit and KubeCon, all integrated in Shanghai. >> And a lot of use cases are developing, certainly on the edge, in China. >> Correct. >> A lot of cross pollination-- >> Cross pollination. >> A lot of fragmentation has been addressed in China, so they've kind of solved some of those problems. >> Yeah, and I think the good news is, as a global community, which is open-source, whether it's Europe, Asia, China, India, Japan, the developers are coming together very nicely, through a common governance which crosses boundaries. >> Yeah. >> And building on use cases that are relevant to their community. >> And what's great about what you guys have done with Linux Foundation is that you're not taking positions on geographies, because let the clouds do that, because clouds have-- >> Clouds have geographies, >> Clouds, yeah they have agents-- >> Edge may have geography, they have regions. >> But software's software. (laughs) >> Software's software, yeah. (laughs) >> Arpit, thanks for coming in. Great insight, loved talking about networking, the deep learning- congratulations- and obviously the IoT Edge is hot, and-- >> Thank you very much, excited to be here. >> Have a good trip to China. Thanks for coming in. >> Thank you, thank you. >> I'm John Furrier here for CUBE Conversation with the Linux Foundation; big event in China, Open Source Summit, and KubeCon in Shanghai, week of June 24th. It's a CUBE Conversation, thanks for watching.

>> Announcer: Live from Nashville, Tennessee, it's theCUBE, covering Commvault GO 2018. Brought to you by Commvault. >> Welcome back to Nashville, Tennessee, the home of hot chicken and Commvault GO this week. I'm Stu Miniman with my co-host, Keith Townsend. Keith wasn't expecting that one. >> I'm looking forward to the hot chicken. >> Absolutely. And happy to welcome to the program first-time guest, Rahul Pawar, who is the head of R&D, research and development, at Commvault. Thank you so much for joining us. >> Thanks for having me on this one. >> Alright, we said, like the hot chicken, I said we need to roll up our sleeves and really get into the sauce-- >> Rahul: Yes, yes. of what we're talking. Alright, enough of the puns on my standpoint. But tell us a little bit about R&D inside, what's your role, what's your team, what's your charter? >> So, we have a team of about 650 very dynamic, young engineers. And what my role, and I'm very excited about that role, is I get to talk with a lot of our customers and partners and understand their pain points. And the majority of my research comes from what the customer is really looking to do and what is hurting them, and trying to solve that and describe. And once I have a problem defined, the team is very, very intelligent at solving them and they come up with various ways to solve it. And then getting that customer satisfaction high is what gives me the high and that's really what's kept me at Commvault for over 17 years now. >> Yeah, 17 years, Rahul. I think back so, 17 years ago, I was working for a storage company. And we talked about data, but it was usually about storing data or protecting data. Now we're talking about how we can get more value out of data. One of the things I was looking at coming into this show is like, okay, you talk about the AI and the ML. Well, how does that fit into this environment? Maybe you can explain why is it different now in 2018? What can you do now that you wouldn't have been able to do 10 years or even five years ago? >> So Stu, you made a good point. Back up, especially, was make a copy, put it on tape, send it to somewhere. Iron mountain, typically. And that has changed now. Everything is available online all the time. And even our thermostat is much smarter than what it was five years back, so we really are expecting, everybody's expecting, a lot more from the retail that is available from all the information that is there and they want to make use of that. So backup can no longer be, hey, I'm backing up these five servers and go figure it out. Backup is now getting tons of VM's, tons of new application swapping in various cloud applications that are coming in. So the IT team is really, really in the middle of this data revolution and getting so much information thrown their way. So that data, and that data is the liquid gold, like Bob and I like to call it, and that has a lot of valuable information. It has information about your patterns, it has information about who is accessing what files, and should they really be accessing it, what data is really, really not needed, and what is the sensitive data that is lurking behind and it could become a problem for you? So that data is a goldmine and the systems and the hard disks are becoming so much cheaper. Storage has become so much cheaper, so having that data accessible all the time, we take it for granted. >> So Rahul, I'd like to say scale breaks things. When I was a young administrator, I literally had a spreadsheet to keep track of my tapes, of where my tapes were, what systems were backed up. So even if I lost my index and my software backup product, I could know where my tapes were at. Now, with organizations with petabytes and petabytes of data, how important is ML, AI to knowing where your data is at and how important is the index to that relationship? >> I really want to say that ML and AI has become what deduplication was five years back, and pretty much everybody is expecting you to have it. Like I said, if my car knows it, if my home knows it, my thermostat knows it, even my phone knows it, like where I'm going, like every week if I travel to a certain place and it knows it, it is something that is expected to be known. And our backup environment has become so dynamic. There's network failures and there's tons of things beyond the control of the backup admin, even the storage admin or the DB-ers or the app developers who are putting in there, that just come in place. And with all of that happening, you need a system that is learning from what is happening and being very smart about doing stuff. So, we learned from yesterday's failures or the failures that were on the backups, we look at the network load that is on right now, the disk load that is on right now, and adapt our backup schedules accordingly. So we know your SLAs. You're trying to get an SLA of a certain number of hours versus minutes, and based on that, we prioritize certain servers over others, or certain VM's that we see brand new over other VM's, and then VM's around certain data stores over others because we want to keep the load on the data storage server or even your network and the proxies minimum, but at the same time we know we are racing against the clock because we want everything to be backed up and even have a secondary copy and all of that. So there we are prioritizing and re-prioritizing our backups and schedules and everything. >> One of the challenges when you talk about automation is there's the technology and then there's the people and in the open to the keynote this morning, the poet was using the GPS analogy >> Yes. and talked about, okay, you have arrived. Well, the admins today, they kind of have their turf that they control versus do I trust that it's doing the job and can automate some of those things and I shouldn't have to worry about it. Does your team get involved in that dynamic? Because I know you listen to the customers how do you help bridge that gap and help? I think of autonomous cars, we said we will soon get to the point, sometime hopefully in the not-too-distant future, where it's not that I don't trust the computers, it's really that I trust them more than I do the people. >> Okay so I'll tell you, trust develops as you use it more. There's a reason why autonomous driving cars still have a steering wheel and a break because, I'm not sure whether I can trust it. But on the other hand, as time passes by, you really see the software in action and you want to see that its really doing the smart thing, and you yield control to it more and more. Like today, I'm like old era, so when I have something important I make an extra copy. Versus my kids, they are on Google files system or cloud files systems. They never even think about making an extra copy. The same thing is going to happen. We do have people who can take control and they can put on their priorities and all of that but we are saying, hey guys, you shouldn't be doing it we are here to help you and we are going to show you and in case you don't like it you can always put your brake on that self driving car or the self driving backup. >> So Rahul would we be remised if we had a researcher on theCUBE and we didn't talk about the art of the possible looking a few years ahead, or even a couple of years ahead. If you've ever been a backup administrator, nothing beats bandwidth. The bandwidth of a station wagon full of tapes. However in this modern digital transformative environment, we have to get data to the cloud as soon as possible. What are some of the unique ways Commvault is tackling getting Big Data from where it's ingested and to the cloud provider so that we can take advantage of stuff like AI, ML, base workloads, and Amazon or Google? >> One thing we have done with the cloud or anything is we have always kept data independent of where it is going. So even if I am taking data from on-print to a cloud provider we will play to their full strength, but we will still keep the data independent where, in case you want to move from one cloud window to another you have that flexibility with Commvault. As for us taking the cloud and its efficiency and using its efficiency what we have done is we always only send re-duplicated encrypted data to the cloud and we have various ways of consuming the cloud. So the cloud is where your storage has become so cheap that you don't have to think about it. In fact, I had a customer who got rid of their whole secondary DR data center, and now they are using the cloud as their DR location and every three months they do the DR test with Commvault, wherein they bring the infrastructure machines up, and its all scripted and orchestrated, they bring the infrastructure machines up, followed by all the VM's and the applications in a certain order. Like database has to come up before AD has to come before exchange anytime it has to come before web server. So all of that happens after their testing is done they have SLA's of four hours and 24 hours on certain servers. After all of that is done they power it off, they get rid of the infrastructure, and then they are back to paying only the storage bill on the cloud. That's just one usage but the cloud has made life so flexible that I don't have to think about my rack space and where does the server go and when do I order it and when does it ship, If I need something I experiment with it, I give it more memory and size and do stuff. Protecting that data and the cloud, and protecting it well, is what we do. We have taken use of all the technologies, like replicating across regions, taking it and replicating it across clouds we have done all of that. >> Keith: Well let's talk about the importance of metadata in all of that. So if I have bits and pieces of data distributed across cloud providers on-prem, how do I keep track of that data? >> That's where our furi index comes in play key because all that is happening is the data is spreading faster than some of the cloud growth because you have data with so many copies and people have made extra copies just to be safe that keeping track of everything, and knowing what is where, and who has access to what, and people change roles, some people leave, who has access after all of that is done? It's very vital and critical for an organization to function So our furi index is keeping track of not just the bare minimum of who has the files and what the files are what we have done is we have worked with several customers where we have allowed them to insert their own custom tags and custom information along with the data. So it's not just the file and file information or the file content awareness. They are able to keep third party extra data along with every piece that is automatically queried from their other databases and inserted in that file. So those are the custom properties that are tagged a lot. >> Stu: Yeah its interesting, you think about metadata I remember five or 10 years ago we were talking about the importance of metadata, but it seems like it's the convergence of the intelligence and the AI paired with that, because it used to be, oh, make sure you tag your files or set up your ontologies or things like that, and now, on our phones, it does a lot of that for us and therefore the enterprise is following a similar methodology. Did we hit a certain kind of tipping-point recently, or is it just some of these technologies coming together? >> I think a lot of that was in the making. We used to have this technology called index cards, where we were keeping track of things, who ever thinks of that, right? Now everything is by search, and that's the new normal. Searching for your thing, thinking that somebody will know what I'm trying to do and telling me ahead of time is where the future is. That's what we are trying to keep up with. >> You're saying my kids don't know the Dewey decimal system because they have Amazon and you know, and now we have a similar thing in business. >> It really to strikes you, for a calculator on a Windows desktop when the kids go and search on the web for a calculator instead of using the calculator app on the desktop, you really know that things have changed and shifted a lot. >> Keith: So thinking about that change and shift before I'm able to add these custom tags to net new data, I'm going to throw you a softball from a use case perspective, but it's a hard technical challenge is, I have 20 years of Commvault data that are data I've backed up with Commvault. Wouldn't it be great if I could teach an ML or AI algorithm to go back and tag that data based on how I tag new data, any requests for that or roadmaps to add that type of capability? >> Alright so if you are a 20 year old Commvault veteran customer, first of all, thank you. (laughing) >> Secondly, the fact that you're index is there and we have built on our existing index and added a lot more attributes to it, we already know a lot about you. If you are starting to beam to our cloud, we know a lot more about how your backups are, and how much you are backing up, and how your licensing is, and what are the typical workloads, and the top error rates, and how the health conditions are, and a lot of that. That is even on your own server dashboard. You don't have to beam it to any public cloud. You could see it on your own dashboard, all those statistics. So we already know all of that information. What we have come and started doing is we are inserting even more and more pieces of intelligence that we are finding because things have changed over the last 20 years. So what used to be just file metadata, user and all of that, now we have a lot more attributes that the file has. >> One of the biggest challenges we see is, I'm a networking person, and when I go to like the Cisco show this year, the network administrator, most of the network that they are responsible for isn't under their purview, and I think we have the same thing in data, a lot of the data that I'm concerned about in my business it's no longer in my four walls and it's spread out in so many different environments. Opportunity? Challenge? Both? >> For us it's very exciting and opportunistic. For our customers and a lot of IT admins if you are dealing with multiple tools to handle that kind of thing its a big challenge. I have met several customers and they wouldn't admit it, but they know that even though their company policy is not to use certain clouds, the people are using it. If their company policy is not to use some doc sharing, people are using it. So, there are two ways you can look at it. You could forget it and then risk. Or you could accept it and analyze everything with Commvault and go ahead. >> So let's talk about Commvault and this ability to know where your data is at with adjacent technology you know data protection is about protecting the data not just from 'oops I lost my data' or even ran somewhere specifically, but security. What is the role of the index or metadata In protecting your data from intruders? >> So as far as 'ran somewhere' is concerned, we have taken a few things. One is, and we are not a 'ran somewhere' production per se, but what we have done is because we are in there and we look at your backup, how often they happen, how much data is changing, adjusted that to seasonality we know per quarter if you have a lot of files changing versus weekends and how things change, adjusted to seasonality if we something that is out of the norm, we are going to alert you. At that point that alert is an actionable alert where you could say, hey, I want to disable data edging on this particular client, or I want to take away access of someone on that. So even data risks like a rogue admin or an accidental admin what we did is we have added almost a two-signature kind of stuff. So if somebody accidentally deletes a client or a storage policy, one admin won't be able to do that. The business workflow says: 'do you also have authentication from Stu?' That 'hey, Keith is trying to delete this'. That's to approve of this and it's and email to which you reply 'yes' or 'no'. The moment it is done, it goes ahead and it deletes it versus it may stop and 'oops' that was an accident Keith didn't really want to do that. So there's that aspect, the second thing is our own media, what we have done is it is completely protected with our drivers, wherein you can't get to it. Only Commvault authenticated processes are able to write to write to our media. When the customer came in this morning and was talking about it, all their infrastructure was affected, but Commvault really hasn't because we had it secured and the ransomer couldn't attack that because they simply were unable to write to it. >> Stu: Alright well Rahul Pawar we really appreciate you giving us an update. Look forward to catching up in the future where we'll see exactly where the research is going. Alright, for Keith Townsend I'm Stu Miniman, we'll be back with lots more coverage here from Commvault GO, in Nashville, Tenessee. Thanks for watching theCUBE. >> Rahul: Thank you Keith, thank you Stu. >> Keith: Thank you.

