(bright upbeat music) >> Welcome back to theCUBE's coverage of VeeamON 2022. We're here at the Aria in Las Vegas. This is day two, Dave Vallante with David Nicholson. You know with theCUBE, we talked about the cloud a lot and the company that started the cloud, AWS. Jeanna James is here. She's the Global Alliance Manager at AWS and a data protection expert. Great to see you. Thanks for coming on theCUBE again. >> Thanks so much for having me, Dave. It's great to be here in person with everyone. >> Yes, you know, we've done a few events live more than a handful. Thanks a lot to AWS. We've done a number. We did the DC Summits. Of course, re:Invent was huge out here last year. That was right in between the sort of variant Omicron hitting. And it was a great, great show. We thought, okay, now we're back. And of course we're kind of back, but we're here and it's good to have you. So Veeam, AWS, I mean, they certainly embrace the cloud. What's your relationship there? >> Yeah, so Veeam is definitely a strong partner with AWS. And as you know, AWS is really a, you know, we have so many different services, and our customers and our partners are looking at how can I leverage those services and how do I back this up, right? Whether they're running things on premises and they want to put a copy of the data into Amazon S3, Amazon S3 Infrequent Access or Amazon S3 Glacier Deep Archive, all of these different technologies, you know Veeam supports them to get a copy from on-prem into AWS. But then the great thing is, you know, it's nice to have a copy of your data in the cloud but you might want to be able to do something with it once it gets there, right? So Veeam supports things like Amazon EC2 and Amazon EKS and EKS Anywhere. So those customers can actually recover their data directly into Amazon EC2 and EKS Anywhere. >> So we, of course, talked a lot about ransomware and that's important in that context of what you just mentioned. What are you seeing with the customers when you talk to them about ransomware? What are they asking AWS to do? Maybe we could start unpacking that a bit. >> Yeah, ransomware is definitely a huge topic today. We're constantly having that conversation. And, you know, five years ago there was a big malware attack that was called the NotPetya virus. And at that time it was based on Petya which was a ransomware virus, and it was designed to go in and, you know, lock in the data but it also went after the backup data, right? So it hold all of that data hostage so that people couldn't recover. Well, NotPetya was based on that but it was worse because it was the seek and destroy virus. So with the ransomware, you can pay a fee and get your data back. But with this NotPetya, it just went in, it propagated itself. It started installing on servers and laptops, anything it could touch and just deleting everything. And at that time, I actually happened to be in the hospital. So hospitals, all types of companies got hit by this attack. And my father had been rushed to the emergency room. I happened to be there. So I saw live what really was happening. And honestly, these network guys were running around shutting down laptops, taking them away from doctors and nurses, shutting off desktops. Putting like taping on pictures that said, do not turn on, right? And then, the nurses and staff were having to kind of take notes. And it was just, it was a mess, it was bad. >> Putting masks on the laptops essentially. >> Yeah, so just-- >> Disinfecting them or trying to. Wow, unplugging things from the network. >> Yes, because, you know, and that attack really demonstrated why you really need a copy of the data in the cloud or somewhere besides tape, right? So what happened at that time is if you lose 10 servers or something, you might be able to recover from tape, but if you lose a hundred or a thousand servers and all of your laptops, all in hours, literally a matter of hours, that is a big event, it's going to take time to recover. And so, you know, if you put a copy of the backup data in Amazon S3 and you can turn on that S3 Object Lock for immutability, you're able to recover in the cloud. >> So, can we go back to this hospital story? 'Cause that takes us inside the disaster potential. So they shut everything down, basically shut down the network so they could figure out what's going on and then fence it off, I presume. So you got, wow, so what happened? First of all, did they have to go manual, I mean? >> They had to do everything manually. It was really a different experience. >> Going back to the 1970s, I mean. >> It was, and they didn't know really how to do it, right? So they basically had kind of yellow notepads and they would take notes. Well, then let's say the doctor took notes, well, then the nurse couldn't read the notes. And even over the PA, you know, there was an announcement and it was pretty funny. Don't send down lab work request with just the last name. We need to know the first name, the last name, and the date of birth. There are multiple Joneses in this hospital so yeah (giggles). >> This is going to sound weird. But so when I was a kid, when you worked retail, if there was a charge for, you know, let's say $5.74 and, you know, they gave you, you know, amount of money, you would give them, you know, the penny back, count up in your head that's 75, give them a quarter and then give them the change. Today, of course, it works differently. The computer tells you, how much change to give. It's like they didn't know what to do. They didn't know how to do it manually 'cause they never had the manual process. >> That's exactly right. Some of the nurses and doctors had never done it manually. >> Wow, okay, so then technically they have to figure out what happened so that takes some time. However they do that. That's kind of not your job, right? I dunno if you can help with that or not. Maybe Amazon has some tooling to do that, probably does. And then you've got to recover from somewhere, not tape ideally. That's like the last resort. You put it on a Chevy Truck, Chevy Truck Access Method called CTAM, ship it in. That takes days, right? If you're lucky. So what's the ideal recovery. I presume it's a local copy somewhere. >> So the ideal-- >> It's fenced. >> In that particular situation, right? They had to really air gap so they couldn't even recover on those servers and things like that-- >> Because everything was infected on on-prem. >> Because everything was just continuing to propagate. So ideally you would have a copy of your data in AWS and you would turn on Object Lock which is the immutability, very simple check mark in Veeam to enable that. And that then you would be able to kick off your restores in Amazon EC2, and start running your business so. >> Yeah, this ties into the discussion of the ransomware survey where, you know, NotPetya was not seeking to extort money, it was seeking to just simply arrive and destroy. In the ransomware survey, some percentage of clients who paid ransom, never got their data back anyway. >> Oh my. >> So you almost have to go into this treating-- >> Huge percentage. >> Yeah, yeah, yeah. >> Like a third. >> Yeah, when you combine the ones where there was no request for ransom, you know, for any extorted funds, and then the ones where people paid but got nothing back. I know Maersk Line, the shipping company is a well studied example of what happened with NotPetya. And it's kind of chilling because what you describe, people running around shutting down laptops because they're seeing all of their peers' screens go black. >> Yes, that's exactly what's happening. >> And then you're done. So that end point is done at that point. >> So we've seen this, I always say there are these milestones in attacks. I mean, Stuxnet proved what a nation state could do and others learned from that, NotPetya, now SolarWinds. And people are freaking out about that because it's like maybe we haven't seen the last of that 'cause that was highly stealth, not a lot of, you know, Russian language in the malware. They would delete a lot of the malware. So very highly sophisticated island hopping, self forming malware. So who knows what's next? We don't know. And so you're saying the ideal is to have an air gap that's physically separate. maybe you can have one locally as well, we've heard about that too, and then you recover from that. What are you seeing in terms of your customers recovering from that? Is it taking minutes, hours, days? >> So that really de depends on the customers SLAs, right? And so with AWS, we offer multiple tiers of storage classes that provide different SLA recovery times, right? So if you're okay with data taking longer to recovery, you can use something like Amazon S3 Glacier Deep Archive. But if it's mission critical data, you probably want to put it in Amazon S3 and turn on that Object Lock for immutability sake. So nothing can be overwritten or deleted. And that way you can kick off your recoveries directly in AWS. >> One of the demos today that we saw, the recovery was exceedingly fast with a very small data loss so that's obviously a higher level SLA. You got to get what you pay for. A lot of businesses need that. I think it was like, I didn't think it was, they said four minutes data loss which is good. I'm glad they didn't say zero data loss 'cause there's really no such thing. So you've got experience, Jeanna, in the data protection business. How have you seen data protection evolve in the last decade and where do you see it going? Because let's face it, I mean when AWS started, okay, it had S3, 15 years ago, 16 years ago, whatever it was. Now, it's got all these tools as you mentioned. So you've learned, you've innovated along with your customers. You listened to your customers. That's your whole thing, customer obsession. >> That's right. >> What are they telling you? What do you see as the future? >> Definitely, we see more and more containerization. So you'll see with the Kasten by Veeam product, right? The ability to protect Amazon EKS, and Amazon EKS Anywhere, we see customers really want to take advantage of the ability to containerize and not have to do as much management, right? So much of what we call undifferentiated heavy lifting, right? So I think you'll see continued innovation in the area of containerization, you know, serverless computing. Obviously with AWS, we have a lot going on with artificial intelligence and machine learning. And, you know, the backup partners, they really have a unique capability in that they do touch a lot of data, right? So I think in the future, you know, things around artificial intelligence and machine learning and data analytics, all of those things could certainly be very applicable for folks like Veeam. >> Yeah, you know, we give a lot of, we acknowledge that backup is different from recovery but we often fall prey to making the mistake of saying, oh, well your data is available in X number of minutes. Well, that's great. What's it available to? So let's say I have backed up to S3 and it's immutable. By the way my wife keeps calling me and saying she wants mutability for me. (Jeanna laughs) I'm not sure if that's a good thing or not. But now I've got my backup in S3, begs the question, okay, well, now what do I do with it? Well, guess what you mentioned EC2. >> That's right. >> The ability exists to create a restore environment so that not only is the data available but the services are actually online and available-- >> That's right-- >> Which is what you want with EKS and Kasten. >> So if the customer is running, you know, Kubernetes, they're able to recover as well. So yes, definitely, I see more and more services like that where customers are able to recover their environment. It might be more than just a server, right? So things are changing. It's not just one, two, three, it's the whole environment. >> So speaking of the future, one of the last physical theCUBE interviews that Andy Jassy did with us. John Furrier and myself, we were asking about the edge and he had a great quote. He said, "Oh yeah, we look at the data center as just another edge node." I thought that was good classic Andy Jassy depositioning. And so it was brilliant. But nonetheless, we've talked a little bit about the edge. I was interviewing Verizon last week, and they told me they're putting outposts everywhere, like leaning in big time. And I was saying, okay, but outpost, you know, what can you do with outpost today? Oh, you can run RDS. And, you know, there's a few ecosystem partners that support it, and he's like, oh no, we're going to push Amazon. So what are you seeing at the edge in terms of data protection? Are customers giving you any feedback at this point? >> Definitely, so edge is a big deal, right? Because some workloads require that low latency, and things like outpost allow the customers to take advantage of the same API sets that they love in, you know, AWS today, like S3, right? For example. So they're able to deploy an outpost and meet some of those specific guidelines that they might have around compliance or, you know, various regulations, and then have that same consistent operational stance whether they're on-prem or in AWS. So we see that as well as the Snowball devices, you know, they're being really hardened so they can run in areas that don't have connected, you know, interfaces to the internet, right? So you've got them running in like ships or, you know, airplanes, or a field somewhere out in nowhere of this field, right? So lots of interesting things going on there. And then of course with IoT and the internet of things and so many different devices out there, we just see a lot of change in the industry and how data is being collected, how data's being created so a lot of excitement. >> Well, so the partners are key for outposts obviously 'cause you can't do it all yourself. It's almost, okay, Amazon now in a data center or an edge node. It's like me skating. It's like, hmm, I'm kind of out of my element there but I think you're learning, right? So, but partners are key to be able to support that model. >> Yes, definitely our partners are key, Veeam, of course, supports the outpost. They support the Snowball Edge devices. They do a lot. Again, they pay attention to their customers, right? Their customers are moving more and more workloads into AWS. So what do they do? They start to support those workloads, right? Because the customers also want that consistent, like we say, the consistent APIs with AWS. Well, they also want the consistent data protection strategy with Veeam. >> Well, the cloud is expanding. It's no longer just a bunch of remote services somewhere out there in the cloud. It's going to data centers. It's going out to the edge. It's going to local zones. You guys just announced a bunch of new local zones. I'm sure there are a lot of outposts in there, expanding your regions. Super cloud is forming right before our eyes. Jeanna, thanks so much for coming to theCUBE. >> Thank you. It's been great to be here. >> All right, and thank you for watching theCUBE's coverage. This is day two. We're going all day here, myself, Dave Nicholson, cohost. Check out siliconangle.com. For all the news, thecube.net, wikibon.com. We'll be right back right after this short break. (bright upbeat music)

(upbeat music) >> Introducer: Live, from San Francisco, celebrating 10 years of high tech coverage, it's theCUBE. Covering VMworld 2019. Brought to you by VM Ware, and it's ecosystem partners. Hello and welcome back to theCUBE's live coverage here in San Francisco, from VMworld 2019. I'm John Furrier with my co-host Dave Vellante. Dave, 10 years of theCUBE, covering VMworld. A lot's changed, a lot's happening, 10 more years. Two great guests here from Veeam. Carey Stanton is VP of Global Alliances and Ken Ringdahl who is the VP of Global Alliance Architecture. Both with Veeam. Love the green. >> Good to see you gents. >> Epic party last night. You guys are known for the legendary party. >> Yes. Andy Rammer? Did you? It was great. We had 2,500 people waving, shouting. Yeah it was great. >> So welcome back to theCUBE. So what's the news for you guys? You're always popping some news out. What's going on here for you guys at VMworld 2019? Top story. >> Top story for us I think is continuation of what we're doing with VMware on VMC and AWS, you know we continue to be the number one data protection workload on VSAN, working with them on their new marketplace as a design partner that they just launched this week as well, so I would say that we are always everything to VMware and then we just continue to ratch it up with continuing to grow out their ACF platform with VSAN and the new marketplace which is their VCPP, which is a big part of our business. >> And the cloud's certainly a big part of the equation for VMware this year. I mean you've seen the announce cloud native support, kubernetes on Vsphere, so they're starting to get their software mojo down on trying to build that next generation platform. You guys are kind of there with your solution. What's the big takeaway technically that's going on that customers should care about in your mind Ken? What do you think? >> Yeah, you know. Certainly this, is a big push towards Cloud I think. You know as Ratmir our co-founder would tell you, "hey, we track VMware." So when VMware started on print we would track along, they've now moved into the public cloud, and we're following along there, so whether it's VMC and AWS, the new relationships with Azure and Google, you know, goodness for us because we provide inherent support there. But you know some of the next generation things, a lot of news about project Pacific, and kubernetes, and next generation cloud native applications and, yeah we're here to support our customers, you know we're looking at all the new things that are there, we've done a lot of things recently about adopting object storage for cloud storage, etc. A lot of things we're doing from a product and technology perspective. >> So Pat Gelsinger, on theCUBE one time said, "If you don't ride the waves you're going to become driftwood." You guys have always been wave riders. When you see something like the project Pacific, what does that mean to you? How do you respond to that? Do you talk to customers? Do you sort of huddle internally and start designing? >> We do. We certainly take a lot of feedback, you know as we all know in tech, there's a lot of things that come and go, some of these things are great ideas, and at VM where you can look at vCloud Air right? You know, it was great momentum for a while and VMware made a very good pivot right? They understood that, we shouldn't compete in the space, we should partner in this space, and so we do the same thing, we look at when we're evaluating new technologies, kubernetes, etc., I mean I think at this point we know kubernetes is here to stay right? That's not a fad. It's very clear, the adoption is clear, so we're evaluating how we participate there. Our customers are largely, on-prem customers but moving to the cloud it's a real hybrid story, and so when we go in and implement our support and look at how we're going to integrate that, it's all about how we help our customers in that world. So when you see a new trend you say what, "We can protect that." Right? It's anything, everything needs to be protected. How do you think about protecting containers? Yeah so we look at it and say, hey look, the way that containers are delivered it's inside of a system regardless so, sometimes it's inside a VM, sometimes it's within a physical system. We can protect what support's there and we're looking at how can we help customers today. A lot of customers are moving their workloads. Similar to when server virtualization came up, it was a little bit of a lift in shift right, I'm going to take what was working on physical, move it to virtual. A lot of customers today are moving what they have in legacy apps and they're just putting 'em into containers just to get there, and then they're building new applications, they're moving in a more stateless fashion. We can support the customers today when they move to a stateful system. And we're evaluation how we support more the stateful longterm view of kubernetes. >> But Carey so you obviously know VMware very well. Yes sir. I've spent some time there. When you think about how the ecosystem's evolving, VMware now is a networking company, they're a storage company, obviously they compute, but they haven't sort of aimed the cannon at data protection, they've left that to the eco system. Your thoughts on how the ecosystem is evolving, your relationship with VMware, and the broader. >> Yeah I think it's not dissimilar to, if we just pick vSAN, storage, primary workload, they don't play in the secondary storage, and they allow their customers to work with reference architectures that we create to say which data protection partner would you like to have. We're fortunate to be number one, if you look at HBE, right, again, they have a lot of partners, we're number one with them, NetApp, Cisco and the like, so you know, VMware's not any dissimilar, so we're fortunate to have that Tier 1 relationship with them that they're looking to us as serving their customers. And then we also have a very close working relationship with them on the engineering side to ensure that we're always protecting their customers, and we have lots of other great meetings this week and lots of other things to be announced in the weeks ahead with working with them and their customers so we're very excited on what they're doing in that space and how we can solve their customers. >> That's interesting. None of the big platform players really have attacked ever, historically, back up, I mean I guess IBM kind of, but that's for different reasons. They'd probably say, "Okay, we've replicated, we're good." Why do you think that is? Is this cause it's so hard? You guys as an industry are that far ahead of the functionality? It's a tough business? >> Well I think it's.. It's big, it's a large business I think it is. It's a 6+ billion dollar town. I think that it's also a legacy. I mean if you look at where Veeam came in and we were disrupters when VMware was it and doing the virtualization, we were disrupting the legacy players without saying all the names what we did, and I believe that with the two decade plus world the data protection has been in, and the evolution, that it would take a lot of work on their part to want to come in and say that they're going to get into that space and try and have a solution that is as credible as Veeam is in the marketplace. So again we're fortunate, we stay very close with them and you know we continue to see them as one of our Tier 1 partners as well. >> Talk about the integration aspect because you're Tier 1, you guys are number 1 recommended with these guys, relationship's strong, integration's are key, for you guys and with VMware but also as customers, look at the Cloud 2.0 world, and you guys are following VMware with multi-Cloud, you guys can play everywhere. You're going to be integrating a lot, so that has to be a core competence for you guys. Can you just talk about, how you guys view your integration with VMware and then, from a customer standpoint, why is it important? >> I'll take the first crack and then pass it Ken, but if you look at two years ago with VMC on AWS when they made those announcements, we were a design partner in, and then they started to evolve that and doing those pilot, we're starting to see those pilots turn into large enterprises deployments. You hear Pat