>> Narrator: Live from Las Vegas, it's theCUBE, covering Veritas Vision 2017. Brought to you by Veritas. (techno music) >> Welcome back to Veritas Vision 2017. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante, and I'm here with my cohost, Stu Miniman. Dave Nettleton is here. He's the group product manager at Google. Dave, thanks for coming on theCUBE. >> Thank you, really excited to be here. >> Alright, let's talk storage and cloud. So Google Cloud Platform, we were at your show in March. Kind of the second coming out party. Diane Green at the helm. Obviously you guys are making serious moves in the enterprise. Give us the update overall and then we'll get into the storage piece. >> Yeah. Well as you say, over the last couple of years a big focus for Google has actually been shifting and focusing on enterprise customers. I think Gartner reflects that about a trillion dollars of IT spend is going to be affected by the cloud over the next three to five years. And Google has some amazing assets that its developed over the last 10 or 15 years that we can bring to bat will really help meet enterprise customers' needs, help them where they are, and really help transform their businesses for the future. So we're excited about that. >> So how's that going? One of the big thrusts that we heard in March was and we saw it. You guys have made some moves bringing in people from enterprise companies In particular, you came from Microsoft. See a lot of guys from Cisco. We saw a lot of guys running around from EMC. Diane herself from VMware, bringing a lot of that enterprise DNA. How is the the patient assimilating with those organs? >> Yeah, actually that's been one of the most exciting parts I think of the journey has been watching the team come together over the last year or two. As you say, bringing together that pool of talent that has entered one and created even new business in the past, it's amazing to see that talent group come together. Diane is doing an amazing job bringing the team together and building out all of the sales functions and other parts of the business that we need for the enterprise. Building out the partner ecosystem, as well, is obviously super critical. And when you marry that together with the technology assets that Google has, it really is giving customers unprecedented levels of capabilities in the cloud to operate their business in new, more efficient ways. >> So Google is really well known for kind of the analytics piece of the business. Look at all the pieces that have spun out of what Google has done. I'm a networking guy by background. I said when PCP was launched I said, "Google's network is second to none." Best network. Really understand when the whole wave of SDN came out. Storage on the other hand, one of those foundational pieces, but it's not the first thing that comes to mind. So give us a little bit of a pedigree of the group, what you're building, what differentiates Google from the other infrastructure as a service and cloud players. >> Yeah, and actually you teed it up beautifully, because one of our in storage big differentiators is actually our ability to leverage the network. So, let me talk you through that a little bit. So Google internally has been building out massive, scalable storage systems for years to power the rest of Google. And as we take those to our enterprise customers we find that we're able to leverage that core infrastructure together with global assets like our network. Two parts of the network actually I talk about. One is our wide area network. That allows us to actually not only store data in regions around the world, but distribute that content through hundreds of points of presence direct to customers very, very quickly. Inside of our data centers we have software defined networks that allow us to separate out compute and storage to really help us then scale these independently so that we can give massive flexibility and cost savings and pass that through to our customers. And how this shows up in our products, perhaps the best example is if you take something like Google Cloud Storage, which is our object storage product, that product is very differentiated in the industry in that it provides a single API that will meet use cases from global content serving for customers like Spotify and Vimeo who want to stream media content around the world, streaming news, web, media, videos, all the way through to archival storage. Last year we launched our cold line storage class, and this is unique in the industry because it is archival storage that's online, and it has the same API and access as all of the rest of Google Cloud Storage. So I can take a single piece of data, a video for example, I could be streaming it out to customers around the world globally, and then after a month or two I might decide that I want to archive it. I can archive that down to our colder storage class, and if a customer wants to set it up again they have instant access to it. >> What we're hearing from customers is something we heard in the keynotes here at the Veritas show is customers' cloud strategy is rather fragmented, and by that I mean they're not all in on one place to spot. Certain companies say that. How does that impact your relationship with customers on storage? How do you interact with their SaaS environment, their on premises solutions, as well as what you have inside Google? >> Yeah. I think fundamentally we believe the world is going to evolve to sort of a multi cloud world, and that includes both on premises and public clouds. And as part of that our strategy is to be, be the most open. And by being the most open that means we need to help customers be portable with their workloads. We need to help them bring their workloads to the cloud for when that's appropriate, but also if it's appropriate to take it back to say on premises to enable them to do that in a very first class way, as well. And we think what will happen is some customers will go all in on a particular cloud. There will be particular use cases and platform capabilities that will be very differentiated that they want to go all in on, and others will take a more portfolio approach. And then partners, such as Veritas and others, are great for helping customers through their information map helping manage that overall portfolio. >> Could you explain that portability? Is Kubernetes a piece of it? Is that the primary piece of it? And maybe explain a little bit more how Veritas fits in, too. >> Yeah, so the overall ecosystem is evolving. Kubernetes is obviously a huge part of that, that environment, for being able to portably move your compute around. In terms of relationship with Veritas, you know, for me it's all about helping customers solve the problems that they have and meet customers where they are. And if customers are leveraging multiple clouds, either because they use investor breed solutions through acquisitions, etc., they need the ability to be able to manage their data across all of those environments. And someone like Veritas with information map is a key partner for us in helping customers meet and manage their needs. >> So what does that mean for storage? So containers obviously for the application portability, mobility. Kubernetes is sort of Google's little lever. Everybody wants to do Kubernetes and you guys are front and center there. So that gives you credibly in the cloud world, not that you didn't have it before, but everybody now wants to belly up to you on that. What does that mean for storage? Is that just sort of like an ice breaker for you guys? Are there other things that you're doing specific to storage to take advantage of your expertise there? >> Yeah, we want to make sure that customers have a really great integrated experience as they build out their application platforms. So we're always working with them to better define and understand their needs and build that out. It is a fast emerging, fast evolving space. APIs are still evolving fast. Different layers of the stack are evolving fast. So we continue to work with customers and just meet their needs through partnerships and also first party platform. >> And as you move up the stack sort of beyond the networking storage and compute into even database, Google has got some amazing database technologies. Are you doing specific things in storage to take advantage of that, making things run faster or more available or recover faster? Can you talk about that a little bit? >> Yeah. The underlying infrastructure at Google powers a lot of our external facing services. So we actually are able to reap very interesting benefits by managing on a single shared TI, technical infrastructure, that we have at Google. But as that surfaces up to customers we have to make sure obviously that they can use it in the ways that best meet their needs. But we want to make sure that we integrate their solutions as easy as possible. So for example, Google Cloud Storage (mumbles) talking about is really well integrated with Dataproc, which is our managed Hadoop product for running big data workloads, and also with something like BigQuery, which is our massively scalable data warehousing solution. So, I can store a lot of my own structured data in Google Cloud Storage and then leverage my entire analytics portfolio to operate over that. And again, a key part of that is the separation of computer networking that we were talking about. When storage is separate from compute and we've used that very powerful software defined network, then that lets us spin up thousands of nodes in something like BigQuery to operate over data and make a very seamless experience for customers. >> So Stu kind of touched on it before. People talk about Google and Google Cloud they point to two things. Obviously the Google app suite, okay, boom. We're a customer. We love it. Everybody is familiar with it. And the other is data, the data king. And they kind of put you in those two boxes. Are you comfortable with that? Is that fair? Is that really the brand that you want? Are you trying to extend that? I wonder if you can comment. >> Yeah. Obviously our strengths have been in analytics and machine learning, and we find that that's a thing that customers are really looking to find ways to add new value to their business. But we also wanted to make sure, we also want to make sure that we're a very trusted provider offering the various high levels of services. And it's not just the capabilities but overall TCO. We want to make it much easier for people to develop new applications on the platform. We talked a little bit about some of our open capabilities, but just in general we want to make it easy for customers to get the best value out of their cloud. So you'll see us doing more and more of that. Things we've done have been like being able to create a custom, custom VM images. You can dial up your memory and size, give you a lot of flexibility to really just hone in and solve the problems that you have. >> So help us square a circle there. When you talk to the cloud, we'll call pure cloud folks, people that, you know, born in the cloud, they developed cloud from day one, no legacy infrastructure. You talk to those guys they're like, "Wow, TCO advantages "from a developer advantage, the speed, etc." When you talk to the legacy enterprise guys they'll tell you, "Oh it's expensive in that cloud. "A lot of people moving back from the cloud." Now of course we know the cloud growth is astronomical. The enterprise growth is flat at best. But there's two different exact polar opposites. Which is the truth? >> I mean the truth is it depends on what you need, right? We think cloud will be a huge disruptor to IT spend over the next several years, it already is. Wind back five or 10 years ago, I don't think people would even be thinking we'd be having the conversations that we have today. People were like, "Security, "I'm not even sure this cloud thing. "Seems like a shared colo facility to me. "I don't think I want to go near that." And it's taken us awhile collectively as an industry to educate really what the cloud is, that it's actually a much more integrated set of services that helps people up level what it is that they can do. But you know, one of the biggest challenges we still face in the industry is just education, skills. You know, it takes time to learn new skills. It's encouraging developers, working with partners, providing solutions to IT that make it much more turnkey for them to use solutions so they don't have to learn deep developer skills or super high end data science skills to get value out of their data. >> One of the hot button topics at this show has been GDPR. How does Google fit into the discussion? How are you helping customers get ready for that? >> Yeah, well obviously we're very well aware of GDPR and are working really hard to make sure that we're going to be meeting the requirements for our customers as we move forward. We take security and compliance incredibly seriously. So yes, expect us to see see us having full GDPR compliance, and then working with partners to make sure that customers can get the confidence that they need for their business. >> So Dave, as a storage technology guy, what are the big trends that you're tracking as it relates to storage that sort of are driving Google's thinking? >> Yeah, great question. So ... So, you know, more and more data is going to be coming out. Like data has traditionally been siloed. People haven't known where their data is. More and more of that data is now going to be shared within a single environment, and it's not just going to be in the cloud. That data is going to reach both onto on premises and also all the way out to the edge. IoT is going to be a huge generator of data. Being able to gather that data, manage that data, provide rich analytics over that data with machine learning and then push that intelligence back out to the edge so that actually data that's produced can just be analyzed right there is going to be super important. I love to say that data is the fuel for analytics and ML, and that fuel is going to be not just in the cloud, on prem, and all the way to the edge and managing that. It's going to be super, super, super interesting. I think network again. Network, once you start to bring low latency networks to your storage you can actually start to do really new and interesting things with your data that you'd never thought of before. If your data, if you can't access it quickly, your data is dark to you. It might as well not be there, right? >> Have things like ... How have things like Flash affected sort of bottlenecks and you mentioned the network. People talk about the network is now the new bottleneck. How is that shaping your thinking? >> Yeah, so storage trends continue, densities get higher, speeds get faster. That's a trend that's been continuing. We've been tracking it, continuing to track it. For me that just means then people will store more data and look to get more value out of that data. Sort of like the latent value of, the latent value of your data is often a function of how quickly you can run machine learning and analytics over that data and get value out of it. And you know we can do things now to analyze data faster than ever before. I was just thinking of an example the other day. I was running a query myself to look at storage usage. It's something I do regularly. And I ran the query and looked at the results. "Oh, that's cool." And then I was like, "Oh, "how many rows of data am I querying here?" And I run that query. Oh, that was like several billion rows of data that I just analyzed in like four seconds. I have no idea how much compute power was ran up in the background to meet that query, but that's the power that these new capabilities will enable over that data. >> Dave, how are customers doing with ... Kind of the thing I want to poke at is in the room data centers utilization is usually abysmal. And the biggest problem we have is when you do a technology you do it the old way. How are they doing at really taking advantage of cloud, getting utilization, utility? I'm sure if they go all serverless and per micro second it would be much better, but how are they doing? >> Well, so one of the beauties of the cloud is of course that it's a pay as you go model, right? And with storage and compute being disaggregated we see customers can provision storage, pay per gig as they go, and then when they need to run compute they just pay for the compute as they need it. They can shape custom compute instances in GCP, so they only pay for the compute that they need. When they finish they can shut them down. And if you're running something like for example a Hadoop workload where traditionally you were provisioning large amounts of compute and storage, sizing for maximum capacity, you no longer need to think about that anymore. You can just store data super cheaply. When you want to run a large 100, 1,000, 10,000 node Hadoop cluster over that data no problem. You spin it up. It spins up in under a minute. Run huge amounts of compute, shut it down, and you're done. And actually what we're finding is that like this is leading ... People are now having to ask new questions of how they manage custom controls in their business, because this is an incredible power that you can give to businesses, but they also want their controls to say, "Hey yeah, don't do that too often, "or if you do I want to manage it "and manage the cost and controls "for departments inside of organizations." So, we're building out the capabilities to help customers with that. >> Last question. Veritas were here. What do you look for in a partner like Veritas? What do you want from Veritas partnership? >> So Veritas is a fantastic partner for us. They really help us do the two things that we strive for, which is meet customers where they are today and help them transform their business for the future. So for our integration with NetBackup really helps customers in the enterprise just use existing products that they know and love and in a very turnkey way use the cloud. That helps them manage the costs and meet a lot of demands they have in their IT environments today super easily, so we love that. It also empowers them to do new things in the future. So the integration with information map we love. Helps customers identify new opportunities in their data and add new value to their business. >> Great, Dave Nettleton, Google, we'll leave it there. Thanks very much for coming on theCUBE. >> Thank you very much, been a pleasure. >> Alright, we'll keep it right there, buddy. Stu and I will be back with our next guest. This is Veritas Vision 2017. You're watching theCUBE. (techno music)

>>Hey everyone, and welcome back to The Cube's live coverage of AWS Reinvented 2022 Live from the Venetian Expo in Las Vegas. We're happy to be back. This is first full day of coverage over here last night. We've got three full days of coverage in addition to last night, and there's about 50,000 people here. This event is ready, people are ready to be back, which is so exciting. Lisa Martin here with Paul Gill and Paul, it's great to be back in person. Great to be hosting with you >>And likewise with you, Lisa. I think the first time we hosted again, >>It is our first time exactly. >>And we come here to the biggest event that the cube ever does during the year. >>It's the Super Bowl of the >>Cube. It's it's elbow to elbow out there. It's, it's, it's full tackle football, totally on the, on the floor of reinvent. And very exciting. This, you know, I've been to a lot of conferences going back 40 years, long as I can remember. Been going to tech conferences. This one, the, the intensity, the excitement around this is really unusual. People are jazzed, they're excited to be here, and that's great to see, particularly coming back from two years of isolation. >>Absolutely. The energy is so palpable. Even yesterday, evening, afternoon when I was walking in, you just feel it with all the people here. You know, we talk to so many different companies on the Q Paul. Every company these days has to be a data company. The most important thing about data is making sure that it's backed up and it's protected, that it's secure, that it can be recovered if anything happens. So we're gonna be having a great conversation next about data resiliency with one of our alumni. >>And that would be Rick Scott, Rick, excuse me, Rick Scott, >>Rick Clark. Rick Clark, say Rick Scott, cloud sales Veritas. Rick, welcome back >>To the program. Thank you. Thank you so much. It's a pleasure being here, you know, thank you so much. You're definitely very excited to myself and 40,000 of my closest cousins and friends all in one place. Yep. Or I could possibly go wrong, right? So >>Yeah, absolutely nothing. So, Rick, so Veritas has made some exciting announcements. Talk to us about some of the new things that you've >>Unveiled. Yeah, we've been, we've been incredibly busy and, you know, the journey that we've been on, one of the big announcement that we made about three or four weeks ago is the introduction, really, of a brand new cloud native data management platform that we call Veritas Alta. And this is a journey that we've been on for the better part of seven years. We actually started it with our, our flex appliances. We continued, that was a containerization of our traditional net backup business in, into a highly secured appliance that was loved by our customers. And we continued that theme and that investment into what we call a scale out and scale up form factor appliance as well, what we called flex scale. And then we continued on that investment theme, basically spending over a billion dollars over that seven year journey in our cloud native. And we call that basically the Veritas altar platform with our cloud native platform. And I think if you really look at what that is, it truly is a data management platform. And I emphasize the term cloud native. And so our traditional technologies around data protection, obviously application resiliency and digital compliance or data compliance and governance. We are the only, the first and only company in the world to provide really a cloud optimized, cloud native platform, really, that addresses that. So it's been fun, it's been a fun journey. >>Talk a little bit about the customer experience. I see over 85% of the Fortune 100 trust Veritas with their data management. That's >>A big number. Yeah. Yeah. It's, it is incredible actually. And it really comes back to the Veritas older platform. We sort of built that with, with four tenants in mind, all driving back to this very similar to AWS's customer obsession. Everything we do each and every day of our waiting moments is a Veritas employee is really surrounds the customer. So it starts with the customer experience on how do they find us to, how do they procure our solutions through things like AWS marketplace and how do they deploy it? And the second thing is around really cost optimization, as we know, you know, to, to say that companies are going through a digital transformation and moving workloads to the cloud. I mean, I've got customers that literally were 20% in cloud a year ago and 80% a year later, we've never seen that kind of velocity. >>And so we've doubled down on this notion of cost optimization. You can only do that with these huge investments that I talked about. And so we're a very profitable company. We've been around, got a great heritage of over 30 years, and we've really taken those investments in r and d to provide that sort of cloud native technology to ultimately make it elastic. And so everything from will spin up and spin down services to optimize the cloud bill for our customers, but we'll also provide the greatest workload support. You know, obviously on-prem workloads are very different from cloud workloads and it's almost like turning the clock back 20 years to see all of those new systems. There's no standard API like s and MP on the network. And so we have to talk to every single PAs service, every single DB PAs, and we capture that information and protect it. So it's really has been a phenomenal journey. It's been great. >>You said this, that that al represents a shift from clouds from flex scale to cloud native. What is the difference there? >>The, the main difference really is we took, you know, obviously our traditional product that you've known for many media years, net backup. It's got, you know, tens of millions of lines of code in that. And we knew if we lifted and shifted it up into the cloud, into an I AEs infrastructure, it's just not, it obviously would perform extremely well, but it wasn't cost optimized for our customer. It was too expensive to to run. And so what we did is we rewrote with microservices and containerization, Kubernetes huge parts of that particular product to really optimize it for the cloud. And not only have we done it for that technology, what we now call alter data protection, but we've done it across our entire port portfolio. That was really the main change that we made as part of this particular transition. And >>What have you done to prepare customers for that shift? Is this gonna be a, a drop in simple upgrade for them? >>Absolutely. Yeah. In fact, one of the things that we introduced is we, we invest still very heavily with regards to our OnPrem solutions. We're certainly not abandoning, we're still innovating. There's a lot of data still OnPrem that needs to move to the cloud. And so we have a unique advantage of all of the different workload supports that we provide OnPrem. We continue that expansion into the cloud. So we, we create it as part of the Veritas AL Vision, a technology, we call it AL view. So it's a single painter glass across both OnPrem and cloud for our customers. And so now they can actually see all of their data protection, all our application availability, single collect, all through that single unified interface, which is really game changing in the industry for us. >>It's game changing for customers too, because customers have what generally six to seven different backup technologies in their environment that they're having to individually manage and provision. So the, the workforce productivity improvements I can imagine are, are huge with Veritas. >>Yeah. You you nailed it, right? You must have seen my script, but Absolutely. I mean, I look at the analogy of, you think about the airlines, what's one of the first things airlines do with efficiency? South Southwest Airlines was the best example, a standardized on the 7 37, right? And so all of their pilots, all of their mechanics, all know how to operate the 7 37. So we are doing the same thing with enterprise data protection. So whether you're OnPrem at the edge or in the cloud or even multi-cloud, we can provide that single painter glass. We've done it for our customers for 30 plus years. We'll continue to do it for another 30 something years. And so it's really the first time with Veritas altar that, that