>> Live from Bellevue, Washington, it's theCUBE, covering Smartsheet ENGAGE '18. Brought to you by Smartsheet. >> Welcome back to theCUBE's continuing coverage of Smartsheet ENGAGE 2018 from Bellevue, Washington, I'm Lisa Martin, and I'm sitting here with a couple of Smartsheeters. We've got Steven Branstetter, the VP of customer and partner success. And, Mike Andrews, you are the VP of strategic accounts. Guys, welcome! >> Thanks for having us. >> You're Smartsheeters! >> That's right. >> We are. >> I have to say, I was very scared to say that on the air, and I did it twice now, and I'm going to stop, 'cause I didn't mess it up. So, Steven, running customer and partner success. I want to start there, because customer success as a term can mean different things to different companies. Something that I read that you wrote recently was customers' feedback saying, "Guys at Smartsheet, you need to be operating a different playbook for customer success." So, first question: How do you define and deliver customer success at Smartsheet? >> Right, so, first of all, customer success is often looked at as a single department, and it's not. It is a whole company effort. You've talked with our product folks, talking with sales, everyone in the organization is part of that customer success. What they're telling us, what the customers are telling us is customer success primarily is about change management. We're going through a transformation that has a lot to do with your product, not everything to do with your product. But, we need help with that transformation. And, what you saw on the keynote was you saw three folks standing up who said, "I, at my organization, signed up "to do this really hard thing." And, we didn't have a playbook as to how to do that thing. What we try to do as a customer success organization, as a company, is make sure we're standing behind that person. So, when that person comes out and says, I can accomplish that thing, that unsolvable thing for our organization, and I can do that on Smartsheet, we want to make sure that person is successful. And so, sometimes, that's the customer success team. Sometimes, that's the training team. Sometimes, that's our consulting team. Sometimes, it's elements of product helping to come alongside them, showing them what's possible. So, customer success at Smartsheet is holistic. It's not meant to be a single department. This is a company effort, so that when folks do raise that hand and take on that impossible task, that we're with them to make sure they can accomplish that. And, that creates the stories that you heard earlier today. >> And, what Steven's talking about is, during the general session this morning, the CEO, Mark Mader, actually went down to the audience and just randomly asked several, maybe three customers to talk about how Smartsheet is empowering them. And, it was really interesting how articulate they were, being put on the spot. But, how they were able to speak so eloquently to how they are facilitating this transformation. You mentioned change management. That's a hard thing to do. >> It is. When you're looking at an enterprise that has a ton of applications, and, Mike, you know this well, being a sales leader, they're comfortable with certain applications, yet companies grow organically by acquisition, and there's a lot of different tools that some groups are married to. Other groups are, eh, I'm not so sure. To transform digitally, cultural transformation is probably step one. So, how are you seeing, and, this is the second part question to you, Mike. How have you evolved CS in Smartsheet to be facilitators of that change management, not only for customers, but for you guys as well? >> So, one of the things we thought early on was, we tried this new thing, it was called Office Hours, and we did it at one of our largest customers, and it was a huge success. Literally, the first day we do it, 400 people show up on this webcast, and it was fantastic. And so, I talk with Mike, and we talked with organizations saying, we have this new thing, Ii's going to be amazing. The feedback was fantastic. We go to that next organization to roll out the same thing, and four people show up instead of 400. >> Wow. >> And so, one of the things that's been really important for us is understanding not all organizations are the same, especially in the enterprise. That, as we create that playbook, there's certain elements that absolutely resonate at, maybe, our tech customers, that don't resonate at all in the manufacturing space or organizations, and that each of those organizations are different. So, we've built a lot of that playbook with an understanding that different elements of it are going to be applicable at different organizations. And, that's the way we've approached it, which has been really successful, where we know there are elements that have to happen. We know there are elements where we need to have scalable programs. Not everything can be one-on-one. But, at most organizations, there has to be some level of one-on-one connection as well. And, whether that's a big Smartsheet day which we'll run, which folks will fly their own folks into, it's almost like a mini ENGAGE conference at their own organization. Or, whether that's all over the Web. So, we'll go to some customers. We'll show up in person, and there's a big meeting room, there's only four people there. And, they tell us, well, there's actually 200 people watching this. And so, it's figuring out that motion, at least at the enterprise, that's different for every organization. But, as you also know, we have a long tail through our organization as well. So, while we have those really large customers, we also have this long tail where we need to meet those customers at scale. We need to provide programs. So, our Center of Excellence is a good example of that. Our Webinar series is good example of that, where we provide these motions that at a scaled element, so even our smallest customer can take advantage of it. >> Awesome, so, Mike, transitioning over to you. So, I love stats. Geeky, very geeky, but I admit it freely. I was looking at Smartsheet, 75000 customers. Here, you have about 1100 companies represented over 20 countries. You guys have presence in half the Fortune 500, 90% of the Fortune 100, lot of customers, pan industry. Some of the things that they were hearing from you guys, or, rather, you're hearing from them is, we want you to build for scale, as you were talking about, Steven. We want you to teach us how to phish. And, they want you, also, to help them do it right and do it fast. How are you helping customers do it right and do it fast? Can you do both at the same time? >> Absolutely, we're proving that. And, I think, something that's really unique about how we go to market, and really the basis of our ethos as a business, is we're obsessed with keeping the software easy to use. And, as we add functionality to not get it heavy and put friction in place. So, when we think about engaging with the biggest companies in the world, we have the benefit of starting from organic adoption, where individuals and teams are using the software. They're experiencing value, they're sharing. They're collaborating. And what we see happen, the dynamic we see happening is, as individuals share and go to directors or VPs, we start from sort of work execution, project management, task tracking, and the next step is often these line of business solutions, whether MNA or product planning or employee engagement. Literally every function in the business can benefit from the ability to configure the software. And, keep in mind, we've already taken off the table the biggest issue. I've been in enterprise software for 30 years. I've sat with a lot of CIOs who've written seven figure checks. And, when they're honest with me, the biggest thing they worry about is: Is this software going to get used? We take that issue off the table. We turn it on its head. And, that ability to have that basis of adoption, to have raving fans who love using the software, and then the added benefit of being able to go higher in an organization with senior leaders who want transparency. They want speed. They want accountability. That configurability to solve bigger and bigger, more complex, more strategic flows is a huge advantage for us. It's, frankly, what fuels us, sort of our passion around serving our customers, because we get such great feedback. >> That configurability that you mentioned, Mike, kind of seems to be how customer success is set up. To be configurable, sort of modular, to be able to adjust it with the agility that's needed to deliver what these customers are needing. So, sounds like, maybe, land and expand. I know we've got a gentleman from the office of the CIO at PayPal who's going to be on shortly with us, really helping the C-Suite at PayPal, which everyone uses to be able to see things more clearly, have that transparency in terms of managing projects. >> Absolutely. >> So, I know Cisco's a customer as well. So, is it pretty typical to start with a function within marketing, for example, where there's a team that, hey, this is innovative. This is going to integrate with Jira and Slack, and all these things. Is that kind of a common sales conversation? >> Absolutely. We practice the principles of the challenge your sale and challenge your customer. And, one of the key elements of the challenge your customer is this idea of a mobilizer. And, the mobilizer does two things. They drive change, and they build consensus. And, what we find is those individuals who are change agents often times love our software, because they can do things that they wouldn't otherwise, they'd have to depend on a consultant or IT. So, we find those individuals and we work with them, and they coach us up on: what are the priorities, who are the key players?" And, that becomes a common play we run to get higher in the organization. The other thing that's happening now, I'm seeing it, really, over the past year, is organizations are starting to choose to sort of play offense with us. So, we'll continue to have that bottoms-up organic growth. But now, we're seeing VPs of marketing or CMOs, or CFOs or COOs realize, hey, you know what? I love the fact I have this base of users who love the software, and I can do things, I can enable priorities or initiatives that span the organization, get away from side-load apps, and have the kind of visibility and speed that's been unheard of. And, we're starting to see that our customers wanting to play offense with us. >> That speed to value element has just been critical. So, you heard in the stories this morning, we have MOD Pizza. Their first solution, the gentleman probably built that in a day. And, that was just to roll out one store, and then they rolled out eight the next year. And, I'm sure they made some modifications there. And then, they need to go from eight to around 200 in a year. And, they were able to do that very quickly. They were able to take an existing solution and make the modifications, add in one more element, which is control center for us, to make it that much more scalable. So, when you talk about the land and expand motion, it's both within the customer as a whole, but on a solution as well, where we have story after story where someone starts a new initiative. They don't know whether it's going to work out. It works out really well, and that effort they put into the initial solution isn't lost. They don't have to switch over to a different application, because it's now gotten too big, or some element like that. The software and the application is able to grow with their growth as a business, which eliminates a lot of those things that often happens in business, where you have to pause something that's growing to replace a software. >> Right, so, in terms of the feedback loop, you obviously, as you were describing, Steven, the customer success program you're running here is very cross-functional, very collaborative. It's product management. It's marketing, it's sales, it's IT. It's all these groups that need to come together. What is the process like, maybe from both of your perspectives, Steven, starting with you, of getting customer feedback when they're engaging with their customer success manager, for example, and they want a feature that is not quite there yet, How do you take that feedback from the customers, from the field, and start to really prioritize that internally? >> So, let me start. So, one of the things we've introduced this year is, as we've grown the field organization, is we're using our own software, and we've built these territory hubs. So, the account exec, the SC, the CSM, the SSR, the internal team, everyone is on the same page, as it relates to what we're doing in the account. And, we run weekly meetings. We check off on priorities and to-dos. So, you have that visibility by use of our own platform. So, everybody's on the same page. And, that idea of signal that we talk about, that Gina Mark talked about, it starts with that team that is right there with the customer, and then we feed it. Often times, I'll let Steven take the hand off. So, we have that signal. We have the pulse right with the customer with these field teams, and then that gets fed. And, I'll let Steven talk about how we drive it here sort of in Bellevue. >> Yeah, so, there's two elements of getting that signal, and I'm sure there's more, if you think about it. But, one is from the internal team, and one is the feedback from the customer. And, we, not surprisingly, have used the Smartsheet application to do that. But, any time we're getting a customer signal. That could be from our community, that could be coming in from a support ticket, that could be a conversation with a customer success manager, could be from any site. That feedback then goes into a Smartsheet form, and that goes directly to the product management team. And, anyone who has submitted that from a support rep perspective, for example, gets visibility to where that stands in the progress. So, is it something we're looking into? Is it in progress? If there's a date to it, what does that look like? So, we get all that. And then, the other element is we are huge users of Smartsheet internally. And, Mark likes to talk about that he is the biggest user of the mobile application across our whole customer base, and he probably is. But, we absolutely eat our own dog food there, or drink our own champagne. >> I like that one better. >> Probably a better one. And, that motion really helps us understand how to use the application, so Dynamic View, which was launched this week. We're going to be one of the biggest users of that right out of the gate. For the example that I just brought up, what Dynamic View allows us to do is it allows us to provide a view of all of those submissions of request, and the right view to the right company, or the right internal stakeholders, so they know exactly what that status is. So, those are two ways that we get that feedback back into our producting. >> Mike, you said you've been in sales for a long time. How helpful in a sales situation is the fact that you do drink your own champagne? >> Huge, it's huge. >> On Smartsheet, I imagine, a lot of companies don't show that. >> It's a really big deal; anybody who's, really anybody in the company. Anybody's who's touching the customer, When I hire people, the ability to have that confidence and understand how to use and speak from personal experience that fuels passion, it fuels credibility, and it's authentic, which is one of our core values. And then, so much of it is the art of the possible on the whiteboard with the customer. This ability to move from an idea, we've literally mapped out processes, and within 30 minutes, the essay's in there, and we've prototyped a solution. And, not only is it a quality solution, but the customer's blown away by the speed with which we've done it. But, that starts with that deep understanding of the platform and all the functionality, and what you can do with it. >> Right, I'm sure that breeds that authenticity that Gene actually talked about. Well, we're almost out of time, but I want to quickly, Steven, talk about the Partner Success Program. You guys partner with Amazon, Oracle, NetSuite, Salesforce, Slack, Google, I'm probably leaving out a few. Talk to us a little bit about the partner evolution as you compete with some of these partners as well. >> Well, I'm going to switch that a little. So, we have two elements of partners. So, we have those technology partners that you're speaking to. And then, we have the solution provider partners and resellers; that's more in my world. But, what's been really exciting about those folks and, we had a big partner day yesterday, so I'm kind of coming off the high of talking with all these folks. And, one of the things that we hear over and over again is whatever their focus is. So, sometimes, that's a geography focus. Sometimes, that's an industry focus. They tell us how much we're missing already. So, they'll say, if I'm focused on the accounting industry, they'll say, you guys don't even know how great your off the shelf application is in the accounting world. And, what they're so excited about is being able to configure it, being able to build the applications on top of Smartsheet. That then, they can bring to that world, so that, from a scale perspective, we don't have to be experts in accounting. We don't have to be experts in any of those different verticals or in those geographies. We can leverage those partners, their expertise, their relationships, in order to bring that to market in each of those areas. >> Any feedback, I know we're out of time. But, any feedback on some of the announcements that came out today from some of your key partners, besides two thumbs way up? >> They were extremely excited about Dynamic View and seeing what's possible from a new solution perspective. They were just like the rest of the customers. So, when there was the final slide showing all the new features we're bringing, all the phones came out to take pictures. It was a great scene, and they were definitely in that mix. >> Excellent, well, Steven and Mike, thanks so much for stopping by theCUBE and sharing with us how you're transforming, how the customers are able to evolve and transform with your technology. We know you have a lot of meetings to get to, so we'll let you go to that. >> Thank you very much. >> Thank you. >> We want to thank you for watching theCUBE. I'm Lisa Martin live at Smartsheet ENGAGE 2018. Stick around, I'll be right back with my next guest. (techno music)