and Sanjay talking about the evolution that they're having and we're seeing the result of that, the customer's saying, "I need what was using on-pram." And that's moving to the Cloud and it just works, the Veeam slogan, it just works. So we're seeing a lot of those deployments for customers taking those enterprise solutions that they had on AWS and scaling them out, and we're going to continue to do that across all of, you know, what Ken was just talking about kubernetes, the Pacific project and others that were.. Again, we're at the table working with them, but I don't think that Veeam is going to stay away, we're only going to get closer as go into new technologies. >> You have to and the tech's getting better too. >> Yeah, what I'll say also is, Veeam, when you look at the data protection landscape we're a pure play ISV. That actually makes us pretty unique because all of our competitors either sell their software on a piece of hardware, or it's at least an option. We have no Veeam whitebox option that you'll see a Veeam label on it, and it really resonates with out partners. We're totally non-competitive, or non-overlapping with our partners and so, they welcome us with open arms as a result of that, and it really helps us drive in. But the integrations are critical and just to quickly make a comment about the last question about sort of the point solutions and why doesn't the big platform players. I'll give you two examples, two public Cloud examples: Azure and AWS, the two primary hyperscale Cloud vendors. They both have backup solutions, AWS has site recovery, sorry, Azure has site recovery, AWS come out with AWS backup. About a year ago they announced that at Reinvent. They need that for point solutions for customers that are looking for a checkbox. Customers, really that more the developer that just needs the base level protection. But they partner with folks like ourselves, for the broader support, for the hybrid support, because silicon angle right? I just read an article yesterday, or two days ago on silicon angle. It's a hybrid cloud world. You guys, talking all about it. That's where our strength is and that's why we have these partners coming to us. You know they build point solutions on their own, again for that checkbox, we're not checkbox, we're deep integrations, we're hybrid cloud, portability, flexibility, reliability. >> And that's smart of the cloud guys to do that because some people want end to end or compliance reasons they have to use the cloud's solution, or it's a requirement, but look at Cloud Trail, and data job's going public. You got New Relic. You got these companies that are winning in adding value with their products through leadership. Not necessarily. Amazon's got a solution out there but they're not really, going down that road. >> And John, and what I would say is also they see, the number of customers and the size of the petabytes that we're driving on the respective clouds, again back to Ken's point on AWS and Azure, I mean that business for us is growing 30 to 35%, month over month, and so they understand the number of customers, and they see that this is a hybrid play, the customers tiering off data to the cloud, but their primary workload is on-prem >> That will give you more EC2 cycles. I mean, crank up the EC2 baby. >> And they know that we're coming out with cloud native solutions as well so, I mean we're doing all the heavy RD investment, solving their customer problems, so again reason number 452 as to why they would want to go in and be disrupted to that? >> As you guys do these integrations, a lot of cloud action, you got VMC on AWS, Cloud Tier with AWS and Azure, you have a bunch of stuff going on with VMware solutions with Cloud Simple. As you work in this multi-cloud world, how are you changing your licensing and pricing models to adapt? Yeah a think Ratmir and Danny were on this week and talking about instances, so we're moving the portability of the license no longer, making the customer have to make a hard decision on the day of licensing with Veeam, is we're saying, hey, the license, it's an instance, it's on-prem, it's an instance in the cloud, you determine what's right for your business and move those licenses. So we were the first to really make that giant leap and we're going to continue to evolve that solution and make it even easier for them to do that, and then another thing that Veeam is there's no tax, we don't charge the customer any money if you want to move those data environments up into the public cloud, and again that's Veeam differentiating as it were, that customer company. We're always focused on what's right for the customer, from the product, right down to the licensing model. Yeah you're tag line is it just works. And I don't know it that's the tag line but that's what customers always say. >> It was for many years. And it's more than just a product, it's the business model. You guys have always been, pretty innovative in that regard. And especially with partners, you and I have talked about this in terms of how you make a transparent for the partner, for the sales reps. On the partner's side, to not care, whether it's they're selling on-prem or if it's a cloud solution. >> And it's been well received as you know we have global resell agreement with the Cisco's and HP's and NetApp's of the world and they're very appreciate to the way that we make it easy for them to sell to their customers and allow them to have that portability of the licenses. >> It's been great following you guys and your events, and getting to see you guys be successful, the product does the talking, and the customers are the references. I mean they vote with their wallets. You know and you guys are a Tier 1 partner. Congratulations. >> Yeah. Thank you very much. >> Final question for you, is the event successful in your mind for you guys? What do you think is happening here? What's the top story coming out of the event overall for the folks that didn't make it here? >> So, first and foremost, huge success, we're 100% back here next year trying to make it even bigger, and I would say that what's coming out of it is just the, the success that our customers show by coming to our booth and showing us that they're looking to keep with Veeam on the journey as they go with VMware. And Ken touches on kubernetes and look at all the new solutions, and so we have an overwhelming support to customers saying, "Hey, I've been with you for the last decade, I want to be with you on the journey." And so that's, we've hear that over and over again this week so very strong. >> Yeah, I think I'll second what Carey said and maybe I'll give you a broader picture. I mean if you look at what VMware's done over the last 12 to 15 months. Last year at VMworld they announced the Cloud Health acquisition, they acquired Security Company, Pivotal, you know they're really broading and they're seeing that hey look, it's not just about on-prem server virtualization, we need to have a very broad story. We need to be relevant in the public cloud, we need to provide some management and multi-cloud capabilities. We're doing the same, but I think VMware is clearly in a period of transition and figuring out.. You know I think VMC and AWS is a great step. You know having the CloudSimple relationship and virtustream for Azure, you know runnig VMware, and Azure, and Google, but I think you'll continue to see that evolve and I think they've put the breadcrumbs down so that as we go forward here in the coming months, weeks, and next year when we're here at VMworld, you'll see that continue. >> And it's certainly a great growth in terms of infrastructure's, code. You're starting to see the Enterprise Cloud start to stand up a little bit. Hybrid cloud's got visibility. It's not as easy as leaving stuff in the cloud, getting the enterprise to work, you guys know that first hand. And Congratulations. Thanks for coming on theCUBE. Thank you very much. Appreciate it >> VMworld 2019 CUBE coverage. Here live in San Francisco, I'm John Furrier, Dave Vellante. We'll be back with more after this short break. (upbeat music)

>> live from Anaheim, California. It's the queue covering nutanix dot Next twenty nineteen. Brought to you by Nutanix. >> Welcome back, everyone to the cubes. Live coverage of nutanix dot Next here in Anaheim, California. I'm your host, Rebecca Night, along with my co host, John Furrier. We have two guests for the segment. We have Ken Ringle. He is the vice president Global Alliance Architecture at Wien. Thanks so much for coming on. The your Cube alum Returning to the >> great to be here again >> And we have Mark Ni Mire. He is the director of product management for data protection Nutanix Thank you for coming on the Cube. So we're one of the big thing when the big announcements today is nutanix mine. I want to talk to you and ask you Ken. What brings nutanix and team together to create Nutanix? Mine? >> Yeah, sure where you know we're super excited. You know, we've been partners for many years. We actually brought a product to market together last year, called the availability for nutanix, which added support for primary workloads. But we hadn't been working together on the secondary side, right where we land are backups And it became very clear, you know, from our customers that they were, You know, we really want to provide that seamless experience, a turnkey experience for our customers. So we started talking together and really, this is over a year in the making, right? We came together and we started brainstorming and it became very clear in a lot of synergies between the companies and and what we could deliver to our customers. So it became obvious. Hey, let's let's bring this together. It was more about the high. Not not not when they're you know, it was It was it was how how do we do it? >> And what were the problems you were trying to solve here? What were the issues that you were hearing from customers? >> So when we talk to customers, a lot of complaints that there are customers are voicing its around the complexity in their backup infrastructure, Right? Nutanix is known for providing simplicity for the primary infrastructure, right, reducing complexity that you typically having your free chair our protection. New tenants mind will provides the same amount ofthe simplicity for your for your lack of infrastructure, a type of converts solution that includes the Wien sell fair to provide data protection services for any workload running in your data center >> Integrations A big part of the modernized in hybrid on cloud with, you know, on premises Private Cloud. As you guys know, integrating it is not always that easy. This's pretty important. You guys been very successful with your partnering. Your product has been successful. Revenues actually show that as the cloud comes into the picture, a lot of people have been tweaking the game there game a little bit on the product side because of the unique differences with Cloud. So with multi cloud, private cloud and hybrid, what changes what's changing in the customer mind right now? Because they got their own premises thing pretty solid, but operationally it feels like cloud. But how does it affect the d Rp? Because this is going to be one of the big conversations. >> Yeah, no question. I mean, when we when we talked to our customers on how they're protecting their data, you know, we hear from a lot of customers is hey, we want to leverage the cloud for for a number of things. And I think the cloud has gone through an evolution right, You know, it's just like anything there's, you know, the great great hey could do all these things. And then people come back to reality. And what we see a lot of our customers doing is is using the cloud for long term data retention, using it as a secondary d our site. You know, you go back five years, you know, customer, especially large customers, all have two physical data centers. So now what? We're seeing a lot of our customers. They have that one physical primary data center, but they're leveraging the cloud. Is there as there d our site, right? So they're they're moving their data there with our recovery capabilities, you know, you can actually get a cloud workload recovered in a disaster scenario quite rapidly. And that's that's been a major change over the especially over the last couple years. >> And then, if you really look at integration, right, the the new Tenants Mind solution to Platform provides integration in six different areas. Integration is sizing, making it very easy to size, or we've identified some form. Factors were building it into new. He's an ex isar, very easy to, uh, to buy single skew that basically provides the hardware hardware support suffer for from from nutanix and suffer from being easy to deploy. Very automated installer that turns the nutanix appliance into a into a mine appliance in a matter of minutes and an easy to manage integrated dashboards Easy to scale right Horse entering is tailing out for capacity, but also for increased performance and then integrated support, where we have a joint support model between the two companies to really help our customers in case there are issues. >> So why why did you choose each other? What was the courtship like and and how how did they have the relationship evolve? >> So if you look at vino and new tenants, we really focus on quality and providing simplicity for our customers. That if that is something that really it was very apparent from the beginning that we have the same view points in the same Mantorras, basically around simplicity, providing quality both off our MPs scores are definitely the highest in the industry, something that is that is practically unheard of. So it was a very natural. I think this company's coming together and providing value together. >> Yeah, I mean, we're maniacal about customer success and customer support and customer satisfaction. That was that was very clear early on. You know, Venus as a peer software company in a way, and we need a partner in order to deliver a full stack solution. Nutanix is there's just a lot of synergies that culture, the companies, the size of the companies, the age of the cos it just It's just a great partnership in a great fit where, you know, there's just we're both moving in the same direction in in concert >> both hard charging cultures to, you know, entrepreneurial high quality was focus on the customer but hard charging. You guys move fast, so well, I got the two experts here on data protection. I gotta ask you about my favorite topic, ransomware, because people are fun and get rid of that tape. I got to get stuff back faster on recoveries. But ransomware really highlights the data protection scenario because they target like departments that maybe understaffed or might be vulnerable or just don't fix their problem. They go back to the well every time that it's everything you want to make some cash and go back. This >> is where >> software. Khun solved a lot of problem. What's your what's your guy's view of the whole ransomware thing? Because it becomes huge. >> Yeah, no question. Way Hear this from a lot of our customers And of course, we can't talk about it when we have customers come to us. But, you know, we've had many customers come to us, and unfortunately, it's after the fact A I you know, I had a ransomware attack and, you know, I lost all this, but now you know I can't let it happen again, but it's really from a backup strategy perspective. It's still important to keep air gap. You know, these ransom where these folks that are building these, these ransomware attacks, they're very intelligent. They've gotten extremely intelligent and how they move from one system to another and they even hide out. So you, you you eliminate a ransomware attack and that thing can come right back. You restore a backup that was a month old that has that sitting and waiting. So, you know, having a solution that can actually test your backups before you put him in production. Haven't air gap, you know, have a mutability on some of your backup date of those. These are all things we talk to our coast. >> You'd be a point about the bridges up because it was just going to a customer about this. They fixed the ransomware paid but didn't fix the problem. Yeah, so it's, like, end of the month and eat some cash right around the end of the month. But, you know, saying they shake him down again. Yes. The wells there, they keep on coming back. So there's, like, community of data perfection. I mean, professionals getting together to kind of get ahead of this problem >> on DH, then the other aspect ofthe basically being able to recover quickly his performance, right? Nutanix platform provides have informed the throughput. So you can very quickly restore your work clothes as well. >> Yeah, that would be a great problem of simplifying. Yeah, exactly. >> So what are the next steps for this alliance? Where where where do we go from here? >> So from from basically we've just finished a round of vested beta testing right way are going to be maniacally focused on the first hundred customers really understanding how they're going to put mine in their data centers. How they were going to use it as in their data sent to protect their Derek. There their workloads and their applications from their own. We have a lot of plans, very interesting plans around Rome Emperor. We can build even tighter integration from a management perspective, but also from a data fabric perspective. Weather that's on prime a weather gets goes into desire clouded nutanix icloud There's a lot of interesting areas that brain and I have been brainstorming on white boarding and so on that you'LL see coming out in the next two versions of the products. >> What's the big customer request? What's the big feature request? What's the big ask from customers for you guys together? >> At the end of the day, you know, our customers are really asking for simplicity. They they want, they want to simplify their environment. I mean, it is moving from specialists generalists, and they and they want a system that works well together. That's going to lower their costs and they want peace of mind. So they want. They want to know their backups are protected, They want to know they can restore. And that's really what we're focused on is providing that to our customers >> and reliable. Have making sure their works hundred percent any new things emerging out the multi cloud thing that you guys see coming down around the quarter that you're getting ready for to help customers simplified any any signals from this multi cloud equation. >> So one of the things I look at is really the lines between on Graham and primary and secondary and tertiary. They're really blurring. Also, the lines between Young Prem and Cloud are blurring as well, but you can replicate data and replicate backups really, really efficiently to wherever it needs to be. So I really see that as a zoo core strength to enable value that plays into the military >> true operational model across whatever environment, and still do the tearing and things you need to do. >> Yeah, no doubt flexibility and being able to support, you know, multiple environments. You know, that's that's that's absolutely what we're after. It's It's what we what we leverage is part of the nutanix ecosystem is is that breath of coverage, but but also given customer choice. >> Just talking to Rebecca, which we love data project. Should I leave lights? Ideo delegate always whimsy will you guys be on next week? This is a huge conversation that used to be a bolt on conversation in the old days of now. Data protection, backup in recovery, disaster planning. All part of a operating model. Holistic picture. Yeah. How is that? We're one hundred percent there yet. And all customers where they still use. This stuff's still kind of like, not forgetting to design in. >> Yeah, I mean, protection. You know where you know, lots of our customers are coming to us because their struggle with legacy solutions and they're looking to modernize their whole infrastructure right there, modernizing where they land. The backups are modernizing the platform that that lands those backups on the infrastructure. And so, you know, that's it's a major problem for our customers and really, you know, you you mentioned, you know, availability and you know, you you go back five years, maybe five, seven, eight years. You know, availability was measured in three nines. Four, ninety five, ninety availability. You know, everyone in the world of of everything cloud and everything sas, you know, availability is one hundred percent or nothing. You know, it's there is no there. There really is no sort of anything but a one hundred percent availability, >> and its security highlights all the problems. So another customer about this ransom, one other ransomware customer they were doing all the backups on tape. Can you imagine? Of course, they're talking for ransom where it's just good on the director. He was still using tape because they can't turn around fast enough. It was a big problem. >> Yeah, you know, it's funny, you know, you you know, we're focused on innovation and next things. But when you you know, you you then have some of those customer conversations. And some of them are still, you know, because of their compliance and processing procedures, There's still, you know, five years behind may be where we are. You know, you've got a you gotto sort of bring them along for the journey to knowing that they're gonna they're gonna trail behind. But for the for the early adopters and the innovators way also have to serve them as well. >> And they got there. They gotta level up themselves to it, son. Them too. They had they had the level of >> So speaking of innovation, you are two different companies. You already talked about this, its energies and the similarities in culture. But you are two companies coming together to build a product. How does that work? I mean, do you do get in the same room? Do you watch the same movies? Do you have a happy you? >> So >> get one brain working on this >> female. Vamos a distributed company. We are distributed company. So it's it's It's a lot of calls and so on. But it's it's really fun to really see it. She had come together and becoming really right. Yes, there's a lot of hard engineering problems that we have to solve in some very deep discussions around layout and things like that. But then doubling it up, working on the joint value prop and working on the joint marketing it really is a very nice wide set of off capabilities and skills that we've been working >> on. And when I went out, I mean, it is hard. It is hard to bring to two things together and work on them jointly. And we've, you know, so far been fairly successful. What I would tell you is it it brings some some advantages to us as well Because we have a best of breed platform. We have a best to breed data protection platform. You know, bringing those together bring some advantages that maybe someone that does all that together on their own don't have because it's not a focus area for them. Right? So, you know, it's our job to make sure we take advantage of that and provide some additional things for our customers that maybe they won't get out of some of those other platforms. >> Well, Mark and Ken, thank you both. So much for coming on the Cube. It was a pleasure having you. >> Thank you very much. >> Thanks for having us. >> I'm Rebecca Knight for John Furrier. We will have Ah, we'Ll have more from nutanix dot Next coming up just a little bit. Stay with us.