we're, we're coming out with something that we've invested for so long and put, put such a huge investment on that can create those changes and that compelling solution for our customers. So as you can see, we're pretty pumped and excited about it. >>Yes, I can >>Use the term data management to describe Alta, and I want to ask about that term because I hear it a lot these days. Data management used to be database, now data management is being applied to all kinds of different functions across the spectrum. How do you define data management in Veritas >>Perspective? Yeah, there's a, we, we see it as really three main pillars across the environment. So one is protection, and we'll talk a little bit about this notion of ransomware is probably the number one use case. So the ability to take the most complex and the biggest, most vast applications. SAP is an example with hundreds of different moving parts to it and being able to protect that. The second is application resiliency. If, if you look at the cloud, there's this notion of, of responsibility, shared responsibility in the cloud. You've heard it, right? Yep. Every single one of the cloud service providers, certainly AWS has up on their website, this is what we protect, here's the demarcation line, the line in the sand, and you, the customer are responsible for that other level. And so we've had a technology, you previously knew it as InfoScale, we now call it alter application resiliency. >>And it can provide availability zone to availability zone, real time replication, high availability of your mission critical applications, right? So not only do we do the traditional backups, but we can also provide application resiliency for mission critical. And then the third thing really from a data management standpoint is all around governance and compliance. You know, ac a lot of our customers need to keep data for five, 10 years or forever. They're audited. There's regulations and different geographies around the world. And, and those regulations require them to be able to really take control of their cloud, take control of their data. And so we have a whole portfolio of solutions under that data compliance, data government. So back to your, your question Paul, it's really the integration and the intersection of those three main pillars. We're not a one trick pony. We've been at this for a long time, and they're not just new products that we invented a couple of months ago and brought to market. They're tried and tested with eight 80,000 customers and the most complex early solutions on the planet that we've been supporting. >>I gotta ask you, you know, we talked about those three pillars and you talked about the shared responsibility model. And think of that where you mentioned aws, Salesforce, Microsoft 365, Google workspace, whatnot. Are you finding that most customers aren't aware of that and haven't been protecting those workloads and then come to you and saying, Hey guys, guess what, this is what this is what they're responsible for. The data is >>You Yeah, I, it's, it's our probably biggest challenge is, is one of awareness, you know, with the cloud, I mean, how many times have you spoken to someone? You just put it in the cloud. Your applications, like the cloud providers like aws, they'll protect everything. Nothing will ever go down. And it's kind like if you, unless your house was ever broken into, you're probably not gonna install that burglar alarm or that fire alarm, right? Hopefully that won't be an event that you guys have to suffer through. So yeah, it's definitely, it wasn't till the last year or so the cloud service providers really published jointly as to where is their responsibility, right? So a great example is an attack vector for a lot of corporations is their SAS applications. So, you know, whether it it's your traditional SA applications that is available that's available on the web to their customers as a sas. >>And so it's certainly available to the bad actors. They're gonna, where there's, there's gonna be a point they're gonna try to get in. And so no matter what your resiliency plan is, at the end of the day, you really need to protect it. And protection isn't just, for example, with M 365 having a snapshot or a recycle bin, that's just not good enough. And so we actually have some pretty compelling technology, what we call ALTA SAS protection, which covers the, pretty much the, the gamut of the major SAS technologies to protect those and make it available for our customers. So yeah, certainly it's a big part of it is awareness. Yeah. >>Well, I understand that the shared responsibility model, I, I realize there's a lot of confusion about that still, but in the SaaS world that's somewhat different. The responsibility of the SaaS provider for protecting data is somewhat different. How, how should, what should customers know about that? >>I think, you know, the, the related to that, if, if you look at OnPrem, you know, approximately 35 to 40% of OnPrem enterprise data is protected. It's kind of in a long traditional problem. Everyone's aware of it. You know, I remember going to a presentation from IBM 20 something years ago, and someone held their push hand up in the room about the dis drives and says, you need to back it up. And the IBM sales guy said, no, IBM dis drives never crash. Right? And so fast forward to here we are today, things have changed. So we're going through almost a similar sort of changes and culture in the cloud. 8% of the data in the cloud is protected today, 8%. That's incredible. Meaning >>That there is independent backup devoted >>To that data in some cases, not at all. And something many cases, the customer just assumes that it's in the cloud, therefore it's always available. I never have to worry about protecting it, right? And so that's a big problem that we're obviously trying to, trying to solve. And we do that all under the umbrella of ransomware. That's a huge theme, huge investment that, that Veritas does with regards to providing that resiliency for our >>Customers. Ransomware is scary. It is becoming so prolific. The bad actors have access to technologies. Obviously companies are fighting them, but now ransomware has evolved into, no longer are we gonna get hit, it's when, yeah, it's how often it's what's the damage going to be. So the ability to help customers recover from ransomware, that resiliency is table stakes for businesses in any industry these days. Does that, that one of the primary pain points that your customers are coming to you with? >>It's the number one pain point. Yeah, it's, it's incredible. I mean, there's not a single briefing that our teams are doing customer meetings where that term ransomware doesn't come up as, as their number one use case. Just to give you something, a couple of statistics. There's a ransomware attack attack that happens 11 times a second right around the globe. And this isn't just, you know, minor stuff, right? I've got friends that are, you know, executives of large company that have been hit that have that some, you know, multimillion dollar ransom attack. So our, our play on this is, when you think about it, is data protection is the last line of defense. Yes. And so if they break through, it's not a case, Lisa, as you mentioned, if it's a case of when Yeah. And so it's gonna happen. So one of the most important things is knowing how do you know you have a gold copy, a clean copy, and you can recover at speed in some cases. >>We're talking about tens of thousands of systems to do that at speed. That's in our dna. We've been doing it for many, many years. And we spoke through a lot of the cyber insurance companies on this particular topic as well. And what really came back from that is that they're actually now demanding things like immutable storage, malware detection, air gaping, right? Anomaly detection is sort of core technologies tick the box that they literally won't ensure you unless you have those core components. And so what we've done is we've doubled down on that investment. We use AI in ML technologies, particularly around the anomaly detection. One of the, the, the unique and ne differentiators that Verto provides is a ransomware resiliency scorecard. Imagine the ability to save uran a corporation. We can come in and run our analytics on your environment and kind of give you a grade, right? Wouldn't you prefer that than waiting for the event to take place to see where your vulnerability really is? And so these are some of the advantages that we can actually provide for our customers, really, really >>To help. Just a final quick question. There is a, a common perception, I believe that ransomware is an on premise problem. In fact, it is also a cloud problem. Is that not right? >>Oh, absolutely. I I think that probably the biggest attack vector is in the cloud. If it's, if it's OnPrem, you've certainly got a certain line of defense that's trying to break through. But, you know, you're in the open world there. Obviously with SAS applications in the cloud, it's not a case of if, but when, and it's, and it's gonna continue to get, you know, more and more prevalent within corporations. There's always gonna be those attack factors that they find the, the flash wounds that they can attack to break through. What we are concentrating on is that resiliency, that ability for customers to recover at speed. We've done that with our traditional appliances from our heritage OnPrem. We continue to do that with regard to resiliency at speed with our customers in the cloud, with partners like aws >>For sure. Almost done. Give me your 30 seconds on AWS and Veritas. >>We've had a partnership for the better part of 10 years. It's incredible when you think about aws, where they released the elastic compute back in 2006, right? We've been delivering data protection, a data management solutions for, for the better part of 30 years, right? So, so we're, we're Junos in our space. We're the leader in, in data protection and enterprise data protection. We were on-prem. We, we continue to be in the cloud as AWS was with the cloud service provided. So the synergies are incredible. About 80 to 85% of our, our joint customers are the same. We take core unique superpowers of aws, like AWS outposts and AWS Glacier Instant retrieval, for example, those core technologies and incorporate them into our products as we go to Mark. And so we released a core technology a few months ago, we call it ultra recovery vault. And it's an air gap, a mutable storage, worm storage, right Once, right? You can't change it even when the bad actors try to get in. They're independent from the customer's tenant and aws. So we manage it as a managed backup service for our customers. Got it. And so our customers are using that to really help them with their ransomware. So it's been a tremendous partnership with AWS >>Standing 10 years of accounting. Last question for you, Rick. You got a billboard on the 1 0 1 in Santa Clara, right? By the fancy Verto >>1 0 1? >>Yeah. Right. Well, there's no traffic. What does that billboard say? What's that bumper sticker about? Vertus, >>I think, I think the billboard would say, welcome to the new Veritas. This is not your grandfather's old mobile. We've done a phenomenal job in, in the last, particularly the last three or four years, to really reinvent ourselves in the cloud and the investments that we made are really paying off for our customers today. So I'm excited to be part of this journey and excited to talk to you guys today. >>Love it. Not your grandfather's Veritas. Rick, thank you so much for joining Paula, me on the forgot talking about what you guys are doing, how you're helping customers, really established that cyber of resiliency, which is absolutely critical these days. We appreciate your >>Time. My pleasure. Thank you so much. >>All right, for our guest and Paul Gilland, I'm Lisa Martin, you're watching the Queue, which as you know is the leader in live enterprise and emerging check coverage.

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey, welcome back, you're ready. Jeff Frick here with theCUBE. We are still getting through the year of 2020. It's still the year of COVID and there's no end in sight I think until we get to a vaccine. That said, we're really excited to have one of our favorite guests. We haven't had him on for a while. I haven't talked to him for a long time. He used to I think have the record for the most CUBE appearances of probably any CUBE alumni. We're excited to have him joining us from his house in Palo Alto. Bill Schmarzo, you know him as the Dean of Big Data, he's got more titles. He's the chief innovation officer at Hitachi Vantara. He's also, we used to call him the Dean of Big Data, kind of for fun. Well, Bill goes out and writes a bunch of books. And now he teaches at the University of San Francisco, School of Management as an executive fellow. He's an honorary professor at NUI Galway. I think he's just, he likes to go that side of the pond and a many time author now, go check him out. His author profile on Amazon, the "Big Data MBA," "The Art of Thinking Like A Data Scientist" and another Big Data, kind of a workbook. Bill, great to see you. >> Thanks, Jeff, you know, I miss my time on theCUBE. These conversations have always been great. We've always kind of poked around the edges of things. A lot of our conversations have always been I thought, very leading edge and the title Dean of Big Data is courtesy of theCUBE. You guys were the first ones to give me that name out of one of the very first Strata Conferences where you dubbed me the Dean of Big Data, because I taught a class there called the Big Data MBA and look what's happened since then. >> I love it. >> It's all on you guys. >> I love it, and we've outlasted Strata, Strata doesn't exist as a conference anymore. So, you know, part of that I think is because Big Data is now everywhere, right? It's not the standalone thing. But there's a topic, and I'm holding in my hands a paper that you worked on with a colleague, Dr. Sidaoui, talking about what is the value of data? What is the economic value of data? And this is a topic that's been thrown around quite a bit. I think you list a total of 28 reference sources in this document. So it's a well researched piece of material, but it's a really challenging problem. So before we kind of get into the details, you know, from your position, having done this for a long time, and I don't know what you're doing today, you used to travel every single week to go out and visit customers and actually do implementations and really help people think these through. When you think about the value, the economic value, how did you start to kind of frame that to make sense and make it kind of a manageable problem to attack? >> So, Jeff, the research project was eyeopening for me. And one of the advantages of being a professor is, you have access to all these very smart, very motivated, very free research sources. And one of the problems that I've wrestled with as long as I've been in this industry is, how do you figure out what is data worth? And so what I did is I took these research students and I stick them on this problem. I said, "I want you to do some research. Let me understand what is the value of data?" I've seen all these different papers and analysts and consulting firms talk about it, but nobody's really got this thing clicked. And so we launched this research project at USF, professor Mouwafac Sidaoui and I together, and we were bumping along the same old path that everyone else got, which was inched on, how do we get data on our balance sheet? That was always the motivation, because as a company we're worth so much more because our data is so valuable, and how do I get it on the balance sheet? So we're headed down that path and trying to figure out how do you get it on the balance sheet? And then one of my research students, she comes up to me and she says, "Professor Schmarzo," she goes, "Data is kind of an unusual asset." I said, "Well, what do you mean?" She goes, "Well, you think about data as an asset. It never depletes, it never wears out. And the same dataset can be used across an unlimited number of use cases at a marginal cost equal to zero." And when she said that, it's like, "Holy crap." The light bulb went off. It's like, "Wait a second. I've been thinking about this entirely wrong for the last 30 some years of my life in this space. I've had the wrong frame. I keep thinking about this as an act, as an accounting conversation. An accounting determines valuation based on what somebody is willing to pay for." So if you go back to Adam Smith, 1776, "Wealth of Nations," he talks about valuation techniques. And one of the valuation techniques he talks about is valuation and exchange. That is the value of an asset is what someone's willing to pay you for it. So the value of this bottle of water is what someone's willing to pay you for it. So everybody fixates on this asset, valuation in exchange methodology. That's how you put it on balance sheet. That's how you run depreciation schedules, that dictates everything. But Adam Smith also talked about in that book, another valuation methodology, which is valuation in use, which is an economics conversation, not an accounting conversation. And when I realized that my frame was wrong, yeah, I had the right book. I had Adam Smith, I had "Wealth of Nations." I had all that good stuff, but I hadn't read the whole book. I had missed this whole concept about the economic value, where value is determined by not how much someone's willing to pay you for it, but the value you can drive by using it. So, Jeff, when that person made that comment, the entire research project, and I got to tell you, my entire life did a total 180, right? Just total of 180 degree change of how I was thinking about data as an asset. >> Right, well, Bill, it's funny though, that's kind of captured, I always think of kind of finance versus accounting, right? And then you're right on accounting. And we learn a lot of things in accounting. Basically we learn more that we don't know, but it's really hard to put it in an accounting framework, because as you said, it's not like a regular asset. You can use it a lot of times, you can use it across lots of use cases, it doesn't degradate over time. In fact, it used to be a liability. 'cause you had to buy all this hardware and software to maintain it. But if you look at the finance side, if you look at the pure play internet companies like Google, like Facebook, like Amazon, and you look at their valuation, right? We used to have this thing, we still have this thing called Goodwill, which was kind of this capture between what the market established the value of the company to be. But wasn't reflected when you summed up all the assets on the balance sheet and you had this leftover thing, you could just plug in goodwill. And I would hypothesize that for these big giant tech companies, the market has baked in the value of the data, has kind of put in that present value on that for a long period of time over multiple projects. And we see it captured probably in goodwill, versus being kind of called out as an individual balance sheet item. >> So I don't think it's, I don't know accounting. I'm not an accountant, thank God, right? And I know that goodwill is one of those things if I remember from my MBA program is something that when you buy a company and you look at the value you paid versus what it was worth, it stuck into this category called goodwill, because no one knew how to figure it out. So the company at book value was a billion dollars, but you paid five billion for it. Well, you're not an idiot, so that four billion extra you paid must be in goodwill and they'd stick it in goodwill. And I think there's actually a way that goodwill gets depreciated as well. So it could be that, but I'm totally away from the accounting framework. I think that's distracting, trying to work within the gap rules is more of an inhibitor. And we talk about the Googles of the world and the Facebooks of the world and the Netflix of the world and the Amazons and companies that are great at monetizing data. Well, they're great at monetizing it because they're not selling it, they're using it. Google is using their data to dominate search, right? Netflix is using it to be the leader in on-demand videos. And it's how they use all the data, how they use the insights about their customers, their products, and their operations to really drive new sources of value. So to me, it's this, when you start thinking about from an economics perspective, for example, why is the same car that I buy and an Uber driver buys, why is that car more valuable to an Uber driver than it is to me? Well, the bottom line is, Uber drivers are going to use that car to generate value, right? That $40,000, that car they bought is worth a lot more, because they're going to use that to generate value. For me it sits in the driveway and the birds poop on it. So, right, so it's this value in use concept. And when organizations can make that, by the way, most organizations really struggle with this. They struggle with this value in use concept. They want to, when you talk to them about data monetization and say, "Well, I'm thinking about the chief data officer, try not to trying to sell data, knocking on doors, shaking their tin cup, saying, 'Buy my data.'" No, no one wants your data. Your data is more valuable for how you use it to drive your operations then it's a sell to somebody else. >> Right, right. Well, on of the other things that's really important from an economics concept is scarcity, right? And a whole lot of economics is driven around scarcity. And how do you price for scarcity so that the market evens out and the price matches up to the supply? What's interesting about the data concept is, there is no scarcity anymore. And you know, you've outlined and everyone has giant numbers going up into the right, in terms of the quantity of the data and how much data there is and is going to be. But what you point out very eloquently in this paper is the scarcity is around the resources to actually do the work on the data to get the value out of the data. And I think there's just this interesting step function between just raw data, which has really no value in and of itself, right? Until you start to apply some concepts to it, you start to analyze it. And most importantly, that you have some context by which you're doing all this analysis to then drive that value. And I thought it was really an interesting part of this paper, which is get beyond the arguing that we're kind of discussing here and get into some specifics where you can measure value around a specific business objective. And not only that, but then now the investment of the resources on top of the data to be able to extract the value to then drive your business process for it. So it's a really different way to think about scarcity, not on the data per se, but on the ability to do something with it. >> You're spot on, Jeff, because organizations don't fail because of a lack of use cases. They fail because they have too many. So how do you prioritize? Now that scarcity is not an issue on the data side, but it is this issue on the people resources side, you don't have unlimited data scientists, right? So how do you prioritize and focus on those opportunities that are most important? I'll tell you, that's not a data science conversation, that's a business conversation, right? And figuring out how you align organizations to identify and focus on those use cases that are most important. Like in the paper we go through several different use cases using Chipotle as an example. The reason why I picked Chipotle is because, well, I like Chipotle. So I could go there and I could write it off as research. But there's a, think about the number of use cases where a company like Chipotle or any other company can leverage your data to drive their key business initiatives and their key operational use cases. It's almost unbounded, which by the way, is a huge challenge. In fact, I think part of the problem we see with a lot of organizations is because they do such a poor job of prioritizing and focusing, they try to solve the entire problem with one big fell swoop, right? It's slightly the old ERP big bang projects. Well, I'm just going to spend $20 million to buy this analytic capability from company X and I'm going to install it and then magic is going to happen. And then magic is going to happen, right? And then magic is going to happen, right? And magic never happens. We get crickets instead, because the biggest challenge isn't around how do I leverage the data, it's about where do I start? What problems do I go after? And how do I make sure the organization is bought in to basically use case by use case, build out your data and analytics architecture and capabilities. >> Yeah, and you start backwards from really specific business objectives in the use cases that you outline here, right? I want to increase my average ticket by X. I want to increase my frequency of visits by X. I want to increase the amount of items per order from X to 1.2 X, or 1.3 X. So from there you get a nice kind of big revenue hit that you can plan around and then work backwards into the amount of effort that it takes and then you can come up, "Is this a good investment or not?" So it's a really different way to get back to the value of the data. And more importantly, the analytics and the work to actually call out the information. >> The technologies, the data and analytic technologies available to us. The very composable nature of these allow us to take this use case by use case approach. I can build out my data lake one use case at a time. I don't need to stuff 25 data sources into my data lake and hope there's someone more valuable. I can use the first use case to say, "Oh, I need these three data sources to solve that use case. I'm going to put those three data sources in the data lake. I'm going to go through the entire curation process of making sure the data has been transformed and cleansed and aligned and enriched and met of, all the other governance, all that kind of stuff this goes on. But I'm going to do that use case by use case, 'cause a use case can tell me which data sources are most important for that given situation. And I can build up my data lake and I can build up my analytics then one use case at a time. And there is a huge impact then, huge impact when I build out use case by use case. That does not happen. Let me throw something that's not really covered in the paper, but it is very much covered in my new book that I'm working on, which is, in knowledge-based industries, the economies of learning are more powerful than the economies of scale. Now think about that for a second. >> Say that again, say that again. >> Yeah, the economies of learning are more powerful than the economies of scale. And what that means is what I learned on the first use case that I build out, I can apply that learning to the second use case, to the third use case, to the fourth use case. So when I put my data into my data lake for my first use case, and the paper covers this, well, once it's in my data lake, the cost of reusing that data in a second, third and fourth use cases is basically, you know marginal cost is zero. So I get this ability to learn about what data sets are most important and to reapply that across the organization. So this learning concept, I learn use case by use case, I don't have to do a big economies of scale approach and start with 25 datasets of which only three or four might be useful. But I'm incurring the overhead for all those other non-important data sets because I didn't take the time to go through and figure out what are my most important use cases and what data do I need to support those use cases. >> I mean, should people even think of the data per se or should they really readjust their thinking around the application of the data? Because the data in and of itself means nothing, right? 