>> Live from Washington, D.C., it's theCUBE! Covering Inforum D.C. 2018. Brought to you by Infor. >> Well, welcome back to Washington, D.C., We are alive here at the Convention Center at Inforum 18, along with Dave Vellante, I'm John Walls. It's a pleasure now, welcome to theCUBE, Leigh Martin, who is the Senior Director of the Dynamic Science Labs at Infor, and good afternoon to you Leigh! >> Good afternoon, thank you for having me. >> Thanks for comin' on. >> Thank you for being here. Alright, well tell us about the Labs first off, obviously, data science is a big push at Infor. What do you do there, and then why is data science such a big deal? >> So Dynamic Science Labs is based in Cambridge, Massachusetts, we have about 20 scientists with backgrounds in math and science areas, so typically PhDs in Statistics and Operations Research, and those types of areas. And, we've really been working over the last several years to build solutions for Infor customers that are Math and Science based. So, we work directly with customers, typically through proof of concept, so we'll work directly with customers, we'll bring in their data, and we will build a solution around it. We like to see them implement it, and make sure we understand that they're getting the value back that we expect them to have. Once we prove out that piece of it, then we look for ways to deliver it to the larger group of Infor customers, typically through one of the Cloud Suites, perhaps functionality, that's built into a Cloud Suite, or something like that. >> Well, give me an example, I mean it's so, as you think-- you're saying that you're using data that's math and science based, but, for application development or solution development if you will. How? >> So, I'll give you an example, so we have a solution called Inventory Intelligence for Healthcare, it's moving towards a more generalized name of Inventory Intelligence, because we're going to move it out of the healthcare space and into other industries, but this is a product that we built over the last couple of years. We worked with a couple of customers, we brought in their loss and data, so their loss in customers, we bring the data into an area where we can work on it, we have a scientist in our team, actually, she's one of the Senior Directors in the team, Dawn Rose, who led the effort to design and build this, design and build the algorithm underlying the product; and what it essentially does is, it allows hospitals to find the right level of inventory. Most hospitals are overstocked, so this gives them an opportunity to bring down their inventory levels, to a manageable place without increasing stockouts, so obviously, it's very important in healthcare, that you're not having a lot of stockouts. And so, we spent a lot of time working with these customers, really understanding what the data was like that they were giving to us, and then Dawn and her team built the algorithm that essentially says, here's what you've done historically, right? So it's based on historic data, at the item level, at the location level. What've you done historically, and how can we project out the levels you should have going forward, so that they're at the right level where you're saving money, but again, you're not increasing stockouts, so. So, it's a lot of time and effort to bring those pieces together and build that algorithm, and then test it out with the customers, try it out a couple of times, you make some tweaks based on their business process and exactly how it works. And then, like I said, we've now built that out into originally a stand-alone application, and in about a month, we're going to go live in Cloud Suite Financials, so it's going to be a piece of functionality inside of Cloud Suite Financials. >> So, John, if I may, >> Please. >> I'm going to digress for a moment here because the first data scientist that I ever interviewed was the famous Hilary Mason, who's of course now at Cloudera, but, and she told me at the time that the data scientist is a part mathematician, part scientist, part statistician, part data hacker, part developer, and part artist. >> Right. (laughs) >> So, you know it's an amazing field that Hal Varian, who is the Google Economist said, "It's going to be the hottest field, in the next 10 years." And this is sort of proven true, but Leigh, my question is, so you guys are practitioners of data science, and then you bring that into your product, and what we hear from a lot of data scientists, other than that sort of, you know, panoply of skill sets, is, they spend more time wrangling data, and the tooling isn't there for collaboration. How are you guys dealing with that? How has that changed inside of Infor? >> It is true. And we actually really focus on first making sure we understand the data and the context of the data, so it's really important if you want to solve a particular business problem that a customer has, to make sure you understand exactly what is the definition of each and every piece of data that's in all of those fields that they sent over to you, before you try to put 'em inside an algorithm and make them do something for you. So it is very true that we spend a lot of time cleaning and understanding data before we ever dive into the problem solving aspect of it. And to your point, there is a whole list of other things that we do after we get through that phase, but it's still something we spend a lot of time on today, and that has been the case for, a long time now. We, wherever we can, we apply new tools and new techniques, but actually just the simple act of going in there and saying, "What am I looking at, how does it relate?" Let me ask the customer to clarify this to make sure I understand exactly what it means. That part doesn't go away, because we're really focused on solving the customer solution and then making sure that we can apply that to other customers, so really knowing what the data is that we're working with is key. So I don't think that part has actually changed too much, there are certainly tools that you can look at. People talk a lot about visualization, so you can start thinking, "Okay, how can I use some visualization to help me understand the data better?" But, just that, that whole act of understanding data is key and core to what we do, because, we want to build the solution that really answers the answers the business problem. >> The other thing that we hear a lot from data scientists is that, they help you figure out what questions you actually have to ask. So, it sort of starts with the data, they analyze the data, maybe you visualize the data, as you just pointed out, and all these questions pop out. So what is the process that you guys use? You have the data, you've got the data scientist, you're looking at the data, you're probably asking all these questions. You get, of course, get questions from your customers as well. You're building models maybe to address those questions, training the models to get better and better and better, and then you infuse that into your software. So, maybe, is that the process? Is it a little more complicated than that? Maybe you could fill in the gaps. >> Yeah, so, I, my personal opinion, and I think many of my colleagues would agree with me on this is, starting with the business problem, for us, is really the key. There are ways to go about looking at the data and then pulling out the questions from the data, but generally, that is a long and involved process. Because, it takes a lot of time to really get that deep into the data. So when we work, we really start with, what's the business problem that the customer's trying to solve? And then, what's the data that needs to be available for us to be able to solve that? And then, build the algorithm around that. So for us, it's really starting with the business problem. >> Okay, so what are some of the big problems? We heard this morning, that there's a problem in that, there's more job openings than there are candidates, and productivity, business productivity is not being impacted. So there are two big chewy problems that data scientists could maybe attack, and you guys seem to be passionate about those, so. How does data science help solve those problems? >> So, I think that, at Infor, I'll start off by saying at Infor there's actually, I talked about the folks that are in our office in Cambridge, but there's quite a bit of data science going on outside of our team, and we are the data science team, but there are lots of places inside of Infor where this is happening. Either in products that contains some sort of algorithmic approach, the HCM team for sure, the talent science team which works on HCM, that's a team that's led by Jill Strange, and we work with them on certain projects in certain areas. They are very focused on solving some of those people-related problems. For us, we work a little bit more on the, some of the other areas we work on is sort of the manufacturing and distribution areas, we work with the healthcare side of things, >> So supply chain, healthcare? >> Exactly. So some of the other areas, because they are, like I said, there are some strong teams out there that do data science, it's just, it's also incorporated with other things, like the talent science team. So, there's lots of examples of it out there. In terms of how we go about building it, so we, like I was saying, we work on answering the business, the business question upfront, understanding the data, and then, really sitting with the customer and building that out, and, so the problems that come to us are often through customers who have particular things that they want to answer. So, a lot of it is driven by customer questions, and particular problems that they're facing. Some of it is driven by us. We have some ideas about things that we think, would be really useful to customers. Either way, it ends up being a customer collaboration with us, with the product team, that eventually we'll want to roll it out too, to make sure that we're answering the problem in the way that the product team really feels it can be rolled out to customers, and better used, and more easily used by them. >> I presume it's a non-linear process, it's not like, that somebody comes to you with a problem, and it's okay, we're going to go look at that. Okay now, we got an answer, I mean it's-- Are you more embedded into the development process than that? Can you just explain that? >> So, we do have, we have a development team in Prague that does work with us, and it's depending on whether we think we're going to actually build a more-- a product with aspects to it like a UI, versus just a back end solution. Depends on how we've decided we want to proceed with it. so, for example, I was talking about Inventory Intelligence for Healthcare, we also have Pricing Science for Distribution, both of those were built initially with UIs on them, and customers could buy those separately. Now that we're in the Cloud Suites, that those are both being incorporated into the Cloud Suite. So, we have, going back to where I was talking about our team in Prague, we sometimes build product, sort of a fully encased product, working with them, and sometimes we work very closely with the development teams from the various Cloud Suites. And the product management team is always there to help us, to figure out sort of the long term plan and how the different pieces fit together. >> You know, kind of big picture, you've got AI right, and then machine learning, pumping all kinds of data your way. So, in a historical time frame, this is all pretty new, this confluence right? And in terms of development, but, where do you see it like 10 years from now, 20 years from now? What potential is there, we've talked about human potential, unlocking human potential, we'll unlock it with that kind of technology, what are we looking at, do you think? >> You know, I think that's such a fascinating area, and area of discussion, and sort of thinking, forward thinking. I do believe in sort of this idea of augmented intelligence, and I think Charles was talking a little bit about, about that this morning, although not in those particular terms; but this idea that computers and machines and technology will actually help us do better, and be better, and being more productive. So this idea of doing sort of the rote everyday tasks, that we no longer have to spend time doing, that'll free us up to think about the bigger problems, and hopefully, and my best self wants to say we'll work on famine, and poverty, and all those problems in the world that, really need our brains to focus on, and work. And the other interesting part of it is, if you think about, sort of the concept of singularity, and are computers ever going to actually be able to think for themselves? That's sort of another interesting piece when you talk about what's going to happen down the line. Maybe it won't happen in 10 years, maybe it will never happen, but there's definitely a lot of people out there, who are well known in sort of tech and science who talk about that, and talk about the fears related to that. That's a whole other piece, but it's fascinating to think about 10 years, 20 years from now, where we are going to be on that spectrum? >> How do you guys think about bias in AI and data science, because, humans express bias, tribalism, that's inherent in human nature. If machines are sort of mimicking humans, how do you deal with that and adjudicate? >> Yeah, and it's definitely a concern, it's another, there's a lot of writings out there and articles out there right now about bias in machine learning and in AI, and it's definitely a concern. I actually read, so, just being aware of it, I think is the first step, right? Because, as scientists and developers develop these algorithms, going into it consciously knowing that this is something they have to protect against, I think is the first step, for sure. And then, I was just reading an article just recently about another company (laughs) who is building sort of a, a bias tracker, so, a way to actually monitor your algorithm and identify places where there is perhaps bias coming in. So, I do think we'll see, we'll start to see more of those things, it gets very complicated, because when you start talking about deep learning and networks and AI, it's very difficult to actually understand what's going on under the covers, right? It's really hard to get in and say this is the reason why, your AI told you this, that's very hard to do. So, it's not going to be an easy process but, I think that we're going to start to see that kind of technology come. >> Well, we heard this morning about some sort of systems that could help, my interpretation, automate, speed up, and minimize the hassle of performance reviews. >> Yes. (laughs) >> And that's the classic example of, an assertive woman is called abrasive or aggressive, an assertive man is called a great leader, so it's just a classic example of bias. I mentioned Hilary Mason, rock star data scientist happens to be a woman, you happen to be a woman. Your thoughts as a woman in tech, and maybe, can AI help resolve some of those biases? >> Yeah. Well, first of all I want to say, I'm very pleased to work in an organization where we have some very strong leaders, who happen to be women, so I mentioned Dawn Rose, who designed our IIH solution, I mentioned Jill Strange, who runs the talent science organization. Half of my team is women, so, particularly inside of sort of the science area inside of Infor, I've been very pleased with the way we've built out some of that skill set. And, I'm also an active member of WIN, so the Women's Infor Network is something I'm very involved with, so, I meet a lot of people across our organization, a lot of women across our organization who have, are just really strong technology supporters, really intelligent, sort of go-getter type of people, and it's great to see that inside of Infor. I think there's a lot of work to be done, for sure. And you can always find stories, from other, whether it's coming out of Silicon Valley, or other places where you hear some, really sort of arcane sounding things that are still happening in the industry, and so, some of those things it's, it's disappointing, certainly to hear that. But I think, Van Jones said something this morning about how, and I liked the way he said it, and I'm not going to be able say it exactly, but he said something along the lines of, "The ground is there, the formation is starting, to get us moving in the right direction." and I think, I'm hopeful for the future, that we're heading in that way, and I think, you know, again, he sort of said something like, "Once the ground swell starts going in that direction, people will really jump in, and will see the benefits of being more diverse." Whether it's across, having more women, or having more people of color, however things expand, and that's just going to make us all better, and more efficient, and more productive, and I think that's a great thing. >> Well, and I think there's a spectrum, right? And on one side of the spectrum, there's intolerable and unacceptable behavior, which is just, should be zero tolerance in my opinion, and the passion of ours in theCUBE. The other side of that spectrum is inclusion, and it's a challenge that we have as a small company, and I remember having a conversation, earlier this year with an individual. And we talk about quotas, and I don't think that's the answer. Her comment was, "No, that's not the answer, you have to endeavor to reach deeper beyond your existing network." Which is hard sometimes for us, 'cause you're so busy, you're running around, it's like okay it's the convenient thing to do. But you got to peel the onion on that network, and actually take the extra time and make it a priority. I mean, your thoughts on that? >> No, I think that's a good point, I mean, if I think about who my circle is, right? And the people that I know and I interact with. If I only reach out to the smallest