live from Las Vegas it's the cube covering Dell technologies world 2019 to you by Dell technologies and its ecosystem partners welcome back to the cube Lisa Martin with Dave Volante hey Dave you too day two of the cubes coverage of Dell technology world 2019 from Las Vegas and as John Ferrier said yesterday with you guys did your open this is a a cube cannon of content we've got two sets three days of coverage excited to welcome to light back to the program but a couple of new guys from Deloitte joining us we have Bob black Dell technologies Global Alliance lead and principal from Deloitte Bob it's great to have you on the program thank you're having us both of you and Louise spend vanina's VP of cloud business development at Deloitte thanks so much for joining us welcome so guys yesterday's keynote was fantastic it really started with this electric and I think that started with the fact that Michael Dell came out to Queen music which I loved but also today Jeff Clarke shot out of a cannon as well one of the things that he talked about was the Dell technologies five key imperatives for IT modernization one of them being hybrid cloud strategy for multi cloud journey we've been talking about multi cloud for a long time it's a big theme here we're gonna get into that with you guys and specifically get the rise of hybrid cloud but before we get into that Bob let's start with you kind of set the stage about your tenure at Deloitte you are an industry veteran so tell us a little bit about your expertise and kind of some of your thoughts on some of the things you've heard the last two days sure well thanks for that for having us so again my name is Bob black I'm the global lead alliance partner for the deltek for Deloitte I've been with the firm for about a little less than three years now I actually came to the firm because I really my former employee really neglected to focus a lot on infrastructure and hybrid cloud right so when I saw this as an opportunity to come to going and and really fashion what that what that should look like at the marketplace and so I think as serving in this role you know we've been able to really drive a lot of what that hybrid cloud messaging throughout the white for a lot of our customers and Louise tell us a little bit about your background well thank you I've been with the firm now for about two years actually came in via an acquisition of a company called day one solutions where we helped kind of provide a shot in the arm in our overall cloud go to market but my experience is I've worked at cloud providers like AWS I've worked with the technology OEMs as well so I've seen this seen the space change for quite some time so this is the second Dell technologies world lots of news yesterday this whole journey a lot of collaboration yesterday as well between really kind of VMware as a linchpin of driving a lot of what Dell technologies is doing we saw a Microsoft on stage everything is about collaborating listening to the customer and enabling customers to be successful in this challenging but very true multi cloud world talk about some of the announcements Bob we'll start with you some of the things that you're hearing and what that without Deloitte can help Dell technologies customers with a successful hybrid cloud strategy what are some other trends you're seeing and hearing well yeah so thank you so I mean it was something else was yesterday we're very fascinating and you know one of the things that we have been seeing in the marketplace is a lot of our clients are restarting to repatriate their applications back from public cloud fact on-premises and that's not to you know say anything bad about the public cloud but what we're seeing is a lot of our clients have thousands or if not tens of thousands of applications in a legacy portfolio that simply are not conducive for public cloud and when there was a shift out to public cloud I think that you know a lot of our clients adopted this lift and shift or Rijo sling type of methodology and most of our clients really didn't find the value in there that they thought they were going to get right they weren't able to modernize the application they weren't saving any money they had dual IT fractured operations and so for because of our reason we're starting to see a lot of that repatriation back on premises and Ilana and I think a lot of our clients see that it's somewhat of a failure but I think what we see this is an opportunity right you know public cloud is a great tool in the toolbox and when it's incorporated with as you heard here up on the stage yesterday you know the data center other other areas on premises and the edge right you can really put together a cohesive hybrid cloud strategy and that's what's really going to drive value of our clients so we've you know we've worked successfully with a number of clients on things like BMC and AWS and yesterday's announcement on VMware and Microsoft and now of course Dell cloud I think is only going to accelerate the adoption of those technologies and solutions to help our clients adopt a hybrid multi cloud strategy I'll add to that I think it's it's kind of an evolution relieve our industries if you go back to the ten years of history of the cube look at how much has changed and and software-defined OEM abstract eight the OS or application away from the hardware component that's been happening for some time and you're now starting to see you know the other side of that from the public cloud moving to on-premises as well so you know really the hybrid definition if it's seamless data and applications across environments will just continue to move to that model and there's some ironies that I want to come back to but I want to ask you Bob but you said some of your clients feel like it was a failure do you feel like it was an edict from up on high go to the cloud and they really didn't have a clear strategy to affect the operating model and that's why they ended up where they are or were there other factors I think so we kind of dubbing at some one times as a management by in-flight magazine if you will right you know this you know I remember talking about client she told me that she had to move everything all her storage out to cloud and when we asked her why this is well that's what everybody else is doing I assume I have to do the same thing right so I looked you know we're not here to disparage anything in the public cloud it is a great you know tool for a lot of our clients it's just making sure that we have that integration that seamless integration of applications and data between you know both on-premises and cloud and that really starts with a really well thought out strategy it actually starts out even before that with trying to understand your applications many of our client made the application and this is hard for me saying this as an infrastructure guy right but the application should drive the placement right and I think a lot of our clients struggle with what makes up their application portfolio right what are the attributes that that make up the application that will help determine the placement of of those applications themselves well that's why I mean I'm interested in how Deloitte looks at infrastructure because Bob you were saying your previous firm really wasn't all in on infrastructure kind of infrastructure I met but when you think about a firm like Deloitte global capabilities deep industry expertise application you know keep those infrastructures more horizontal how do you guys look at the the opportunity why does it form structure so important to you well it's really to support next generation applications right again going back to if you think of hybrid as an approach of seamless integration across environments and you start developing these new applications towards you know different operating models or service oriented models the underlying infrastructure is not really built for that so you're gonna need to go through IT modernization refresh is within a new type of technologies and maybe what you were doing before so these applications I think move away from kind of legacy environments to more modernize type environments cloud or on-premises ultimately you're gonna have to address that IT on-premise need so what is that path for a customer who like you were saying Bob who thought well we've really failed at this they they went to public cloud because everybody else was suddenly I mean we all know time is a major issue that with any every business transforming because first survival and for competitive advantage how do you help a customer take that what they perceive as a failure and a lot of times that can be a positive effort right gotta opportunity to learn align exactly give us an example it may be a favorite customer by name or industry where you said alright we let's step back we have to have a strategy you're evaluating all these applications letting them as you said both drive where they should be run how they should be deployed how they should be secured how do you kind of help that repatriation it from a speed perspective so they can get to market faster and turn that failure of a failure into a success yeah I'll give you one example I mean we working with we're working with a retail client not too long go and you know they had they bought the you know they drank the kool-aid in decided they went through everything on some public clown right but again what they found was unless you're willing to refactor an application right to take advantage of cloud-based services and more agile nature of application development what they found was and it wasn't really providing the value that they were hoping for right and so again when you're left with that lift and shift with a tree hosting strategy it just didn't really provide anything so what we wanted to do is work with them to kind of put together a business case a more well defined strategy to say hey look let's let's let's accept the fact that these applications are not conducive to the cloud and right and how do we drive value across the entire application portfolio across both on-premises and in the public cloud right and then I think the other thing that we need to talk about too is I think a lot of times and it's been brought up here several times is I think a lot of lot of clients out there they talk about public cloud and the data center but that's it right there we're going to start to see distributed computing in the edge right we're going I think a lot of clients forget that people need to get to the cloud there's going to be this edge competing right so the journey starts there but it certainly doesn't end there either yeah it kind of comes to mind another insurance client that we had a large insurance company and they're a major cloud adopter but overall it represents still a small portion of their overall IT but they need that speed to market so leveraging technologies like pivotal to be able to quickly obtain that and then but as far as their overall application strategy every net new generation app they're gonna try to they're a cloud first mandate as well and we're seeing that a lot more prevalent from I will explore this a little bit because I saw stat I had some IDC survey so the 80% of companies say that half their workloads are gonna come back on Prem by I don't know when but anyway that repatriation conversation so okay so the insurance company their claims app going into the cloud but if if I'm a CIO and you're gonna bring back workloads I want that experience to be very cloud like I want the tool chains to be similar so obviously some of the announcements here today but that's non-trivial so I presume that's where you guys come in okay you guys you guys do well with complex so how do you help customers create that cloud variants that can substantially mimic the public cloud on-premise yeah and so very much we took first take a look at the operating model right if the in the way that they've adopted cloud mostly Cline's have been kind of a workload by workload and you start having these siloed environments of operations but what they want is the premise of you know how you operate cloud we know for the way that you do the rest of your legacy applications for IT so the evolution has been is they want to take a look at this holistically across all of their enterprise portfolio but not just from a technology enablement organization change you know can you bring in the right talent can you retain the right talent you know and is this the kind of environment where you can enable them to go create you know those things that might provide a bit of bigger ROI to market as well as automate and relentlessly where you can in how you approach services so I have a thesis I want to test with you guys so and I've been saying a few times this week at multi cloud it's all the hype but I think it's been a symptom of multi vendor shadow IT line of business initiatives and now we sort of have this you know airline magazine home s and and I think executives are realizing wow we have to get control of this provide the agility of the cloud the cost structure potentially but also the corporate edicts of security and compliance is that a valid premise is that how we got here has it been more deliberate than that and you know where do you see it going yeah I mean we've seen somebody even from this M&A right well you might be all-in with one cloud provider predominantly and you're continuing to move in that direction so you can gain some maturity before you introduce a multi cloud model but what happens we can just made an acquisition of something very strategic to your company oh crap they're on there with some other completely different kind of provider and then but you still have to roll that into how your overall IT governance so again these are just areas of the conversation where it's at a completely different level than you know which console are you going to use we'll try to manage all and that's and it's it depends whether or not you've sort of migrated into the the acquirers cloud or you leave it alone yeah but but how do you make how do you help customers make that distance I mean you're at the board level you're dealing with CIOs application heads line of business heads I mean your Deloitte you get some visibility I think we'll just kind of go back to what I was saying earlier right I think having a better view of the application portfolio is what's going to start that process right you know again going back to what I said earlier the we have clients who have thousands and thousands of applications and and they have they don't have the slightest clue on what's in that portfolio let alone the attributes that make up those those applications right so really starting there to understand you know what's important to them what are their requirements understanding that application portfolio a little bit more right and and marrying those with those requirements that's what's really going to drive a lot of these decision-making we have one of our methods of engagement is something we start off with in a kind of a shorter sprint model or our cloud value calculator and really trying to quantify what's that what's that investment really worth what are we what do we expect back you know are you moving it for TCO reasons or is there something that more profound that can happen back to the bottom line of the business and really try to put it in those type of terms then you know just a technology move yeah because I was thinking you know the cost of repatriation has got to be pretty significant so in terms of looking at and working with say the retail customer that you mentioned or the insurance example Luis that you mentioned when you're helping them identify the right strategy and really looking at the business imperatives for doing this quickly this repatriation what are some of the business outcomes that say this retailer is achieving that makes this repatriation cost expense very much worth it yeah I think it's I think it's peak right you know I mean we talked about cost a lot but I mean cost has become less of a factor in decision-making I mean our clients regardless the industry they're looking for speed to market right you know how do I deliver services how do I change faster than my you know my competitors right you know so it's it's it's it really right now it's all about speed how do I do things faster you know obviously cost is a factor of that right but you know beating your competitor out to the month I mean we always talk about you know regardless with the company is there everybody's a technology company all right and I actually believe that right you know so if the faster that you can do the faster you get your products and services to market that's what spread that's what's really does this customer this retail customer have it hey where we are 2x faster to market now that we've gone through this yeah as a matter of fact I don't know if we can't talk about some of those specifics but yeah they have definitely seen an uptick in the speed the market to be able to deliver new services to help drive business with the strategic foundation that they derive with you guys yes I'll add you know the other thing is regulations are changing worldwide right every day when Tina data sovereignty laws whatever it may be and if your cloud provider is not in that region or doesn't have the same level of services that you have say in the US what do you do so again other reasons for kind of having those does that reap retried it repatriated model so that you can kind of go wherever you need to go as far as your business expansion excellent guys I wish we had more time there's so much to talk about here but Louise Bob thanks so much for stopping by the Conservatives County with David mean we appreciate it have a great rest of your show Thank You Jay thank you Lisa our pleasure for Dave Volante I'm Lisa Martin live from Las Vegas if you thought your heard dogs barking by the way you weren't imagining that we are right next to Michael's angel paws one of my favorite parts of this entire event with about 15,000 people or so you're watching the cube live from day 2 of our coverage of gel technology world 2019 thanks for watching [Music]

(electronic music) >> Narrator: Live from Las Vegas, it's theCUBE. Covering NetApp Insight 2018. Brought to you by NetApp. >> Welcome back to theCUBE's coverage of NetApp Insight 2018. I'm Lisa Martin. I've been here all day with Stu Miniman, and we've had a jam-packed agenda of guests. We're now coming to the end of our program. We bring back two CUBE alumni. We've got Bharat Badrinath, welcome back. I feel like it's deja vu. The VP of Product and Solutions Marketing at NetApp. And Ken Ringdahl, also an alumni, VP of Global Alliance Architecture from Veeam. Hey, guys. Thanks for stopping by towards the end of the day. I'm sure you guys have both lots of meetings today. Let's talk a little bit about the NetApp-Veeam partnership. NetApp bought Veeam a few months ago, Ken. The reseller relationship that Veeam has with NetApp was announced. Let's talk about the impetus of that, that momentum coming from joint partners, customers, channel partners? Tell me a little bit about that from Veeam's perspective. >> Yeah, sure. I think earlier this year, we announced that resell relationship, which went live in March. So VeeamON was in May, so we were just at the early stages of that, and we've seen some good momentum. We've expanded that relationship. And now we're able to jointly sell the whole portfolio. And I'd say it's a combination of two things: and really it's customers and partners, right? So, we had a lot of success in the channel. Veeam and NetApp have been partnering together on the channel for, you know, five, seven years. A long time now. And just based on the success of our meeting on the channel and then customer demand and partner demand, you know, we decided to expand our relationship and go deeper and really go deeper not only from a go-to-market perspective, but from a product perspective. We're getting even closer together and driving more business and integration and really highlighting the value of the NetApp platform. >> What's NetApp's reaction to when the channel and customers are saying, "Hey, guys." Tell us about that, Bharat. >> We obviously are here to make sure the customers have a great experience with it. And Veeam brings in something which is unique in the market for the customer, so we've heard it from our customers, our joint customers saying that better integration is going to help them. Being the stewards of the customers' data, we want to make sure the data is protected. And Veeam brings that expertise into the market. We integrate better to make it more seamless for the customer, which is what we're doing as we expand this partnership to the next level. >> Both Veeam and NetApp were pretty early in learning into this hybrid, multi-cloud world. Wondering if you have any good customer examples you might be able to share as to customers that are kind of moving towards this future that we're talking about in the partnership. >> Yeah, sure, I mean at Veeam our goal is to really provide a hybrid environment. We started in the virtual world. We expanded to physical. We've gone to cloud. You know, we see NetApp with a very strong presence on-prem. They obviously have strong relationships with the public cloud vendors and have done a really good job of pivoting the strategy and embracing the cloud, which is what we've done at Veeam as well. We see our customers.. they're really choosing cloud. They're choosing best of breed now, right? So, they don't say, "Hey, I'm a single cloud strategy. I don't do just one cloud here. I'm saying best of breed. Maybe I'm doing my machine-learning and AI and Google, And I'm doing my cloud native apps in AWS, and I'm doing my Microsoft native workloads in Azure." And so really you do need to provide that hybrid solution. That's really what we've looked to focus on is taking the strength of where we came up and providing that best solution in the virtual world, extending that to physical, and now going to the cloud. You know, we see lots and lots of customers that they just want a comprehensive solution. They don't want point solutions, a point solution here, a point solution there. They want a comprehensive solution, and so it comes down to two companies really I think that have a very strong strategy for that hybrid world, for best of breed solutions that we can work together in all those facets. >> Yeah, and I think our strategy and Veeam's strategy are pretty aligned when you look at the hybrid cloud, when you look at our data fabric, (inaudible) in the market, and what we are doing to stitch together on-prem and