55, is that fast or slow? Is that old or young? Well, it depends on a whole lot of things. Am I walking or am I in a brand new Corvette? So it just, it's funny to me that the data in and of itself really doesn't have any value and doesn't really provide any direction into a decision or a higher order, predictive analytics until you start to manipulate the data. So is it even the wrong discussion? Is data the right discussion? Or should we really be talking about the capabilities to do stuff within and really get people focused on that? >> So Jeff, there's so many points to hit on there. So the application of data is what's the value, and the queue of you guys used to be famous for saying, "Separating noise from the signal." >> Signal from the noise. Signal from a noise, right. Well, how do you know in your dataset what's signal and what's noise? Well, the use case will tell you. If you don't know the use case and you have no way of figuring out what's important. One of the things I use, I still rail against, and it happens still. Somebody will walk up my data science team and say, "Here's some data, tell me what's interesting in it." Well, how do you separate signal from noise if I don't know the use case? So I think you're spot on, Jeff. The way to think about this is, don't become data-driven, become value-driven and value is driven from the use case or the application or the use of the data to solve that particular use case. So organizations that get fixated on being data-driven, I hate the term data-driven. It's like as if there's some sort of frigging magic from having data. No, data has no value. It's how you use it to derive customer product and operational insights that drive value,. >> Right, so there's an interesting step function, and we talk about it all the time. You're out in the weeds, working with Chipotle lately, and increase their average ticket by 1.2 X. We talk more here, kind of conceptually. And one of the great kind of conceptual holy grails within a data-driven economy is kind of working up this step function. And you've talked about it here. It's from descriptive, to diagnostic, to predictive. And then the Holy grail prescriptive, we're way ahead of the curve. This comes into tons of stuff around unscheduled maintenance. And you know, there's a lot of specific applications, but do you think we spend too much time kind of shooting for the fourth order of greatness impact, instead of kind of focusing on the small wins? >> Well, you certainly have to build your way there. I don't think you can get to prescriptive without doing predictive, and you can't do predictive without doing descriptive and such. But let me throw a really one at you, Jeff, I think there's even one beyond prescriptive. One we're talking more and more about, autonomous, a ton of analytics, right? And one of the things that paper talked about that didn't click with me at the time was this idea of orphaned analytics. You and I kind of talked about this before the call here. And one thing we noticed in the research was that a lot of these very mature organizations who had advanced from the retrospective analytics of BI to the descriptive, to the predicted, to the prescriptive, they were building one off analytics to solve a problem and getting value from it, but never reusing this analytics over and over again. They were done one off and then they were thrown away and these organizations were so good at data science and analytics, that it was easier for them to just build from scratch than to try to dig around and try to find something that was never actually ever built to be reused. And so I have this whole idea of orphaned analytics, right? It didn't really occur to me. It didn't make any sense into me until I read this quote from Elon Musk, and Elon Musk made this statement. He says, " I believe that when you buy a Tesla, you're buying an asset that appreciates in value, not depreciates through usage." I was thinking, "Wait a second, what does that mean?" He didn't actually say it, "Through usage." He said, "He believes you're buying an asset that appreciates not depreciates in value." And of course the first response I had was, "Oh, it's like a 1964 and a half Mustang. It's rare, so everybody is going to want these things. So buy one, stick it in your garage. And 20 years later, you're bringing it out and it's worth more money." No, no, there's 600,000 of these things roaming around the streets, they're not rare. What he meant is that he is building an autonomous asset. That the more that it's used, the more valuable it's getting, the more reliable, the more efficient, the more predictive, the more safe this asset's getting. So there is this level beyond prescriptive where we can think about, "How do we leverage artificial intelligence, reinforcement, learning, deep learning, to build these assets that the more that they are used, the smarter they get." That's beyond prescriptive. That's an environment where these things are learning. In many cases, they're learning with minimal or no human intervention. That's the real aha moment. That's what I miss with orphaned analytics and why it's important to build analytics that can be reused over and over again. Because every time you use these analytics in a different use case, they get smarter, they get more valuable, they get more predictive. To me that's the aha moment that blew my mind. I realized I had missed that in the paper entirely. And it took me basically two years later to realize, dough, I missed the most important part of the paper. >> Right, well, it's an interesting take really on why the valuation I would argue is reflected in Tesla, which is a function of the data. And there's a phenomenal video if you've never seen it, where they have autonomous vehicle day, it might be a year or so old. And he's got his number one engineer from, I think the Microprocessor Group, The Computer Vision Group, as well as the autonomous driving group. And there's a couple of really great concepts I want to follow up on what you said. One is that they have this thing called The Fleet. To your point, there's hundreds of thousands of these things, if they haven't hit a million, that are calling home reporting home every day as to exactly how everyone took the Northbound 101 on-ramp off of University Avenue. How fast did they go? What line did they take? What G-forces did they take? And every one of those cars feeds into the system, so that when they do the autonomous update, not only are they using all their regular things that they would use to map out that 101 Northbound entry, but they've got all the data from all the cars that have been doing it. And you know, when that other car, the autonomous car couple years ago hit the pedestrian, I think in Phoenix, which is not good, sad, killed a person, dark tough situation. But you know, we are doing an autonomous vehicle show and the guy who made a really interesting point, right? That when something like that happens, typically if I was in a car wreck or you're in a car wreck, hopefully not, I learned the person that we hit learns and maybe a couple of witnesses learn, maybe the inspector. >> But nobody else learns. >> But nobody else learns. But now with the autonomy, every single person can learn from every single experience with every vehicle contributing data within that fleet. To your point, it's just an order of magnitude, different way to think about things. >> Think about a 1% improvement compounded 365 times, equals I think 38 X improvement. The power of 1% improvements over these 600,000 plus cars that are learning. By the way, even when the autonomous FSD, the full self-driving mode module isn't turned on, even when it's not turned on, it runs in shadow mode. So it's learning from the human drivers, the human overlords, it's constantly learning. And by the way, not only they're collecting all this data, I did a little research, I pulled out some of their job search ads and they've built a giant simulator, right? And they're there basically every night, simulating billions and billions of more driven miles because of the simulator. They are building, he's going to have a simulator, not only for driving, but think about all the data he's capturing as these cars are riding down the road. By the way, they don't use Lidar, they use video, right? So he's driving by malls. He knows how many cars are in the mall. He's driving down roads, he knows how old the cars are and which ones should be replaced. I mean, he has this, he's sitting on this incredible wealth of data. If anybody could simulate what's going on in the world and figure out how to get out of this COVID problem, it's probably Elon Musk and the data he's captured, be courtesy of all those cars. >> Yeah, yeah, it's really interesting, and we're seeing it now. There's a new autonomous drone out, the Skydio, and they just announced their commercial product. And again, it completely changes the way you think about how you use that tool, because you've just eliminated the complexity of driving. I don't want to drive that, I want to tell it what to do. And so you're saying, this whole application of air force and companies around things like measuring piles of coal and measuring these huge assets that are volume metric measured, that these things can go and map out and farming, et cetera, et cetera. So the autonomy piece, that's really insightful. I want to shift gears a little bit, Bill, and talk about, you had some theories in here about thinking of data as an asset, data as a currency, data as monetization. I mean, how should people think of it? 'Cause I don't think currency is very good. It's really not kind of an exchange of value that we're doing this kind of classic asset. I think the data as oil is horrible, right? To your point, it doesn't get burned up once and can't be used again. It can be used over and over and over. It's basically like feedstock for all kinds of stuff, but the feedstock never goes away. So again, or is it that even the right way to think about, do we really need to shift our conversation and get past the idea of data and get much more into the idea of information and actionable information and useful information that, oh, by the way, happens to be powered by data under the covers? >> Yeah, good question, Jeff. Data is an asset in the same way that a human is an asset. But just having humans in your company doesn't drive value, it's how you use those humans. And so it's really again the application of the data around the use cases. So I still think data is an asset, but I don't want to, I'm not fixated on, put it on my balance sheet. That nice talk about put it on a balance sheet, I immediately put the blinders on. It inhibits what I can do. I want to think about this as an asset that I can use to drive value, value to my customers. So I'm trying to learn more about my customer's tendencies and propensities and interests and passions, and try to learn the same thing about my car's behaviors and tendencies and my operations have tendencies. And so I do think data is an asset, but it's a latent asset in the sense that it has potential value, but it actually has no value per se, inputting it into a balance sheet. So I think it's an asset. I worry about the accounting concept medially hijacking what we can do with it. To me the value of data becomes and how it interacts with, maybe with other assets. So maybe data itself is not so much an asset as it's fuel for driving the value of assets. So, you know, it fuels my use cases. It fuels my ability to retain and get more out of my customers. It fuels ability to predict what my products are going to break down and even have products who self-monitor, self-diagnosis and self-heal. So, data is an asset, but it's only a latent asset in the sense that it sits there and it doesn't have any value until you actually put something to it and shock it into action. >> So let's shift gears a little bit and start talking about the data and talk about the human factors. 'Cause you said, one of the challenges is people trying to bite off more than they can chew. And we have the role of chief data officer now. And to your point, maybe that mucks things up more than it helps. But in all the customer cases that you've worked on, is there a consistent kind of pattern of behavior, personality, types of projects that enables some people to grab those resources to apply to their data to have successful projects, because to your point there's too much data and there's too many projects and you talk a lot about prioritization. But there's a lot of assumptions in the prioritization model that you can, that you know a whole lot of things, especially if you're comparing project A over in group A with project B, with group B and the two may not really know the economics across that. But from an individual person who sees the potential, what advice do you give them? What kind of characteristics do you see, either in the type of the project, the type of the boss, the type of the individual that really lends itself to a higher probability of a successful outcome? >> So first off you need to find somebody who has a vision for how they want to use the data, and not just collect it. But how they're going to try to change the fortunes of the organization. So it always takes a visionary, may not be the CEO, might be somebody who's a head of marketing or the head of logistics, or it could be a CIO, it could be a chief data officer as well. But you've got to find somebody who says, "We have this latent asset we could be doing more with, and we have a series of organizational problem challenges against which I could apply this asset. And I need to be the matchmaker that brings these together." Now the tool that I think is the most powerful tool in marrying the latent capabilities of data with all the revenue generating opportunities in the application side, because there's a countless number, the most important tool that I found doing that is design thinking. Now, the reason why I think design thinking is so important, because one of the things that design thinking does a great job is it gives everybody a voice in the process of identifying, validating, valuing, and prioritizing use cases you're going to go after. Let me say that again. The challenge organizations have is identifying, validating, valuing, and prioritizing the use cases they want to go after. Design thinking is a marvelous tool for driving organizational alignment around where we're going to start and what's going to be next and why we're going to start there and how we're going to bring everybody together. Big data and data science projects don't die because of technology failure. Most of them die because of passive aggressive behaviors in the organization that you didn't bring everybody into the process. Everybody's voice didn't get a chance to be heard. And that one person who's voice didn't get a chance to get heard, they're going to get you. They may own a certain piece of data. They may own something, but they're just waiting and lay, they're just laying there waiting for their chance to come up and snag it. So what you got to do is you got to proactively bring these people together. We call this, this is part of our value engineering process. We have a value engineering process around envisioning where we bring all these people together. We help them to understand how data in itself is a latent asset, but how it can be used from an economics perspective, drive all those value. We get them all fired up on how these can solve any one of these use cases. But you got to start with one, and you've got to embrace this idea that I can build out my data and analytic capabilities, one use case at a time. And the first use case I go after and solve, makes my second one easier, makes my third one easier, right? It has this ability that when you start going use case by use case two really magical things happen. Number one, your marginal cost flatten. That is because you're building out your data lake one use case at a time, and you're bringing all the important data lake, that data lake one use case at a time. At some point in time, you've got most of the important data you need, and the ability that you don't need to add another data source. You got what you need, so your marginal costs start to flatten. And by the way, if you build your analytics as composable, reusable, continuous learning analytic assets, not as orphaned analytics, pretty soon you have all the analytics you need as well. So your marginal cost flatten, but effect number two is that you've, because you've have the data and the analytics, I can accelerate time to value, and I can de-risked projects as I go use case by use case. And so then the biggest challenge becomes not in the data and the analytics, it's getting the all the business stakeholders to agree on, here's a roadmap we're going to go after. This one's first, and this one is going first because it helps to drive the value of the second and third one. And then this one drives this, and you create a whole roadmap of rippling through of how the data and analytics are driving this value to across all these use cases at a marginal cost approaching zero. >> So should we have chief design thinking officers instead of chief data officers that really actually move the data process along? I mean, I first heard about design thinking years ago, actually interviewing Dan Gordon from Gordon Biersch, and they were, he had just hired a couple of Stanford grads, I think is where they pioneered it, and they were doing some work about introducing, I think it was a a new apple-based alcoholic beverage, apple cider, and they talked a lot about it. And it's pretty interesting, but I mean, are you seeing design thinking proliferate into the organizations that you work with? Either formally as design thinking or as some derivation of it that pulls some of those attributes that you highlighted that are so key to success? >> So I think we're seeing the birth of this new role that's marrying capabilities of design thinking with the capabilities of data and analytics. And they're calling this dude or dudette the chief innovation officer. Surprise. >> Title for someone we know. >> And I got to tell a little story. So I have a very experienced design thinker on my team. All of our data science projects have a design thinker on them. Every one of our data science projects has a design thinker, because the nature of how you build and successfully execute a data science project, models almost exactly how design thinking works. I've written several papers on it, and it's a marvelous way. Design thinking and data science are different sides of the same coin. But my respect for data science or for design thinking took a major shot in the arm, major boost when my design thinking person on my team, whose name is John Morley introduced me to a senior data scientist at Google. And I was bottom coffee. I said, "No," this is back in, before I even joined Hitachi Vantara, and I said, "So tell me the secret to Google's data science success? You guys are marvelous, you're doing things that no one else was even contemplating, and what's your key to success?" And he giggles and laughs and he goes, "Design thinking." I go, "What the hell is that? Design thinking, I've never even heard of the stupid thing before." He goes, "I'd make a deal with you, Friday afternoon let's pop over to Stanford's B school and I'll teach you about design thinking." So I went with him on a Friday to the d.school, Design School over at Stanford and I was blown away, not just in how design thinking was used to ideate and bring and to explore. But I was blown away about how powerful that concept is when you marry it with data science. What is data science in its simplest sense? Data science is about identifying the variables and metrics that might be better predictors of performance. It's that might phrase that's the real key. And who are the people who have the best insights into what values or metrics or KPIs you might want to test? It ain't the data scientists, it's the subject matter experts on the business side. And when you use design thinking to bring this subject matter experts with the data scientists together, all kinds of magic stuff happens. It's unbelievable how well it works. And all of our projects leverage design thinking. Our whole value engineering process is built around marrying design thinking with data science, around this prioritization, around these concepts of, all ideas are worthy of consideration and all voices need to be heard. And the idea how you embrace ambiguity and diversity of perspectives to drive innovation, it's marvelous. But I feel like I'm a lone voice out in the wilderness, crying out, "Yeah, Tesla gets it, Google gets it, Apple gets it, Facebook gets it." But you know, most other organizations in the world, they don't think like that. They think design thinking is this Wufoo thing. Oh yeah, you're going to bring people together and sing Kumbaya. It's like, "No, I'm not singing Kumbaya. I'm picking their brains because they're going to help make their data science team much more effective and knowing what problems we're going to go after and how I'm going to measure success and progress. >> Maybe that's the next Dean for the next 10 years, the Dean of design thinking instead of data science, and who knew they're one and the same? Well, Bill, that's a super insightful, I mean, it's so, is validated and supported by the trends that we see all over the place, just in terms of democratization, right? Democratization of the tools, more people having access to data, more opinions, more perspective, more people that have the ability to manipulate the data and basically experiment, does drive better business outcomes. And it's so consistent. >> If I could add one thing, Jeff, I think that what's really powerful about design thinking is when I think about what's happening with artificial intelligence or AI, there's all these conversations about, "Oh, AI is going to wipe out all these jobs. Is going to take all these jobs away." And what we're actually finding is that if we think about machine learning, driven by AI and human empowerment, driven by design thinking, we're seeing the opportunity to exploit these economies of learning at the front lines where every customer engagement, every operational execution is an opportunity to gather not only more data, but to gather more learnings, to empower the humans at the front lines of the organization to constantly be seeking, to try different things, to explore and to learn from each of these engagements. I think it's, AI to me is incredibly powerful. And I think about it as a source of driving more learning, a continuous learning and continuously adapting an organization where it's not just the machines that are doing this, but it's the humans who've been empowered to do that. And my chapter nine in my new book, Jeff, is all about team empowerment, because nothing you do with AI is going to matter of squat if you don't have empowered teams who know how to take and leverage that continuous learning opportunity at the front lines of customer and operational engagement. >> Bill, I couldn't set a better, I think we'll leave it there. That's a great close, when is the next book coming out? >> So today I do my second to last final review. Then it goes back to the editor and he does a review and we start looking at formatting. So I think we're probably four to six weeks out. >> Okay, well, thank you so much, congratulations on all the success. I just love how the Dean is really the Dean now, teaching all over the world, sharing the knowledge and attacking some of these big problems. And like all great economics problems, often the answer is not economics at all. It's completely really twist the lens and don't think of it in that, all that construct. >> Exactly. >> All right, Bill. Thanks again and have a great week. >> Thanks, Jeff. >> All right. He's Bill Schmarzo, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (gentle music)