group of people, I'm not getting really out beyond my initial circle. So I think that's a very good point, and I think that that's-- we have to find ways to be more interactive, and pull from different areas. And I think it's interesting, so coming back to data science for a minute, if you sort of think about the evolution of where we got to, how we got to today where, now we're really pulling people from science areas, and math areas, and technology areas, and data scientists are coming from lots of places, right? And you don't always have to have a PhD, right? You don't necessary have to come up through that system to be a good data scientist, and I think, to see more of that, and really people going beyond, beyond just sort of the traditional circles and the traditional paths to really find people that you wouldn't normally identify, to bring into that, that path, is going to help us, just in general, be more diverse in our approach. >> Well it certainly it seems like it's embedded in the company culture. I think the great reason for you to be so optimistic going forward, not only about your job, but about the way companies going into that doing your job. >> What would you advise, young people generally, who want to crack into the data science field, but specifically, women, who have clearly, are underrepresented in technology? >> Yeah, so, I think the, I think we're starting to see more and more women enter the field, again it's one of those, people know it, and so there's less of a-- because people are aware of it, there's more tendency to be more inclusive. But I definitely think, just go for it, right? I mean if it's something you're interested in, and you want to try it out, go to a coding camp, and take a science class, and there's so many online resources now, I mean there's, the massive online courses that you can take. So, even if you're hesitant about it, there are ways you can kind of be at home, and try it out, and see if that's the right thing for you. >> Just dip your toe in the water. >> Yes, exactly, exactly! Try it out and see, and then just decide if that's the right thing for you, but I think there's a lot of different ways to sort of check it out. Again, you can take a course, you can actually get a degree, there's a wide range of things that you can do to kind of experiment with it, and then find out if that's right for you. >> And if you're not happy with the hiring opportunities out there, just start a company, that's my advice. >> That's right. (laughing together) >> Agreed, I definitely agree! >> We thank you-- we appreciate the time, and great advice, too. >> Thank you so much. >> Leigh Martin joining us here at Inforum 18, we are live in Washington, D.C., you're watching the exclusive coverage, right here, on theCUBE. (bubbly music)

>> Hello everyone, welcome to a special CUBE conversation. We're here in our Palo Alto studios. I'm John Furrier host of theCUBE and we're here with Bipul Sinha, Co-Founder and CEO of Rubrik, one of the hottest startups in Silicon Valley. Great to have you here in the cube. Thank you so much for this opportunity. So thanks for coming in. You guys have $292 million in funding led the Series A with Lightspeed, Series B with Greylock Series C with Khosla, Series D with IVP. You've got celebrities like Kevin Durant, Frank Slootman, rockstar investors. Great momentum. John Thompson. Just join your board recently. He's on the board of Microsoft as well.  All since 2014, like short time. Congratulations. >> Thank you so much. And we have been very fortunate to have the market traction and demand for Rubrik's for what is now cloud data management product. When we started the company we saw a market need around simplification, cloud enablement and really automating, orchestrating, backup recovery, recovery archive and DR across on-premises and the cloud. >> You guys. Had it been pretty good run here. You've got a new CFO. Talk about that. News, I want to get that out. There was the new CFO, we have >> Our new CFO is Murray Demo. We hired him out of Atlassian where he, he joined the company and took the company public and then the company next two years become like a very fast growing, very successful public company. Our goal is to build Rubrik into the next 30-, 40-years iconic company and we're building a management team that, that will have the firepower and the and the talent to take this company to really become the standard for data management. >> Yeah. I want to get to that. That's I think the big story for you guys is that you've now come out of nowhere, but it's just, you know, the classic startup story, great investors, but you know, we'd go to all the events. We see you guys out there just all of a sudden, just a massive runs. You put the foundation together. Um, you've publicly said you, you're on a $300 million run rate. Great numbers. So great growth. What's take us inside Rubrik. I mean, how is this all working when you guys got good funding? You've got a great management team. What's the core strategy? How it. Why is it working for you guys? >> The core of Rubrik is our culture because technology evolves product. What is invariant is Rubrik's, culture, our culture of transparency, the culture of velocity. The culture of relentlessness is actually drives Rubrik. When we bring new employees into Rubrik, we tell them that it's not about what makes your boss happy or what makes the CEO of this company happy. What moves the agenda of this company? Always think about how do we make or give Rubrik the best opportunity that company can get and we'd drive on that basis so there is no ego, there is no superiority that sales is better than or engineering is a 'know-it-all' and Gods. It's all about how do we collectively build the foundation of a long lasting large public company. >> So that early DNA about that DNA. Where's that come from? The come from the product side engineering side. What? Where's that core DNA of that teamwork come from. >> The core DNA of the team is Google, Facebook, Oracle software. Essentially folks who built the largest scale distributed system, very strong industrial strength enterprise product that powers most of the large enterprises in the world, so we took these two thoughts, of Oracle-like industrial product and Google, Facebook, Amazon- like a scale-out distributed infrastructure and brought together in a single product. >> It's interesting. Lightspeed does it. A lot of interesting deals that were once poo-pooed by many in the industry. Nutanix was one and you mentioned Facebook, Google, these are not, I won't say cloud native. They basically built the cloud. They had to build their own hyperscale or they build their own infrastructure all on open source so you have that generational DNA with it from the tech standpoint and and market standpoint. And Nutanix is a great example because they, you know, they brought all this together. This is a new new kind of view. This is a modern perspective that you guys are taking. I want to ask you as you look at the cloud, and a lot of people were poo-pooing Amazon in the early days and look at them, they've run the table, the number one by miles and public cloud. No one's even close in my opinion, but you know, this is a whole new seat change, so you've got Facebook, you've got the Google's got the Nutanixs is of the world out there who were doing things different. Now are the standard. What are you guys doing that someone might say, I don't really get that yet. Or poo-pooing it that you think is a modern approach and that's different. >> See, the issue really is that how do enterprises take advantage of public cloud simplicity, agility, scale, without being bothered by it because the word, because the cloud is a programmatic paradigm, enterprise previously has been a declarative paradigm. How do you bring these two worlds together and really create a seamless platform where enterprises can automate, orchestrate and secure their data, and that has been the vision of Rubrik. The vision of Rubrik is simplicity at the scale with cloud-enabled a single software fabric across on premises and public cloud. That has been the vision of the company and we have been delivering our product from the very beginning. On this vision, we are just adding one blade after the next, after the next blade to really go be a single software platform across multiple clouds and data centers. >> That's great. Again, sounds like data's at the center of the value proposition from your. From your good discussion. Clearly Facebook status center, their value proposition, although under a lot of criticism today, Google as a data company, as companies realize that data is critical for their business, how do they transform it from what used to be because the old way was fenced-off data warehouse or some sort of batch siloed software stack and now that with all kinds of new things like GDPR for instance, and it's coming around the corner, all these headaches are emerging where it's like, wow, this is really painful, but they want to get to a seamless way, so what's going on there? Can you explain in simple way that that transition from the old data modeling where you had siloed stacks or you know, old fenced out data warehouses to something that's really agile somewhere data's a part of the intellectual property, part of the software fabric. >> This is a really insightful question because you have a dichotomy here. The dichotomy is on one side, data is the biggest strengths and biggest asset for all enterprises. On the other side there is a. there is a risk of a bad uses of that data and and and companies private or people's private information getting out. So how do enterprises or businesses create a platform where they can secure their data, they can provide access to the data, to the relevant people or applications in a very controlled and secure way and at the same time protect this strategy asset from tech, from ransomware, from just proliferating or losing, so, so the traditional industry focused on really building a storage platforms for it, but our view is that the storage platform is just the keeper of the data, but the real issue is that how do you automate, orchestrate and secure access to the data because data can be on premises, data can be public clouds, but really this data control plane that actually manages and secures and provides access to this data is the critical piece and that's the Rubrik's focus. >> All right, let's get into. I want to get into the new product announcement before we get there. I want to get your thoughts on architecture because a lot of people have been enamored and using successfully Amazon web services and some are saying that, oh, Amazon is the roach motel. Why don't you check in, you can check out with respect to your data center saying data portability is coming around the corner, but to move data around the cloud is not that easy. Um, so customers are building on Amazon but they also might have azure. So multi-cloud is out there and you can also. Google's got some great stuff going on with Tensorflow and other things that they'd got rolling out, but there's not a one cloud fits all for all workloads. Certainly in the enterprise. And then you've got the on premise, a dynamic. How do, how do you view that? Because now that's an opportunity for you guys, but also a challenge for the customers where they start using the public cloud for business benefits and then realize, well we got a lot of data in there and then it becomes a data opportunity and problem. What's your view of that landscape? >> So the VC, the whole data management, it is Rubrik is creating a whole new better diamond platform because architects really. We thought about this as something where you combine the data and metadata together so that you data becomes self describing. This is a very architectural thing that Rubrik debt because when data understand where it came from and who he he or she is, then you can take this data from on premises to the cloud and powered it on or go from cloud to cloud and power it on some other place, so this core fundamental vision and architecture of data plus deeply connected together and mobile is what really powers Rubrik and that is the fundamental platform and fundamental architecture of Rubrik and that is our view in the future. Saying that once you create the self describing data and this will see a data from the underlying infrastructure, then you give the true power of the data back to the customer because data knows where it came from, which application it is associated with, who has access to it and who can use it. That's where you see the real power of multi-cloud, multi data center, independence of data and application from the infrastructure. >> So you believe data should be friction-less with respect to where it should go at any given time. >> Absolutely. I mean that's where the power, the enterprises and businesses can realize from their data because they can actually collaborate, they can give more access to their data, to their own users without worrying about the wrong data falling into the wrong hands. Can they actually transcreate transport of the data? Can they not stuck in one infrastructure versus take the data wherever they find data to be most applicable, easiest to use and more secure. >> That's great. So we don't want to jump into a new announcement. Before we get there. I want you to just take a minute to explain, um, Rubriks, target customer that you guys are serving today. You get 900 employees, you've got over $300 million run rate in business. Who's buying the product? Why was it a physician? Who's the buyer? What's the value proposition of the offering? >> So we sell into a enterprises. So we are not an SMB product. We sell into the enterprise, I would buy it as our cloud architects, our buyers, our infrastructure architects are buyers are virtualizing architects, uh, folks who are thinking about automation, orchestration, security of the data, recoverability of the data, protection from ransomware, things like that. And that's our core technical and economic buyers and, uh, and, and the core businesses or people who have, um, who have employees more than. So, cloud transformation is classic. Absolutely functional guys are involved in. That's the big driver for Rubrik. Rubin's growth is indexed on the cloud, about has it on their agenda. >> All right, so let's get into the hard news. You guys are launching Rubrik's Polaris, the industry's first SaaS platform for data management applications. I'm smiling because whenever I see first I want to know what that means. I've seen data application platforms out there. I've seen SaaS. So SaaS is not new. What makes you guys first talk about this dynamic, about polaris? What, what is it? Why is it first? >> So the way we see our customers use multiple clouds and multiple data centers is they have some applications running on premises. Some applications running in the cloud, they're building a lot of new applications in the cloud, so essentially cloud is is fragmenting their data and applications and we have Rubrik core product or cloud data management product, wherever they run their application, so Rubrik product runs on premises. Rubrik product runs in the cloud to protect the application. >> Was that the first dynamic that it's on-prem? It's oncloud, >> Yeah, that's our first product and then what we will working with our customers was that once we have this setup, how do you bring all of your applications and all of your data under the single system of record and that is the Rubrik Polaris Platform which is complimentary to our first hybrid cloud product were to the single system of record, which is a global catalog of all the applicants and data content as well as workflows as well as security as well as orchestration, and we expose this to open apis for Rubrik as well as other third party vendors to really build applications no matter where application runs. >> So these applications, the data management application that people or Rubrik will build on top of politics is for compliance, for governance, for auditing, for search across all the infrastructure. So you guys are offering also an ecosystem play with the Polaris. You're enabling others to build on top of it. Absolutely. This is kind of like force.com platform for all your data management. >> So we started salesforce, a Mulesoft had an announcement and that got a lot of attraction. What does that mean to you guys? Because that's. You see sap, salesforce has been very successful for a SaaS platform as well as Mulesoft. What does that acquisition mean to the marketplace and how do you guys fit into that dynamic vis-a-vis that trend? >> Salesforce did a great strategic acquisition or Mulesoft because they realize that if they combine applications on premises as well as in the cloud, then they create a single platform for all the structure data applications, but our view is that this is just half of the problem are that half of the problem is on a structured data across many applications and all the Meta data Rubrik. Polaris is our SaaS platform across on-premise cloud. A single system of record with Apis were Rubrik will deliver data management applications for control, for governance, for compliance, for security across all applications that enterprises are managing, whether they're. Are these applications run on premises or in the cloud, >> And the unstructured data too is that metadata you're talking about, it's critical data. >> It's metadata is application data is is all your unstructured data, >> So bottom line is announced that why would you put this in a single sound byte for customers? What does it mean for me if I'm a customer? For you guys, what's the value proposition of this new product? >> If you want to manage your business with compliance, with governance, with security and access Rubrik delivers a single platform for all your data management needs, >> Platform Polaris from Rubrik enabling an ecosystem first time, bringing all that data together from the data center people. Thanks for coming on the cube. Great to see you. Congratulations on all your success. Thank you so much for the opportunity and thanks for stopping by. I'm job here for cube conversation. Exclusive News here with Rubrik at theCUBE in Palo Alto. Thanks for watching.

>> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE! Covering Women In Data Science Conference 2018. Brought to you by-- >> Coverage of Women in Data Science 2018. I am Lisa Martin. We're at Stanford University. This is where the big in-person event is, but there are more than 177 regional WiDS events going on around the globe today. They are in 53 countries, and they're actually expecting to have about 100,000 people engaged with WiDS 2018. Pretty awesome. I'm joined by one of the speakers for WiDS 2018, Dawn Woodard, the senior data science manager of maps at Uber. Welcome to theCUBE! >> Thank you so much, Lisa. >> It's exciting to have you here. This is your first WiDS, and you are already a speaker. Tell us a little bit about what attracted you to WiDS. What was it that kind of spoke to you as a female leader in data science? >> Well, I tried to do a fair amount of reach-out to women in data science. I really feel like I've been blessed throughout my career with inspiring female mentors, including my mother, for example. Not every woman comes into her career with that kind of mentorship, so I really wanted to reach out and help provide that to some of the younger folks in our community. >> That's fantastic. One of the things that's remarkable about WiDS, one, is the growth and scale that they've achieved reaching such big, broad audiences in such a short time period. But it's also from a thematic perspective, aiming to inspire and to educate data scientists worldwide, and of course, to support females in that. What are some of the, tell us a little bit about your talk is Dynamic Pricing and Matching in Ride Sharing. What are some of the takeaways that the audience watching the livestream and here in person are going to hear from your talk? >> There are two technical takeaways, and then there's one non-technical takeaway. The first technical takeaway is that the matching algorithms that we use are really designed to reduce the amount of time that riders and drivers have to spend waiting in the app. For drivers, that means that we're working to increase the amount of time that they spend on-trip and getting paid. For riders, that means that we're reducing the amount of time that they have to wait to be picked up by a car. That's the first takeaway. The second takeaway is around dynamic pricing, and why it's important in ride-hailing services in particular. It turns out that it's really important in creating a seamless and reliable experience, both for riders and for drivers, so I talk through the technical reasons for that. Interestingly, these technical arguments are based not just on machine learning and statistics, but also on economic analyses and some optimization concepts. The third takeaway is really that data science is this incredibly interdisciplinary environment in which we have economics, statistics, optimization, machine learning, and more. >> It's really, data sciences has the opportunity, or really is, very horizontal. Every sector, every area of our lives is impacted by it. I mean, we think of all of us that use Uber and ride-sharing apps. I think that's one of the neat things that we're hearing from the event and from the speakers like yourself is these demarcated lines of career paths are blurring, or some of 'em are evaporating. And so, I think having the opportunity to talk to the younger generation, showing them how much impact they can make in this field has got to sort of be maybe, I would even guess, invigorating for you, as someone who's been in the tech in both industry and academia for a while. >> Absolutely. I think about data science as being the way that we learn about the world, statistics and data science. So, how do we use data to learn about the world, and how do we use data to improve, to make great products, to make great apps, for example. >> Exactly. Tell me a little bit about your career path. You have your PhD in statistics from Duke University. Tell me about how you got there, and then how you also got into industry. Were you always a STEM fan as a kid, or was it something that you had a passion for early on, or developed over time? >> I was always passionate about math and science. When I was an undergraduate, I did an internship with a defense contractor. That's how I got interested in machine learning in particular. That's where it took off. I decided to get a PhD in statistics from there. Statistics and machine learning are really closely related. And then, continued down that path throughout my academic career, and now my career in tech. >> What are some of the things that you think that prepared you for a being a female leader? Was it those mentors that you mentioned before? Was it the fact that you just had a passion for it and thought, "If I'm one of the only females in the room, I don't care. "This is something that's interesting to me." What were some of those foundational elements that really guided you? >> One is the inspiration of some women in my life, and if we have to be completely honest, I'm a person who, when, the very rare times in my career when somebody has acted like I couldn't hack it or couldn't make it, it always really got me angry. The way that I channeled that was really to turn it around and to say, "No problem. "I'm going to show you that I can go well beyond "anything that you had conceived of." >> You know, I love that you said that, 'cause Margot Gerritsen, one of the founders of WiDS actually said a couple hours ago, a few years ago, when they had this idea, from concept to first conference was six months, and she said she almost thought of it like a revenge conference. Like, "We can do this!" I think it's kind of, when they had this idea in 2015, the fact that even in 2015, there's still not only demand for, but the demand is growing. As we're seeing, the statistics that show a low percentage of women that have degrees in engineering, I want to say 20%, but only 11% of them are actually working in their field. We still have a lot of work to do to ignite the fire in this next generation of prospective leaders in technology. There's still a lot of groundwork to make up there. I think we're hearing that a lot at WiDS. Are you hearing that in your peer groups as well? >> Absolutely. I think one of the things that I've really focused on is mentoring women as leaders and managers within my organization, and I really find that that's an amazing way to reach out, is not just to reach out myself, but also to do that through female leaders in my own organization. For example, I've mentored and managed two women through the transition from individual contributor to manager. Just watching their trajectory afterwards is incredibly inspiring. But then, of course, those female managers bring in additional female contributors, and it grows from there. >> Right. And you have a pretty good, pretty diverse team at Uber. Tell us a little bit about your rise at Uber. One of the things that I saw on your LinkedIn profile, that you achieved pretty quickly in the first three years, or probably less, was that you led the marketplace data science team through a period of transformative growth. You started that team with 10 data scientists, and by the time you transitioned into your next role, there were 49 data scientists, including seven managers. How were you able to come in and make such a big impact so quickly? >> Well, the whole team chipped in in terms of hiring and reaching out. But at the time when I joined Uber, data science was still relatively small. Those 10 people were being asked to do all of the pricing and matching algorithms, all of the data science for Uber Pool, all of the data science for Uber Eats. We just had one person in each of these areas, and those people very quickly stepped up to the plate and said, "Okay, I need help." We worked together to help grow their teams. It's really a collaborative effort involving the whole team. >> The current team that you're managing, what does that look like from a male/female ratio standpoint? >> The current team is more than 50% female at this point, which is something that I'm really proud of. It's definitely not only my achievement. There was a manager who was leading the team just before I switched to leading maps, and that person also helped increase the presence of women in data science for Uber's mapping organization. The first data scientist on maps at Uber was a woman, actually. >> That's fantastic. And you were saying before we went live that there's a good-sized contingent of women data scientists at Uber today that are participating in WiDS up in San Francisco? >> That's right, yes. We're live-streaming it. There's a Women in Data Science organization at Uber, and that organization is sponsoring the internal events for the live stream, not just for my talk, but really, the whole conference. >> That's one of the things that Margot Gerritsen was also saying, that from a timing perspective, they really knew they were on to something pretty quickly, and being able to take advantage of technology, live streaming, they're also doing it on Facebook, gives them that opportunity to reach a bigger audience. It also is, for you and your peers as speakers, gives you an even bigger platform to be able to reach that audience. But one of the things I find interesting about WiDS is it's not just the younger audience. Like Maria Klawe had said in her opening remarks this morning and before, that the optimal time that she's found of reaching women to get them interested in STEM subjects is first year college, first semester of college. I actually had the same exact experience many years ago, and I didn't realize that was a timing that was actually proven to be the most successful. But it's not just young women at that stage of their university career. It's also those who've been in tech, academia, and industry for a while who, we're hearing, are feeling invigorated by events like WiDS. Do you feel the same? Is this something that just sort of turns up that bunsen burner maybe a little bit higher? >> Oh, it's incredibly empowering to be in a room full of such technically powerful women. It's a wonderful opportunity. >> It really is, and I think that reinvigoration is key. Some of the things like, as we look at what you've already achieved at Uber so far, and we're in 2018, what are some of the things that you're looking forward to your team helping to impact for Uber in 2018? >> In 2018, we're looking to magnify the impact of data science within Uber's mapping organization, which is my main focus right now. Maps at Uber does several things. Think of Uber as being a physical logistics platform. We move people and things from point A to point B. Maps, as our physical world, really impacts every aspect of the user experience, both for riders and for drivers. And then, whenever we're making a dispatch decision or a pricing decision, we need to know something about how long it would take this driver to get to this rider, for example, which is really a mapping prediction. We are looking at increasing the presence of data science within the mapping organization, really bringing that perspective to the table, both at the individual contributor level, but really also growing leadership of data science within the mapping organization so that we can help drive the direction of maps at Uber through data-driven insights. >> Data-driven insights, I'm glad that you brought that up. That's something that, as we talk about data science. Data science is helping to make decisions on policy, healthcare, so many different things, you name it. It really seems like these blurred lines of job categories, as businesses use data science, and even Uber, to extend, grow the business, open new business models, so can the next generation leverage data science to just open up this infinite box, if you will, of careers that they can go into and industries they can impact by having this foundation of data science. >> Absolutely. Well, any time we have to make a decision about what direction we go in, right, as a business, for example, as an organization, then doing that starting from data, understanding what is the world really like, what are the opportunities, what are the places in which we as a company are not doing very well, for example, and can make a simple change and get an incredible impact? Those are incredibly powerful insights. What do you think, last question-ish, 'cause we're getting low on time. We talk a lot about, there's the hard skills/soft skills. Soft is kind of a weird word these days to describe that. You know, statistical analysis, data mining. But there's also this, the softer skills, empathy, things like that. How do you find those two sides, maybe it's right brain/left brain, as being essential for people to become well-rounded data scientists? >> The couple of soft skills that I really look for heavily when I'm hiring a data scientist, one is being really focused on impact, as opposed to focused on building a new shiny thing. That's quite a different approach to the world, and if we stay focused on the product that we're creating, that means that we're willing to chip in, even if the work that's being done is not as glamorous, or is not going to get as much attention, or is not as fancy of a model. We can really stay focused on what are some simple approaches that we can use that can really drive the product forward. That kind of impact focus, and also, that great attitude about being willing to chip in on something, even if it's not that fancy or if I'm not going to get in the limelight for doing this. Those are the kinds of soft skills that really are so critical for us. >> Attitude and impact. I've heard impact a number of times today. Dawn, thank you so much for carving out some time to chat with us on theCUBE. We congratulate you on being a speaker at this year's event, and look forward to talking to you next year. >> Thank you, Lisa. >> We want to thank you for watching theCUBE. We are live at Stanford for the third annual Women in Data Science Conference, hashtag #WiDS2018. Get involved in the conversation. It is happening in over 53 countries. After this short break, I will be right back with my next guest. (fast electronic music)

>> Voiceover: Live from Washington D.C., it's theCube, covering AWS Public Sector Summit 2017, brought to you by Amazon Web Services and its partner Ecosystem. >> Good morning, welcome back here on theCube, the Silicon Valley or Siliconangle TV flagship broadcast, here as we continue our coverage live from the Nation's capital, Washington D.C., the AWS Public Sector Summit 2017. I'm John Walls, we're glad to have you hear on theCube along with John Furrier, good morning. >> Morning. >> Good night? >> Great night. I had two great meetings, learned some information, got some exclusive material for a story that has to do with government stuff. >> So you were kind of working then weren't you? >> I'm always working. We're in D.C. I want to put my ear to the ground and bring all these stories back to my show, Silicon Valley Friday Show, which has been on hiatus during the month of May and June for all theCube events. >> Slacker. >> I got some great metadata as they say. (laughter) >> Good about data. >> I went home and watched the Nat's game. That was my big night. Jeff McAllister is with us now, he is the GM of the Americas for Druva and Jeff, glad to have you on theCube, we appreciate the time. >> Oh gee, thank you for the opportunity and it's a pleasure to meet you. >> Alright so you guys are all data, all the time on the Cloud right? >> That's right. >> All about data protection and security, availability. Tell us a little big more just about Druva and then we'll get into maybe your relationship with AWS but first off about you, about Druva. >> I've been fortunate to be with Druva since we really embarked on our enterprise strategy. I've been part of the team that made the investment a couple of years ago to start to pursue FedRAMP and some of the specifications for the Federal Government. And as you know, we are Cloud native. We are for the Cloud and built on the Cloud. We've been a partner with AWS for over eight years now. So we've had a very strong working relationship with them and the opportunity to come and speak here today and with you gentlemen, has really been tremendously exciting and frankly they're absolutely wonderful partners to go to market with. >> Yeah, talk about a minute about how integral that obviously is to your business to have not just a relationship, but to have the relationship that you do with AWS. >> Well, AWS obviously provides a world-class platform on which to build a service like ours. For our customers, it means tremendous levels of security, tremendous data durability, a reliability and availability of that data, but also the idea that many of our customers are very mobile. They have great geographic dispersion among their employees. Their employees are engaging in other