cloud. Veeam happens to be a great partner to help protect that data as we work with the customer along this journey. And today Veeam just announced an SEI part of it as well. Just making sure that we are helping the customer through every aspect of the journey. >> I'm wondering if you might have.. Since the deal was announced earlier this year, any specific customer examples--even anonymized-- that you could share? >> I'm sure there are lots of customers we have had jointly. I don't have any specific ones at this moment. >> There's a few I can highlight. Probably one of the top ten international banks, AMEA. That's a really, really large deal that we're working to get closed. It's multi-million dollars to both of us. Very, very large deal. I think we're seeing success. Veeam's strength has always been sort of in the commercial world, and we're moving up into the enterprise. That's a big impetus for the partnership quite honestly 'cause NetApp has a lot of strength 'specially with the ONTAP system in enterprise. So, I think we're really sort of dovetailing each other. Veeam is bringing NetApp into more of our commercial deals. NetApp is bringing us into more enterprise deals. But really it's across the board: large banks, even healthcare and other deals as well. I don't know if there's any specific names I can call out, but I can tell you it really stretches the entire sort of stretches vertical, all different types, different sizes, different types of customers. >> We just had Dave Hitts on a little bit ago, Stu and I did today, and he kind of talked about in the last five years, really a big revolution at NetApp that has been around 26 years. Ken, you mention that NetApp and Veeam have been partners for about five to seven years. I'm curious what Veeam's perspective is of NetApp's digital and IT and cultural transformation to now go out boldly and say, "We're the data authority," and really kind of wrap their strategy around cloud. >> Yeah, sure. I would say we are in a data-driven world. Data is the currency in the cloud world. We look at ourselves as being the stewards of data availability. NetApp has the strength in that primary data management. There's really a natural dovetail between the two of us and a natural hand-off, where we can provide the entire end-to-end from primary to DR to secondary and really about sort of managing the placement of that data, the value of that data, and the availability of that data. It's incredibly important. I think together we cover that end-to-end. >> Bharat, one of the messages we've been hearing today is talking about there's a lot of complexity out there. NetApp's goal, like many companies in this space, is to try to help simplify. What is the partnership, the integration, reselling.. How does that help simplify solutions for companies? >> Absolutely. As you heard earlier, it was all about providing a comprehensive stack end-to-end, but what makes it simple is when it is comprehensive and integrated, right? So, when the two companies' engineering teams work together to drive that integration, that results in simplicity, which our customers and our partners.. For our partners, it's assurance that we're both working together, so it makes the solution more reliable, works well, as advertised, if you will. And the customer premise is for customers. It's the simplicity in the form of integration, which comes in where the two companies' engineering teams are driving towards that. >> Last question, Ken, for you. In terms of kind of following on what Bharat was saying, the customers now not only need that simplicity, they expect it. I'm curious where is that in that, in the selling motion, where is that conversation? Is it with some of the folks that are down in the technical weeds, who are looking to drastically improve recovery time and recovery point objectives? Or are you also having conversations at the business level of the business going, maybe it's a legacy not cloud-native that needs to go, "We have so much data, which is an advantage, but how do we use that?" Are you seeing those business leaders, business unit leaders in C-levels involved in this conversation with Veeam and NetApp? >> Yeah, yeah, no question. I think traditionally Veeam has really been compelled by the Backup Administrator, by the IT director. Because the product is so easy to try, you can download it, you can try it for free.. Our whole "It Just Works" has been our tagline because it is just so simple to get started with Veeam. We make it simple to get up and running and to manage your backups and also give some of that power back to your customers. In fact, just a quick sidebar. Had dinner last night with a longtime Veeam customer, longtime NetApp customer, and they said, "Hey, look, NetApp is my storage vendor of choice. Veeam is my backup data protection vendor of choice. And they come together well. And NetApp does such a great job from primary to leveraging the snapshot replication," but he told me about this great story. He said, "We had somebody at midnight needed to recover a file. We have self-restore capabilities that they were able to give that power to their end users to go recover a file to their server instead of calling up and opening a ticket. Instead of what took maybe eight hours to go through a whole process to get a storage admin and then a backup admin took eight minutes." I think it talks to the value of the NetApp platform in providing that availability and the simplicity of the Veeam system to be able to give that power and take what might be complex and make it very simple. So, back to your original question, Lisa, about.. We've traditionally really sort of been very, very valuable to that backup administrator, IT admin. As we move further into the enterprise, of course that goes up into VP of IT, all the way up to the CIO. I think our relationship is really bringing us both ways. We can come bottom-up, NetApp can come top-down. And we're hitting both sides and really that whole stack of influencer to buyer to decision-maker in that whole stack. >> Bharat, last question for you. We've got a few seconds left. I'm curious when a customer says, "Veeam is our backup, and recovery, NetApp is our storage," how does that, in this day as, "Hey, cloud is the heart of our strategy," how do you react to, "NetApp is our storage provider?" >> I don't see those as exclusive things. We manage the data on-prem, and Veeam, given their abilities in the hybrid cloud, if a customer considers us as on-prem storage company, that is great. We're working with them to change that impression, to get with them on their journey to the cloud. So we don't want to force them to get into the cloud, but as they move to the cloud, we want to be there to make sure we can manage the data in the cloud. And Veeam, given their hybrid capabilities and where they've been and what they do with the customer, and their ability to manage monthly cloud maps really well, to what we offer the customers. Of course we'd like our customers to change their perception to not just view NetApp as on-prem storage but as a cloud vendor as well, but it takes time for them to change their perception, and we're working very hard on that. As you saw today in the keynote as well, you're starting to see customers.. It has to be driven by the customer need. Sometimes they realize certain things are done better in the cloud, which drives them to the cloud. We want to be there to provide that service for them as they move. >> Well, Bharat and Ken, thanks so much for stopping by at the end of the day here. We appreciate your time, and we look forward to, in 2019, maybe hearing more from that big AMEA bank and some of the great successes they're achieving with this partnership. >> Thank you for having us. >> Absolutely, thank you. >> Our pleasure. We want to thank you for watching. This wraps up theCUBE's full day. I'm Lisa Martin with Stu Miniman. We've had a great day, Stu, talking with NetApp executives, customers, partners, and we want to thank you for watching. Hope you've learned a lot, and of course, watch the replays at theCUBE.net. For Stu, I'm Lisa, thanks for watching. We'll see you next time. (electronic music)

>> From Orlando, Florida, it's theCUBE. Covering SAP SAPPHIRE NOW 2018. Brought to you by NetApp. Welcome to theCUBE, we are on the ground in the NetApp booth at SAP SAPPHIRE 2018. I'm Lisa Martin, I'm hanging out with Keith Townsend. Today we are joined by RJ Bibby, who is the SAP Global Alliance Chief of Staff at NetApp. RJ, welcome to theCUBE. >> Thank you, we're so glad you guys are here. >> So, this, is a huge event. There are, I've heard 20,000 attendees live, but they were saying at the keynote this morning, Bill McDermott was, over a million people are expected to engage with the SAP experience both in person and online, that's enormous, enormous. SAP and NetApp have been partners for 17 plus years now. Right, you've got thousands of customers that run SAP on NetApp. What's current with the partnership? What's going on there from your perspective? >> Well thanks Lisa, thanks Keith. But first off I want to thank you all for being here. We're ecstatic for having theCUBE in our booth. We haven't been back here as a sponsor in a couple years. So being a platinum sponsor, 40 people here on the ground from all over the world. Like you said, we're about 26,000 people this week. So, really busy, we're in our 18th year. This year, as a partnership with SAP. To answer that question, it's really exciting. We have a very unique partnership with SAP. It's a true 360 partnership, and what I mean by that. One, we co-innovate together. So we're doing co-innovation where NetApp on SAP on NetApp. What that mean is basically a lot of the SAP products, like hybris, like Ariba, SuccessFactors are built on NetApp. We're doing co-innovation on blockchain, on HANA, IOT. So, we're really looking at that next phase of automation in data management. And we'll get into data management in a bit. We're both customers to each other. We just had our CIO met with a customer success office this morning to talk about some of the integrations of products that we're doing. Second year in a row SAP has been our largest customer. So the growth on that end is great, and then lastly the GODA market, and that's really what I really do from the alliance side. So, heavily around HANA acceleration. How we constantly are helping our customers move to HANA with our NetApp data fabric and ONTAP, our core signature products that deploy SAP. And we're very focused on industry, very focused on a global to local partner for life. We both have really warm, loyal customers. And then there's a kind of G100 strategic approach too. So that's the partnership, it's been a lot of fun. And we're gonna see where it goes in 2018. >> So RJ, talk to us a little bit more. Add some color on this relationship between Netapp and SAP in the market. NetApp, data driven company. SAP, probably the premier data analysis, analytics. We saw on stage from customer experience all the way to backend. You can't do that without a solid, robust infrastructure that's focused on SAP. What are some of the key technologies and strategy that NetApp and SAP have teamed together, bring together such successes? >> No, great question Keith. Really it goes down to the core of data. So NetApp has done a transformation the last two years, where we're gonna be now the data management company for hybrid cloud. So in that core, customers are looking to do a bunch of different things with NetApp. We want to manage, transport, analyze and protect data. A lot of data on SAP. So they're modernizing their data centers, how do we move to the hybrid cloud? With our ONTAP product, which is really a software capability, really turning into a software company in the cloud, as is SAP. So the core products of HANA, SuccessFactors, Ariba, Field Glass, Concur, all the things from an operations standpoint that's been automated for their business is kind of built on NetApp. Is built on NetApp, a lot of them. So our approach to the customer is how do we help the experience? And, we're doing that transformation internally, so we're going through it with SAP. There's lessons there. SAP did this and moved to a kind of cloud company a couple years ago on NetApp. Those are some of the core instances, but there's a modernizing a data center approach, there's a hybrid cloud. But it still just comes down to, oh my God, data runs my business. I'm really scared about it from protection. There's too much of it. How do I monetize? What are the analytics behind it? And that's what NetApp is really on the forefront of doing. Our CIO talked about this, this week. He's going to talk about it this week, about choice. What we're hearing for customers is, I need choice. I need to move my data around on PRAM, into whatever hybrid hyperscaler environment you want fast, efficient, with analytics read outs. So that's kind of the approach we're starting to take to market. I find it to be a very consultative of approach where it's Mister Customer, SAP NetApp, whoever your hybrid cloud choice is, who your SI is, the other technology partners are. We're all together collectively, almost like a innovation program for a customer approach. And that's kind of, not my secret, but one of my secrets of how we're going to market with the sales teams. >> I'm curious, NetApp is 26 years old. 26 years young maybe. I worked there I was telling you, for a few years as well. On marketing, which was awesome. Lot of evolution from a storage perspective. You say NetApp runs a SAP, SAP runs a NetApp as well. Talk to us about this maybe SAP as an influencer of the evolution of NetApp from storage company to now as you said, data authority for hybrid cloud. >> Yeah, no great question. I think it started where we wanted, we saw that software was kind of taking over. The automation, right? So it's almost like storage is a service. In my four years at NetApp, we never approach SAP as a partner or the customer talking about storage or infrastructure per se. Kind of around this data management methodology a while back. I think SAP has been an influencer internally fpr us in a couple regards. One they have the state of the art, a lot of the software operations. Things that we needed to run the business. There's been some kinks, there's some things that we probably need to customize that fit our business. NetApp's really unique, we're about 6 billion dollars, with 10,000 employees, with three business units. And we're a very unique company. The culture is awesome, we're empowered. Salespeople on the ground are empowered. Me helping run the alliance, we can be very strategic on how and what we want to do. Hey, we want to have CUBE at SAPPHIRE, absolutely do it, as an example. So, with that empowerment, we've been able to look at the best of breed in tools. And I think the tools are helping us from looking at the business and really how the customer experience. I'll give you one example on that. We're listening to our customers and how they want to transform their data, in their data on SAP. Well, I need to also be able to look at the analytics internally on okay, does my customer need a technical refresh? What are they doing on SAP? Is it SAP on Oracle? What products do they have of NetApp? Do our salespeople properly enabled on selling SAP on Cloud? Are they talking to their counterparts at the account from SAP, from a CISCO or Fujitsu, AWS, and then whatever SI. So there's a lot of complexity, there's an art and a science to it. And it's in our transformation in SAP from the tools perspectives at the core of that. >> RJ let's talk about the alliance beyond just the SAP to NetApp. This is really complex, I mean even with the tools, you know, ONTAP on the cloud, ONTAP in your data center. ONTAP kind of in the fog later, wherever you want to say that's at. That normalizes the data, it kind of validates the NetApp as the data driven company. However, when you go to an enterprise and you say that, you know what, this thing that used to live at my data center is now spread across these three different environments. It's really hard to figure out. How do SIs play a role into shaping the strategy in this alliance? Yeah, that's a whole other layer right? The complexity, 'cause I find, I came from the SI side of the house. I worked at Accenture for a really long time prior to my career in the partnership side. You know, I think they're very good from a consultative approach of hey, how do we want to design this thing? How do we want to implement it? How do we want to run it? And where does everybody's silo of stuff or technologies fall into that? I think the art part of it is hey, as NetApp or with MRSI, hey man, how do we help design with you? How do we consult the end to end approach here? I think we're the expert from an end to end data management approach. So there's some butting of heads at times depending on which SI, because they do. They have these long standing executive partnerships. There's a lot of investment from SIs at the account. I was just at a leadership conference with Accenture. And they're spending three billion dollars on three different things around automation. One, training. They can't get people, it's still about people in process. How do we get the process and tools in place? Where do we need to go merger and acquisition on the latest products? And how do we implement with that ecosystem? So I always think it's a work in progress. It's gone well, I think that's something I'd like to see us improve on. I think the SAP to NetApp partnership is advanced. In a lot of regards to that. It's like anything, it's also like when you look at salespeople internally at NetApp with our transformation. How do you get people out of the conference zone talking to their infrastructure lead, their line of business lead. And elevate to the cloud conversation. Going to the CXO, I think the Chief Security Offer is the key executive now in our sales process. Because of data protection. And that's something that we do well, and that's something they own, and I'm always trying to be creative. There might lots of dollars to protect data. How do we turn that into a whole strategy conversation with all the partners? >> So let's go a little deeper on NetApp's value propositions. You know what? Infrastructure is infrastructure, why should it matter? How do you guys differentiate between your competitors and running S4 HANA, the cloud strategy, you know what, end memory databases, storage is no longer needed, that's not true, but what's the story? >> Good question. The story for us is the ONTAP product that we have, the software because what we can do is deploy SAP really fast. Really fast, just some stats. You can get 45% project timeline savings with our deployment of SAP. The secret sauce in that is, the tools of the replication in the snapshot. When you're doing constant development ongoing maintenance, we can do snapshots in real time. That is the key thing that keeps the production going live faster. >> You know, because CICD is not something that we do, I've managed SAP for a long time. And CICD wasn't exactly a concept in SAP. So we rely on the infrastructure a lot to do. So snapshots is an amazing example of how you bring the CICD approach to something that is stayed as SAP. You can't just shut down SAP for the weekend to apply a update five times a year. >> Correct, so hours and hours of down time, where we can do it in three hours. A lot of times it's real time. I was just at a HANA Conference in Vegas and we got a lot of one on one time with customers. It was awesome, and that was the biggest things they said they need more of NetApp. And the differentiator is we're continue to expand our approach to managing the data, and I need the replication and the cloning specifically to run the production value end to end. So that's the other part of it. It's really just doing that end to end landscape management of SAP and Non-SAP workloads. The one thing that's great about the cloud part of this is you do need a lot of storage, and it's software based storage. So I think the approach in NetApp is going in the right direction. I've been working with SAP as a partner now for 12 years. I think that this is probably the best momentum I've had with SAP ever. And one of the reasons why is one, data is the story, right? What does Bill McDermott always say? Data is now the currency. Well today he was saying now trust is the currency, which is completely true too. But from the data being the currency perspective, it's now the end game for both of us. So we've kind of, in all companies, have gone into the middle. That's kind of not only the messaging, but kind of the central thing we're trying to deliver value on. And the choice, I want to keep saying the fact that customers now want choice on where they put their data. That's the thing that we're really promoting here at SAPPHIRE this week. >> Last question RJ. >> Last? All day! >> I know, I know right? Speaking of choice, you mentioned customers want choice. They do want choice. You talked about value, delivering value. From a competitive perspective, customers have choice. They've got other storage vendors they can work with. Give us your best elevator pitch. What makes NetApp and SAP different and better than say, some of those, maybe orange colored competitors? >> Sure, no, no, it's a great question. The biggest differentiator is just the fact that we are the one company out there that can provide data management in any hybrid environment. AWS, a hyperscaler, Microsoft, Google, we're doing cloud volumes just announced a Google Cloud platform. You know, we're one of the premier technology from HANA and Azure. So I think number one it's that. Secondly, we can deploy SAP really quickly, which consumes licenses. So one, the customer really likes that. Two, SAP sales loves it 'cause then it gives them a chance to go back to the customer. And then just the end to end data management that we can provide our customers value. I would say choice one. >> Awesome, well I said a few minutes ago to Keith that Bill McDermott is probably the most energetic C level that I've ever seen. Your energy level RJ, right there with his. >> You know why? 'Cause it's go time, it's SAPPHIRE day one. >> The stage might have exploded if we had them both at the same time. >> That would've been fun. >> Pyrotechnics on day one! Well RJ thank you so much, not only for visiting with Keith and me this morning. But also of having theCube in the NetApp booth at SAPPHIRE. >> We love it, we can't wait. Thanks everybody. >> I'm Lisa Martin with Keith Townsend at theCUBE on the ground at SAPPHIRE NOW day one. Stick around, we'll be right back with our next guest.