>> Narrator: Live from San Francisco, it's theCUBE, covering RSA Conference 2020 San Francisco. Brought to you by SiliconANGLE Media. >> Welcome back everyone. This is theCUBE's coverage from RSA Conference on Moscone South. I'm John Furrier, host of theCUBE. You know, cybersecurity is changing, and the next technology is right around the corner, and it's got to be invented somewhere, and of course Accenture Labs is part of it. Our next guest is Lisa O'Connor, Global Security R&D Lead for Accenture Labs. Lisa's working on some of those hard problems all around the world. Thank you for joining me today. Thanks for coming on. >> Thank you for having me. >> So, we always get the good scoop from Accenture, because you have a lot of smart people in that company. You know, they know their stuff. I know you got a huge analytics team. I've talked to Jean-Luc Chatelain before, and I know you got a massive amount of, deep bench of talent. But as you have to go do the applied R&D, and maybe some of the crazy ideas, you got to start thinking about where the puck is going to be. >> Absolutely. >> You got to understand that. Well, it's pretty clear to us that Cloud is certainly there. Palo Alto Networks had a disappointing earnings yesterday, because their on-premises business is shifting to the Cloud. You're seeing hybrid operating model and multicloud for the enterprise, but now you got global challenges. >> We absolutely do. >> Huge, so what are you guys working on that's coming? Tell us. >> So we're working on lots of exciting things, and Cloud is one of them. But, some of the things I'm so passionate about in labs, and I have the best job at Accenture. Don't tell anyone. (laughs) I do. So, we are working on, like Jean-Luc is working on applied intelligence, we are working on robust AI. So, when we think about AI in the future, how do we feel that, and know that it's okay? How do we put it out there and know it's safe in production, we've done the right training, we've made our model resilient to what's out there? One of the things we see happening, and I love AI, love it. It has great potential, and we get great insights out of it, but a lot of times we stop, we get the insights, and we say, "Okay, it's in the box, we got a couple hits there, "we're good, it's good." No, maybe not. And so really, it's learning and creating the actually applied attacks on AI, and then figuring out what the right defenses are. And, depending on what type of machine learning you're using, those defenses change. And so, we're having a great time in our lab in Washington D.C., working on basically defending AI and building those techniques, so that what we put out as Accenture is robust. >> You know, it's interesting, AI, you watch some of the hardcore, you know, social justice warriors out there going after Amazon, Google, you know, because they're doing some pretty progressive things. Oh, facial recognition, you got AI, you got Alexa. You know, a lot of people are like, "Oh, I'm scared." But, at the end of the day, they also have some challenges like network security, so you have all this AI up and down the stack. And, one thing I like about what's being talked about in the industry is the shared responsibility model. So, I got to ask you, as AI becomes exciting, but also, balancing, frightening to people, how do you get that shared responsibility model, so we get it right, do the experimentation, without people freaking out? (laughs) So, it's kind of like this weird mode we're in now, where I want to do more AI, because I think it benefits society, but everyone's freaking out. >> Yeah, so, in our tech vision that we just launched, The Tech Vision 2020, there's a lot of talk about value and values, which is really important when we think about AI because we can get great value out of it, but there's a values piece of it and it's how we're using it, how we're getting those insights. Because, the one thing, we have this circle, and it's between customer experience, because the companies that do customer experience well are going to excel, they're going to keep their clients, they're going to do amazing things, they're going to become sticky. But, to do that well, you have to be a good custodian of their data and their information, and curated experiences that they want, and not the creepy ones, not the ones they don't want. And so, we really look at that trust is necessary in that ecosystem, in building that, and keeping that with clients. So, that's something that came out of our technology vision. And, in fact, we're going to be talking at the Executive Women's Forum, this is tomorrow, and we're going to be having a panel on AI, and defending it, which will be very interesting. >> Make sure your people film that conference. We'd like to get a view of it on YouTube after. We love those conferences, really insightful. But, I want to get back to what you were talking about, the fun side. >> Yeah. >> You got a lot of new things on, your guys are kicking the tires on, scratching the surface on. You have two operating labs, one in Washington D.C., and one in Israel. What city in Israel? Is it in Tel Aviv or-- >> Herzliya. >> Okay, did not know. >> Yeah, the tech district, just north of Tel Aviv. It's the hotspot. >> So, Silicon Valley, D.C., and Israel, hotbeds of technology now. >> Yes. >> What's coming out of those labs, what's hot? >> Oh, there's so much exciting stuff coming out of our lab in Herzliya. One of the things that we have, and it's something that's been long and coming, it's been brewing for a while, but it's really looking at creating a model of the enterprise security posture. And, when I say a model of it, I'm talking about a cyber digital twin. Because, so much we can't do in our production networks, we don't have the capabilities. We can look around the room, but we don't have the capabilities on the SOCs team side, to ingest all this stuff. We need a playground where we can ask the what-ifs, where we can run high performance analytics, and we do that through a temporal knowledge graph. And, that's a hard thing to achieve, and it's a hard thing to do analytics at scale. So, that's one of the big projects that we're doing out of our Israel lab. >> Are you saying digital twins is a framework for that? >> Yeah. >> Does it really work well with that? >> So the knowledge graph, we can create digital twins around many things, because a digital twin is a model of processes, people, technologies, the statefulness of things, and configurations, whatever you want to pull in there. So, when we start thinking about, what would we take in to create the perfect enterprise security posture? What would give us all the insights? And, then we can ask the questions about, okay, how would an adversary do lateral movement through this? I can't fix everything that's a 10, but I could fix the right ones to reduce the risk impactfully. And, those are the kind of what-ifs that you can do. >> That's real sci-fi stuff, that's right around the corner. >> Yeah, it is. >> That simulation environment. >> It is. >> What-ifs. Oh my god, the company just got hacked, we're out of business. That's your simulation. You could get to, that's the goal, right? >> It absolutely is, to ask those good business questions about the data, and then to report on the risk of it. And, the other thing, as we move to 5G, this problem's getting bigger and bigger, and we're now bringing in very disparate kinds of compute platforms, computing-at-the-edge. And, what does that do to our nice little network model that we had, that our traditional systems are used to defending against? >> I mean, just the segmentation of the network, and the edge opens up so much more aperture-- >> Yes, it does (laughs). >> to the digital twin, or a knowledge graph. You brought up knowledge graph, I want to get your thoughts on this. I was just having dinner last night with an amazing woman out of New York. She's a Ph.D. in computer science. So, we're talking about graphs, and I love riffing on graph databases. But, the topic came up about databases in general, because with the cloud, it's horizontally scalable, you've got all kinds of simulation, a lot of elasticity going on, there's a lot of software being written on this. You got time series database, you got relational database, you got unstructured, and you got graphs. You got to make them all work together. This is kind of the unique challenge. And, with security, leveraging the right database, and the right construct is a super important thing. How do you guys look at that in the labs? Because, is it something that you guys think about, or is it going to be invisible someday? >> Oh, we think about it a lot. In fact, we've had a number of research projects over the last five years now, actually six years, where we've really pivoted hard in cyber security to graph databases. And, the reason for that is, the many-to-many relationships, and what we can do in terms of navigating, asking the questions, pulling on a thread, because in cyber hunting, that's what we're doing. In many of these use cases that we're trying to defend an enterprise, we're following the next new path based on the newest information of now what the challenge is, or what the current configuration is. So, that's really important. So, graph databases enable that so well. Now, there's still the architecture challenge of, okay, when I ask a query, what am I doing? Am I disrupting the whole apple cart? Do I have to process everything over, or is there a way to do that elegantly, where I can ask my query, and because of how I've structured it in storage, I can do it much better, and I can do it much more efficiently. And that, I think, is where the opportunities are. >> I got to tell you, I'm getting exited now on this whole database discussion, because you think about the logic around what you just said. A graph database with that kind of complexity, when you factor in contextually different things happening at any given time, the database needs to be parsed and managed differently. >> Yes. >> That's a huge challenge. >> It is a great research challenge, which is why we're doing it. >> What is that, how far along are we going to be able to have this dynamic, self-evolving, self-governing, self-healing data modeling? Is that coming soon, or... >> Yeah, I hope so. We wrote about it a couple of years ago. >> You did? >> The self-healing enterprise, aspirational. But I think, I mean, we try to get to real time, right? And, we try to get to real time, and again, refactoring. As we talk about what an adversary is going to do, or lateral movement through a business process, we're talking about a lot of computational horsepower to recalculate all that, process it again, update it, and then again present that back. So the number of things we're asking, how we're asking it becomes also very important to the structure. >> Just, it goes zooming up a little bit, high level, what we're really talking about here is value >> of the data. >> Absolutely. >> And, when you get into the valuation of the nodes, and the arcs, and all that graphs, and other databases, you got to know what to pay attention to. It's kind of like going into the hospital and hearing all these alarms going off. At some point you don't know what's, until they hear a flat line, or whatever. >> Right. That's a bad one. >> I mean, well that's obvious. But, now sometimes there's so many alerts, there's so many alarms. How do you understand at any given time what to pay attention to, because obviously when someone's having a problem you want to pay attention to it. If it's a security alert, that's prioritized. >> And the devil is in the analytics, right? What's the question we're asking, and the analytics that give us that prioritization? And that's non-trivial, because there are a lot of other folks that are doing prioritization in a different manner. To do it at scale, and to do it, not just one hop out, but I want to go all the way to the crown jewels, I want that whole path navigated, and I want to know where to cut along that path. That's a hard thing to do. And so, we've actually developed, and we've submitted patents for them, but we've developed new analytics that'll support that. >> Awesome. Well Lisa, I want to ask you kind of a, I'll give you a plug here, just going to get it out, because I think it's important. Skills gap's a big thing, so I want to give you a minute to explain, or share what you're looking for in your hiring. Who are you looking for? What kind of, the make-up of individual, obviously? Maybe, do you use straight, more academic paper kind of people, or practitioners? I mean, when you look to hire, what are some of the priorities that you look for, and who would thrive in an Accenture Lab's environment? >> Oh, my goodness. >> Take a minute to share what you're looking for. >> Yeah, so we love people that think out of the box, and those kinds of people come from very different backgrounds. And so, part of that is, some of them we look for Ph.D.'s, that have wonderful applied skills, and applied is a key word there. White papers are great, I need to be able to prove something, I need to be able to demo something that has value. So, having the applied skills to a business challenge is really important. So, that sort of ground, understanding the business, very important too. But, our talent comes from many different areas. I mean, I kind of joke, my lab looks like the UN, it's wonderful. I have people from across the globe that are in our cyber security lab. I have, in our Washington D.C. lab, we're 50% women, which is also exciting, because we want different experiences, and we shoot for cognitive diversity, right? So, we're looking for people that think differently about solving problems, and are not encumbered by what they've seen in the past, because we're trying to be tip of spear. And, I'm sure you know that from Paul Daugherty. >> Yeah. >> We are trying to be three to five years over the horizon. >> You guys got a good narrative. I always love talking to Accenture, they have a good vision. So, I got to ask you, the next logical question is, obviously, in the news, you see everyone talking about breaches, and ya know, it's not a breach if the door's open, you just walk in. They're really walking in, nothing was really breached, you're just giving it to them. >> Yeah. It's a passive invitation. >> (laughs) Hey come on in. Human error is a big part of it, but then, breach is obviously targeted, phishing, and all that good stuff. But, as those stories get told, there's a whole nother set of stories that aren't being told that are super important. So, I'd love to get your thoughts on, what are the most important stories that we should be talking about that aren't being talked about? >> Yeah, so I have two that are front-of-mind for me. One theme we come back to, and it's not sexy, it's hygiene. It is IT hygiene, and so many of the large companies, and even medium, small companies, we have legacy technology, and keeping that adds complexity, it adds to the whole breadth and depth of what we have to manage and defend. Keeping that attack surface simple and small, cloud-enabled, all those good things, is a real asset and it makes it much easier to defend. So, that's kind of the first non-sexy one, hygiene. The other one I'll say that I think is a challenge that we are not dealing with yet, quantum computing, right? And so, we're on the way to getting our post quantum cryptography in place, but there's another dimension to it, and it's our histories. So, all of the things that have passed on the wire, all the communications with the key exchanges, all that brilliant stuff, is sitting somewhere. Once we get to that point where this becomes very routine, and it's coming fast, we predicted eight years, two years ago. >> So, all that exhaust is somewhere, pent up. >> It's somewhere that, we have to think about how much data we're keeping as custodians, how we're managing it, and then we have to think about the exposure from our past, and say, "Okay, what does that mean that, that was out there?" "Is it aged enough that it doesn't have value?" And, I think there's a real triage that needs to be done, and certainly data management. >> I think, you know, the hygiene brings up a good point. It reminds me of the story Andy Jassy was telling about the mainframe customer that they couldn't find who had the password. They had to find their person, who was retired 10 years earlier to get the password. You don't forget things, but also, there's a human component in all this. Humans and machines are working together. >> Absolutely. >> And. that's a huge part of it. It's not just machines dominating it all, there's going to be a human component, there's a societal impact that we're seeing with information. And, whether that's out in the open, or behind closed doors, there's all kinds of things looming. >> There are, and I think one of the things in the companies that we're seeing who are embracing innovation well, are doing a lot of retraining. Because, the things that people are excellent at, AI is not good at, and the things that AI is good at, are not at all what people are good at. So, the good news is there is a beautiful teaming there, if we retool the skills, or if we re-envision those roles, so that people can get into those roles, and I think that's really important, because I'd rather see AI do all the heavy lifting well, and be trustworthy, and robust and all those great things, and the people be doing the much smarter things that require a human. >> Does the process serve the purpose? Does the purpose serve the process? Same kind of question, right? >> Exactly. >> AI, you can't have great AI that does nothing. >> That's right. >> (laughs) So, it has to be relevant. >> It absolutely does. >> Relevance is kind of a big thing. >> And we own that context, right? Humans own that context. >> Yeah. Yeah. Yeah. Well, thanks for coming in, and sharing the insight. Really appreciate it. Final question, it's always tough to pick your favorite child, but what is your most coolest thing you're working on right now? >> I'll tell you, the cyber digital twin stuff is so cool. >> The what? >> The cyber digital twin stuff is so cool. When you see the power of what that picture, and the analytics can do, we'll show ya. >> Do you have a demo of that now? >> We absolutely do. >> You do. Is it online, or is it more in person you got to see it? >> More in person. >> Okay. >> Folks can reach out, yeah. >> We'll have to get the exclusive on that. >> We do. >> I love those simulations. I think it's very beneficial. >> It is. >> A lot of learning. I mean, who doesn't want practice? >> Well, and a picture, you know that is worth a million dollars. It's just incredible to look at it, and it clicks. It clicks of all the potential things you could ask or do. And, that's the exciting part now, as we show this with customers' and we co-innovate with customers', they're coming up with a laundry list of questions. >> And, this is the beautiful thing about cloud, is that new capabilities are emerging every day, and you could use the good ones. Lisa O'Connor is here. Thank you very much for sharing your insights. Global Security R&D Lead for Accenture Labs. TheCUBE coverage, getting all the signal here on the show floor, extracting that from all the noise. I'm John Furrier, thanks for watching. (upbeat music)

(upbeat music) >> Announcer: From San Francisco, it's theCUBE. Covering Accenture Tech Vision 2020. Brought to you by Accenture. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at the Accenture San Francisco Innovation Hub on the 33rd floor of the Sales Force Tower in downtown San Francisco. It's 2020, the year we know everything with the benefit of hindsight. And what better way to kick off the year than to have the Accenture Tech Vision reveal, which is happening later tonight, so we're really happy to have one of the authors who's really driving the whole thing. He's Michael Blitz, the managing director of the Accenture Tech Vision 2020, a very special edition. Michael, great to see you. >> Hey, thanks for having me. >> Absolutely, so you've been doing this for a while. I think we heard earlier, this thing's been going on for 20 years? >> It is. >> You've been involved for at least the last eight. >> Michael: I think a little bit more than that. >> More than that, so what's kind of the big theme before we get into some of the individual items? >> Yeah, so I mean, I think right now, what we're really talking about is that our real big theme is this: We the digital people. And it's that recognition that says that we've fundamentally changed. When you start looking at yourself and your lives, it's that you've gotten to a point where you're letting your cell phone track you. Your car knows where you are probably better than your spouse does. You're handing your key to Amazon and Walmart so they can deliver packages in your house. And more than that is that actually, we're trying to start to revolve our lives around this technology. I look at my own life, and we just sold our second car, specifically because we know that Uber and Lyft exist to fill that void. >> Right, well you don't have to look much further than phone numbers. How many people remember anybody's phone number anymore, right, 'cause you don't really have to. I think it's the 15th anniversary of Google Maps. >> Michael: Yep. >> This year, and to think of a world without Google Maps, without that kind of instant access to knowledge, is really hard to even fathom. But as you said, we're making trade-offs when we use all these services, and now, some of the costs of those things are being maybe more exposed? Maybe more cute or in your face? I don't know, what would you say? >> Yeah, I mean, I think what's happening now is that what we're realizing is that it's changed our relationship with companies. Is that suddenly we've actually brought them into our lives. And, on one hand, they're offering and have the ability to offer services that you could never really do before. But on the other hand is that, if I'm going to let somebody in my life, suddenly they don't have to just provide me value and this is useful, is that they actually, people are expecting them to retain their values, too. So, how they protect your data, what they're good for the community, for the environment, for society, whether it's sustainable or not. Is that suddenly, whereas people used to only care about what the product you're getting, now how it's built and how your company's being run is starting, it's just starting to become important, too. >> Right, well it's funny, 'cause you used to talk about kind of triple bottom line, shareholders, customers and your employees. And you talked about, really, this kind of fourth line, which is community and really being involved in the community. People care, suddenly you go to conferences where we spend a lot of time all the utensils are now compostable and the forks are compostable. And a lot of the individual packaging stuff is going away. So people do care. >> They do, and there's a fourth and a fifth. It says that your community cares, but your partners do, too. Is that you can't, I'm going to say, downgrade the idea that your B2B folks care is that suddenly, we're finding ourselves tied to these other companies, and not just in a supply chain, but from everything. And so, you're not in this alone in terms of how you're delivering these things. But now it's becoming a matter that says, Well, man, if my partners are going to get pummeled because they're not doing the right thing or they don't have that broad scope, that's going to reflect on me, too. And so, now you're suddenly in this interesting position where all of the things that we suspected were going to happen around digital connecting everybody is just starting to, and I think that's going to have a lot of positive effects. >> Yeah, so one of the things you talked about earlier today, in an earlier presentation was kind of the shift from kind of buyer and seller, seller and consumer, to provider and collaborator. Really kind of reflecting a very different kind of a relationship between the parties as opposed to this one-shot transactional relationship. >> No, and that's right, and it doesn't matter who you're talking about, is that, if you're hiring folks for skills that you're assuming that they're going to learn, that's going to be different in three years, in five years, you're essentially partnering with them in order to take all of you on a journey. When you start talking about governments, is that you're now partnering with regulators. You look at companies like Tesla, who are working on regulations for electric cars, they're working on regulations around battery technology. And you see that this go-it-alone approach isn't what you're doing. Rather, it's becoming much more holistic. >> Right, so we're in the innovation hub, and I think number five of the five is really about innovation today. >> Michael: It is. >> And you guys are driving innovation. And, rest in peace, Clayton Christensen passed away, Innovator's Dilemma, my all-time favorite book. But the thing I love about that book is that smart people making sound decisions based on business logic and taking care of existing customers will always miss discontinuous change. But you guys are really trying to help big companies be innovative. What are some of the things that they should be thinking about, besides, obviously, engaging with Mary and the team here at Innovation Hub? >> Yeah, no, and that's the really interesting thing is that when we talked about innovation, you know, five or even 10 years ago, you were talking about, just: How do I find a new product or a new service to bring to market? And now, that's the minimum stakes. Like, that's what everybody's doing. And I think what we're realizing as we're seeing tech become such a big part is that we all see how it's affecting the world. And a lot of times that things are good is that there's no reason why you wouldn't look at somebody like a Lyft or Uber and say that it's had a lot of positive effects. But from the same standpoint is that, you ask questions of: Is it good for public transit? It is good for city infrastructure? And those are hard questions to ask. And I think where we're really pushing now is that question that says: We've got an entire generation of not-tech companies, but every company that's about to get into this innovation game, and what we want them to do is to look at this not the way that the tech folks did, that says, here's one service or one technology, but rather, look at it holistically that says: How am I actually going to implement this, and what is the real effects that it's going to have on all of these different aspects? >> Right, Law of Unintended Consequences is always a good one. >> Michael: It is. >> And I remember hearing years ago of this concept of curb management. I'm like, Curb management, who ever thought of that? Well, drive up and down in Manhattan when they're delivering groceries or delivering Amazon packages and FedEx packages and UberEats and delivery dog food now. Where is that stuff being staged now that the warehouse has kind of shifted out into the public space? So, you never kind of really know where these things are going to end up. >> No, and I'm not saying that we're going to be able to predict all of it. I think, rather, it's that starting point that says that we're starting to see a big push that says that these things need to be factored and considered. And then, similarly, it's the, if you're working with them up-front, it becomes less of a fault, on a fight of whose fault it is at the end, and it becomes more of a collaboration that says, How much more can we do if we're working with our cities, if we're working with our employees, if we're working with our customers? >> Right, now another follow up, you guys've been talking about this for years, is every company is a tech company or a digital company, depending on how you want to spin that. But as you were talking about it earlier today, in doing so and in converting from products to service, and converting from an ongoing relationship to a one-time transaction, it's not only at that point of touch with a customer, but you've got to make a bunch of fundamental changes back in your own systems to support kind of this changing business model. >> Now, and that's right, and I think this is going to become the big challenge of the generation, is that we've gotten to a point where just using their existing models for how you interact with your customers or how you protect their data or who owns the data, all of these types of things, is that they were designed back when we were doing single applications, and they were loading up on your Windows PC. And where we're at now is that we're starting to ask questions that