parts of the world. So availability of that Cloud and that Cloud infrastructure, in local areas is tremendously important. And for our Federal customers, the certification for ITAR and other things that are specific to that market, having a platform like GovCloud, built specifically to their specifications, to service them, creates great leverage for us and our customers. >> John F.: I mean, eight year relationship, and that's going back. >> Yes it is. >> And they're only 10 years old and they spent their 10th birthday going on their 11th year, just AWS. So, obviously they saw some federal action right away, or public sector action right away. Nature of the Cloud, very friendly to developers back then. But still it was building blocks foundational back then. >> That's right, exactly. >> What's changed? How would you chronicalize that change other than the massive growth we've seen in the market place which we've chronicalized as well but I mean, from your perspective in the public sector, this is on a nice trajectory. >> I've been in the business now for over 30 years. Started out at Data General through Sun Microsystems and I've seen much of the industry change. The one thing that has been very impressive with the public sector, is that the interval in product innovation would come to the public sector a year or two years behind what we saw in the commercial marketplace. That time and space is absolutely shrinking down to nothing. They are pursuing the same business continuity, data transformation issues the Cloud-first strategies that our commercial customers are. And frankly, the government worker today has become more mobile. And the requirements to protect that data and secure it, are at an all-time high. And the AWS platform in combination with what we do, really provides a level of security that is hard to do on your own. >> So yesterday, we talked about a term I coined, or phrase I coined, around the seminal moments in GovCloud's history and really in the Amazon public sector. Is called "the shot heard around the Cloud", and that was the CIA deal where AWS came in and beat IBM, which had a lock-in spec and they're old-school IBM, they know how to sell. The sponsorships, they had everything locked and loaded. Who knows what they were doing, wining and dining. You know how the Federal Government is? >> Jeff: That's right. >> Things were very much picked out, everything's buttoned up and then boom, Shadow IT is happening, Amazon wins. Since then, we've seen a lot of change in how people are securing, how people are deploying. >> Jeff: Right. >> No better example than data protection because there's no wall, there's no firewall. You're in the middle of it. Talk about that dynamic about how the no walls, no perimeter in the Cloud has changed the role of data and data protection. >> Sure. So, gone are the days where we can dictate the device, how somebody wants to work, what solutions they're going to use. Cloud applications like Office 365, Box, Slack, other, have really created an environment where the IT folks, want to stimulate innovation, stimulate the work in places where people want to get done. But then provide the same level of protection and governance that they would on a non-platform solution. So, watching that evolution take place, its really driven us to really have to be mindful that we're in the performance business and with that performance we have to be respectful of the requirements from a security and protection standpoint that our customers call for. FIP certification became fundamental for us being able to service the government. That led us into the pursuit now of FedRAMP, which we're now FedRAMP ready. But all of those things provide the infrastructure to allow them to embrace these new strategies and this digital transformation, be it in my Cloud-first strategy or my mobility strategy, and be able to extend that same level of security that I would need, and provide that flexibility for my users to get their jobs done. >> Yeah and honestly, Cloud native, as you know, we love Cloud native, we've covered it. >> We do too. >> Covered it from day one. (laughs) Cloud-first is kind of like a moniker that people use. >> Sure. >> Kind of an ethos. It's more of a manifesto, it's more agile. But really Amazon has never hidden the ball in the fact what they believe the future will be and that is API economy. And from day one it's all about APIs and they believe that you should have APIs everywhere. The Cloud has no perimeter so that changes the security game. But the one thing that's emerged out of all this, is a new SaaS business model for businesses and government, and federal, and education. So everything's as a service. >> Jeff: Correct. >> That is a huge deal and this is maybe nuanced a bit, but how does public sector turn into a service model with the Cloud? 'Cause that's something that everyone's kind of going at. You have Cloud natives great, we're going to be Cloud natives, check. But really what they're getting to is, everything's as a service. >> Right. It's created a lot of flexibility in the buying process. First of all, you're bringing that elasticity of demand, right? So they are able to embrace the idea that, I only pay for the services I actually consume. So, should I have a movement in employees, should I change in structure, should my usage suddenly spike, I have the ability to adjust on the fly. That's a big part of it. But the other piece of it is that we can deliver our service at a fixed price cost for a certain period of time within that government fiscal year. So not only does it become easy to manage technologically, but from a budget stand point, it makes it a very predictable cost. I'm no longer having an explosion of data that I have to manage and go off books to try and find data to provide those IOPS and storage on sight. I can simply continue to go at the same budget level that I've already set aside. >> One dynamic that has come up while you brought this up, 'cause I think it's relevant to what we were just talking about is, lock-in. Right? I mean the word lock-in has always been vendor lock-in but really that's on one side of the coin. The other side of the coin is user lock-in. So last night, one of my secret meetings I had last night was with a senior government official and we were talking about how, they're all pissed 'cause they got Microsoft Surfaces instead of Macs. They wanted Macs. So they were just handed a bunch of Microsoft Surfaces. No offense Microsoft, I love the Surface personally, but I've got a Mac here. The point is, they didn't want it. >> Jeff: Right. >> It was forced down their throat. >> Let's just shut that for a moment here. (laughs) >> This is the old way. We made a decision, we're going with this product. So this is really the flexibility point is, very interesting, 'cause now with the Cloud, you can actually do these really agile deployments. >> Jeff: Exactly. >> And give people more choice. >> That's right. The time to value on these products, we have a very large defense contractor inside the Beltway. We were able to deploy to 23,000 users worldwide in under six weeks. But we understand that we're in the performance business and the idea that our customers could leave us at any point in time when the term is up, keeps us very conscious of the specifications that they require. And frankly, it requires us to be innovative on their behalf. Certainly taking their feedback, but really starting to anticipate their requirements, so that we continue to earn that business year over year. And frankly, if you want to talk about lock-in, SaaS provides tremendous flexibility to switch when a contractor isn't performing to spec, versus a perpetual license where I'm locked in for the duration. >> And that's a fear obviously that they're going to use their dollars wisely. I want to get you to weigh in on Druva's digital transformation in back of the customer. Obviously you guys are doing well, you're in the sweet spot, data protection is a hot area. It's one of the hottest area no one really kind of looks at, but it's really hot with the Cloud. What impact are you having with customers and how are you rolling out your value proposition to the public sector? What are the key highlights? I mean, how do they work with you? Is it FedRAMP? Is it GovCloud? Just take us through your value proposition with respect to the- >> Our value proposition, I think is fairly unique. So first, we run on the most wildly accepted Cloud platform by the public sector, AWS GovCloud. Without question the market leader there. We bring all of our experience from the commercial marketplace into that same experience on GovCloud. With the added certifications of FIPS, certification 140-2 moderate. Our FedRAMP in process. We're also HIPPA certified so that we have the ability to address HHS and FDA as some of our customers. 'Cause they also process a lot of personal information that is unique to that particular agency. But at the end of the day, the piece that really is most interesting to our public sector customers is, one, this is a very easy service to bring to the Cloud at lower cost and frankly higher value. The plethora of features and the security, the ease of management that we bring, relieving them of having to manage hundreds of terrabytes of data and apps on behalf of this service, is tremendously beneficial. The predictability of the cost year over year, makes it very very easy to manage. But I think the biggest thing that people have come to embrace is that the innovation that takes place in the Cloud comes to market so much faster in the Cloud. Just think of the QA cycles and how they've been reduced 'cause we're QAing for one platform. Being able to consistently, quarter in, quarter out, deliver that additional feature set and additional value, at no additional cost to our customers, is really what they've really gelled around. >> How do you guys handle the certification processes that are going? I'm sure there'll be more. I mean, they're coming. With all the free-flowing data, I'm sure there's going to be a lot of regulations and policies and governance issues. But you've got to move fast. How do you guys move fast to certify? Is there a secret sauce? Is there a secret playbook? How do you guys stay on top of it? 'Cause automations, machine learning, what's the secret sauce? >> You know, I think it's interesting, part of the uniqueness that is Druva I think is, our ability to anticipate market demand. I think we have a very experienced team of individuals. Look at the choice to go to AWS eight years ago. It was unthinkable at that time, but its turned out to be a visionary sort of choice. We identified that FedRAMP and FIPs certification, three or four years ago, was an absolute mandate to play in this marketplace. So we went there way ahead of our success in the market but we saw a very unique opportunity to go there. So I think it's just a tremendously creative group of people. It's a very dynamic marketplace. And it's one that requires a little bravery and a little bit of thinking in advance of the marketplace. I don't know that we have any magic sauce, but so far it's worked pretty well. I think it's worked out alright. >> I always ask just to see. >> Although that's a good question. >> To that point though, eight years ago when you went, it was a leap right? >> It was. >> Big leap. And now here you are 2017, things are rolling along. I imagine your sale or your pitch has taken on a different tone because you have so much proof in the pudding now, right? >> Oh, it does. A long time ago it was strictly backup. We've now moved into governance, e-discovery, the idea of user behavior analysis so I can find anomalies that may occur so that I can avoid Cryptolocker or other sorts of viruses or things that may be able to affect the operation of my customers. All of those things have come into play that weren't there four years ago. So it's really been an advancement of the added services beyond what we just did in backup, that have really kind of driven the business and differentiated us from the market. But it's still kind of fundamentally that idea that I'm going to protect your data, make it available to you and separate now from your device and really help you manage your data wherever you're doing your work. >> I know we're running tight on time, I do want to get one more question in from your perspective because again, present and creation is really a benefit to Druva, congratulations on that. You get to ride the wave and now the wave is bigger and more sets coming in. That's to use the surfing analogy. But talk about the perspective from your personal standpoint, just the changes going on in this marketplace right now. Teresa Carlson, when we were commenting on our opening, how tenacious she's been. She's knocked on a lot of doors. Eight years ago, what the hell's cloud? No one even knew what it was right? And then the shot heard around the Cloud with the CIA deal and just more and more and more in them, this is just a great business opportunity for Amazon Web Services, not just the enterprise, which they're doing well in now. >> Right. >> They own the startup market. This could be, it could have a 90% market share of public sector. >> That's right, that's right. >> John F.: Talk about the change. What's going on? Is it the perfect storm? Is it like right now, what's the progress. >> Well you know, it seems like its a perfect storm but for somebody who's been banging at it for the last four or five years, it seems to be a little bit more evolutionary. But it's interesting, when I started at Druva, if I looked across our opportunities across the Americas. It was fairly evenly split between the idea that I'm going to do this on premise or I'm going to do it in the Cloud. Today, if I look across all o6f North America and all the commercial entities and public sector entities that we're dealing with, we're probably engaged in well over 500 opportunities at any one time, literally less than two, quarter over quarter, is now on premise. People have come to embrace the idea that this is a place where I can conduct business safely and securely. And frankly, for us, you look at that digital transformation or business transformation, we become two really compelling services to start and experiment with moving to the Cloud. So very often, we are the tip of that spear. Lets backup our endpoint devices to the Cloud, let's get out of that business, 'cause we can do it much more effectively with Druva than we can for ourselves at less cost. >> It's almost the reverse of what on prem was. I've had many opportunities where I've bumped into IT practitioners, friends and what not in the industry. "Oh, I forgot to do the backup plan. I got the procurement going on." It's kind of an afterthought, it's been kind of an afterthought. I am oversimplifying but generally, it's not the primary. When you go outside the walls of a company, into the Cloud where there's no perimeter, it's the first conversation. >> That's right. >> So I hear what you're saying and I totally agree. This is unique, it's a complete flip around. >> Well it's amazing. So often, we're backing up server data to the cloud. So now it used to be just backing up to the Cloud. Now it's, I have the application running in the Cloud and I want to back it up and secure it into another Cloud. It's completely morphing into all sorts of interesting places. But the part that's really interesting is that we will bring to our customers disaster recovery, for example. Well that's a service, we turn it on and if you never experience the disaster, you don't pay for it. It just creates a whole new mindset of how we're going to think and how we're going to approach the infrastructure that we're now building. >> No license fee. It's just if you need it, you get whacked on it and you deserve to get whacked on it because you need the service. >> Well, they know what the cost will be. We've set it up for a nominal fee but if you're fortunate enough that you never experience the problem, why should you pay for it. So literally cutting that price in half, removing the requirement of 2XL Servers and 430 tip. >> John F.: It's a new operating model. >> That's right. And the flexibility that it creates to change to your computing requirements is just phenomenal. >> Well, phenomenal, I think would be a way to describe your ascent as well. >> Oh thank you. >> So congratulations on that front. Glad you could be with us Jeff, at the show. Continued success and we hope to see you down the road on theCube. >> John, John, it was a real pleasure. >> John W.: First time right? >> It was, it was, thank you. >> John W.: You're a tour alum now or a Cube alum. (laughs) >> John F.: Cube alumni. >> Good to have you with us. >> Jeff: Thank you, thank you so much. >> Jeff McAllister with Druva. Back with more here from AWS Public Sector Summit 2017 on theCube. You're watching live in Washington D.C..