(Music) >> Announcer: Live from the Bill Graham Auditorium, in San Francisco, it's theCUBE. Covering Pure Storage accelerate, 2018. Brought to you by Pure Storage. >> Welcome back to theCUBE, we are live at Pure Storage Accelerate, 2018 at the Bill Graham Civic Auditorium in San Francisco. I'm Lisa Martin sporting Prince today, with Dave Vellante sporting The Who. And I'm sandwiched, most importantly, between two Celtics fans. And the Warriors are across the bay. We'll save that for after the conversation. So we want to welcome to theCUBE for the first time Ken Ringdahl the VP of Global alliance Architecture. From Veeam, welcome. >> Great. Thank you, Lisa. >> Dave: Well the truth be told, we're afraid of the warriors, okay. We really don't want to play the Warriors. >> Oh really, alright. >> And we're not afraid of many people in Boston, but I don't know, they look pretty good. >> Well, I appreciate the honesty, that's pretty cool. >> Well... Though they lost last night. Right? We're going to start the sports talk now. >> Yep. >> Iguodala was out, they showed some foulability. So, anyway. >> We digress to- >> We'll be back to it later on in this segment stay tuned. >> Alright, so you're just fresh off Veeam On, last week. We're impressed that you still have a voice, you've recovered from that. Tell us a little bit about some of the things that are new with Veeam and Pure. So just a month ago, in April, new intergradation between VM availability platform, and Pure Storage flash a way to deliver business continuity, agility, intelligence for the Cloud era. Expand a little bit upon that. >> Yeah, sure, I mean really this integration with Pure Storage, in the VM backup and replication product, end of last year we introduced this new functionality called Universal Storage API. And what this really is, is a way for us to enable our partners to take control of their destiny a little bit more. It's a program we invite our partners into, you know Pure is one of the first that we integrated with, and invited into the program very early. We announced this last year, and we've now finished the integration, as you've mentioned, we announced it last month. It's now been out there, and I think the number I heard earlier today is that we've already had a couple hundred downloads and deployments. So that's just great adoption, and just shows the pent up demand for that. But what we've integrated is the ability for our partners, our storage partners in particular to integrate with our storage snapshot technology to really off load the snapshot from the VMware side, and really put more of it on the storage side, and take it really off the production environment. And so it's a better together story where you know we take the feature that we've introduced into the backup and replication, and Pure built this plug-in, and they integrate with their own APIs and we jointly test and develop, and release that plug-in. And they can install it with VM backup and replication, and it really takes the mention, it takes that load off the production environment. So that snapshot without this integration, it's a VMware snapshot, that snapshot stays open as long as the backup is. Which can be minutes, and you know tens of minutes potentially for a large system. But now we shrink that down literally to just seconds. So we take a VMware snapshot, we take the Pure snapshot, we close the VMware snapshot. And typically it's like 10-12 seconds long where as opposed to the minutes, and even tens of minutes from before. So, really it's really offloading a lot of that back up impact, and we're able to do it in a very secure quiesce fashion from the production environment. >> Lets roll back and understand that a little bit better. >> Ken, if you could explain it to us and our audience. In the 2008, seven, eight, nine timeframe. Virtualization Gem of VMware in particular started to take hold. And you ended up replacing a bunch of physical servers with virtual servers, which was awesome, because all those physical servers were underutilized, except for one major workload, which was backup. So when you did want to do the backup, you didn't have enough resources. Veeam's ascendancy coincided with that trend, so there was a simplicity component, but it seems like what you're describing now is another instantiation of offloading that bottle neck. So what was the journey to Veeam's efficiency in a virtualization environment? >> Ken: Yeah if you look at that journey, and Veeam really grew up in the virtualization age, right. So backup prior to VM, or virtualization was all agent based, it was physical. So everything was over the wire, and Veeam went and said, hey look you know we see VMware really sort of growing, and we see that trend towards virtualization, right, and at this point, what's the world 95 percent virtualized, at this point the only workloads that aren't virtualized are really legacy work loads. And so we made a significant leap forward in a data protection stance, by integrating with the hyper visors. So instead of off loading that into the individual guests, right. The Windows guest, the Linux guest. We said, okay we're going to go the hyper visor. Right? And we're going to do this in an agent less fashion, so that you don't have to go an visit every little, every system that you're looking to backup. That was sort of the first step, right. Now what we're saying is we can do even better. And we can off load the hyper visor, and off load that to the storage system. So we can have a very small impact on the hyper visor, really minimize that. And now really put that workload on the storage system which has a lot of extra cycles and availability, and we can go straight to the backup environment. And not through the VM, or through the hypervisor to get there. >> Dave: So VMware admins, they don't like snapshots because it's overhead intensive, it clogs up their system if you will. This capability makes that transparent, or irrelevant to them? >> It does, it minimizes them to such a small degree that it's a blip. You know it's a little blip on the radar, as opposed to when you snapshot a VM you're essentially quiescing that VM, so everything sort of slows down for a very short period of time. And what happens is that it spawns another virtual disc. So while that snapshot is open this other virtual disc is being written to. And then when you close that snapshot, and you remove that snapshot, that disc gets merged back in, right. This is generally how VMware snapshots work. And what we're saying is we're going to minimize as much as we possibly can. The data that goes in there, so if you think of a running virtual machine, if you're merging back in a Gigabyte disc versus a disc that has 10 Megabytes, you know that's going to be really, really quick, as opposed to, you know if you keep that snapshot open for a long period of time that merge operation, and it just slows things down, and we're trying to minimize that impact on the system. >> Lisa: So business benefits; I get the performance improvements that this integration with Pure facilitates, if we think of this in the context of digital business transformation, where companies that are doing well, have the ability to really glean actionable insights from their data to be able to drive, you know, new products and get products to market faster. Is this actually going to facilitate a company being able to get new products to market faster? >> Absolutely, so there a feature inside of VM backup and replication we call data labs. And what data labs is, is the ability to take a production snapshot, in this case, we're talking about a pure snapshot, and be able to stand that up in a sandbox environment. And you can run DEV tests, you can apply your Windows' patches in an environment that literally matches production. And it's a key differentiator. It's a key differentiator for Veeam, and it's enabled by the Pure Snapshot integration that you have this environment, and even if you have an infected system, you go put it over in data labs, it's sandboxed, so you can put in a private network so it doesn't have any connectivity. Say if you have a worm, or some other ransom ware, you can run analytics, you can run diagnosis on any of that, and not worry about it infecting any other environment, nor does it put work load on your production environment. So you get patched Tuesday, right, and we all know that Windows' patches don't always go as they seem, right? So data labs, let's take that Pure snapshot, let's stand up a virtual environment, which exactly matches production, let's test that patch, right. And we have confidence there, so when we go to production, we have confidence because we've already done it. We've already run that in production. So there's a lot of value in that capability. >> So we were at Veeam On last week fresh off the Kool-Aid injection. It's all orange here, it was all green at Veeam in Chicago. The messaging there was all about multi-cloud and hyper availability in this multi-cloud world. We're hearing a lot about cloud like function here, but of on prem activity. Of course multi-cloud includes on prem, so I wonder if you could dove tail your messaging last week, what you're seeing in the field, and what you're seeing with the partnership with companies like Pure. >> Yeah no question. I mean the Veeam platform, and really you saw it last week at Veeam On we talked kind of about sort of private cloud, and public cloud and our ability to orchestrate, and really stretch across all those environments, and we know that customer all the way from SMB all the way up to enterprise, right. They have remote offices, branch offices some of them use the cloud, some of them use multiple data centers, and really they need their data protection to be able to stretch across those environments. They don't want point solutions in each of those locations. They want a platform that they can trust, and have visibility, right. That's one of the five stages that we talked about about hyper availability, like last week. Is visibility, they want visibility across those clouds. Phase two is aggregation, they want to be able to aggregate all these different places. And that's what we provide our customers with the platform is backup, visibility, aggregation, orchestration, automation. And we provide them on different stages of that journey for our customers. We have different products, services and integration actions with our partners, that really help our customers along that journey. >> We know from our research, the crew at Wiki Bond does some great work on this. We know that data protection, and orchestration are moving up on the list of CXO priorities. At the same time, for a lot of IT practitioners who are under real budget constraints it's like trying to sell more insurance to a 24 year old. So those are kind of two countervailing trends, what are you seeing in the market place? >> What we're seeing is customers, you know down time is really is gone. I mean, I think last week we heard in one of our keynotes, you know you roll back a couple of years, you were talking about availability in terms of five-nines, right? Now it's zero. I mean people don't talk about down time because down time can't exist, and customers need that sense of security and availability. You know, it will happen, lets face it even Amazon, the best data centers in the world, go down, right, there's been some notable S3 outages, but it's about how fast can you recover. And you're talking about low RPOs, and one of the things that this week at Pure Accelerate we're hearing a lot about rapid recovery, flash blade, and the ability and you take rapid recovery and flash blade, and you combine that with the Veeam platform and our instant recovery, and you can get to near zero time recovery, in your environments. To really provide that security, and lets face it, time is money for a lot of our customers, right? So they longer they're down, the more time their losing money, they need availability, and the RPOs are near zero these days. = [Dave] The other thing, if I may just follow up, just one follow up. The other thing our research shows is the average Fortune 1000 company, over a three or four year period is leaving, literally, a billion plus dollars on the table because of poorly architected backup, or inadequate backup. So that's a huge opportunity for you and others, obviously. There's a lot of opportunity right now for vendor turn. That's the other thing our research shows, is that people aren't wed to their backup and recovery vendor. So, does that resonate with customers, are they because of digital, for example, are you seeing that tipping point, that critical mass occur, and then if you could tie that in to sort of your partnership with Pure, I'd be interested in that. >> Sure, yeah, no doubt about it. We're seeing customers, you know, they want that flexibility and that portability. One of the things we do with out platform, it's one of our unique selling features is is that it is agnostic, right. And I'll tie it back to Pure in a moment, but you know when we back up, we back up in a storage agnostic fashion. So any Veeam backup that lands on a disc on the tape anywhere, can be reconstituted, can be re imported, so even if you have a full disaster scenario, we can go stand that back up some where else, and fully consume that backup and restore it, and we have direct restore capabilities. We can port those backups and direct restore them. For example, a direct restore Azure, for example. So that flexibility, and portability is extremely valuable. Now, bring that back to Pure, some of the things we're doing around rapid recovery around the snapshot integration, we talked about is we're really enabling customers to have high performing primary storage environments. High performing secondary storage environments. And really bring that together in a way that works. We talked about multi cloud, right, you know, remote data centers and work across, and aggregate and give visibility. That's really where the Veeam Pure story together, becomes really strong because you've got an incredibly high performing primary and secondary with a highly flexible, portable secondary data protection environment. And you get the capability to get to the cloud. You know DL, a lot of customers looking to the cloud for DR, because they don't have to stand up infrastructure there. When they need it, they can spin it up, and then they can bring it back. And there's a lot of value there. >> I hear a lot of harmony, but I actually read recently, online, that a different analyst firm called the Pure Veeam relationship a match of opposites. Now they say opposites attract, and you've done a great job of talking about the integration, do you agree that it's a good blending of opposites, and if so what's that kind of symbiotic benefit that those bring to each other? >> Yeah, I don't know that I saw that report, but what I would say you know, there's a lot of synergy, we're growing at a very rapid rate, I think. When I looked at Pure, and I look at Veeam we grew 36 percent last year, I think Pure is growing at like 50 percent year over year. We have NPS scores, our NPS score is 73, we're really proud of that. The Pure NPS score, I think I saw- >> 83. >> Ken: 83. >> Dave: I didn't think it could be higher than 73. >> It's incredible. It is incredible, and I think there is a lot of synergy, the size of the organizations, I think the age of our organizations, the aggressiveness that we have, we have joint competitors in the market, so I think there's a lot of synergies between where we are as an organization, as Veeam, and where Pure is. I wish I read the article in terms of the opposites, because I'd love to understand. >> Personally, as a long time analyst, I would say the similarities are greater than the differences. >> Sure sounds like it. >> You're both about a billion dollars, you're both growing at lets call it 35-40 percent a year. You're both pursuing platforms, your both really aggressive, you're insanely passionate about your customers and winning. And you like colors, you like green, they like orange. Alright, we got to talk a little sports here. >> Lisa: Speaking of green. >> I'm going to start somewhere else though because I asked this question of a number of folks at Veeam On. If you were, Ken, if you were Robert Kraft would you have traded Tom Brady? >> {Ken] No. >> Elaborate. >> I think when you look at a, the guy was the MVP of the league last year, so that by itself stands on it's own, but you have to look and the Patriots have always been about, sort of you know, trading or moving on a year or two early, versus a year or two late. So you could make that case with Tom Brady, but I think there's always exceptions, and when you look at, I mean he is basically like an adopted son of Robert Kraft and the organization. He's brought five Superbowls, he's basically, he built Patriot place, you know. Robert Kraft built Patriot place on the backs of Tom Brady and Bill Belichik to that extent. But how do you move on from someone who's brought you so much success, that has been under market. You know, get paid under market so that they can go and do other things, and have flexibility with the gap. I just don't know how you could move on from that. >> So, that's consistent now, I think it's four for four of people we've asked, Boston fans. So appreciate that feed back. Let's talk a little hoops, you know Celtics we were feeling pretty good, up two zip, now it's tied two-two. Houston, Golden state, tied two-two. Those two teams have proven they could win on the road, Celtics haven't proven that yet. What are your thoughts on that series? >> Yeah so certainly Cleveland came storming back, I think the stories of the down fall of the Cavs were clearly over exaggerated. They came back in a big way. I think they Celtics started to figure out the Cavs in quarters two, three, and four. They got themselves in a big hole in the first quarter in the last game. I feel good, the Celtics are nine and O at home this year in the post season. You know, it's basically the best of three, and they have two of them at home, so. The Cavs will have to break serve if they want to win the series. >> Dave: If they're lucky enough to get through to the finals, which would be unbelievable, do they have any shot against the Warriors? >> So, I think to say they have no shot is probably going a little too far, but- >> Dave: Got to play the game. >> You know you got to play the games, and the Celtics have, traditionally, matched up well against the Warriors. I mean least year, the Celtic actually came into Oracle, and broke, I don't know, what was it, like a 50 game home winning streak or something. So, you know, and that was a team that didn't have Kyrie, or Gordon Haywood, and I know they're still out so the future looks bright for the Celtics. But in the context of this years finals, certainly, if I were a betting man, I'd be putting my money behind the Warriors, but I don't doubt that Brad Stevens could come up with a scheme that could steal a couple of games, and make people in the Bay area feel a little uneasy. >> Would love to see a non Lebron Final, you know. >> Yeah I think as the words would like the Celts >> Sorry Brandon, sorry buddy. >> A little diversity, you know three years in a row we've had the same things, so I'll extend my support to the Celtics in honor of both of you guys. >> Alright, and we can talk, if they get to the finals then we can take it from there. >> I can't imagine what the day after the Superbowl was like for both of you. We won't go there. >> I still haven't recovered, so. >> (laughs) Awesome, well Ken, thanks so much for stopping by. Congrats on being a CUBE alumni, now. We look forward to seeing you Veeam World in just a few months time. >> Yes, great. Thank you. We'll be there for sure. >> For Dave Vellante, I am Lisa Martin. You're watching theCUBE live from Pure Accelerate 2018. Stick around, Dave and I will be back with a wrap in just a moment. (music)