says, All right, in this new world, what do I have to fundamentally do differently? And sometimes that can be as simple as asking a question that says, you know, there's a consortium of pharma folks who have created a joint way for them to develop all of their search algorithms for new drugs. But they're using block chain, and so they're not actually sharing the data. So they do all the good things, but they're pushing that up. But fundamentally, that's a different way to think about it. You're now creating an entirely new infrastructure because what you're used to is just handing somebody the data, and what they do with the data afterwards is kind of their issue and not yours. And so now we're asking big, new questions to do it. >> Right, another big thing that keeps coming up over and over is trust. And again, we talked a little earlier. But I find this really ironic situation where people don't necessarily trust the companies in terms of the people running the companies and what they're going to do with their data, but they fundamentally trust the technology coming out of the gate and this expectation of: Of course it works, everything works on my mobile phone. But the two are related, but not equal. >> Michael: No, I mean, they're not, I mean, and it's really pushing this idea that says we've been looking at all these, I'm going to say scary headlines, of people not trusting companies for the last number of years, while at the same time, the adoption for the technology has been huge. So there's this dichotomy that's going on in people, where at one point, they like the tech. You know, I think the last stat I saw is that everybody spends up to six-and-a-half hours a day involved on the internet, in their technology. But from the same standpoint is that they worry about who's using it and how and what is going to be done. And I think where we're at is that interesting piece that says we're not worried about a tech lash. We don't think that people are going to stop using technology. Rather, we think it's really this tech clash that says they're not getting the value that they thought out of it, or they're seeing companies that may be using this technologies that don't share the same values that they do, and really, what we think this becomes, is the next opportunity for the next generations of service providers in order to fill that gap. >> Right, yeah, don't forget there was a Friendster and a MySpace before there was a Facebook. >> Yeah, there was. >> So, nothing lasts forever. So, last question before I let you go, it's a busy night. The first one was the I in experience, and I think kind of the user experience doesn't get enough light as to such a defining thing that does move the market if, again, I love to pick on Uber, but the Uber experience compared to walking outside on a rainy day in Manhattan and hoping to hail down a cab is fundamentally different, and I would argue, that it's that technology put together in this user experience that defined this kind of game-changing event, as opposed to it's a bunch of APIs stitching stuff together in the back. >> No, that's right, and I think where we're at right now is that we're about to see the next leap beyond that. Is that, most of the time when we look at the experiences that we're doing today, they're one way. Is that people assume that, Yeah, I have your data, I'm trying to customize. And whether it's an ad or a buying experience or whatever, but they're pushing it as this one-way street, and when we talk about putting the I back in experience, it's that question of the next step to really get people both more engaged as well as to, I'm going to say improve the experience itself, means that it's going to become a partnership. So you're actually going to start looking for input back and forth, and it's sometimes going to be as simple as saying that that ad that they're pushing out is for a product that I've already bought. Or, you know, maybe even just tell me how you knew that that's what I was looking for. But it's sometimes that little things, the back and forth, is how you take something from, what can be a mediocre experience, even potentially a negative one, and really turn it into something that people like. >> Yeah, well, Michael, I'll let you go. I know you got a busy night, we're going to present this. And really thankful to you and the team, and congratulations for coming up with something that's a little bit more provocative than, Cloud's going to be big, or Mobile's going to be big, or Edge is going to be big. So this is great material, and thanks for having us back. Look forward to tonight. >> No, happy to do it, and next year we'll probably do it again. >> [Jeff\ I don't know, we already know everything, it's 2020, what else is unknown? >> Everything's going to change. >> All right, thanks again. (upbeat music)

>> Announcer: From San Francisco, it's theCUBE, covering Accenture Tech Vision 2020, brought to you by Accenture. >> Hey, welcome back, everybody. Jeff Rick here with theCUBE. We're high atop San Francisco on a beautiful day at the Accenture San Francisco Innovation Hub, 33rd floor of the Salesforce Tower, for the Accenture Tech Vision 2020 reveal. It's where they come up with four or five themes to really look forward to, a little bit innovative, a little bit different than cloud will be big or mobile will be big. And we're excited to have, really, one of the biggest brains here on the 33rd floor. She's Teresa Tung, the managing director of Accenture Labs. Teresa, great to see you. >> Nice to see you again. >> So I have to tease you because the last time we were here, everyone was bragging on all the patents that you've filed over the years, so congratulations on that. It's almost kind of like a who's who roadmap of what's happening in tech. I looked at a couple of them. You've got a ton of stuff around cloud, a ton of stuff around Edge, but now, you're getting excited about robots and AI. >> That's right. >> That's the new passion. >> That's the new passion. >> All right, so robots, one of the five trends was robots in the wild, so what does that mean, robots in the wild, and why is this something that people should be paying attention to? >> Well, robots have been around for decades, right? So if you think about manufacturing, you think about robots. But as your kid probably knows, robots are now programmable, kids can do it, so why not enterprise? And so, now that robots are programmable, you can buy them and apply them. We're going to unlock a whole bunch of new use cases beyond just those really hardcore manufacturing ones that are very strictly designed in a very structured environment, to things in an unstructured and semi-structured environment. >> So does the definition of robot begin to change? We were just talking before we turned on the cameras about, say, Tesla. Is a Tesla a robot in your definition or does that not quite make the grade? >> I think it is, but we're thinking about robots as physical robots. So sometimes people think about robotics process automation, AI, those are robots, but here, I'm really excited about the physical robots; the mobile units, the gantry units, the arms. This is going to allow us to close that sense-analyze-actuate loop. Now the robot can actually do something based off of the analytics. >> Right, so where will we see robots kind of operating in the wild versus, as we said, the classic manufacturing instance, where they're bolted down, they do a step along the process? Where do you see some of the early adoption is going to, I guess, see them on the streets, right, or wherever we will see them? >> Well, you probably do see them on the streets already. You see them for security use cases, maybe mopping up a store after, where the employees can actually focus on the customers, and the robot's maybe restocking. We see them in the airports, so if you pay attention to modern airports, you see robots bringing out the baggage and doing some of the baggage handling. So really, the opportunities for robots are jobs that are dull, dirty, or dangerous. These are things that humans don't want to or shouldn't be doing. >> Right, so what's the breakthrough tech that's enabling the robots to take this next step? >> Well, a lot of it is AI, right? So the fact that you don't have to be a data scientist and you can apply these algorithms that do facial recognition, that can actually help you to find your way around, it's actually the automation that's programmable. As I was saying, kids can program these robots, so they're not hard to do. So if a kid can do it, maybe somebody who knows oil and gas, insurance, security, can actually do the same thing. >> Right, so a lot of the AI stuff that people are familiar with is things like photo recognition and Google Photos, so I can search for my kids, I can search for a beach, I can search for things like that, and it'll come back. What are some of the types of AI and algorithms that you're applying with kind of this robot revolution? >> It's definitely things like the image analytics. It's for the routing. So let me give you an example of how easy it is to apply. So anybody who can play a video game, you have a video game type controller, so when your kid's, again, playing games, they're actually training for on the skilled jobs. Right, so you map a scene by using that controller to drive the robot around a factory, around the airport, and then, the AI algorithm is smart enough to create the map. And then, from that, we can actually use the robot just out of the box to be able to navigate and you have a place to, say, going from Teresa, here, and then, I might be able to go into the go get us a beer, right? >> Right, right. >> Maybe we should have that happen. (laughs) >> They're setting up right over there. >> They are setting up right there. >> That's right. So it's kind of like when you think of kind of the revolution of drones, which some people might be more familiar with 'cause they're very visible. >> Yes. >> Where when you operate a DJI drone now, you don't actually fly the drone. You're not controlling pitch and yaw and those things. You're just kind of telling it where you want it to go and it's the actual AI under the covers that's making those adjustments to thrust and power and angle. Is that a good analogy? >> That is a great analogy. >> And so, the work that we would do now is much more about how you string it together for the use case. If a robot were to come up to us now, what should it do, right? So if we're here, do we want the robot to even interact with us to get us that beer? So robots don't usually speak. Should speaking be an option for it? Should maybe it's just gesturing and it has a menu? We would know how to interact with it. So a lot of that human-robot interface is some of the work that we're doing. So that was kind of a silly example, but now, imagine that we were surveying an oil pipeline or we were actually as part of a manufacturing line, so in this case it's not getting us a beer, but it might need to do the same sort of thing. What sort of tool does Theresa need to actually finish her job? >> Yeah, and then, the other one is AI and me. And you just said that AI is getting less complicated to program, these machines are getting less complicated to program, but I think most people still are kind of stuck in the realm of we need a data scientist and there are not a lot of data scientists and they got to be super, super smart. You've got to have tons and tons of data and these types of factors, so how is it becoming AI and me, Jeff who's not necessarily a data scientist. I don't have a PhD in molecular physics, how's that going to happen? >> I think we need more of that democratization for the people who are not data scientists. So data scientists, they need the data, and so, a lot of the hard part is getting the data as to how it should interact, right? So in that example, we were saying how does Teresa and Jeff interact with the robot? The data scientist needs tons, right, thousands, tens of thousands of instances of those data types to actually make an insight. So what if, instead, when we think about AI and me, what about we think about, again, the human, not the, well, data scientists are people too. >> Right, right. >> But let's think about democratizing the rest of the humans to saying, how should I interact with the robot? So a lot of the research that we do is around how do you capture this expert knowledge. So we don't actually need to have tens of thousands of that. We can actually pretty much prescribe we don't want the robot to talk to us. We want him to give us the beer. So why don't we just use things like that? We don't have to start with all the data. >> Right, right, so I'm curious because there's a lot of conversation about machines plus people is better than one or the other, but it seems like it's much more complicated to program a robot to do something with a person as opposed to just giving it a simple task, which is probably historically what we've done more. Here, you go do that task. Now, people are not involved in that task. They don't have to worry about the nuance. They don't have to worry about reacting, reading what I'm trying to communicate. So is it a lot harder to get these things to work with people as opposed to kind of independently and carve off a special job? >> It may be harder, but that's where the value is. So if we think about the AI of, let's say, yesterday, there's a lot of dashboards. So it's with the pure data-driven, the pure AI operating on its own, it's going to look at the data. It's going to give us the insight. At the end of the day, the human's going to need to read, let's say, a static report and make a decision. Sometimes, I look at these reports and I have a hard time even understanding what I'm seeing, right? When they show me all these graphs, I'm supposed to be impressed. >> Right, right. >> I don't know what to do versus if you do. I use TurboTax as an example. When you're filing TurboTax, there's a lot of AI behind the scenes, but it's already looked at my data. As I'm filling in my return, it's telling me maybe you should claim this deduction. It's asking me yes or no questions. That's how I imagine AI at scale being in the future, right? It's not just for TurboTax, but everything we do. So in the robot, in the moment that we were describing, maybe it would see that you and I were talking, and it's not going to interrupt our conversation. But in a different context, if Teresa's by herself, maybe it would come up and say, hey, would you like a beer? >> Right, right. >> I think that's the sort of context that, like a TurboTax, but more sexy of course. >> Right, right, so I'm just curious from your perspective as a technologist, again, looking at your patent history, a lot of stuff on cloud, a lot of stuff on edge, but we've always kind of operated in this kind of new world, which is, if you had infinite compute, infinite storage, and infinite bandwidth, which was taking another. >> Yes. >> Big giant step with 5G, kind of what would you build and how could you build it? You got to just be thrilled as all three of those vectors are just accelerating and giving you, basically, infinite power in terms of tooling to work with. >> It is, I mean, it feels like magic. If you think about, I watch things like "Harry Potter", and you think about they know these spells and they can get things to happen. I think that's exactly where we are now. I get to do all these things that are magic. >> And are people ready for it? What's the biggest challenge on the people side in terms of getting them to think about what they could do, as opposed to what they know today? 'Cause the future could be so different. >> That is the challenge, right, because I think people, even with processes, they think about the process that existed today, where you're going to take AI and even robotics, and just make that process step faster. >> Right. >> But with AI and automation, what if we jumped that whole step, right? If as humans, if I can see everything 'cause I had all the data and then, I had AI telling me these are the important pieces, wouldn't you jump towards the answer? A lot of the processes that we have today are meant so that we actually explore all the conditions that need to be explored, that we do look at all the data that needs to be looked at. So you're still going to look at those things, right? Regulations, rules, that still happens, but what if AI and automation check those for you and all you're doing is actually checking the exceptions? So it's going to really change the way we do work. >> Very cool, well, Teresa, great to catch up and you're sitting right in the catbird seat, so exciting to see what your next patents will be, probably all about robotics as you continue to move this train forward. So thanks for the time. >> Thank you. >> All right, she's Teresa, I'm Jeff. You're watching theCUBE. We're at the Accenture Tech Vision 2020 Release Party on the 33rd floor of the Salesforce Tower. Thanks for watching. We'll see you next time. (upbeat music)

>> Announcer: From San Francisco, it's theCUBE, covering Accenture Tech Vision 2020. Brought to you by Accenture. >> Hey, welcome back, everybody. Jeff Frick here from theCUBE. We are high atop San Francisco at the Accenture Innovation Hub, 33rd floor of the Salesforce Tower. It's a beautiful night, but we're here for a very special occasion. It's the Tech Vision 2020 reveal, and we are happy to have the guy that runs the whole thing, he's going to reveal on stage a little bit later, but we got him in advance. He's Paul Daugherty, the chief technology and innovation officer for Accenture. Paul, great to see you as always. >> Great to see you, Jeff, too. It is a beautiful evening here, looking out over the Bay. >> If only we could turn the cameras around, but, sorry, we can't do that. >> Yeah. >> All right, so you've been at this now, the Tech Vision's been going on for 20 years, we heard earlier today. >> Yeah. >> You've been involved for almost a decade. How has this thing evolved over that time? >> Yeah, you know, we've been doing the Vision for 20 years, and what we've been trying to do is forecast what's happening with business and technology in a way that's actionable for executives. There's lots of trend forecasts and lists and things, but if you just see a list of cloud, or-- >> Jeff: Mobile's going to be really big. (laughs) >> AI, mobile, it doesn't really help you. We're trying to talk a little bit about the impact on business, impact to the world, and the decisions that you need to make. What's changed over that period of time is just the breadth of the impact that technology's having on people, so we focus a lot of our Visions on the impact on humans, on individuals, what's happening with technology, what the impact on business, we can talk about that a little bit more, but business is certainly not the back office of companies anymore. It's not just the back office and front office, either. In business, it's instrumental in the fabric of how every part of the company operates, their strategy, their operations, their products and services, et cetera, and that's really the trajectory we've seen. As technology advances, we have this accelerating exponential increase in technology, the implications for executives and the stakes just get higher and higher. >> It's weird, there are so many layers to this. One of the things we've talked about a lot is trust, and you guys talk about trust a lot. But what strikes me as kind of this dichotomy is on one hand, do I trust the companies, right? Do I trust Mark Zuckerberg with my data, to pick on him, he gets picked on all the time. That might be a question, but do I trust that Facebook is going to work? Absolutely. And so, our reliance on the technology, our confidence in the technology, our just baseline assumption that this stuff is going to work, is crazy high, up to and including people taking naps in their Teslas, (laughs) which are not autonomous vehicles! >> Not an advisable practice. >> Not autonomous vehicles! So it's this weird kind of split where it's definitely part of our lives, but it seems like kind of the consciousness is coming up as kind of the second order. What does this really mean to me? What does this mean to my data? What are people actually doing with this stuff? And am I making a good value exchange? >> Well, that's the, we talk in the Vision this year about value versus values, and the question you're asking is getting right at that, the crux between value and values. You know, businesses have been using technology to drive value for a long time. That's how applying different types of technology to enterprise, whether it be back to the mainframe days or ERP packages, cloud computing, et cetera, artificial intelligence. So value is what they were talking about in the Vision. How do you drive value using the technology? And one thing we found is there's a big gap. Only 10% of organizations are really getting full value in the way they're applying technology, and those that are are getting twice the revenue growth as companies that aren't, so that's one big gap in value. And this values point is really getting to be important, which is, as technology can be deployed in ways that are more pervasive and impact our experience, they're tracking our health details-- >> Right, right. >> They know where we are, they know what we're doing, they're anticipating what we might do next. How does that impact the values? And how are the values of companies important in other ways? The values you have around sustainability and other things are increasingly important to new generations of consumers and consumers who are thinking in new ways. This value versus values is teeing up what we call a tech-clash, which isn't a tech-lash, just, again, seeing people reacting against tech companies, as you said earlier, it's a tech-clash, which is the values that consumer citizens and people want sometimes clashing with the value of the models that companies have been using to deliver their products and services. >> Right. Well, it seems like it's kind of the "What are you optimizing for?" game, and it seems like it was such an extreme optimization towards profitability and shareholder value, and less, necessarily, employees, less, necessarily, customers, and certainly less in terms of the social impact. So that definitely seems to be changing, but is it changing fast enough? Are people really grasping it? >> Well, I think the data's mixed on that. I think there's a lot of mixed data on "What do people really want?" So people say they want more privacy, they say they want access and control of their data, but they still use a lot of the services that it may be inconsistent with the values that they talk about, and the values that come out in surveys. So, but that's changing. So consumers are getting more educated about how they want their data to be used. But the other thing that's happening is that companies are realizing that it's really a battle for experience. Experience is what, creating broader experiences, better experiences for consumers is what the battleground is. A great experience, whether you're a travel company or a bank or a manufacturing company, or whatever you might be, creating the experience requires data, and to get the data from an individual or another company, it takes trust. So this virtuous circle of experience, data, and trust is something that companies are realizing is essential to their competitive advantage going forward. We say trust is the currency of the digital and post-digital world that we're moving into. >> Right, it's just how explicit is that trust, or how explicit does it need to be? And as you said, that's unclear. People can complain on one hand, but continue to use the services, so it seems to be a little bit kind of squishy. >> It's a sliding scale. It's really a value exchange, and you have to think about it. What's the value exchange and the value that an individual consumer places on their privacy versus free access to a service? That's what's being worked out right now. >> Right, so I'm going to get your take on another thing, which is exponential curves, and you've mentioned time and time again, the pace of change is only accelerating. Well, you've been saying that, probably, for (laughs) 20 years. (Paul laughs) So the curve's just getting steeper. How do you see that kind of playing out over time? Will we eventually catch up? Is it just presumed that this is kind of the new normal? Or how is this going to shake out? 