>> [Voiceover] Live from Silicon Valley, it's theCube, covering Mobile World Congress 2017. Brought to you by Intel. >> Okay, welcome back everyone. We're live here in Palo Alto, California inside theCube's new studios, 4500 square feet in Palo Alto, just opened up last month and excited to be here. Breaking down Mobile World Congress all day, from 8 a.m. to 6, today and tomorrow. As their day ends, we're going to pick up the coverage, do the analysis, get some commentary and reaction to all the news and also the big trends and my next guest here in the studio is Saar Gillai, friend of the Cube, Cube alumni, and former HPE Senior Vice President GM of the teleco business. Ran the cloud, then a variety of things with Meg Whitman at HPE, now he's independent board member and in between gigs on the beach, clipping coupons as we say, Saar, great to see you, looking good. >> Great to be here, nice studio. >> I'm excited that you could come in, this is exactly why we're having our show here in this new studio because a lot of folks that don't take the big trek to Barcelona who don't have to can come in and talk to us and you've been a veteran of Mobile World Congress for many years. Again, you ran, and actually built the cloud biz and also built the, I won't say NFV biz, but essentially the teleco communications division for HPE so you know a lot about what's happening in the industry and more importantly Mobile World Congress. This is the year that all the accelerant is coming to the table, all the rocket fuel is being poured out on to the bonfire, the matches are going to be lit, it's called 5G, it's called IOT, internet of things, internet of people, the devices look good, they all want to be Apple, they all want to be over the top, running entertainment, smart cities, cars, 5G is the holy grail, we're done. But seriously, where is the meat on the bone on this thing? Is it real, is this transformation hype or reality at Mobile World Congress? >> Yes. (laughing) >> Yes, hype or yes, it's real? >> There's a lot of hype, but there's some reality. I mean I think first of all, 5G is the latest thing, it used to be LTE, now it's 5G. What does 5G actually mean? Really, for people, what 5G means is you should have a lot more capacity, right? So 5G talks about even up to one gigabit in certain cases, lower latency and so forth. Now the thing about wireless is you know, there's no secrets in wireless, okay? It's not like... >> It's a physics game. >> ...Yeah, it's physics, it's not like ethernet, where you can go from one meg to 10 meg then all you have to do is run more line and you're good. If that was so easy in wireless right now, we'd all be getting one gigabyte right, but we're not. So the only way you increase capacity in wireless is through smaller cells, and there are some mimo technologies and so forth. You know, 5G talks abouts technologies that will enable you to do that, but it's much more of an evolution than revolution and people need to understand that. There's no fundamental shift, what they're talking about in 5G is adding a lot more bandwidth. Today, most of the frequencies being used are sub five gigahertz, those are great frequencies to go through walls, they're not that great in terms of capacity and there's not that much of them. Like AT&T might have 60 megahertz, that's the entire capacity they have in the U.S. And that's not much. And so they're talking about using millimeter waves, other things like 27 gig, 28 gig, 60 gig. Now, those do have a lot more capacity, they have other problems, they don't go through walls. So, I think instead of thinking about 5G, we need to think about, okay, what problems are we trying to solve? Like, what problems is this going to solve? I think in some sense, it's... ...while everyone wants more broadband, some of it is a solution looking for a problem. >> [John] Yeah, it's a field of dreams too dynamic, build it, they will come. That has been a network operator concept, right? And then we know the operators, and you and I have talked about this on theCube many years, the operators are having business model challenges, problems, challenges, there are opportunities, but at the same time there is a bigger picture I want to get your thoughts on. So in a vacuum, there's limitations, there's physics, but now, you're looking at a connected network, and this is the end to end concept, so under the covers of wireless, assuming wireless has its topology, architectural things you could do, smaller cells, different frequencies, how it's going through walls is preferred, longer distance, longer latency through walls, that's the ideal scenario. But I think there's a bigger picture around the different types of wireless networks, but there's cars, there's mobility, actual true mobility, 60 miles an hour in a car versus walking down the street or sitting in a stadium or at home. These are use cases. How much of it is a wireless problem versus another problem? NFV, end to end, virtualization, help us parse that through, how should we think about this? >> There's two issues, there's a wireless problem, we can talk about the different segments that make sense and don't make sense or how much they have to evolve to make sense. And then fundamentally, the networks are very... ...they're not that agile as we know. Which is why NFV really, if you remove NFV, and you just... ...NFV's about creating agility, and yes their doing for virtualization and yada yada, but it's about creating agility, creating automation, right? You can't have these... ...a lot of these networks were designed years ago even 3GPP, this is a decade old. And so yes, there's a lot of work that has to be done and creating much more agility in a network because the network isn't built for that. Just if you think about even simple things like number of subscribers that can go on and off, right? Okay, if you have a cell phone. Like today, if you look in the world there might be 80 billion subscribers, lets say. If you look at the number of cell phones and so forth. But once you start IOT, you might have 100 billion because every device will be a network. That's a different management system, right? Also, those devices may go on and off every day, right? Because you buy a new device, you plug it in the wall. Okay, phones, you don't start a new phone every day, right? People buy a phone to use it, so, the network becomes much more dynamic, the back end has to be more dynamic, that has a side effect and so there's a lot of work that has to be done on the backend to make it more dynamic. That's the backend problem and then, you know, they are working on it. >> [John] And the bright spots there are what? What are the bright spots happening today, this week at Mobile World Congress and the trends around the backend? >> Well, you know, I mean Mobile World Congress is a show, right? And these are not sexy things, so we probably won't hear a lot about them, but you hear about orchestration, automation, network virtualization, basically moving all this through the cloud paradigm, where you have a lot more flexibility. I mean if you think about what's happening NFV these days for example, you don't hear a lot about it, but what is happening a lot is onboarding work. Okay, we've talked a lot about it, from that hype, now we're into build-out, right? So you hear less about it, but stuff is actually happening. >> [John] So it's operational. >> [Saar] It's operational stuff. >> Yeah >> [Saar] They're modifying the system so that they can be ready to work when you get to that point. On the radio side, I think the important thing to understand is like you said exactly, there's multiple use cases for 5G. The most interesting and immediate one, potentially is to use wireless to compete against cable. Which is fixed wireless access. You know, there, the telecos for years have wanted to do it, there was this whole discussion about fiber, then it turned out fiber's expensive. >> [John] Yeah, you've got to trench it, you've got to provision it to your home, you've got to roll a truck. >> Took Google a few years to figure that out, but even for Google, it's expensive. >> [John] People who have done that said you're crazy, but Google's has had so many deep pockets and Facebook does the same thing with their kind of R&D projects. >> They figured it out, there are technologies, millimeter wave is a bit hard because it doesn't go through walls, but I think when we talk about capacities, it's not for your mobile phone, it's for other things, it's mostly for fixed wireless access. There's a whole discussion about cars, I personally, because we're talking about opinions here, I don't understand the problem so much because the reality is the car's going to be a mobile data center. So 90 percent of the data that's generated by the car will be kept in the car and the car will be sending analytics and metrics up so it doesn't need gigabits. It's not like every time you turn you need to get it an instruction. Maybe that's what the network guys want you to believe because then you need like zero latency. But you don't need that, it's much easier to invest in a better system in the car. So the car's not going to figure out... >> [John] The car is a computer, it's not a peripheral. >> Yeah, it's a peripheral. >> [John] It's a data center to your point. >> [Saar] It's a full data center. It's the edge that computes, so I don't think that's an issue, I think the car will need coverage and so forth. >> But that's a different thing, cars are great examples so let's take on this one. 'Cause this is a perfect mental model. A car is going to have all this capacity like a big computer >> [Saar] It's a data center. >> [John] Or a data center. >> [Saar] A mini data center. >> A lot of things, a lot of instrumentation, a lot of software, glue. >> [Saar] It's going to have 10 computers, big systems, it's like a little data center. >> But it also moves fast, so it's a true mobile data center. So it needs mobility. So mobility has trade-offs, right, with the wireless piece at least. Depends on how you're uploading. >> Again, it depends on the capacity. Mobility has certain elements when you get into Doppler Effect and so on. It's always a capacity trade off. All of you have talked on your cell phone or used data on your cell phone in your plane, we know this. When the plane's landing, that's 150 miles an hour when the plane lands, okay, and it works pretty well. >> We all cheat, don't turn on your cell phones, we're landing. >> [Saar] Yeah, exactly. >> We've all done it. >> So again, if you want to run a gigabit, it's a problem, if you want to run less it's not such a big deal if a car's going 60 miles an hour. So it depends. Now if you define the use cases, I need a gigabit for every car, there's a million cars, that's a problem. If you define the use case, as something else it's not a big problem. There is a problem though, and I think that is something that the 5G is trying to address in terms of more on the backend of density. >> Density in terms of signal, or density in terms of... >> In terms of support, so for example, the one place you can never use a cell phone is in a conference, because too many people are trying to get on at the same time. It's a statistical model. >> [John] A-station issues. >> It breaks, and so with a car also, you're going to have high density because if you have a traffic jam, all these cars are talking or receiving, so that's a bigger issue. And 5G does talk about that as well, but that's a bigger issue than pure capacity. Pure capacity, great, I'll give you this much megahertz... >> [John] I agree with that. >> You can do that on WiFi today. >> [John] I totally agree with that. So let's take a step back I want to get a little color on Mobile World Congress. Talk about what's going on right now. So it's dinner, people at parties, what goes on? People want to always ask me, John what always happens? First of all, Barcelona is a great city as you know, we've been there together for some HP events, as well as for Mobile World Congress. What's happening, you always make the comment it's a Biz Dev show which means it's business development going on. All the top executives go there, deals are being cut, but it's also a large trade show as you will for mobility. >> I think like you said, from my experience, the biggest value of Mobile World Congress is not the show itself, with all due respect to the show, it's the fact that everybody and anybody who is somebody is there, that's why we're not there. So you can meet people. And so if you want to meet a bunch of people, teleco leaders and so forth, that's what you do. This is the place. You all say, okay, we'll meet at Mobile World Congress. So like for example, when I was down there I would basically go back to back from in the morning until 10 at night, in meetings, dinners, whatever with CEOs of various telecos or CEOs of partners and so on. Everybody's there, and I never actually got to see the show because I never got out of a meeting. And most of what happens there is that. That's amazing because again, everybody's there. >> [John] There's a huge ecosystem involved. Talk about that ecosystem because this is the dynamic. And first of all, we don't have to go there because we've got theCube here so we're there virtually, digitally, and that's what we do now. This is great, in the studio, we save ourselves the three day flight to go to Barcelona. It is crazy there, but it is about the community there, because you have that opportunity to get the feedback, do deals. >> [Saar] A lot of deals around there. >> [John] A lot of deals happening, also feedback, trying to connect the dots and having the right product strategies. What are some of the things that you think is happening right now from a business standpoint in these meetings, right now? Are people still scratching their heads on over the top, is it the classic problems, what's the current state of the union? >> Well, you saw Vimpel Com change their name to some other thing, so I think what you're seeing right now, is there's still sort of multiple dynamics going on. One dynamic is there's people maneuvering around how 5G ends up closing and there was some discussions about that, there was some release done about hey we should speed it up and then Enrique said no this is silly. So there are some discussions, there is some maneuvering going on like any time when you're doing a spec, when does it freeze, when does it not freeze? Some of the telecos want this, and so forth. That's sort of in the background going on. They're still trying to figure out, you know, business model is still an issue. The people are experimenting and you're going to see a lot of that, experimenting with apps, experimenting with these monetization strategies. So there's a lot of that going on, trying to figure out, okay, how do we monetize the network in a better fashion? >> What do you think the best path is from your perspective? Just putting your industry hat on, if you had to kind of lay down some epic commentary to the teleco bosses, hey you got to cannibalize your own, get out in front, what would you advise them in terms of what to get out in front of, what to double down on? >> I think some of them are actually doing this, but I think first of all, I think they should forget about worrying about the technology. I mean, technology is very important, we need to take care of that, but really, they need to know what are they good at? What are they strong at? So their strong at a customer relationship, they have customers that they quote unquote have as partners, those customers, and they're very strong so what can you do with that partnership as opposed to all kinds of other random stuff. Now, if you look at what they're doing, they're doing different things. Some of them are like buying different media companies, so there's no easy path, but they're going to have to use their strength as opposed to try to become somebody they're not. They're not going to become Google, they're not going to become Amazon, they're not going to become one of those guys. They do need to become more cloudified just to be efficient, but that's because that's sort of the... ...just to play, you have to pay that card, but they're not going to be better than the existing, but they do have a very strong relationship with customers, they could probably sell them more things if they focus on good customer service. Customers are happy to work with them if they get a good deal and a frictionless environment. So, I would certainly encourage all of them, and I know many of them are focused on this, to improve your frictionless interaction for the customer. If the customer has a frictionless interaction and gets a good deal, they'll do business with them. >> Are you worried about the teleco's customer relationship when they have this decoupling trend kind of happening where the consumers want to take their phone or device and uncouple it from the network and just add more mobility across networks. So if there's better connectivity, I could be able to hop between Verizon, AT&T, whoever, that seems to be something that a lot of folks technically are saying from an architectural standpoint, having that personal centric view versus a network centric tie-in. Is that on the radar at all? Or is that still kind of in a way, fantasy? >> It's like people are still using AOL, right? >> [John] Who? Who? (laughing) >> They voted for Trump then, huh? >> I'm not going there right now, we can discuss that later. The point is, the primary area where there's problems in that area is roaming. And there's a lot of discussion about roaming. 