(techno music) >> Announcer: It's the Cube, covering Sapphire Now 2017, brought to you by SAP Cloud Platform, and HANA Enterprise Cloud. >> Hey, welcome back to our exclusive SAP coverage here in our studio in Palo Alto, our 4,500 square foot studio. I'm John Furrier. Our three days, we're on third day, of Sapphire Now 2017 coverage. I'm on the phone with RJ Bibby, who's the SAP Global Alliance Manager for SAP. Handles the relationship. RJ, great to have you on the phone and thanks for calling in from Orlando, really appreciate it. >> RJ: You bet, John. Love the Cube. Love SiliconANGLE. We're great partners. It's been a great week and looking forward to talking to you about it. >> Tell us what's going on on the ground. First, give us the updates on day three. So, pretty much everyone's coming-- And always a great activities at night as well. So, SAP, a lot of business done during the day. They work hard. They play hard. But, day three, what's it like? What's settling in as the storylines for Sapphire 2017? >> RJ: Yeah, absolutely. So, you're starting to feel-- You've gone through about-- We're in our third tour. For the partner's community, we're in day four, cause we had the partner day. Last night was the big partner night. We actually NetApped with our partners with Cisco and KPIT did a private event at Universal Studios at the Jimmy Fallon Theme Park that was highly successful. What was great about today, was in the morning, we kicked off will Bill McDermott on stage with Kobe Bryant and Derek Jeter. And it was all about leadership and mentorship and experience in being in the business, whatever industry that you're in for so long and how you just stay creative, hungry, and passionate. And it was packed. One of the comments was they couldn't believe, on the day after the big party night of all the partners that you still have a lot of energy on the floor. Ultimately, it's still about data, which is great for our business that we can get into at NetApp. There's a lot of buzzword bingo going on here, John, all week, whether it's machine to machine, blocked chain, Cloud-- And at the end of it, it's still our customers who we've talked to a lot this week, and wow. What are we going to do with out data? How do we analyze it? And how do we improve that user experience based on all this data that we have? And I think that's one of the things that I see on the floor that's almost overwhelming with the amount of people, 30,000, all the partners. Just a lot of information. And lastly, I'll say, the good news with that is everybody is hungry for content. Whether it's a mini-theater, whether it's at one of the booths, interactions one-on-one, it's people are hungry for what is happening in the industry. And I think that's exciting for all of us. >> Well, we do our part and try and get as much coverage as possible, even if we are going to do it from Palo Alto. Question for you on NetApp. I mean, you guys have been-- The scuttlebutt in Silicon Valley is that NetApp is doing very well with the Hyperscale (mumbles). I know for a fact. I've interviewed the former CEO and others within NetApp. They were really on early with AWS. And obviously, AWS a big part of the announcement at Sapphire. So, you guys are kind of like getting these relationships with these key players. It's changed a little bit of the business model, or culture within NetApp. What's different about NetApp right now? With resect to some of the big players that you've had relationships with. It's not this new relationship with SAP. You guys have a deep relationship. What's changing as the CloudWave hits, as the DataWave hits? Those are the biggest waves hitting the world right now. How are you guys playing in that world? And share some insight there. >> RJ: Absolutely. Great question. 'Cause the world is going through digital transformation and so is NetApp. So, we are actually celebrating our 25th year as a company right now and we've been a traditional, global technology and data management company. And, the digital shift to Hybrid Cloud is where we're moving. So, specifically with partners like AWS, Microsoft and Azure, the Hyperscalers like CenturyLink, it's how we can help our customers really collect, transport, analyze, protect data, in whatever environment they want to hold their data. Whether it's On-Premises, if your in a Cloud, you can choose whatever Hyperscaler you want. You still have to deal with the data. And then, how do we manage it? How do we consume it? Where is dead data that needs to be taken out? So, data's the currency and with our data fabric methodology and tools from software, hardware, we're really able to help manage that complete life cycle, whether it's SAP, or any other type of environment we hold. So, the exciting thing for us, and the stock prices is showing that at an all time high, is what Bill McDermott said on Monday, in the keynote, or excuse me, Tuesday, "Data is the currency. "Our new mission statement is we're trying "to empower our customers to change the world with data." So, back to the buzzWord bingo comment I made earlier, we're still dealing with fact that we have all these great technologies: all these censors, machine-to-machine, On-Print to Cloud. At the heart of everything is the data and what you do with it. And I think that one of the things that NetApp does and the best in the world of, is we continually evolve digital transformations with the tools on how we deal with data. So, that's high level. >> How about the data dynamic? >> Data is the fundamental story, in my opinion. Cloud has been around, the Clouderati. We were part of that from the beginning. Now, Cloud is mainstream. Amazon stock prices looking like a hockey stick now, it's going straight up. But, that took years of development, right? I mean, you saw the Cloud formation coming, really, in the mid-2000s and then, really at 2008, -09, -10 was the foundational years and then the rest is history. Data's now going through the same thing. As people get over themselves and say, "Okay, big data's not a dupe. It's everything." IOT is certainly highlighting a lot of that. SAP has recognized that legacy systems have to move to a MultiCloud and certainly multi-vendor world in a whole new way. But, at the end of the day, you still got to store this stuff. So, that's your business. How are you keeping up with the moving train of data as is architecturally shifts in the marketplace? >> RJ: Great question. I think that we have some of the best minds in Silicon Valley. Again, been there 25 years. I think with the deep relationships we have with companies like SAP. On the front end, I think the one thing that we bring as a value to SAP is the consumption model, life exists. Through owning the data and the user experience, we're able to enable and accelerate the license consumption to the edge. Right from application in to the system. From an architectural standpoint, it still comes down to the thing that we are creating and blabs and launching around, like the data fabric, the tool system, really software. The software that can help from an analytical perspective affect the user experience. Everybody wants it live. And the other part is the data protection and the DR aspect of it. And I think that's another core competency that we're continuing to develop as a service for the customer. So, I hop I've answered your question. >> Yup. >> RJ: But if-- >> (mumbles) a bottom line then, why NetApps? Say I'm a customer. Okay, I get the SAP. Why should I go with you guys over new the Delium see powerhouse over there, or the White-Box Storage? >> RJ: At the end of the day, we are best at capitalizing the value of data in the Hybrid Cloud. Nobody can help collect, analyze, test, and do life-cycle management live like NetApp can. And that's the reason that we are going more upstream, selling like we say at EPC, always selling to the CXO. I think we're changing the landscape from a true storage company on the infrastructure side to a full end-to-end Hybrid Cloud data management portfolio company. And it's been proven by the acquisition of Salazar from bringing Slash in to the portfolio, our cloning, and snapshot capabilities. So, anywhere in the stack at any time during the day when you're looking live at your operations or your data that you can take live snapshots. Just so if there's a glitch from a data protection side, or there's some type of spike from a request on the ticketing side or demand side of your system. So, I think that's some of the things that we're differentiating. And that's the reason that the AWSs and the Azures and the SAPs are so excited about co-innovating together to again, improving the customer experience with their data. >> RJ, final question. What's the net-net? What's the bumper sticker for you this year at Sapphire 2017? What's the walk-away revelation? >> RJ: Well, I think from the SAP side, it's the revelation on the push of Leonardo. I think that SAP-- I'd like to see them continue to hone out the 'what' and the 'if' from partners with Leonardo from blotching in machine-to-machine and IOT. For us, it is the beautiful fact that now at the center of everything that SAP and the ecosystem is trying to do is around the data side of it and it's the actual currency. And the fact that we have kind of the leading-edge tools to enhance the customer experience with our platform for customers' and partners' data is really, really exciting for us. And we're excited. We're all psyched to be partnered with the Cube. And everything we do is in the Cloud. So, I'm here to help. >> Alright. >> RJ, thanks so much for takin' the time callin' in from Orlando. RJ Bibby, SAP Global Alliance Executive with NetApp. He runs the the relationship with NetApp. And again, it's been a long-term relationship. I remember takin' photos on my phone, way back in the day, years ago. So, not a new relationship and continued momentum. Congratulations and thanks for sharing the insight from Orlando. 'Preciate it. >> RJ: You bet. Thanks for the partnership. Have a great day. >> 'Kay, more coverage from the Cube in Palo Alto on SAP, Sapphire 2017 after this short break. Stay with us. (techno music)

>> Welcome to the Intel AI Lounge. Today, we're very excited to share with you the Precision Medicine panel discussion. I'll be moderating the session. My name is Kay Erin. I'm the general manager of Health and Life Sciences at Intel. And I'm excited to share with you these three panelists that we have here. First is John Madison. He is a chief information medical officer and he is part of Kaiser Permanente. We're very excited to have you here. Thank you, John. >> Thank you. >> We also have Naveen Rao. He is the VP and general manager for the Artificial Intelligence Solutions at Intel. He's also the former CEO of Nervana, which was acquired by Intel. And we also have Bob Rogers, who's the chief data scientist at our AI solutions group. So, why don't we get started with our questions. I'm going to ask each of the panelists to talk, introduce themselves, as well as talk about how they got started with AI. So why don't we start with John? >> Sure, so can you hear me okay in the back? Can you hear? Okay, cool. So, I am a recovering evolutionary biologist and a recovering physician and a recovering geek. And I implemented the health record system for the first and largest region of Kaiser Permanente. And it's pretty obvious that most of the useful data in a health record, in lies in free text. So I started up a natural language processing team to be able to mine free text about a dozen years ago. So we can do things with that that you can't otherwise get out of health information. I'll give you an example. I read an article online from the New England Journal of Medicine about four years ago that said over half of all people who have had their spleen taken out were not properly vaccinated for a common form of pneumonia, and when your spleen's missing, you must have that vaccine or you die a very sudden death with sepsis. In fact, our medical director in Northern California's father died of that exact same scenario. So, when I read the article, I went to my structured data analytics team and to my natural language processing team and said please show me everybody who has had their spleen taken out and hasn't been appropriately vaccinated and we ran through about 20 million records in about three hours with the NLP team, and it took about three weeks with a structured data analytics team. That sounds counterintuitive but it actually happened that way. And it's not a competition for time only. It's a competition for quality and sensitivity and specificity. So we were able to indentify all of our members who had their spleen taken out, who should've had a pneumococcal vaccine. We vaccinated them and there are a number of people alive today who otherwise would've died absent that capability. So people don't really commonly associate natural language processing with machine learning, but in fact, natural language processing relies heavily and is the first really, highly successful example of machine learning. So we've done dozens of similar projects, mining free text data in millions of records very efficiently, very effectively. But it really helped advance the quality of care and reduce the cost of care. It's a natural step forward to go into the world of personalized medicine with the arrival of a 100-dollar genome, which is actually what it costs today to do a full genome sequence. Microbiomics, that is the ecosystem of bacteria that are in every organ of the body actually. And we know now that there is a profound influence of what's in our gut and how we metabolize drugs, what diseases we get. You can tell in a five year old, whether or not they were born by a vaginal delivery or a C-section delivery by virtue of the bacteria in the gut five years later. So if you look at the complexity of the data that exists in the genome, in the microbiome, in the health record with free text and you look at all the other sources of data like this streaming data from my wearable monitor that I'm part of a research study on Precision Medicine out of Stanford, there is a vast amount of disparate data, not to mention all the imaging, that really can collectively produce much more useful information to advance our understanding of science, and to advance our understanding of every individual. And then we can do the mash up of a much broader range of science in health care with a much deeper sense of data from an individual and to do that with structured questions and structured data is very yesterday. The only way we're going to be able to disambiguate those data and be able to operate on those data in concert and generate real useful answers from the broad array of data types and the massive quantity of data, is to let loose machine learning on all of those data substrates. So my team is moving down that pathway and we're very excited about the future prospects for doing that. >> Yeah, great. I think that's actually some of the things I'm very excited about in the future with some of the technologies we're developing. My background, I started actually being fascinated with computation in biological forms when I was nine. Reading and watching sci-fi, I was kind of a big dork which I pretty much still am. I haven't really changed a whole lot. Just basically seeing that machines really aren't all that different from biological entities, right? We are biological machines and kind of understanding how a computer works and how we engineer those things and trying to pull together concepts that learn from biology into that has always been a fascination of mine. As an undergrad, I was in the EE, CS world. Even then, I did some research projects around that. I worked in the industry for about 10 years designing chips, microprocessors, various kinds of ASICs, and then actually went back to school, quit my job, got a Ph.D. in neuroscience, computational neuroscience, to specifically understand what's the state of the art. What do we really understand about the brain? And are there concepts that we can take and bring back? Inspiration's always been we want to... We watch birds fly around. We want to figure out how to make something that flies. We extract those principles, and then build a plane. Don't necessarily want to build a bird. And so Nervana's really was the combination of all those experiences, bringing it together. Trying to push computation in a new a direction. Now, as part of Intel, we can really add a lot of fuel to that fire. I'm super excited to be part of Intel in that the technologies that we were developing can really proliferate and be applied to health care, can be applied to Internet, can be applied to every facet of our lives. And some of the examples that John mentioned are extremely exciting right now and these are things we can do today. And the generality of these solutions are just really going to hit every part of health care. I mean from a personal viewpoint, my whole family are MDs. I'm sort of the black sheep of the family. I don't have an MD. And it's always been kind of funny to me that knowledge is concentrated in a few individuals. Like you have a rare tumor or something like that, you need the guy who knows how to read this MRI. Why? Why is it like that? Can't we encapsulate that knowledge into a computer or into an algorithm, and democratize it. And the reason we couldn't do it is we just didn't know how. And now we're really getting to a point where we know how to do that. And so I want that capability to go to everybody. It'll bring the cost of healthcare down. It'll make all of us healthier. That affects everything about our society. So that's really what's exciting about it to me. >> That's great. So, as you heard, I'm Bob Rogers. I'm chief data scientist for analytics and artificial intelligence solutions at Intel. My mission is to put powerful analytics in the hands of every decision maker and when I think about Precision Medicine, decision makers are not just doctors and surgeons and nurses, but they're also case managers and care coordinators and probably most of all, patients. So the mission is really to put powerful analytics and AI capabilities in the hands of everyone in health care. It's a very complex world and we need tools to help us navigate it. So my background, I started with a Ph.D. in physics and I was computer modeling stuff, falling into super massive black holes. And there's a lot of applications for that in the real world. No, I'm kidding. (laughter) >> John: There will be, I'm sure. Yeah, one of these days. Soon as we have time travel. Okay so, I actually, about 1991, I was working on my post doctoral research, and I heard about neural networks, these things that could compute the way the brain computes. And so, I started doing some research on that. I wrote some papers and actually, it was an interesting story. The problem that we solved that got me really excited about neural networks, which have become deep learning, my office mate would come in. He was this young guy who was about to go off to grad school. He'd come in every morning. "I hate my project." Finally, after two weeks, what's your project? What's the problem? It turns out he had to circle these little fuzzy spots on these images from a telescope. So they were looking for the interesting things in a sky survey, and he had to circle them and write down their coordinates all summer. Anyone want to volunteer to do that? No? Yeah, he was very unhappy. So we took the first two weeks of data that he created doing his work by hand, and we trained an artificial neural network to do his summer project and finished it in about eight hours of computing. (crowd laughs) And so he was like yeah, this is amazing. I'm so happy. And we wrote a paper. I was the first author of course, because I was the senior guy at age 24. And he was second author. His first paper ever. He was very, very excited. So we have to fast forward about 20 years. His name popped up on the Internet. And so it caught my attention. He had just won the Nobel Prize in physics. (laughter) So that's where artificial intelligence will get you. (laughter) So thanks Naveen. Fast forwarding, I also developed some time series forecasting capabilities that allowed me to create a hedge fund that I ran for 12 years. After that, I got into health care, which really is the center of my passion. Applying health care to figuring out how to get all the data from all those siloed sources, put it into the cloud in a secure way, and analyze it so you can actually understand those cases that John was just talking about. How do you know that that person had had a splenectomy and that they needed to get that pneumovax? You need to be able to search all the data, so we used AI, natural language processing, machine learning, to do that and then two years ago, I was lucky enough to join Intel and, in the intervening time, people like Naveen actually thawed the AI winter and we're really in a spring of amazing opportunities with AI, not just in health care but everywhere, but of course, the health care applications are incredibly life saving and empowering so, excited to be here on this stage with you guys. >> I just want to cue off of your comment about the role of physics in AI and health care. So the field of microbiomics that I referred to earlier, bacteria in our gut. There's more bacteria in our gut than there are cells in our body. There's 100 times more DNA in that bacteria than there is in the human genome. And we're now discovering a couple hundred species of bacteria a year that have never been identified under a microscope just by their DNA. So it turns out the person who really catapulted the study and the science of microbiomics forward was an astrophysicist who did his Ph.D. in Steven Hawking's lab on the collision of black holes and then subsequently, put the other team in a virtual reality, and he developed the first super computing center and so how did he get an interest in microbiomics? He has the capacity to do high performance computing and the kind of advanced analytics that are required to look at a 100 times the volume of 3.2 billion base pairs of the human genome that are represented in the bacteria in our gut, and that has unleashed the whole science of microbiomics, which is going to really turn a lot of our assumptions of health and health care upside down. >> That's great, I mean, that's really transformational. So a lot of data. So I just wanted to let the audience know that we want to make this an interactive session, so I'll be asking for questions in a little bit, but I will start off with one question so that you can think about it. So I wanted to ask you, it looks like you've been thinking a lot about AI over the years. And I wanted to understand, even though AI's just really starting in health care, what are some of the new trends or the changes that you've seen in the last few years that'll impact how AI's being used going forward? >> So I'll start off. There was a paper published by a guy by the name of Tegmark at Harvard last summer that, for the first time, explained why neural networks are efficient beyond any mathematical model we predict. And the title of the paper's fun. It's called Deep Learning Versus Cheap Learning. So there were two sort of punchlines of the paper. One is is that the reason that mathematics doesn't explain the efficiency of neural networks is because there's a higher order of mathematics called physics. And the physics of the underlying data structures determined how efficient you could mine those data using machine learning tools. Much more so than any mathematical modeling. And so the second thing that was a reel from that paper is that the substrate of the data that you're operating on and the natural physics of those data have inherent levels of complexity that determine whether or not a 12th layer of neural net will get you where you want to go really fast, because when you do the modeling, for those math geeks in the audience, a factorial. So if there's 12 layers, there's 12 factorial permutations of different ways you could sequence the learning through those data. When you have 140 layers of a neural net, it's a much, much, much bigger number of permutations and so you end up being hardware-bound. And so, what Max Tegmark basically said is you can determine whether to do deep learning or cheap learning based upon the underlying physics of the data substrates you're operating on and have a good insight into how to optimize your hardware and software approach to that problem. >> So another way to put that is that neural networks represent the world in the way the world is sort of built. >> Exactly. >> It's kind of hierarchical. It's funny because, sort of in retrospect, like oh yeah, that kind of makes sense. But when you're thinking about it mathematically, we're like well, anything... The way a neural can represent any mathematical function, therfore, it's fully general. And that's the way we used to look at it, right? So now we're saying, well actually decomposing the world into different types of features that are layered upon each other is actually a much more efficient, compact representation of the world, right? I think this is actually, precisely the point of kind of what you're getting at. What's really exciting now is that what we were doing before was sort of building these bespoke solutions for different kinds of data. NLP, natural language processing. There's a whole field, 25 plus years of people devoted to figuring out features, figuring out what structures make sense in this particular context. Those didn't carry over at all to computer vision. Didn't carry over at all to time series analysis. Now, with neural networks, we've seen it at Nervana, and now part of Intel, solving customers' problems. We apply a very similar set of techniques across all these different types of data domains and solve them. All data in the real world seems to be hierarchical. You can decompose it into this hierarchy. And it works really well. Our brains are actually general structures. As a neuroscientist, you can look at different parts of your brain and there are differences. Something that takes in visual information, versus auditory information is slightly different but they're much more similar than they are different. So there is something invariant, something very common between all of these different modalities and we're starting to learn that. And this is extremely exciting to me trying to understand the biological machine that is a computer, right? We're figurig it out, right? >> One of the really fun things that Ray Chrisfall likes to talk about is, and it falls in the genre of biomimmicry, and