'Cause people aren't great at exponential curves. It's just not really in our DNA. >> Yeah, but I think that's the world we're operating in now, and I think the exponential potential is going to continue. We don't see a slowdown in the exponential growth rates of technology. So artificial intelligence, we're at the early days. Cloud computing, only about 20% enterprise adoption, a lot more to go. New adoptions are on the horizon, things like central bank digital currencies that we've done some research and done some work on recently. Quantum computing and quantum cryptography for networking, et cetera. So the pace of innovation is going to accelerate, and the challenge for organizations is rationalizing that and deciding how to incorporate that into their business, change their business, and change the way that they're leveraging their workforce and change the way that they're interacting with customers. And that's why what we're trying to address in the Vision is provide a little bit of that road map into how you digest it down. Now, there's also technology foundations of this. We talk about something at Accenture called living systems. Living systems is a new way of looking at the architecture of how you build your technology, because you don't have static systems anymore. Your systems have to be living and biological, adapting to the new technology, adapting to the business, adapting to new data over time. So this concept of living systems is going to be really important to organizations' success going forward. >> But the interesting thing is, one of the topics is "AI and Me," and traditional AI was very kind of purpose-built. For instance, Google Photos, can you find the cat? Can I find the kids at the beach? But you're talking about models where the AI can evolve and not necessarily be quite so data-centric around a specific application, but much more evolutionary and adaptable, based on how things change. >> Yeah, I think that's the future of AI that we see. There's been a lot of success in applying AI today, and a lot of it's been based on supervised learning, deep learning techniques that require massive amounts of data. Solving problems like machine vision requires massive amounts of data to do it right. And that'll continue. There'll continue to be problem sets that need large data. But what we're also seeing is a lot of innovation and AI techniques around small data. And we actually did some research recently, and we talk about this a little bit in our Vision, around the future being maybe smaller data sets and more structured data and intelligence around structured data, common-sense AI, and things that allow us to make breakthroughs in different ways. And that's, we used to look at "AI and Me," which is the trend around the workforce and how the workforce changes. It's those kinds of adaptations that we think are going to be really important. >> So another one is robotics, "Robots in the Wild." And you made an interesting comment-- >> Paul: Not "Robots Gone Wild," "Robots in the Wild," "Robots in the Wild." >> Well, maybe they'll go wild once they're in the wild. You never know. Once they get autonomy. Not a lot of autonomy, that's probably why. But it's kind of interesting, 'cause you talk about robots being designed to help people do a better job, as opposed to carving out a specific function for the robot to do without a person, and it seems like that's a much easier route to go, to set up a discrete thing that we can carve out and program the robot to do. Probably early days of manufacturing and doing spot welding in cars, et cetera. >> Right. >> So is it a lot harder to have the robot operate with its human partner, if you will, but are the benefits worth it? How do you kind of see that shaking out, versus, "Ah, I can carve out one more function"? >> Yeah, I think it's going to be a mix. I think there'll be, we see a lot of application of the robots paired with people in different ways, cobots in manufacturing being a great example, and something that's really taking off in manufacturing environments, but also, you have robots of different forms that serve human needs. There's a lot of interesting things going on in healthcare right now. How can you support autistic children or adults better using human-like robots and agents that can interact in different ways? A lot of interesting things around Alzheimer's and dealing with cognitive impairment and such using robots and robotics. So I think the future isn't, there's a lot of robots in the wild in the form of C-3POs and R2-D2s and those types of robots, and we'll see some of those. And those are being used widely in business today, even, in different contexts, but I think the interesting advance will be looking at robots that complement and augment and serve human needs more effectively. >> Right, right, and do people do a good enough job of getting some of the case studies? Like, you just walked through kind of the better use cases, the more humane use cases, the kind of cool medical breakthroughs, versus just continued optimization of getting me my Starbucks coupon when I walk by out front? (Paul laughs) >> Yeah, I'm not sure. >> Doesn't seem like I get the pub, like they just don't get the pub, I don't think. >> Yeah, yeah, yeah, maybe not. A little mixology is another (Jeff laughs) inflection that robots are getting good at. But I think that's what we're trying to do, is through the effort we do with the Vision, as well as our Tech for Good work and other things, is look at how we amplify and highlight some of the great work that is happening in those areas. >> So, you've been doing it for a decade. What struck you this year as being a little bit different, a little bit unexpected, not necessarily something you may have anticipated? >> I think the thing that is maybe a tipping point that I see in this Vision that I didn't anticipate is this idea that every company's really becoming a technology company. We said eight years ago, "Every business "will be a digital business," and that was, while ridiculed by some at the time, that really came true, and every business and every industry really is becoming digital or has already become digital. But I think we might've gotten it slightly wrong. Digital was kind of a step, but every company is deploying technology in the way they serve their customers, in the way they build their products and services. Every product and service is becoming technology-enabled. The ecosystem of technology providers is critical to companies in every industry. So every company's really becoming a technology company. Maybe every company needs to be as good as a digital native company at developing products and services, operating them. So I think that this idea of every company becoming a technology company, every CEO becoming a technology CEO, technology leader, is something that I think will differentiate companies going forward as well. >> Well, really, good work, you, Michael, and the team. It's fun to come here ever year, because you guys do a little twist. Like you said, it's not "Cloud's going to be really big, "mobile's going to be really big," but a little bit more thoughtful, a little bit more deep, a little bit longer kind of thought cycles on these trends. >> Yeah, and I think the, if you read through the Vision, we're trying to present a complete story, too, so it's, as you know, "We, the post-digital people." But if you look at innovation, "The I in Experience" is about serving your customers differently. "The Dilemma of Smart Machines" and "Robots in the Wild" is about your new products and services and the post-digital environment powered by technology. "AI and Me" is about the new workforce, and "Innovation DNA" is about driving continuous innovation in your organization, your culture, as you develop your business into the future. So it really is providing a complete narrative on what we think the future looks like for executives. >> Right, good, still more utopian than dystopian, I like it. >> More utopia than dystopia, but you got to steer around the roadblocks. (Jeff chuckles) >> All right, Paul, well, thanks again, and good luck tonight with the big presentation. >> Thanks, Jeff. >> All right, he's Paul, I'm Jeff. You're watching theCUBE. We're at the Accenture innovation reveal 2020, when we're going to know everything with the benefit of hindsight. Thanks for watching, (laughs) we'll see you next time. (upbeat pop music)

(upbeat instrumental music) >> Narrator: Live from San Francisco, Celebrating 10 years of high tech coverage, it's theCUBE. Covering VMworld 2019. (upbeat instrumental music) Brought to you by VMware and its ecosystem partners. Hello and welcome back everyone. theCUBE's Live coverage here in San Francisco, California. I'm John Furrier, Dave Vellante, VMworld 2019 coverage. Dave, 10 years of Cube coverage, Yip! we started out 10 years ago. VMworld is the last show standing. Our next guest is Todd Forsythe, CMO of Veritas. Great to see you, first time on theCUBE. Thanks Todd. It is inaugural. (John laughs) Aafter 25 years in the industry, it's crazy. With your talent, I think we're going to have a good segment here. I'm sure we will. Very entertaining. No, seriously we've known each other, we were on an advisory board together. You're a prolific marketer, you do a lot of great things. You're progressive, you try new things with startups, but also you got to run a big operation. >> Todd: That's right. MarTech stacks, you like to look at platforms. This is a re-platforming of the internet we're seeing with Cloud 2.0, and I want to get your thoughts on this, because you got a unique perspective at Veritas, you know, an older brand modernized in real time. That's right. New products refresh in a massively changing growth, still growth market. It's a data business. Absolutely. That's right, a 100%. So what's your take on this? As you look at the landscape, you've got the modern brand, you got to take it out there, new products. You know it's interesting, I had a really fascinating conversation yesterday with a customer, and the customer said, "You know, I was walking through the expo hall, "and I saw Amazon, I saw Microsoft, "I saw IBM, I saw Dell Technologies, "I saw Kubernetes, I saw Pure, I saw Nutanix, "I thought I was in my own data center." And it's interesting, I think about our business, and in our business, data doesn't care. Like you know data doesn't care if you're running a modern architecture. Data doesn't care if you're running Legacy. So what we're really focused on is helping companies manage data in highly complex, and extremely demanding environments regardless of their infrastructure. And Cloud 2.0 speaks to the complexity of that, because you know, these, and we were talking earlier with VMware about these categories that used to exist, these Gartner Magic Quadrants. You know, you can't put something that's not a silo in a silo, you're horizontally disrupting. And data does that, data has to move around and it's got to move everywhere. So there's no more silo boxes of categories. A 100% agree, you know it's interesting, we launched Enterprise Data Services earlier this year, and that was the precise reason why, because we've relooked at what data protection is. Data protection is no longer backing up your data from a cloud to a cloud from your on Prime, it's a much broader category. It covers how your data becomes available, how resilient you are, understanding where your data is, how it's categorized so you can respond to ransomware attacks, manage regulations around the world. So our view of data protection is a platform that is horizontal and cuts across. Well you guys, I mean the heritage of Veritas is the original data management company, right? Yeah. With no hardware agenda, and so my question for you, Todd, is what attracted you to Veritas? Softball question, so the most amazing customers any company could possibly imagine, Global 2000, the top telecommunications companies, the top banks, top stock exchanges. Secondly a product strategy that's really zoned in, back to your point about this, a platform that cuts across all of these diverse technologies and solves problems for customers that abstracts them from the complex environments that they're in so they could focus on outcomes, and Greg has done an amazing job recruiting a top notch leadership team. So it was really great product, good leaders. Okay now, follow up is you guys, you know, number one, top anyway, right and with Gartner Magic Quadrant, everybody wants a piece of your hide, (chuckles) the whole industry is coming at you. So, what's the sort of messaging strategy to keep top spot from both outward facing and also product development? Sure, sure. So we look at two types of competitors. Competitors that are offering point solutions, predominately playing in the mid-market, and when you're a large financial institution, and you have a highly complex environment, you're in a multi-cloud world, and you can't afford to have a siloed backup data. So you need to understand how your data is classified, where it's stored. So if you're responding to ransomware, and that ransomware attack is targeted to a specific server, you need to know if you have PII there, or if you have cat pictures there. If you have cat pictures there, then, (chuckles). >> John: Let 'em have it. Let 'em have it, exactly. (John laughs) So our platform cuts across protection, availability, and insights, which categorizes your data. So the data gets categorized in NetBackup, extends to the analytics platform, so you know where your data is, and you could take action on your data. The hard question of the day, instead of a softball I'll give you a hard one, you got to refresh the brand of Veritas has got a lot of pros and cons. The pros are, you know, well-known, a lot of customers, I got a customer question later, but the brand is important, because you have the new modern platform products, platform and products. Yeah, yeah. You got to get the name, Veritas has old meaning. You have a lot of older customers, you have legacy customers. How are you going to go out there and refresh? Is there any new plans there? We have a ton of plans. You know, we have the product, we have the customers. The product, the platform product is amazing. We are a quiet company, so we need to be noisier in the marketplace, and we need to insert ourselves into relevant conversations that are top of mind with CEOs and CIOs, whether it's ransomware. If you look at all of the ransomware attacks, It's a huge opportunity I read like two weeks ago in the State of Texas there were 10, 15 municipalities that were attacked. At the same time. At the same time! and we have a solution that can help customers recover from ransomware, so we have to insert ourselves in those dialogues because we have a very, very specific point of view that can help customers. Well but to John's point, right? Everybody that you compete with will say, we have a solution that, you know, helps solve ransomware. So, how do you separate from the pack? Like I said, everybody's trying to take pick you off. You know, we want a piece of that install base, right? 'Cause you got to keep the install base, and you got to keep growing, right? I'll lay down the gauntlet. I would love any competitor to showcase how they can support 500 workloads, a 150 storage targets, 60 cloud providers, at enterprise scale with a high degree of reliability. Our differentiator is we can cut across these very complex, very demanding IT environments, at scale. I want to get your thoughts on the customer journey question, because I think, you know, you mentioned the customer base, we've been following what you guys do. I mean I ran, I was in Bahrain for a regional Amazon event I was covering in the Middle East, and in the exhibit was this Veritas, and they recognized, hey, there's theCUBE guy. I was like, hey, thanks for watching. But seriously, you guys are everywhere. You got huge customers, but you probably have a lot of customers that are trying to go from here to there, VMware was talking almost specifically around, you know, you got the enterprise scale world, and they want that cloud Nirvana, and then there's that missing middle in between. So, you probably have a lot of transformational stories. What is the patterns of customer profile that you see? Some of them making the journey? Some of them are having a hard time? What's the state of the mind of the customer that you guys have? Well, we are definitely seeing a hybrid, multi-cloud world as you've heard here this week. 52% Of our customers are running in a hybrid cloud environment, and we have a core relationship with their legacy infrastructure, and our customers are asking our help to extend their data protection, and their NetBackup environment into the cloud, to backup the cloud, and across new modern workloads. So our customers are pulling us into their environments to do more. And that's cloud and hybrid basically. Cloud and hybrid. Public cloud and on premises. And our customers are also realizing that they're responsible for backing up their own data in the cloud. There's this misperception in the industry that if I move to a cloud provider, the cloud provider will manage my data, when in actuality you are still responsible for your data. You know, Amazon with security has a shared responsibility model. They say, okay, we protect the EC2, the infrastructure for S3, et cetera. You're responsible for pretty much everything else. And I think that you could draft off that message. Yeah, yeah! You guys too, a couple of years ago, had a great event call Veritas Vision where everybody came in, and then you changed that. Now you sort of go to where the customers are, and I'm wondering, how's that working out? It predated you. Yeah! Yeah, yeah, yeah, yeah, yeah! So I won't ask you why that decision was made, but you know, how's it working out? I mean, a company like yours, there was like four, 5,000 people there, it was a really good event. So, a great question, and a highly relevant question, because we're just about to launch our series. So, you know, having run large, large, large user conferences, and you look at distribution of your customers and, you know, you typically find that 80% of your customers are coming from the US. You look at our customer base, global international customers. We have a high percentage of customers that are outside of the U.S. So, our strategy is let's take our user conference, let's take our message, let's take our value proposition to the customer. So, we are kicking off next month an entire series around the world, Germany, Paris, Rome, Seoul, Bali, Singapore, Melbourne, our vision series, where it's our anti-user conference. We're taking content directly to our customers. Is this regional or are those cities based? How is that segment? City based. So it's like an Amazon Summit kind of thing you go to? Yeah, yep. Okay so as a follow-up. So, as a seasoned pro in this space, why either, or, why not do both? I mean there's a budget obviously is one thing, it's expensive to run these events, I get it, but. I would prefer to put more money to where the customer is at. The field, kind of. Yeah, into the fields, you know, one of the life lessons of being a marketer is go to where the customer is. Don't try to get the customer to come to you. Well, your head of sales will love that message, you're going well. (Todd laughs) So our strategy is to go where the customer is. Yeah, and that does help sales actually. So, while you're on that point, you're a very progressive marketer, for the folks that don't know you, I'll share with them that, you know, you like to try things, and you love start-ups, and you love to promote new things. The marketing stack, I've said on theCUBE, and we'd love to have you challenge us if you want, love to debate it, I said, the MarTech Stack just didn't pan out. I mean, it worked? No, no, it didn't, no! Did it work? Is it evolving? Is it siloed? Is the cloud changing the MarTech Stack? So again, pretty aggressive statement, but my point is, email marketing was great for that generation, still is. There's new organic flows, maybe I'm biased, but I'd love to get your thoughts. How is the marketing tech world evolving with cloud computing? So, I'm going to say something provocative. >> John: Okay, all right, here we go. I think the CRM industry has gotten B2B marketing wrong. What I mean by that is you look at most CRM capabilities in B2B and they're focused on an individual. They're focused on a lead, they're focused on nurturing an individual, but if you look at our customers and enterprise, individuals don't buy, buying groups, committees, and accounts buy. So where we're focused is looking at accounts, and understanding account company based behavior that shows buying intent and triggers, which then initiate our marketing. So it's not built around a lead, it's built around-- >> John: So account based marketing? Account based marketing, but account based insight and intelligence around, is there a project or buying opportunity? And you know our good friends at Manigo, that's what they do, which is AI driven, trigger-based marketing. And that's where I think the industry is going. And what's your thoughts on organic marketing, because one of the things that's hot, is we live this world with theCUBE, and we've been kind of pioneering this model where co-creating content together and pushing it out into these digital streams is an organic process. It's technically earned media and PR parlance, but we're seeing the evolution of the CMO-like action around storytelling, right? And so, like community based storytelling, it's an organic function, it's hard to control. You can't just buy it, it's got to be kind of nurtured or enabled. That's right. What's your view on that? Because this is an emerging trend we're seeing, VM were just reorganizing a whole storytelling integrated group of PR pros, that are acting like the marketing, in their marketing. Well you know, one of the most active, customer segments we have is our VOX Community, and if you think to your point about co-creation of content in collaboration, our VOX Community collaborates on solving problems that customers have, they call that-- >> John: Can you take a minute to explain, what is VOX Community to us? VOX is a community of our technical users, where they help each other share best practices and solve problems. >> Dave: A lot of how-to? A lot of how-to-- Not Vox Media. Not Vox Media, correct. Just need to make sure to get that out there. Forums, there's videos. Is this your community, or is it third-party? Veritas. Okay, Veritas. It's a Veritas community, yeah. And then to your other point, John, the marketing world has changed. We've quickly moved into a world where we now have an anonymous relationship with our customer, with email, with direct mail. Yeah, we're always driving to registration to capture a name, that world is long gone. A Facebook show that's been weaponized, so you know. Yeah, that's right. It's the data business, at the end of the day. The user experience is horrible, right? Everybody hates that, and so yeah, there are other ways now you can use data, you can infer. Yeah, that's right, exactly. You can read the tea leaves, and probably make a pretty high prediction, or highly accurate prediction. What is the most under reported trend that you think marketers should look at in terms of capabilities that are working out in the field for you? I would say the ability to leverage predicative analytics, call it AI or machine learning, understand what's happening at an account, and whether there's a buying trigger. I think accessing that information, learning from that information in terms of how should you initiate a selling motion, and then enabling the sales force with that intelligence, I think is a wide open territory. All right, we got a-- So a couple of other things. If I can? Yeah! Just to get it in. So you guys made a big platform enhancements a couple of years ago, and then a big eight dot, whatever it was, eight dot something, two, three, five. I think it was 8.2. Customer momentum, can you update us on that? Maybe even customer examples, and then I've got a partner question for you. Yeah, so I talked about the value of the platform, and we'll take Renault as an example. So Renault, a NetBackup customer, Renault wanted to make their virtualized SAP environment highly available, and they looked at a variety of different solutions, and they looked at some solutions that were homegrown and others, and they realized just extending the Veritas platform was faster time to market, 60% cost savings. So, there's a perfect example of a customer leveraging our platform play. And a couple partner questions. So, you know, we're here at VMworld, so your VMware partnership obviously pretty important, and then we're at Pure Accelerate next month, you guys are there, you got a big presence there, I know you got a tight partnership with them. That's right. Give us the partner update. Partner update, so we have very solid relationships with Amazon, Microsoft, VMware, Google, Nutanix, Pure, and that's where we're really doubling down in terms of technology integration, joint go-to-market. >> Dave: Great. And the community site is vox.veritas.com, I just was checking it out. Thank you for the plug. There's a church one, there's a religious one, not to be confused with Vox Media, so just want to make sure everyone got that URL. We think community is super important. Thanks for coming on theCUBE Excellent! and sharing your insights. Thank you gentlemen. Thank you. Todd Forsythe, CMO of Veritas. More live coverage of VMworld after this short break. (upbeat dance music)