55 or 60 percent of people turn off data when they go overseas because the roaming fares are so incredibly expensive which makes no sense. Why would I have a longer cost because I happen to have an AT&T contract in Europe, I'm not using more data than somebody in Europe and it's going through the backend of the internet anyways. So I think there... >> [John] It's a great way to jack the user with more fees. >> But that's not sustainable. >> Of course. >> I think there, you're going to see pressure of people and there's some companies who provide apps, and cards and sim cards, but there's now soft ways of doing it, there you're going to see pressure and I think eventually that will go the way of the messaging, where... >> [John] Like WhatsApp >> Yeah, they'll come up with something that will allow you to have data at a much cheaper rate, I don't know, does it make sense to switch carriers in the local market if you have a good price? I mean, what's the point? So again it all comes back to, do they give you a frictionless service? If they give you a frictionless service that is at reasonable cost, then you'll use it. So you've got to look at places... ...where their going to have people leave them is where they don't do that. And there are places they don't do that, roaming is one of those places. >> [John] So I've got to ask you about IOT, obviously it's the hottest trend, AI's more of the mental model that people get their arms around, they see virtual reality, augmented reality, they call that AI, it's more of a mental model, it's really not AI, but IOT is really where the action is. People see networks, where devices as you mention are coming on and off, you just don't provision those as static devices. They're very dynamic. Your take on the IOT market, what's your view on that? Because a lot of action happening there. >> I've been involved in IOT and different people have different names for what T means, I won't go there here. >> [John] T and P, things and people. Let it be watch... >> [Saar] Well T could mean things, it could mean other things too, but the point is, IOT ...I was working in a company that was doing IOT when we called machine to machine, if we had called it IOT, it would have been better. The point is IOT is, this is extremely fragmented, it's a super super fragmented market. And it has different ecosystems. The more complex part of IOT is not the front end, it's the backend of how do you manage devices how do you tie them to some app, how do you configure them, provision them? >> [John] 'Cause of the backend, infrastructures are different. Some are IT based... >> Think about it, you've got all these devices how do you upgrade them? How do you make sure they don't start a denial of service attack on their own? How do you provision them, how do you manage their life cycle? HPE has some product in that area, a global connectivity platform, but other people as well. So, this is a bigger problem. The backend is a much bigger problem than the front end. What's the problem? Hypothetically, I can stick a SIM card into anything and it's now a device. Most of these things do not have a high bandwidth. Low bandwidth coverage is pretty good in urban centers, not if you go to Utah, but other places. So, the biggest problem is backend. Now obviously there's a lot of advancements that can be done on the front end too, because of power issues. The biggest problem with IOT is depending what you want, you have a power issue. For example, we used to do this back in the day, you built these little devices, you stick them on containers, and then you can find out where the container is at any given moment. That's great, but how long does this thing last? I think IOT is a very big thing that's happening, I think most of the problem in IOT is not in the front end, it's in the back. >> [John] Yeah, I would agree with that. Also it allows you to get more data too. Another problem is storing up more data which is security, data, IT management, basic stuff. >> [Saar] Very basic stuff, and that stuff is hard to fix because again usually IOT is not a green field that are going to connect to something that exists. You're just augmenting it with IOT like if it's a power meter or something so now you have this existing ecosystem that has to interact with something that is brand new and so there are various companies who build interfaces and how to solve it, there's management issues. But I think IOT is real. >> [John] So let's talk about cloud. So cloud you also had your hand in at HP as well, you had a wireless background, the folks might not know that, going back before then. The cloud really is an opportunity, we see that with Amazon and then Microsoft's now got their stock up and so obviously cloud, it's a bigger game, it's hybrid, it's happening and then you have all these other fringe things developing around the mobility piece. How is the cloud changing the Mobile World Congress game? Because now it's a show that kind of blends. It feels like CES on one hand, it feels like Cloud World on another. It feels like IOT and teleco world, and all these things are kind of in a melting pot. >> Well I think to me, when I look at Mobile World Congress, I think of okay, it's teleco world, really because whatever the telecos happen to be doing is what the show is about, right? If you think about the telecos, we're talking about companies that have a capital like I think AT&T spends like 20 billion dollars a year or something in that range. We're talking about hundreds of billions of dollars of capital budget and whatever those folks are interested in is what shows up in that show. >> [John] So it's still a Teleco show dominant, you don't see that changing at all? >> No, but what teleco is, is changing, right? I mean they have broadcast, so what I'm saying is whatever the telecos are interested in is what shows up in that show. Drones, cars, telecos have their hands in all these things. And that's why it shows up in the show. Because ultimately the show is about the telecom space and the primary players. If you go down there, the booths that are as big as a country club, you know the Ericssons and so forth, it's a teleco show and people who want to be relevant like Intel as they want to be more relevant in wireless is building a bigger and bigger booth over there. But what teleco is really is about connecting things and as there's more things to connect, telecos get involved in other things. If they see a business opportunity, for example drones, lets go back to talk about that because there's a whole drone day and all this other stuff there. I mean drones need higher... ...I don't think they need so much bandwidth although it depends what kind of video you want to do. But they do need reliable connectivity. It's something useful, right? And today, you could argue connectivity it's not super reliable, it's pretty reliable, but we all have dropped calls every five minutes, right? I mean if a drone drops a call, that may not be that easy. There's use cases around these things >> [John] And back to your earlier point, I think this is the most important for the folks to listen to and hone in on is that there's a use case in every corner depending on the view of the market. Drones is one. Take virtual reality, augmented reality, that's another. IT, enterprises connecting, entertainment over the top, smart cities, these are all kind of nuanced areas. >> But when people want to understand, separate the hype from the non-hype, see if you can understand the use case. If you can't understand the use case or if the use case seems out there, then the technology is probably out there. Technology on it's own is fascinating, but if there's no use case that makes sense right here right now like again, for example, if I got a gigabit to my phone right now, would it make a difference in my life? An extra 20 hours of battery life would make a difference in my life more than a gigabit. >> That's a good point, right now battery life is more important than connectivity, but as the network transformation which is a big buzz word for this show is coming to the surface, that's an end to end architecture with software. So we think about traversing cloud, software, delivering of apps and services that's different. Now the apps have more headroom in that case, but then to your point, the backend's got to be... ...under the hood has to be smarter. Or is network transformation not yet there? >> Well I think what's happened is that the OTT, the OTTs started developing 20 years later, and surprise surprise, when you develop 20 years later, you have advantages, doesn't matter who you are, so their backend is a much further generation than the teleco's backend and so that's why when you connect to OTT services, it's consumer experience, it feels seamless and so forth, when you connect to the telecos backend, it's sort of a mismatch. And so they need to sort of fix that and that's part of the NFV transformation they're working on and again it's not because they had any limitations its because they had existing stuff, it's much easier to build from scratch. >> Final comment on Mobile World Congress this year, and outlook for the next year, your thoughts? >> We need to parse out what actually comes out of there. It's still early, I think 5G, 5Gs going to be what people are going to talk about, this is the thing. It means multiple things, but that's because the entire teleco world, if you think about it, if you look at the revenue of the suppliers and so forth who have been in a holding pattern ever since 4G sort of, in China, they finished 4G deployment and so the next big capital spending is going to be 5G, and so you're going to see the providers push anything to get that going. That's just the bottom line. >> Great. Saar Gallai, final comment, what are you working on now? Obviously we got to know you at a personal level with HPE, I've seen your roles, and the last one was really handling that teleco business which you grew up from a handful of people to hundreds of people, thousands of people. You're a land grabber, kingdom builder, empire builder. What are you up to now, what are you looking at for opportunity? I know you're doing some investing, you have some independent boards, what's your world like now here in Silicon Valley, what's your activities look like, and what's your thoughts on the valley in general and entrepreneurship and your activities? >> First of all, what I'm doing, the good news is I'm sitting here in Silicon Valley and so I'm very busy doing various interactions with bbcs, with startups, consulting, looking at different businesses, there's so much interesting things going on. Every morning you can look at new things that people bring over. Whether they're teleco related or not teleco related. Just some amazing things going on. Something from new wireless protocols to cloudification and so on, and I also sit on a few boards so I'm spending a lot of time doing that, looking at different things. >> [John] What's exciting for you right now? What's getting you jazzed up? >> There's so many different things, like I said, I think... >> [John] What's the coolest thing? >> The coolest things, some of the most cool things are.. >> [John] Confidential? >> Yeah, stealthy, I would say. I'm looking at some wireless stuff that's pretty revolutionary that I think could be new protocols that sort of change the whole dynamic of how wireless works, that's pretty interesting. And then I'm looking at some other things that are just how you apply cloud to different problems in the world. If you look at the cloud paradigm, it's existed for a fair amount of time now, but although we talk about it all day, most of the things in the world, most of the apps, most of the problem sets are not leveraging any in the cloud. They're still at best using >> [John] Recycled IT. >> Recycled IT, or sometimes even Windows 98 for all you know, right? In Africa, Africa went to wireless directly, they never did wired. So there may be a lot of industries that never go from Windows to proper data centers. They just go straight from basic Windows directly to the cloud. There's lots of opportunities that are interesting there. I'm looking at a few CEO options. But it's very exciting, there's so much going on. There's just so many things happening. >> [John] Well let's get into that tomorrow, you're going to come by tomorrow at 4:30, folks watching tomorrow at 4:30 Pacific Time, Saar will be back in the studio, we're going to dig in to the entrepreneurial landscapes, I think one of the things that you highlighted that we were talking about earlier is that sometimes you have technology looking for a problem, and the reality is that most of the game changing opportunities come out of left field that no one sees, these are the revolutionary game changers, the new technology, the hard stuff, not just some app that gets built, it's the real hardcore tech that could be applied to some of these real problems. And I think that's going to be the key. Saar Gallai here inside the studio, breaking down Mobile World Congress with theCube here in Palo Alto covering what's happening in Barcelona. We got some still phone-ins, late night in Barcelona, we're going to make those shortly, be right back with more coverage after this short break. (soft music)