how we actually replicate biologic evolution in our technical solutions so if you look at, and we're beginning to understand more and more how real neural nets work in our cerebral cortex. And it's sort of a pyramid structure so that the first pass of a broad base of analytics, it gets constrained to the next pass, gets constrained to the next pass, which is how information is processed in the brain. So we're discovering increasingly that what we've been evolving towards, in term of architectures of neural nets, is approximating the architecture of the human cortex and the more we understand the human cortex, the more insight we get to how to optimize neural nets, so when you think about it, with millions of years of evolution of how the cortex is structured, it shouldn't be a surprise that the optimization protocols, if you will, in our genetic code are profoundly efficient in how they operate. So there's a real role for looking at biologic evolutionary solutions, vis a vis technical solutions, and there's a friend of mine who worked with who worked with George Church at Harvard and actually published a book on biomimmicry and they wrote the book completely in DNA so if all of you have your home DNA decoder, you can actually read the book on your DNA reader, just kidding. >> There's actually a start up I just saw in the-- >> Read-Write DNA, yeah. >> Actually it's a... He writes something. What was it? (response from crowd member) Yeah, they're basically encoding information in DNA as a storage medium. (laughter) The company, right? >> Yeah, that same friend of mine who coauthored that biomimmicry book in DNA also did the estimate of the density of information storage. So a cubic centimeter of DNA can store an hexabyte of data. I mean that's mind blowing. >> Naveen: Highly done soon. >> Yeah that's amazing. Also you hit upon a really important point there, that one of the things that's changed is... Well, there are two major things that have changed in my perception from let's say five to 10 years ago, when we were using machine learning. You could use data to train models and make predictions to understand complex phenomena. But they had limited utility and the challenge was that if I'm trying to build on these things, I had to do a lot of work up front. It was called feature engineering. I had to do a lot of work to figure out what are the key attributes of that data? What are the 10 or 20 or 100 pieces of information that I should pull out of the data to feed to the model, and then the model can turn it into a predictive machine. And so, what's really exciting about the new generation of machine learning technology, and particularly deep learning, is that it can actually learn from example data those features without you having to do any preprogramming. That's why Naveen is saying you can take the same sort of overall approach and apply it to a bunch of different problems. Because you're not having to fine tune those features. So at the end of the day, the two things that have changed to really enable this evolution is access to more data, and I'd be curious to hear from you where you're seeing data come from, what are the strategies around that. So access to data, and I'm talking millions of examples. So 10,000 examples most times isn't going to cut it. But millions of examples will do it. And then, the other piece is the computing capability to actually take millions of examples and optimize this algorithm in a single lifetime. I mean, back in '91, when I started, we literally would have thousands of examples and it would take overnight to run the thing. So now in the world of millions, and you're putting together all of these combinations, the computing has changed a lot. I know you've made some revolutionary advances in that. But I'm curious about the data. Where are you seeing interesting sources of data for analytics? >> So I do some work in the genomics space and there are more viable permutations of the human genome than there are people who have ever walked the face of the earth. And the polygenic determination of a phenotypic expression translation, what are genome does to us in our physical experience in health and disease is determined by many, many genes and the interaction of many, many genes and how they are up and down regulated. And the complexity of disambiguating which 27 genes are affecting your diabetes and how are they up and down regulated by different interventions is going to be different than his. It's going to be different than his. And we already know that there's four or five distinct genetic subtypes of type II diabetes. So physicians still think there's one disease called type II diabetes. There's actually at least four or five genetic variants that have been identified. And so, when you start thinking about disambiguating, particularly when we don't know what 95 percent of DNA does still, what actually is the underlining cause, it will require this massive capability of developing these feature vectors, sometimes intuiting it, if you will, from the data itself. And other times, taking what's known knowledge to develop some of those feature vectors, and be able to really understand the interaction of the genome and the microbiome and the phenotypic data. So the complexity is high and because the variation complexity is high, you do need these massive members. Now I'm going to make a very personal pitch here. So forgive me, but if any of you have any role in policy at all, let me tell you what's happening right now. The Genomic Information Nondiscrimination Act, so called GINA, written by a friend of mine, passed a number of years ago, says that no one can be discriminated against for health insurance based upon their genomic information. That's cool. That should allow all of you to feel comfortable donating your DNA to science right? Wrong. You are 100% unprotected from discrimination for life insurance, long term care and disability. And it's being practiced legally today and there's legislation in the House, in mark up right now to completely undermine the existing GINA legislation and say that whenever there's another applicable statute like HIPAA, that the GINA is irrelevant, that none of the fines and penalties are applicable at all. So we need a ton of data to be able to operate on. We will not be getting a ton of data to operate on until we have the kind of protection we need to tell people, you can trust us. You can give us your data, you will not be subject to discrimination. And that is not the case today. And it's being further undermined. So I want to make a plea to any of you that have any policy influence to go after that because we need this data to help the understanding of human health and disease and we're not going to get it when people look behind the curtain and see that discrimination is occurring today based upon genetic information. >> Well, I don't like the idea of being discriminated against based on my DNA. Especially given how little we actually know. There's so much complexity in how these things unfold in our own bodies, that I think anything that's being done is probably childishly immature and oversimplifying. So it's pretty rough. >> I guess the translation here is that we're all unique. It's not just a Disney movie. (laughter) We really are. And I think one of the strengths that I'm seeing, kind of going back to the original point, of these new techniques is it's going across different data types. It will actually allow us to learn more about the uniqueness of the individual. It's not going to be just from one data source. They were collecting data from many different modalities. We're collecting behavioral data from wearables. We're collecting things from scans, from blood tests, from genome, from many different sources. The ability to integrate those into a unified picture, that's the important thing that we're getting toward now. That's what I think is going to be super exciting here. Think about it, right. I can tell you to visual a coin, right? You can visualize a coin. Not only do you visualize it. You also know what it feels like. You know how heavy it is. You have a mental model of that from many different perspectives. And if I take away one of those senses, you can still identify the coin, right? If I tell you to put your hand in your pocket, and pick out a coin, you probably can do that with 100% reliability. And that's because we have this generalized capability to build a model of something in the world. And that's what we need to do for individuals is actually take all these different data sources and come up with a model for an individual and you can actually then say what drug works best on this. What treatment works best on this? It's going to get better with time. It's not going to be perfect, because this is what a doctor does, right? A doctor who's very experienced, you're a practicing physician right? Back me up here. That's what you're doing. You basically have some categories. You're taking information from the patient when you talk with them, and you're building a mental model. And you apply what you know can work on that patient, right? >> I don't have clinic hours anymore, but I do take care of many friends and family. (laughter) >> You used to, you used to. >> I practiced for many years before I became a full-time geek. >> I thought you were a recovering geek. >> I am. (laughter) I do more policy now. >> He's off the wagon. >> I just want to take a moment and see if there's anyone from the audience who would like to ask, oh. Go ahead. >> We've got a mic here, hang on one second. >> I have tons and tons of questions. (crosstalk) Yes, so first of all, the microbiome and the genome are really complex. You already hit about that. Yet most of the studies we do are small scale and we have difficulty repeating them from study to study. How are we going to reconcile all that and what are some of the technical hurdles to get to the vision that you want? >> So primarily, it's been the cost of sequencing. Up until a year ago, it's $1000, true cost. Now it's $100, true cost. And so that barrier is going to enable fairly pervasive testing. It's not a real competitive market becaue there's one sequencer that is way ahead of everybody else. So the price is not $100 yet. The cost is below $100. So as soon as there's competition to drive the cost down, and hopefully, as soon as we all have the protection we need against discrimination, as I mentioned earlier, then we will have large enough sample sizes. And so, it is our expectation that we will be able to pool data from local sources. I chair the e-health work group at the Global Alliance for Genomics and Health which is working on this very issue. And rather than pooling all the data into a single, common repository, the strategy, and we're developing our five-year plan in a month in London, but the goal is to have a federation of essentially credentialed data enclaves. That's a formal method. HHS already does that so you can get credentialed to search all the data that Medicare has on people that's been deidentified according to HIPPA. So we want to provide the same kind of service with appropriate consent, at an international scale. And there's a lot of nations that are talking very much about data nationality so that you can't export data. So this approach of a federated model to get at data from all the countries is important. The other thing is a block-chain technology is going to be very profoundly useful in this context. So David Haussler of UC Santa Cruz is right now working on a protocol using an open block-chain, public ledger, where you can put out. So for any typical cancer, you may have a half dozen, what are called sematic variance. Cancer is a genetic disease so what has mutated to cause it to behave like a cancer? And if we look at those biologically active sematic variants, publish them on a block chain that's public, so there's not enough data there to reidentify the patient. But if I'm a physician treating a woman with breast cancer, rather than say what's the protocol for treating a 50-year-old woman with this cell type of cancer, I can say show me all the people in the world who have had this cancer at the age of 50, wit these exact six sematic variants. Find the 200 people worldwide with that. Ask them for consent through a secondary mechanism to donate everything about their medical record, pool that information of the core of 200 that exactly resembles the one sitting in front of me, and find out, of the 200 ways they were treated, what got the best results. And so, that's the kind of future where a distributed, federated architecture will allow us to query and obtain a very, very relevant cohort, so we can basically be treating patients like mine, sitting right in front of me. Same thing applies for establishing research cohorts. There's some very exciting stuff at the convergence of big data analytics, machine learning, and block chaining. >> And this is an area that I'm really excited about and I think we're excited about generally at Intel. They actually have something called the Collaborative Cancer Cloud, which is this kind of federated model. We have three different academic research centers. Each of them has a very sizable and valuable collection of genomic data with phenotypic annotations. So you know, pancreatic cancer, colon cancer, et cetera, and we've actually built a secure computing architecture that can allow a person who's given the right permissions by those organizations to ask a specific question of specific data without ever sharing the data. So the idea is my data's really important to me. It's valuable. I want us to be able to do a study that gets the number from the 20 pancreatic cancer patients in my cohort, up to the 80 that we have in the whole group. But I can't do that if I'm going to just spill my data all over the world. And there are HIPAA and compliance reasons for that. There are business reasons for that. So what we've built at Intel is this platform that allows you to do different kinds of queries on this genetic data. And reach out to these different sources without sharing it. And then, the work that I'm really involved in right now and that I'm extremely excited about... This also touches on something that both of you said is it's not sufficient to just get the genome sequences. You also have to have the phenotypic data. You have to know what cancer they've had. You have to know that they've been treated with this drug and they've survived for three months or that they had this side effect. That clinical data also needs to be put together. It's owned by other organizations, right? Other hospitals. So the broader generalization of the Collaborative Cancer Cloud is something we call the data exchange. And it's a misnomer in a sense that we're not actually exchanging data. We're doing analytics on aggregated data sets without sharing it. But it really opens up a world where we can have huge populations and big enough amounts of data to actually train these models and draw the thread in. Of course, that really then hits home for the techniques that Nervana is bringing to the table, and of course-- >> Stanford's one of your academic medical centers? >> Not for that Collaborative Cancer Cloud. >> The reason I mentioned Standford is because the reason I'm wearing this FitBit is because I'm a research subject at Mike Snyder's, the chair of genetics at Stanford, IPOP, intrapersonal omics profile. So I was fully sequenced five years ago and I get four full microbiomes. My gut, my mouth, my nose, my ears. Every three months and I've done that for four years now. And about a pint of blood. And so, to your question of the density of data, so a lot of the problem with applying these techniques to health care data is that it's basically a sparse matrix and there's a lot of discontinuities in what you can find and operate on. So what Mike is doing with the IPOP study is much the same as you described. Creating a highly dense longitudinal set of data that will help us mitigate the sparse matrix problem. (low volume response from audience member) Pardon me. >> What's that? (low volume response) (laughter) >> Right, okay. >> John: Lost the school sample. That's got to be a new one I've heard now. >> Okay, well, thank you so much. That was a great question. So I'm going to repeat this and ask if there's another question. You want to go ahead? >> Hi, thanks. So I'm a journalist and I report a lot on these neural networks, a system that's beter at reading mammograms than your human radiologists. Or a system that's better at predicting which patients in the ICU will get sepsis. These sort of fascinating academic studies that I don't really see being translated very quickly into actual hospitals or clinical practice. Seems like a lot of the problems are regulatory, or liability, or human factors, but how do you get past that and really make this stuff practical? >> I think there's a few things that we can do there and I think the proof points of the technology are really important to start with in this specific space. In other places, sometimes, you can start with other things. But here, there's a real confidence problem when it comes to health care, and for good reason. We have doctors trained for many, many years. School and then residencies and other kinds of training. Because we are really, really conservative with health care. So we need to make sure that technology's well beyond just the paper, right? These papers are proof points. They get people interested. They even fuel entire grant cycles sometimes. And that's what we need to happen. It's just an inherent problem, its' going to take a while. To get those things to a point where it's like well, I really do trust what this is saying. And I really think it's okay to now start integrating that into our standard of care. I think that's where you're seeing it. It's frustrating for all of us, believe me. I mean, like I said, I think personally one of the biggest things, I want to have an impact. Like when I go to my grave, is that we used machine learning to improve health care. We really do feel that way. But it's just not something we can do very quickly and as a business person, I don't actually look at those use cases right away because I know the cycle is just going to be longer. >> So to your point, the FDA, for about four years now, has understood that the process that has been given to them by their board of directors, otherwise known as Congress, is broken. And so they've been very actively seeking new models of regulation and what's really forcing their hand is regulation of devices and software because, in many cases, there are black box aspects of that and there's a black box aspect to machine learning. Historically, Intel and others are making inroads into providing some sort of traceability and transparency into what happens in that black box rather than say, overall we get better results but once in a while we kill somebody. Right? So there is progress being made on that front. And there's a concept that I like to use. Everyone knows Ray Kurzweil's book The Singularity Is Near? Well, I like to think that diadarity is near. And the diadarity is where you have human transparency into what goes on in the black box and so maybe Bob, you want to speak a little bit about... You mentioned that, in a prior discussion, that there's some work going on at Intel there. >> Yeah, absolutely. So we're working with a number of groups to really build tools that allow us... In fact Naveen probably can talk in even more detail than I can, but there are tools that allow us to actually interrogate machine learning and deep learning systems to understand, not only how they respond to a wide variety of situations but also where are there biases? I mean, one of the things that's shocking is that if you look at the clinical studies that our drug safety rules are based on, 50 year old white guys are the peak of that distribution, which I don't see any problem with that, but some of you out there might not like that if you're taking a drug. So yeah, we want to understand what are the biases in the data, right? And so, there's some new technologies. There's actually some very interesting data-generative technologies. And this is something I'm also curious what Naveen has to say about, that you can generate from small sets of observed data, much broader sets of varied data that help probe and fill in your training for some of these systems that are very data dependent. So that takes us to a place where we're going to start to see deep learning systems generating data to train other deep learning systems. And they start to sort of go back and forth and you start to have some very nice ways to, at least, expose the weakness of these underlying technologies. >> And that feeds back to your question about regulatory oversight of this. And there's the fascinating, but little known origin of why very few women are in clinical studies. Thalidomide causes birth defects. So rather than say pregnant women can't be enrolled in drug trials, they said any woman who is at risk of getting pregnant cannot be enrolled. So there was actually a scientific meritorious argument back in the day when they really didn't know what was going to happen post-thalidomide. So it turns out that the adverse, unintended consequence of that decision was we don't have data on women and we know in certain drugs, like Xanax, that the metabolism is so much slower, that the typical dosing of Xanax is women should be less than half of that for men. And a lot of women have had very serious adverse effects by virtue of the fact that they weren't studied. So the point I want to illustrate with that is that regulatory cycles... So people have known for a long time that was like a bad way of doing regulations. It should be changed. It's only recently getting changed in any meaningful way. So regulatory cycles and legislative cycles are incredibly slow. The rate of exponential growth in technology is exponential. And so there's impedance mismatch between the cycle time for regulation cycle time for innovation. And what we need to do... I'm working with the FDA. I've done four workshops with them on this very issue. Is that they recognize that they need to completely revitalize their process. They're very interested in doing it. They're not resisting it. People think, oh, they're bad, the FDA, they're resisting. Trust me, there's nobody on the planet who wants to revise these review processes more than the FDA itself. And so they're looking at models and what I recommended is global cloud sourcing and the FDA could shift from a regulatory role to one of doing two things, assuring the people who do their reviews are competent, and assuring that their conflicts of interest are managed, because if you don't have a conflict of interest in this very interconnected space, you probably don't know enough to be a reviewer. So there has to be a way to manage the conflict of interest and I think those are some of the keypoints that the FDA is wrestling with because there's type one and type two errors. If you underregulate, you end up with another thalidomide and people born without fingers. If you overregulate, you prevent life saving drugs from coming to market. So striking that balance across all these different technologies is extraordinarily difficult. If