>> Announcer: Live from New York, it's theCube! Covering AWS Global Summit 2019. Brought to you by Amazon Web Services. >> Welcome back, here in New York City for the AWS Summit. I'm Stu Miniman and my cohost is Corey Quinn. And I'm happy to welcome two guests from NetApp. First to my right, welcome back to the program from another cloud show earlier this year. Jonsi Stefansson, who's the CCO and Vice President for Cloud Services. And to his right, well it's a first time on the program. I actually was on one of his earlier podcasts, Jeff Dickey, who's joining NetApp as the chief technologist inside that same cloud and data services group. Jeff, welcome and Jonsi welcome back. >> Thank you, Stuart. >> Thank you. >> Okay so Jonsi, let's start with you. So we've watched the cloud and data services. From my words it's like almost, I want a new brand. It's like this is not the ONTAP, everywhere, you know, best NFS, you know the number one thing there, it's about multi cloud, it's about getting the value out of my data that transformation we've seen overall in what was known as the storage industry. There are a lot of new people, a lot of new products, and it's the you know the and is I think there was one NetApp term is all of the history and the things you could trust, but a lot of new things. So give us the updates on what's exciting in your world. >> Yeah absolutely, I mean of course we are still relying on that old trusted ONTAP and WAFL storage operating system in the back end, but we have extracted a lot of that into a more automation or you're consuming it in a more autonomous way. We are actually taking all the the storage norms that the traditional storage admin is really used to, you know tweaking and all of that. That's all done and managed by us. It's fully as a service and we are more focused on the data management capabilities of ONTAP than the actual storage system or the performance of that storage operating system. I mean we are in a very unique position as NetApp. I mean we have a very strong foothold in the enterprise. And now we have integrated services with all the public clouds. I mean fully native integrated services either going through their own console or at their own APIs or with our own UI. So the data management capabilities that we are actually bringing to the table is you can seamlessly migrate from the core to the edge and to the cloud, depending on where you want your data to reside. So our goal is actually to do something very similar as Kubernetes has done to the application layer. They have made it completely mobile, there is no longer that VM format issues that you had in the old days. It's basically just a kernel module, I can move it wherever on top of a hypervisor of choice or a public cloud of choice. But that has always been sort of left behind on some propriety box sitting there. But NetApp like I said, NetApp is in this very unique position of being able to move, migrate, replicate and split the data according to your strategy whether it's on-premise or the public cloud. >> All right, Jeff, would love to hear your viewpoint as what you're hearing from customers. I've known you for many years. Talk about that journey towards cloud and what is cloud and how does it fit into their customer environment. Give us what brought you into NetApp and some of the conversations you're having if you've been digging in with the NetApp team. >> Well the coming to NetApp is actually a long story. I've known the Green Cloud folks for a long time. I think was the first kind of US partner of theirs and had been a big fan of first their cloud and then their software so I was really excited when the data acquisition happened and you know for about a year I was learning like the stuff they're working on and that was blowing my mind and again, I've worked with almost every storage company out there so it was exciting to, like the future of what was happening and then after the acquisition of Stackpoint which I was currently working with, so it's like NetApp kind of took my two favorite companies in a short time so I said, hey, I want to be working on, you guys are doing the coolest stuff that I've seen right now and the roadmap is blowing my mind, I want to join. So it's been a great time here. I think what's most unique, what I've found is that the typical, when you're doing cloud consulting, you go after the low-hanging fruit. It's very simple strategy. You know, if you were to go to a customer and say, "Let's take your highest demanding, "most revenue generating systems "and we're going to migrate those to AWS first." Well they're going to look at the $10 billion contract and you know the two year engagement and say no, we're not going to do that. You go for the low-hanging fruit. But because of the products that have come out and what we're doing in the public clouds, we're for the first time we have NFS, you know like basically SLA performant file system in the cloud that can handle the biggest, baddest on-prem apps. So now that we're able to do that, what customers are doing, they are now we're taking those big ones and it's accelerating the whole journey of the cloud because instead of creating more of a chasm between your public cloud infrastructure and your on-prem, there's a lot of people, you know face it, if you've got a $50 million budget, you're putting it mostly into cloud and some of your on-prem, which again is still generating a lot of revenue, is not getting the love it needs and it's not becoming cloud either and you have this kind of chasm. So it think it's great that with the customers we're working with, they're very excited to be moving what they thought they were never going to be able to move because it just wasn't there. And now they have native connections to all the services they love, like you know, here at AWS. So it's just great 'cause you know, yes they're consolidating their data and you're having less silos, that's exciting. But what excites me most is what are they going to do next and after that what're they going to do next with that? Like as they learn how to use their data and connect more to cloud services and our cloud services and the public cloud services, they're going to be able to do way more than they ever thought they would. >> Something that I think would resonate with a number of folks has been that, I go a little bit back, I'm a little older than I look, although I wear it super well. And I cut my teeth on WAFL and working with SnapMirror and doing all kinds of interesting things with that, it's easy to glance, walk around the expo hall and glance at it and figure huh, I see there's a NetApp booth. You must still be trying to convince AWS to let you shove a filer into us-east-1. That's not really what your company does anymore in the traditional sense but I think a lot of people may have lost that message. From a cloud perspective, what is NetApp doing in 2019? >> So I mean we are really, really software focused. So I mean we are doing a lot of work. We are containerizing that WAFL operating system, we are really excited about launching that as alpha today. That basically means launching as an alpha in October. That basically means that you could get all the ONTAP data management goodies on top of any storage operating system on top of any physical or persistent discs in any of these different public clouds. EPS, Volumes, Google PDs or Azure, we wanted to make it so anybody can actually deploy ONTAP. We've always have that story with ONTAP Select but being able to containerize it, I don't know if we can actually. So we can actually reap the benefits of Kubernetes when it comes to high availability, rapication, auto-scaling and self-healing capabilities to make it a much more robust scale out as well as scale up solution. So that's truly our focus. And our focus for 2019 is of course, we've been really, really busy with our heads down coding for a long, long time or for a long time. Very short time in NetApp terms, but in cloud terms, very, very long. Like for the last 18 months. But now we're really sort of integrating our entire portfolio where we have monitoring, deep analytics, compliancy, Kubernetes, storage providers, schedulers. So everything is sort of gelling together now. >> So I think back a couple of years ago, if you talked to Amazon, the answer to everything was move everything to the public cloud. Today, Amazon at least admitted that hybrid cloud is a thing. They won't say hybrid necessarily but you know with the outposts and what they're doing with their partnership with VMware and the like, they're doing that. When I look at customers, most of them have multi cloud. Now when we say multi cloud it means they have lots of clouds and whether or not they're tied together, they're not doing that and while Amazon won't admit to it and isn't looking to manage in that environment, they're playing in that because if I have lots of clouds, one of them is likely AWS. NetApp sits at the intersection of a lot of this. You have your huge install base inside the data center, you're working very much with Amazon, and the other cloud providers. What I'm hoping to get from you is your insight on customers, you know, where are they today, what are they struggling with in that hybrid or multi cloud world and where do you see things maturing as we go the next couple of years? >> Well I mean, the fact of the matter is, 83% of all workloads still recite on-premise. Whether it stays like that or doesn't, I mean AWS is doing Outpost, Google is doing Anthos, Azure is doing Azure Stack. And the good thing is we are actually playing with all of them we are collaborating on all these different projects, both on the storage layer as well as on the application life cycle management. From our point of view, it is really important that we start tying all the infrastructure related stuff into the application layer so you're actually managing everything from that layer and down. So for a developer like me, it's actually really simple to actually do all the tasks and completely manage my own solution. Of course I need operations to be managing the infrastructure but I should be oblivious to it as a developer and what we are actually seeing customers doing now more and more and it's actually really impressing coming here to New York and meeting all these financial companies, they have always been like probably the slowest movers to the public cloud because of compliancy reasons and other stuff, but they are actually really adopting it. They have segmented out their workloads and really know what teams are allowed to provision and are supposed to be running in the public cloud in order to tap into the innovation that's happening there and what teams are only allowed to work on on-premise environments. So it sort of relates into the true cloud concept. The true cloud concept being everything is a cloud and there is no lock in, have the freedom of choice where to provision, where to spin up your workloads. So we're seeing that more and more from our customers. Wouldn't you agree? >> Yeah, totally agree. >> Yeah, Jeff I wonder if you could give a little bit more as you said, NetApp's done quite a few acquisitions in the last couple of years. What sort of things should people be thinking about NetApp that they might not have a couple years ago? >> Well I know, I'll tell a quick story. My first day as a NetApp employee was at KubeCon in Seattle and I remember I was wearing the Net badge and I had a friend that I was partnered with and he looked at my badge and says, "NetApp? "Like the box in the closet people?" And I just like well I mean not anymore. You know and I think that's the biggest thing. You mean Network Appliance? >> Those of us that have know NetApp long enough. >> Now it's internet application, right? Now it's a little bit different. I think the big thing is you know, it's not just a storage. I mean storage is a key component, and it's very important, but that's not the only thing and I think that on the cloud side it's very important because we're still maintaining this relationship with our storage appliances and everything but we have more buyers now so we can go across the company and say, "What are you doing? "Are you an SRE? "Are you a developer lead? "Are you a VP of operations?" We have all these products that work for them yet in the end, it's a single vision to the deep insights of everything they're doing with us. >> Just quick followup on that, I think when NetApp bought a Kubernetes company, it was like okay, I'm trying to understand how that fits when I look at NetApp's biggest partners, I think VMware, Cisco, Red Hat, all going heavily after software solutions including the kubernetes piece so how does NetApp do differently because you still have strong partnerships there. >> I think we're in a strong place because now we're doing two things, we're bringing the apps to the data and the data to the apps. So it's, where do you want to be? There's the right place for your app. There's a lot of choice now and now we have, you know, now you can choose. Where is this going to live best? Where is this going to operate? Where is this going to serve our customers best? What's going to be the most cost effective? You know, being able to deploy and manage. You know, type in a couple characters and your entire production of Kubernetes deployment is backed up into where you want. Like there's just you know, the apps are nothing without data, the data is nothing without the app right? So it's bringing those two together. I think it's very important to kind of get out there. My job is getting that out that it's not storage silos, this is about your apps. What are you doing with it? Where do you want your apps, and what is that data, how is the data helping your apps grow? You know, we're helping people move forward and innovate faster with these products. >> I mean both companies, my company Green Cloud and the Stackpoint company, we were really, really early adopters of Kubernetes and we've always taken both companies very application-centric point of view on Kubernetes while most everybody else have taken a very infrastructure-centric approach. We were two staffed of companies just developers and we always sort of felt like, because it's a very common misunderstanding that Kubernetes was actually built for developers. It wasn't. It is an infrastructure play, built and developed by the Google SREs to run code. So everything that we are adding on top of it and beneath it, it ties it all together. So I mean for a developer working on our Kubernetes offerings, he's basically working in his own element, he's just doing commands and magic happens in the packet. We tie the development branch to a specific Node Pole. We apply the staging branch to another one and the production environment, once you commit that, then it actually goes through like an SRE process where they are basically the gate keepers, where they actually either allow or say hey we found the bug or we are not able to deploy this according to our standards. So tying it all together, all the way from the storage layer all the way up to the application layer is what we are all about. And I got the same question when we were acquired. When we were Green Cloud, we were in a really, really, good situation where we had term sheets from three different companies. I'm not allowed to say which ones, but everybody, once I sold it to NetApp they were like, "Why NetApp?" But if you go to KubeCon, and you are always there, there is a very live matrix on what the biggest problems are with Kubernetes and persistent volume clearance and storage and data management hasn't been sold yet. And that's where we believe that we have a unique way of offering those data management capabilities all the way up the stack. >> All right well Jonsi and Jeff, thank you for giving us the update there, absolutely. Corey Quinn, I'm Stu Miniman. We'll be at KubeCon later this year in San Diego we're at Amazon re:Invent. Always go to theCUBE.net to see all the shows that we're at as well as hit the search and you can see the thousand of videos. Always no registration to be able to check that out so check all out all the interviews. And as always, thanks for watching theCUBE. (light techno music)

>> From the Salesforce Tower in downtown San Francisco, it's theCUBE covering Accenture TechVision 2019. Brought to you by SiliconANGLE Media. (electronic music) >> Welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Salesforce Tower, the 33rd floor, brand new Accenture Innovation Hub, five stories here in the building, the ribbon cutting this morning, and we're really excited to have our next guest. He's been on many times, I think the first time in 2013. Fresh off the plane from Davos, Paul Daugherty, great to see you. >> It's great to be here Jeff and thanks for joining us at this event. It's a really big day for us here. >> Absolutely, now I didn't get your title in, I give you Chief Technology and Innovation Officer. You're really at kind of the forefront, so let's jump into the TechVision. This is something you guys do every year. You pick five kind of big trends that we should be taking a look at. There's a lot of detail. People can (laughs) take their time to read through it. But, I just want to touch on some of the highlights. What are some of the big changes from when we sat down a year ago? >> We have five trends this year. The number of trends varies a little bit, but the, you know, I think the one key takeaway and highlight from the Vision this year is this idea, the big idea, that we're entering the post-digital era, and I think many people will be surprised by that. They'll go what do ya mean post-digital? >> When you said that earlier today, I'm like post? We're just right in the meat of it aren't we? >> Right, but just to contextualize that a little bit, last year companies spent 1.1 trillion dollars on digital transformation. 94% of companies are doing some stage of digital transformation. 68% of them said they're pretty well set with their digital transformation. >> They said they're set? >> They're in good shape. Now you can question it. >> Does that surprise you? >> I question it, yes, it surprises me, and we're not sure that that's entirely-- >> Accurate? >> Representative, >> That's okay. >> But nonetheless, what is true is that every organization is adopting digital, and the question we're asking in the Vision is if everybody's doing digital, what's going to differentiate you? And, we believe that that's the characteristics of the post-digital environment where what you did leading up to now isn't going to be enough to differentiate you and lead to success in the future. In the post-digital era, it's about some new business concepts about how you shape your business and new technologies and some new corporate obligations that are going to be instrumental in your success as an organization. >> I want to dig into that a little bit 'cause I think it's a really interesting conversation. At the ribbon cutting this morning, we had representatives from the city and county of San Francisco, a representative from, I think, San Francisco State academic institution, and you said in some earlier remarks today that the responsibility for the company has moved beyond kind of stewardship for their customers, stewardship for their employees and their shareholders, but really they've got to be kind of active contributors to the community. And, that's been kind of called out over the last couple years especially in the tech industry that hey, you can't just do this stuff willy-nilly. You got to kind of take responsibility for what you can do. >> Yeah, well put, and that's one of the key things that we've been talking about in prior Visions, if you'll recall. This year, it's a big theme. The importance of this is, it's not just because it feels good. It's not just because you want to create good headlines. It's instrumental to your business success to be responsible, to create trust with your workers, employees, consumers and citizens and people in the communities you live in, and I'll explain why. What's happening is, we're creating increasingly intimate technology-enabled experiences for consumers. Think about implantable medical devices to prevent epileptic seizures. Think about the monitoring devices we use. Think about the information that's collected on us. People swipe on Tinder 1.1 million times per second, 3.7 million Google searches per second, 178 million emails per second, 266,000 hours of Netflix tracking every pause, play, fast forward, yeah per second, 266,000 hours. There's so much information collected on us out there. Our information is being used in so many different ways, and the technology is enabling companies to create individualized services for you that are great for consumers, but they're only going to be great if companies build the trust with their customers to get that data from them and if they honor the boundaries of responsibility to make sure they can sustain those products and services. >> But Paul, you scare me to death because every day we hear this breach, that breach, this breach, that breach. It's almost now-- >> Three billion identities in 2018 alone stolen. >> That's half the world, right, or almost. So, it's almost like okay, that's going to happen. And now that you're getting all this additional information, now you can tie the information from my phone that I'm takin' eight trips to 7-Eleven a day and spending way too much time on my couch not movin' around and how those things are going to tie together. One, for kind of the ethics of how the information is used when they have it, and two, it is probably going to get breached. An amazing concept you talked about earlier today, a digital twin. We hear about it from GE all the time for a jet engine, but to have a digital twin of me in some data base, that's, uh, you know, it's with everything, right? There's a good side and a scary side. >> There is, but I think this is where the idea of trust becomes very important. We need to think about, companies need to think about these services and their consumers in different ways. A lot of people, including myself, in the past have used phrases like data is the new oil. Data's the gold of artificial intelligence in this digital age we're living. I think that's dead wrong, and we got to change the mindset. Data isn't fuel or gold. Each piece of data is a fragment of a person and represents a part of a person's activity and identity, and I think if you change your thinking that way, and if you take a view that it's not all about optimizing the use of data, but it's about carefully using data in the right way that builds trust and provides value for the consumer, and you get that equitable exchange of value, that's what the future's all about. >> Right, so one of the topics, and again, we don't have time to go through all of 'em here, and you're going to give a presentation later, it's kind of just the whole machine and human interaction and how that's evolving. Specifically, I want to ask in terms of the work world. We hear about RPA, and everybody should have their own bots, and you can have bionic legs, so that you don't hurt your back if you're doing lifting. So, as you guys kind of look at how these things are melding, it's going to be an interesting combination of people with machines that are going to enable this kind of next gen of work. >> Yeah, no it'll be interesting. I think the important thing that we need to really think about is that like anything else, all these technologies are being designed by us, and we're deciding how to use them. We're deciding the principals around it, so this is about how do we design the world we want which gets back to the theme around responsibility and such. If you look at it, we find that workers are actually optimistic about the technology. Two thirds of workers are positive and optimistic about how all this technology's going to improve their job to even increase career prospects, but only half of those workers believe that their companies are going to provide them with the right training and learning. When we're talking about the human plus trend in here, the human plus worker trend is that it's not a nice to have for companies to provide learning platforms and train their employees. It's critical to their success because the jobs are changing so fast, roles are changing so fast, that if you as a company don't invest in a learning platform to continuously advance your people to fill the new jobs as they're being redefined every day, you as a company are going to get left behind, and that's what we're talking about in the human plus trend of the Vision. >> Right, another thing we hear all the time in terms of how technology's advancing on accelerating curves and people aren't so good at accelerating curves, but very specifically how no one person in one particular industry really has visibility as to what's happening in all these tangential. What's happening in health care? What's happening in drugs? What's happening in logistics? I'm in the media business, so I don't know. You guys are really sitting in an interesting catbird seat because you can see the transformation and the impacts of technology across this huge front, and it's that movement across that front which is really accelerating this thing way faster than people realize I think. >> Yeah it is, and it's a great position to be in to be able to look across like that. The thing I would say though is that unlike other eras of technology earlier, we're seeing remarkably broad industry adoption of these concepts. It's a little different in each industry as you just said, but every industry is looking at this. The interesting thing to me is one of the most common requests that I get from CEOs and from the C-Suite is they want to pull together a workshop, and they want to talk about their strategy and where they're going, and very often, more often than not now, they're saying, and I want to hear from people outside my industry. I want to hear what's happening over there. If I'm in insurance, I might want to hear what's happening in retail, or you know, they want to hear about different industries because they understand that the change is happening differently. They want to make sure they're not missing a pattern that they could apply in their own industry. >> Right, so last question before I let you go. You're speaking all the time. You're talkin' to customers. You go to cool shows like Davos and get to hang out with other big-brained people, but you get to participate in all these things, and now you have this facility. What does the Innovation Hub and these resources enable you to do with the clients that you couldn't do as we sit here in this beautiful new facility? >> Yeah, that's a great question. It's something we've worked on really hard over the last four or five years. It's creating what we call our Innovation Architecture, and it's, what we think, a unique way of putting together capability from research and thought leadership to our Accenture Ventures which is our venture capital investing arm to Accenture Labs which is our R and D and inventors to our studios where we co-create with clients to our industry professionals, the 2,000 people here in Northern California that are working with our clients everyday, and we can put all that together to turn the idea, the research, into results very quickly for our clients, and I don't think anybody can do it in the same way we can by co-like-heading all this and by the sheer investment we put into this. We invest over 800 million dollars a year in research and development, over a billion dollars a year in training for our people, and that results in things like 6,500, 6,500 patents that we generate, more than anybody else in our sector, and 1,400 of those come from our people right here in the San Francisco Innovation Hub, so it's an amazing place for innovation right here. >> All right, well Paul, thanks again for taking a few minutes. I know it's a busy day. You're gettin' ready to go present the findings for people. Where should they go to learn more about the TechVision? >> Go to accenture.com dot, uh, accenture.com/techvision. I think at midnight tonight Pacific Time it'll be out there, but by the time they see this, they'll probably have access to it, thanks. >> Paul, thanks for takin' a minute and good luck tonight. >> Always fun, thanks Jeff. >> He's Paul, I'm Jeff, you're watchin' theCUBE. We're at the Accenture Innovation Hub in downtown San Francisco in the Salesforce Tower. Thanks for watchin'. (electronic music)