it were easy, the FDA would've done it four years ago. It's very complicated. >> Jumping on that question, so all three of you are in some ways entrepreneurs, right? Within your organization or started companies. And I think it would be good to talk a little bit about the business opportunity here, where there's a huge ecosystem in health care, different segments, biotech, pharma, insurance payers, etc. Where do you see is the ripe opportunity or industry, ready to really take this on and to make AI the competitive advantage. >> Well, the last question also included why aren't you using the result of the sepsis detection? We do. There were six or seven published ways of doing it. We did our own data, looked at it, we found a way that was superior to all the published methods and we apply that today, so we are actually using that technology to change clinical outcomes. As far as where the opportunities are... So it's interesting. Because if you look at what's going to be here in three years, we're not going to be using those big data analytics models for sepsis that we are deploying today, because we're just going to be getting a tiny aliquot of blood, looking for the DNA or RNA of any potential infection and we won't have to infer that there's a bacterial infection from all these other ancillary, secondary phenomenon. We'll see if the DNA's in the blood. So things are changing so fast that the opportunities that people need to look for are what are generalizable and sustainable kind of wins that are going to lead to a revenue cycle that are justified, a venture capital world investing. So there's a lot of interesting opportunities in the space. But I think some of the biggest opportunities relate to what Bob has talked about in bringing many different disparate data sources together and really looking for things that are not comprehensible in the human brain or in traditional analytic models. >> I think we also got to look a little bit beyond direct care. We're talking about policy and how we set up standards, these kinds of things. That's one area. That's going to drive innovation forward. I completely agree with that. Direct care is one piece. How do we scale out many of the knowledge kinds of things that are embedded into one person's head and get them out to the world, democratize that. Then there's also development. The underlying technology's of medicine, right? Pharmaceuticals. The traditional way that pharmaceuticals is developed is actually kind of funny, right? A lot of it was started just by chance. Penicillin, a very famous story right? It's not that different today unfortunately, right? It's conceptually very similar. Now we've got more science behind it. We talk about domains and interactions, these kinds of things but fundamentally, the problem is what we in computer science called NP hard, it's too difficult to model. You can't solve it analytically. And this is true for all these kinds of natural sorts of problems by the way. And so there's a whole field around this, molecular dynamics and modeling these sorts of things, that are actually being driven forward by these AI techniques. Because it turns out, our brain doesn't do magic. It actually doesn't solve these problems. It approximates them very well. And experience allows you to approximate them better and better. Actually, it goes a little bit to what you were saying before. It's like simulations and forming your own networks and training off each other. There are these emerging dynamics. You can simulate steps of physics. And you come up with a system that's much too complicated to ever solve. Three pool balls on a table is one such system. It seems pretty simple. You know how to model that, but it actual turns out you can't predict where a balls going to be once you inject some energy into that table. So something that simple is already too complex. So neural network techniques actually allow us to start making those tractable. These NP hard problems. And things like molecular dynamics and actually understanding how different medications and genetics will interact with each other is something we're seeing today. And so I think there's a huge opportunity there. We've actually worked with customers in this space. And I'm seeing it. Like Rosch is acquiring a few different companies in space. They really want to drive it forward, using big data to drive drug development. It's kind of counterintuitive. I never would've thought it had I not seen it myself. >> And there's a big related challenge. Because in personalized medicine, there's smaller and smaller cohorts of people who will benefit from a drug that still takes two billion dollars on average to develop. That is unsustainable. So there's an economic imperative of overcoming the cost and the cycle time for drug development. >> I want to take a go at this question a little bit differently, thinking about not so much where are the industry segments that can benefit from AI, but what are the kinds of applications that I think are most impactful. So if this is what a skilled surgeon needs to know at a particular time to care properly for a patient, this is where most, this area here, is where most surgeons are. They are close to the maximum knowledge and ability to assimilate as they can be. So it's possible to build complex AI that can pick up on that one little thing and move them up to here. But it's not a gigantic accelerator, amplifier of their capability. But think about other actors in health care. I mentioned a couple of them earlier. Who do you think the least trained actor in health care is? >> John: Patients. >> Yes, the patients. The patients are really very poorly trained, including me. I'm abysmal at figuring out who to call and where to go. >> Naveen: You know as much the doctor right? (laughing) >> Yeah, that's right. >> My doctor friends always hate that. Know your diagnosis, right? >> Yeah, Dr. Google knows. So the opportunities that I see that are really, really exciting are when you take an AI agent, like sometimes I like to call it contextually intelligent agent, or a CIA, and apply it to a problem where a patient has a complex future ahead of them that they need help navigating. And you use the AI to help them work through. Post operative. You've got PT. You've got drugs. You've got to be looking for side effects. An agent can actually help you navigate. It's like your own personal GPS for health care. So it's giving you the inforamation that you need about you for your care. That's my definition of Precision Medicine. And it can include genomics, of course. But it's much bigger. It's that broader picture and I think that a sort of agent way of thinking about things and filling in the gaps where there's less training and more opportunity, is very exciting. >> Great start up idea right there by the way. >> Oh yes, right. We'll meet you all out back for the next start up. >> I had a conversation with the head of the American Association of Medical Specialties just a couple of days ago. And what she was saying, and I'm aware of this phenomenon, but all of the medical specialists are saying, you're killing us with these stupid board recertification trivia tests that you're giving us. So if you're a cardiologist, you have to remember something that happens in one in 10 million people, right? And they're saying that irrelevant anymore, because we've got advanced decision support coming. We have these kinds of analytics coming. Precisely what you're saying. So it's human augmentation of decision support that is coming at blazing speed towards health care. So in that context, it's much more important that you have a basic foundation, you know how to think, you know how to learn, and you know where to look. So we're going to be human-augmented learning systems much more so than in the past. And so the whole recertification process is being revised right now. (inaudible audience member speaking) Speak up, yeah. (person speaking) >> What makes it fathomable is that you can-- (audience member interjects inaudibly) >> Sure. She was saying that our brain is really complex and large and even our brains don't know how our brains work, so... are there ways to-- >> What hope do we have kind of thing? (laughter) >> It's a metaphysical question. >> It circles all the way down, exactly. It's a great quote. I mean basically, you can decompose every system. Every complicated system can be decomposed into simpler, emergent properties. You lose something perhaps with each of those, but you get enough to actually understand most of the behavior. And that's really how we understand the world. And that's what we've learned in the last few years what neural network techniques can allow us to do. And that's why our brain can understand our brain. (laughing) >> Yeah, I'd recommend reading Chris Farley's last book because he addresses that issue in there very elegantly. >> Yeah we're seeing some really interesting technologies emerging right now where neural network systems are actually connecting other neural network systems in networks. You can see some very compelling behavior because one of the things I like to distinguish AI versus traditional analytics is we used to have question-answering systems. I used to query a database and create a report to find out how many widgets I sold. Then I started using regression or machine learning to classify complex situations from this is one of these and that's one of those. And then as we've moved more recently, we've got these AI-like capabilities like being able to recognize that there's a kitty in the photograph. But if you think about it, if I were to show you a photograph that happened to have a cat in it, and I said, what's the answer, you'd look at me like, what are you talking about? I have to know the question. So where we're cresting with these connected sets of neural systems, and with AI in general, is that the systems are starting to be able to, from the context, understand what the question is. Why would I be asking about this picture? I'm a marketing guy, and I'm curious about what Legos are in the thing or what kind of cat it is. So it's being able to ask a question, and then take these question-answering systems, and actually apply them so that's this ability to understand context and ask questions that we're starting to see emerge from these more complex hierarchical neural systems. >> There's a person dying to ask a question. >> Sorry. You have hit on several different topics that all coalesce together. You mentioned personalized models. You mentioned AI agents that could help you as you're going through a transitionary period. You mentioned data sources, especially across long time periods. Who today has access to enough data to make meaningful progress on that, not just when you're dealing with an issue, but day-to-day improvement of your life and your health? >> Go ahead, great question. >> That was a great question. And I don't think we have a good answer to it. (laughter) I'm sure John does. Well, I think every large healthcare organization and various healthcare consortiums are working very hard to achieve that goal. The problem remains in creating semantic interoperatability. So I spent a lot of my career working on semantic interoperatability. And the problem is that if you don't have well-defined, or self-defined data, and if you don't have well-defined and documented metadata, and you start operating on it, it's real easy to reach false conclusions and I can give you a classic example. It's well known, with hundreds of studies looking at when you give an antibiotic before surgery and how effective it is in preventing a post-op infection. Simple question, right? So most of the literature done prosectively was done in institutions where they had small sample sizes. So if you pool that, you get a little bit more noise, but you get a more confirming answer. What was done at a very large, not my own, but a very large institution... I won't name them for obvious reasons, but they pooled lots of data from lots of different hospitals, where the data definitions and the metadata were different. Two examples. When did they indicate the antibiotic was given? Was it when it was ordered, dispensed from the pharmacy, delivered to the floor, brought to the bedside, put in the IV, or the IV starts flowing? Different hospitals used a different metric of when it started. When did surgery occur? When they were wheeled into the OR, when they were prepped and drapped, when the first incision occurred? All different. And they concluded quite dramatically that it didn't matter when you gave the pre-op antibiotic and whether or not you get a post-op infection. And everybody who was intimate with the prior studies just completely ignored and discounted that study. It was wrong. And it was wrong because of the lack of commonality and the normalization of data definitions and metadata definitions. So because of that, this problem is much more challenging than you would think. If it were so easy as to put all these data together and operate on it, normalize and operate on it, we would've done that a long time ago. It's... Semantic interoperatability remains a big problem and we have a lot of heavy lifting ahead of us. I'm working with the Global Alliance, for example, of Genomics and Health. There's like 30 different major ontologies for how you represent genetic information. And different institutions are using different ones in different ways in different versions over different periods of time. That's a mess. >> Our all those issues applicable when you're talking about a personalized data set versus a population? >> Well, so N of 1 studies and single-subject research is an emerging field of statistics. So there's some really interesting new models like step wedge analytics for doing that on small sample sizes, recruiting people asynchronously. There's single-subject research statistics. You compare yourself with yourself at a different point in time, in a different context. So there are emerging statistics to do that and as long as you use the same sensor, you won't have a problem. But people are changing their remote sensors and you're getting different data. It's measured in different ways with different sensors at different normalization and different calibration. So yes. It even persists in the N of 1 environment. >> Yeah, you have to get started with a large N that you can apply to the N of 1. I'm actually going to attack your question from a different perspective. So who has the data? The millions of examples to train a deep learning system from scratch. It's a very limited set right now. Technology such as the Collaborative Cancer Cloud and The Data Exchange are definitely impacting that and creating larger and larger sets of critical mass. And again, not withstanding the very challenging semantic interoperability questions. But there's another opportunity Kay asked about what's changed recently. One of the things that's changed in deep learning is that we now have modules that have been trained on massive data sets that are actually very smart as certain kinds of problems. So, for instance, you can go online and find deep learning systems that actually can recognize, better than humans, whether there's a cat, dog, motorcycle, house, in a photograph. >> From Intel, open source. >> Yes, from Intel, open source. So here's what happens next. Because most of that deep learning system is very expressive. That combinatorial mixture of features that Naveen was talking about, when you have all these layers, there's a lot of features there. They're actually very general to images, not just finding cats, dogs, trees. So what happens is you can do something called transfer learning, where you take a small or modest data set and actually reoptimize it for your specific problem very, very quickly. And so we're starting to see a place where you can... On one end of the spectrum, we're getting access to the computing capabilities and the data to build these incredibly expressive deep learning systems. And over here on the right, we're able to start using those deep learning systems to solve custom versions of problems. Just last weekend or two weekends ago, in 20 minutes, I was able to take one of those general systems and create one that could recognize all different kinds of flowers. Very subtle distinctions, that I would never be able to know on my own. But I happen to be able to get the data set and literally, it took 20 minutes and I have this vision system that I could now use for a specific problem. I think that's incredibly profound and I think we're going to see this spectrum of wherever you are in your ability to get data and to define problems and to put hardware in place to see really neat customizations and a proliferation of applications of this kind of technology. >> So one other trend I think, I'm very hopeful about it... So this is a hard problem clearly, right? I mean, getting data together, formatting it from many different sources, it's one of these things that's probably never going to happen perfectly. But one trend I think that is extremely hopeful to me is the fact that the cost of gathering data has precipitously dropped. Building that thing is almost free these days. I can write software and put it on 100 million cell phones in an instance. You couldn't do that five years ago even right? And so, the amount of information we can gain from a cell phone today has gone up. We have more sensors. We're bringing online more sensors. People have Apple Watches and they're sending blood data back to the phone, so once we can actually start gathering more data and do it cheaper and cheaper, it actually doesn't matter where the data is. I can write my own app. I can gather that data and I can start driving the correct inferences or useful inferences back to you. So that is a positive trend I think here and personally, I think that's how we're going to solve it, is by gathering from that many different sources cheaply. >> Hi, my name is Pete. I've very much enjoyed the conversation so far but I was hoping perhaps to bring a little bit more focus into Precision Medicine and ask two questions. Number one, how have you applied the AI technologies as you're emerging so rapidly to your natural language processing? I'm particularly interested in, if you look at things like Amazon Echo or Siri, or the other voice recognition systems that are based on AI, they've just become incredibly accurate and I'm interested in specifics about how I might use technology like that in medicine. So where would I find a medical nomenclature and perhaps some reference to a back end that works that way? And the second thing is, what specifically is Intel doing, or making available? You mentioned some open source stuff on cats and dogs and stuff but I'm the doc, so I'm looking at the medical side of that. What are you guys providing that would allow us who are kind of geeks on the software side, as well as being docs, to experiment a little bit more thoroughly with AI technology? Google has a free AI toolkit. Several other people have come out with free AI toolkits in order to accelerate that. There's special hardware now with graphics, and different processors, hitting amazing speeds. And so I was wondering, where do I go in Intel to find some of those tools and perhaps learn a bit about the fantastic work that you guys are already doing at Kaiser? >> Let me take that first part and then we'll be able to talk about the MD part. So in terms of technology, this is what's extremely exciting now about what Intel is focusing on. We're providing those pieces. So you can actually assemble and build the application. How you build that application specific for MDs and the use cases is up to you or the one who's filling out the application. But we're going to power that technology for multiple perspectives. So Intel is already the main force behind The Data Center, right? Cloud computing, all this is already Intel. We're making that extremely amenable to AI and setting the standard for AI in the future, so we can do that from a number of different mechanisms. For somebody who wants to develop an application quickly, we have hosted solutions. Intel Nervana is kind of the brand for these kinds of things. Hosted solutions will get you going very quickly. Once you get to a certain level of scale, where costs start making more sense, things can be bought on premise. We're supplying that. We're also supplying software that makes that transition essentially free. Then taking those solutions that you develop in the cloud, or develop in The Data Center, and actually deploying them on device. You want to write something on your smartphone or PC or whatever. We're actually providing those hooks as well, so we want to make it very easy for developers to take these pieces and actually build solutions out of them quickly so you probably don't even care what hardware it's running on. You're like here's my data set, this is what I want to do. Train it, make it work. Go fast. Make my developers efficient. That's all you care about, right? And that's what we're doing. We're taking it from that point at how do we best do that? We're going to provide those technologies. In the next couple of years, there's going to be a lot of new stuff coming from Intel. >> Do you want to talk about AI Academy as well? >> Yeah, that's a great segway there. In addition to this, we have an entire set of tutorials and other online resources and things we're going to be bringing into the academic world for people to get going quickly. So that's not just enabling them on our tools, but also just general concepts. What is a neural network? How does it work? How does it train? All of these things are available now and we've made a nice, digestible class format that you can actually go and play with. >> Let me give a couple of quick answers in addition to the great answers already. So you're asking why can't we use medical terminology and do what Alexa does? Well, no, you may not be aware of this, but Andrew Ian, who was the AI guy at Google, who was recruited by Google, they have a medical chat bot in China today. I don't speak Chinese. I haven't been able to use it yet. There are two similar initiatives in this country that I know of. There's probably a dozen more in stealth mode. But Lumiata and Health Cap are doing chat bots for health care today, using medical terminology. You have the compound problem of semantic normalization within language, compounded by a cross language. I've done a lot of work with an international organization called Snowmed, which translates medical terminology. So you're aware of that. We can talk offline if you want, because I'm pretty deep into the semantic space. >> Go google Intel Nervana and you'll see all the websites there. It's intel.com/ai or nervanasys.com. >> Okay, great. Well this has been fantastic. I want to, first of all, thank all the people here for coming and asking great questions. I also want to thank our fantastic panelists today. (applause) >> Thanks, everyone. >> Thank you. >> And lastly, I just want to share one bit of information. We will have more discussions on AI next Tuesday at 9:30 AM. Diane Bryant, who is our general manager of Data Centers Group will be here to do a keynote. So I hope you all get to join that. Thanks for coming. (applause) (light electronic music)