(bright, peppy music) >> Announcer: From Chicago, it's theCUBE. Covering Veritas Vision Solution Day 2018. Brought to you by Veritas. >> Hello everyone, welcome to Chicago. We're here covering the Veritas Solution Day. Veritas, last year, had the Veritas Vision Conference and they brought together all their customers. This year they decided to go around the world, I think they have six or seven of these across the globe. And we just were in New York a few weeks ago at Tavern on the Green. We're here at the Palmer House in Chicago. Iconic hotel. About 60 to 70 customers here. Of course Chicago's a big opportunity for companies like Veritas because there's such a good customer base here. But what I want to do now is set up what's going on in the data protection business. According to a number of sources, Gartner, IDC Data, other survey data, certainly anecdotally when we talk to customers, about half of the customers that we talk to are going to replace their data protection platform within the next five years. Why is that? Well, there are a number of factors that are affecting that and I want to talk about the reasons why, the implications to the market, and what that means for customers. So if you look back 10 years ago, there was a similar dynamic going on catalyzed by the ascendancy of virtualization. What was happening is that you had all these servers that were underutilized and so the brilliance of virtualization was we're going to consolidate those servers, virtualize the compute power, dramatically increase the utilization and reduce the physical capacity that's on the floor. So you can get rid of stuff. Get rid of servers, spend less, and get more value out of that asset. Because you had all these underutilized hardware assets. Data protection backup in particular was the one workload that actually could use all that compute power. Why, because at the end of the day, you're backing up this huge stream of data. And so as a result, when you had to do a full backup, you didn't have the physical resources. So people had to rethink how they architected backup because of virtualization. So you now have a similar dynamic, but for different reasons. Some of the big trends that are going on here. The first one is of course digital. So digital means data and it's all about how you get value out of your data because data is increasingly an important asset. People are realizing that protecting that data is more and more important. As a result, people are rethinking just the definition of recovery. Recovery has to be faster, you've got to be always on in this digital world. So digital transformation is critical. You can't just bolt on backup as you have for the last 20, 30, 40 years really. Backup has been a bolt on. You've also got cloud. Everybody wants cloud-like. So you're seeing a shift from improving or dealing with resource utilization and allocation, as I explained in the virtualization world, now to automation. Why automation? Because people want a cloud-like experience. They realize they can't just shove all their data into the public cloud. There's data all over the place, and I'll talk about that in a moment in terms of distributed data, but specifically people want a cloud-like experience. What does that mean? That means they want pay-as-you-go, they want simple deployment, they want fast seamless recovery, and they want a lot of automation. While the price of technology comes down year after year, the price of people doesn't. And you can't just keep throwing people at the infrastructure problem, because it's so complex, you have to automate. And you want to shift resources toward higher value activities. Digital transformation, dev opps, application development. So this distributed data world, this multi-cloud world, and I'll talk a little bit more about that in a moment when I discuss the Edge, it's becoming a forcing function. Multi-cloud is a forcing function to rethink your backup. Because you've got different infrastructures, a service providers, you've got SAS providers, you've got all kinds of clouds that are popping up all over the lines of business and within your own data centers. As a result, you need to think about how do I catalog all that data, how do I protect that data, how do I govern that data, how do I deal with things like GDPR and make sure that I'm in compliance. So it becomes a much more complicated equation, and the variables are distinct. For example, I don't really understand what point in time means anymore. If you have distributed data, what does it mean to have a point in time copy? Point in what time? Who's the master? So you need some kind of controls in that multi-cloud world. That's a forcing function to rethink your backup. The other thing is platform. Platform beats products. I'll talk about that in a moment. People for years have looked at backup as purely insurance. Everybody hates buying insurance, we all know that, so you're seeing people trying to get more out of their backup and recovery platforms. For instance, integrating disaster recovery. So that's becoming an integral part of people's strategies. You're also seeing analytics becoming more and more important. People are trying to, because all the data sits in the corpus of the backup, people are saying why don't we analyze that data and get more out of it. Why don't we take snapshots of that data and make it available to dev opps. And what about ransomware, which again I'll talk about in a moment. Could I maybe look at anomalies in that data to determine if there are some problems. Many, many use cases emerging. Data classification, governance, I mentioned GDPR before, so you're seeing backup shift from pure insurance to a higher value business opportunity. And then of course, there's security, there's compliance, there's governance, ransomware is critical. Organizations are creating air gaps, meaning disconnecting from the internet, so that if they get hit with a ransomware attack they can isolate their data, but just even that is not enough. People can get through air gaps by physically putting in, whatever. Sticks or malware et cetera. So you still have to be able to use analytics to look at that corpus of backup data and identify anomalies. But again, because of those security risks and because of the importance of digital transformation and data people are rethinking how they do data protection. And finally, there's the Edge. We are living in a distributed world, it's a multi-cloud world, as I said before it's a forcing function, and the Edge is one of those clouds, if you will, which changes the way in which you think about backup. How does it change. Locality of the recovery data. If you've got Edge data, if you've got multi-cloud, you've, as I said before, got to have a global catalog and recover that data locally. Another thing to think about is SLAs. In a cloud world, you, the customer, are responsible for the recovery. Well, the cloud vendor can get the light back on on the disc system, or the computer, or the compute system, you are responsible for the people and the process to recover your business. That is not the cloud vendor's responsibility so you need to think about that. And think about recovery as recovery at the business level, not just recovery of the data, but recovery, getting your business back online. There's also the three laws of the cloud. We learned this from Pat Gelsinger this August at VMworld. The laws of physics, the laws of economics, and the law of the land. Those will dictate where you put data and how you back up that data. So all of this has created a new landscape in the data protection business. Let's run down that landscape. Who are the leaders. You've got Dell EMC, you've got Veritas, you've got Convault, and you've got IBM. Those guys comprise probably 2/3 or more of the marketplace. And you have startups like Cohesity and Rubrik who have raised hundreds of millions of dollars going after them and challenging them. You've got a whole new set of players that are taking new approaches. Actifio, for example, got the whole copy data management thing going. Datrium is creating end to end, both primary storage and data protection backup in the same platform with a software-based cloud-like, SAS-like offering. You've got companies like Zerto and Imanis Data that are specialists. You've got companies like WANdisco, again, taking new approaches. And then you have Oracle, with the Oracle recovery appliance, which is totally changing the way in which backup worked for Oracle databases exclusively. Taking a database-led approach to backup. And then of course you've got the storage players that are part of the ecosystem even though they're not directly competing with backup software vendors. Guys like Pure, NetApp, InfiniteApp. They're partnering with backup vendors. And then of course, there's the cloud guys. AWS, Azure, Google. The thing to think about as customers, really three things. Platform versus product. What's the platform look like? Is it an API-based platform? Because you want to program to that platform infrastructurer's code, you want to support your dev opps infrastructure. The second is cloud-like pricing, and cloud-like deployment. You want a cloud-based operating model to simplify your operations and lower your IT labor costs and shift those costs to more strategic efforts and initiatives such as digital transformation and application development. And the third is ecosystem alignment. Make sure that your backup software vendor and you backup solution vendors are all, their ecosystem is aligned with your ecosystem. Because you're going to get more facile integration and problem-solving and flexibility if those systems align. So take a look at that as well. Couple of things I want to mention and emphasize. New application development models. Cloud Native, Kubernetes. Function, you know people call it server-less, but function-based programming. Really to support dev opps and infrastructure as a code. That is going to have implications on how you protect data. And finally AI. How can you talk about anything today without talking about AI. Anticipatory staging of data for recovery, as in the example. Predicting where problems are going to occur. Machine intelligence will increasingly play a role in this whole landscape. So, as you can see, there's a lot going on. This is why data protection is such a hot space. That's why the VCs are getting in. It's why the incumbents like Veritas, Dell EMC, IBM, Convault, those that I mentioned are trying to re-platform and hang on to their large install bases and ultimately grow them. And it's why companies in the startup and the niche spaces, are tucking in and identifying new opportunities to participate. So that's a quick overview of what's going on here at the Veritas Vision Solution Day from Chicago. We'll be here all day talking to customers, talking to practitioners, technologists, and executives. So keep it right there, you're watching theCUBE. I'm Dave Vellante. Be right back. (bright music)

>> Narrator: From Chicago, it's theCUBE. Covering Veritas Vision Solution Day, 2018. Brought to you by Veritas. >> Welcome back to Chicago everybody, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, we're here covering the Veritas Vision Solution Day. Veritas last year had a big tent event thay thousands and thousands of customers. They decided this year to go out to the customers. Like us, we go out to the events, we extract the signal from the noise. Jeff Kroth is here, he's the manager of data management and analytics at Softchoice, which is a Veritas partner. Welcome to theCube, thanks for coming on, Jeff. >> Thanks for having me. >> So tell me more about Softchoice, what's your sort of niche and differentiation in the market? >> Sure, so Softchoice is about a two billion dollar North American IT Solution proivder, we're actually the number three Global Midmarket Managed Service provider. We provide the breadth and coverage across a variety of vendors, helping our customers modernize their IT infrastructure. >> So Midmarket is unique, you know, it's not big enough to have like thousands of people do it, data protection for example, they're Generalists, typically, IT Generalists, they're not small, not like the CEO doing the back up. So talk a little bit about the unique aspects of Midmarket from your perspective. >> Well I think some of the things that we bring to the bare Midmarker is helping customers who don't have that deep IT staff with our technology mentorship, with our skills transfer that we provide our customers, we have a managed service that we provide which really helps our customers do more with what they have. >> So data protection is one of the hottest topics going here at VMworld in August, and for the last two years it's been probably one of the hottest topics. That along with Cloud and obviously the AWS partnership with VMWare. Why is data protection so hot right now? What are the factors? >> I would say data protection and data management is hot. It actually comes back to the underlying data behind it, they say, Gardener says data is the new gold and the new natural resource. Well if you don't have your data protected, available, and modernized, you can't leverage things like data analytics to get the most out of your data. Our customers, we see, customers use data as a competitive advantage. Go back look at Blockbuster and Netflix, they weren't able to take advantage of their data and understand that, so really to me data protection is the foundation and building block to grow into an analytics environment where you're really taking advantage of the underlying data for that competitive advantage. >> And I want to do a little tangent here, cause when you hear things like, "data is the new oil, its the new gold," it's actually, in our view, even more valuable, and here's why. Oil, you can put a quart of oil in your car or in your house, but you can't put the same quart in both. Data, using the Netflix example, you can use the same data in a variety of different ways. So in some regards, it's even more valuable. So I guess the bottom line here is digital transformation, which is real, is all about how you use data and that has direct implications on how you protect data, doesn't it? >> It does. >> And so, the other thing is Cloud. You hear a lot of talk about Cloud, and Multicloud, and we're moving into this world of more distributed data. What kind of challenges does that present for customers? >> I mean we are a big Microsoft partner and have a big partnership with Azure, you know, helping our customers on that Cloud journey I think is an important part. One of the things and one of the trends that we're finding is ensuring that you're monerizing your current data platform as you do that data migration to the Cloud. One of the things we see is customers really struggle with cost containment as they make that Cloud migration. So being able to understand what the data is and ensuring that you're only moving the right amount of data and the right workloads to the Cloud to keep costs down, I think is one of the important things, one of the things we're helping our customers, making sure they're getting real value out of the Cloud and doing that cost containment. >> We heard this morning Joe T was talking about some Cloud repatriation and you definitely are seeing it he gave an example of a large company in Dubai who said, "we're going all in on Cloud," and they went all in on Cloud and said, "wow, this is really expensive." Make sense, right? Renting is often times more expensive than owning. So I look at that as, you know, those that have had to repatriate, a lot of that is poor planning so how do you help your customers plan which work loads should be in the Cloud and follow those laws of economics, and physics, and governance, you know the law of the land, how do you help them? >> So it's really a couple of things, we have a couple of assessments that we use to help customers understand their existing workloads and what makes sense to move to the Cloud and what makes sense to keep on premise. So that's an assessment that Softchoice offers. The other thing is aligning to Veritas's 360 data management strategy is really getting a deeper understanding of what that data is that you have so you're aligning the right costs associated with that data to decide what you move to the Cloud and what stays on prem and I think that's a big thing, it's really understanding what that data is and aligning it to what needs to be moved. >> We talked to senior leaders in IT and business, they tell us that if you got to move to the Cloud you really want to change the operating model, that's where you're going to get the biggest bang for the buck. What does that mean in terms of data protection? If you're going to go digital, go Cloud, change your operating model, that's going to have implications on data protection, isn't it? And what do you see as the-- >> It is, and what I think we're seeing in Softchoice as a whole, you know we are a big proponent of the Cloud, what I think we see that, you really don't think that customers are going to go fully Cloud. It's really taking that hybrid approach and aligning what applications make sense to go to the Cloud, what applications make sense to stay on prem. So really having that full view of your environment so you can make intelligent decisions on what to move to the Cloud and what to keep on prem, aligning to the usage of that data. >> Now what about your partnership with Veritas? You kind of exclusive Veritas, you work with other back up vendors? Maybe talk about that a little bit and then what do you see as Veritas's strengths and what's on their to-do list? >> Yeah, so we're a Veritas Gold Partner both in the US and in Canada. We're not an exclusive to Veritas, we like to take a very agnostic approach and really help customers understand what their environment looks like and what makes sense for them. Veritas is a key player as part of our data management strategy and going down the road of our analytics strategy, helping customers really understand the value of their data. You can't get into the analytics world unless your data is in the right place so, again we like to take an agnostic approach but Veritas does align very well from a data management strategy for Softchoice. >> Why, why is that? Is that their stack, they've just been around longer, they focus a lot on governance, and I heard things like categorization, throwing out Federal rules of civil procedure today, that's a long history, so why, what's so special? >> I would say it's the overall breadth of their portfolio, it's helping customers back up to Cloud, back up for the Cloud, it's helping customers do things like DR and replication. It's really getting that full 360 view, you know one of the things we're big on is things like Infomap and Data Insights and really helping customers really understand what the underlying data is, associating the cost with that, so as they move workloads to the Cloud they get a full understanding of what they're moving so they're just not blindly moving things to the Cloud, helping keep costs down. Again, when customers, like as in the example we saw earlier today, a lot of customers think that Cloud is a logical strategy for them but over time they see that it increases cost. So it's really about aligning the right sizing of your environment, moving the right applications, the right data to the Cloud and using that as part of your overall strategy. We really see customers really taking a hybrid approach, it's not ever going to be fully public Cloud, it's not going to be fully private Cloud, it's going to be a combination. >> So we're going to ask you about the competitive landscape cause you are sort of Switzerland here, even though got an affinity, it seems, to Veritas, but you've seen a lot of VC money move into the space, you're seeing a lot of specialists emerge, you've seen some startups come after the Incumbents like Veritas, certainly you know Commvault's another, IBM's another, of course DELL EMC, add those guys up they probably have three quarters on the market place so of course the startups are going to come after them. And they're got shiny new toys and probably developing in Cloud Native and probably talking all the right language. But how do you squint through the hype from the marketing side and sort of help customers figure out how they're going to have the greatest business impact? >> I mean I think that's a good point. I think we're seeing a lot of small niche players that are born in the Cloud or have this shiny new marketing collatoral that they're going to market with and I think what's important for us is making sure our customers understand a full road map on what they're trying to do. So, we do see a lot of upstarts that are going after some of the Veritas, the IBM, the DELL EMC businesses, the world. But it's really making sure you're not taking a point solution and trying to go forward with that, it's understanding Portfolio, like Veritas's that has that depth and breadth and really has that history and background. You know, Veritas has been doing this forever and they really know their stuff. >> Yeah, so we've stressed that platforms are important to pay attention to, you know an API based platform is going to beat a product every time and have some legs. It might be it might have other implications in terms of complexities, but it can drive your business forward as opposed to your point, being a point product. And I'm curious as to your thoughts, particularly as it relates to analytics, which is in your title. For years people have looked at back up as just insurance, people that are trying to get more out of it. But how are people using the corpus of back up data and analytics use cases, why the affinity between data protection and analytics? >> I think data protection and data management are kind of clumped into one category. If you don't have a modernized IT infrastructure and you don't have a good data management strategy, it's impossible, you know poor data in, poor data out. You can't make intelligent analytics decisions or have that data for your analytics team if the information isn't there and accessible and good data. So it's really having a very keen data management strategy enabling your analytics users to have the right data to make the right decisions, cause if you don't have the right data you can't make the right decisions, and no analytics tool can go in and make informed decisions based off bad data. So data management is definitely part of the overall analytic strategy cause it's really the first step. >> And why the, in the back up corpuses, because you've got visibility on that data and it's the logical-- >> Sure. >> The logical one place, even if it's virtual, to actually be able to do those analytics, right? >> Exactly. >> Okay, and then I'll give you the last word. Thing's that your learning here today at the Vision Event, customers obviously Chicago, big customer center, you're based in Atlanta another big customer center. We were just in New York a few weeks ago meeting some pretty senior level folks. What are you learning here, what's the conversation like? >> I think the one key thing that I've taken out is that really customers aren't going full Cloud. It's you know, I think I saw a stat and 92% of customers are taking a hybrid approach and leveraging a really full data management policy to be able to handle on prem, to be able to handle private Cloud, public Cloud, and the combination. Really having that tool set to give you visualizations across an entire hybrid IT infrastructure I think it important. And that's really one of the key takeaways. >> We would agree, we've talked for quite some time now, years actually how organizations can't just shove data into the Cloud, they can't just put their business up into the public Cloud, rather they need to move the Cloud operating model to their business. it's very clearly, that's the trend, you're seeing so many signs of that. AWS and VMware partnering up. You certainly saw Google do that and this summer with Istio on prem, Microsoft obviously with Azure Stack, huge presence in hybrid Cloud. So those predictions are coming true. Jeff thanks very much for coming to theCUBE, great to see you. >> Yep, thanks for having me. >> Oh you're very welcome. Alright, keep it right there everybody, this is Dave Vellante, we'll be back from Veritas Vision Day in Chicago at the Palmer House Hotel, you're watching theCube. (soft techno music)

>> From Tavern on the Green in Central Park, New York it's theCUBE. Covering Veritas Vision Solution Day, brought to you by Veritas. >> Welcome back to New York City everybody. We're here in the heart of Central Park at Tavern On the Green, a beautiful facility. I'm surrounded by Yankee fans so I'm like a fish out of water. But that's okay, it's a great time of the year. We love it, we're still in it up in Boston so we're happy. Dave Vellante here, you're watching theCUBE, the leader in live tech coverage. Nathan Hall is here, he's the field CTO at Pure Storage. Nathan, good to see you. >> Good to see you too. >> Thanks for coming on. >> Thanks. >> So you guys made some announcements today with Veritas, what's that all about? >> It's pretty exciting and Veritas, being the market leader in data protection software. Now our customers are able to take Veritas's net backup software and use it to drive the policy engine of Snapshots for our FlashArrays. They're also able to take Veritas and back up our data hub, which is our new strategy with FlashBlade to really unify all of data analytics onto a single platform. So Veritas really is the solution net back up that's able to back up all the workloads and Pure is the solution that's able to run all the workloads. >> So what if I could follow-up on that, maybe push you a little bit? A lot of these announcements that you see, we call them Barney deals, I love you, you love me, we go to market together and everything's wonderful. Are we talking about deeper integration than that or is just kind of press release? >> Absolutely deeper integration. So you'll see not just how-to guides, white papers, et cetera, but there's actual engineering-level integration that's happening here. We're available as an advanced disk target within that back up, we've integrated into CloudPoint as well. We certify all of our hardware platforms with Veritas. So this is deep, deep engineering-level integration. >> Yeah, we're excited about Pure, we followed you guys since the early days. You know we saw Scott Dietzen, what he built, very impressive modern architecture, you won't be a legacy for 20, 25 years so you've got a lot going for you. Presumably it's easier to integrate with such a modern architecture, but now at the same time you got to integrate with Veritas, it's been around for about 25 years. We heard a lot about how they're investing in API-based architectures, and microservices, and containers and the like, so what is that like in terms of integrating with a 25-year-old company? >> Well I think, from Pure's perspective we are API first, we're RESTfull APIs first. We've done a ton of integrations across multiple platforms whether it's Kubernetes, Docker, VMware, et cetera, so we have a lot of experience in terms of how to integrate with various flavors of other infrastructure. I think Veritas has done a lot of work as well in terms of maturing their API to really be this kind of cloud-first type of API, this RESTful API, that made our cross-integration much easier. >> You guys like being first, there were a number of firsts, you guys were kind of the first, or one of the first with flash for block. You were kind of the first for file. You guys have hit AI pretty hard, everybody's now doing that. You guys announced the first partnership with NVIDIA, everybody's now doing that. (laughs) You guys announced giving away NVME as part of the Stack for no upcharge, everybody's now doing that. So, you like to be first. Culturally, you've worked at some other companies, what's behind that? >> Well culturally, this is best company I've worked at in terms of culture, period, and really it all starts with the culture of the company. I think that's why we're first in so many places and it's not just first in terms of first to market. It's really about first in terms of customer feedback. If you look at the Gartner Magic Quadrant we're up, we've been at leaders quadrant for five years in a row. But this year, we're indisputably the leader. Furthest to the right on the X-axis, furthest north on the Y-axis and that's all driven by just a customer-obsessed culture. We've got a Net Promoter Score of 86.6 which is stratospheric. It's something that puts us in the top 1% of all business-to-business companies, not just tech companies. So, it's really that culture about customer obsession that drives us to be first. Both to market, in a lot of cases, but also just first in terms of customer perception of our technology. >> You guys were a first at really escape velocity, the billion dollar unicorn status, and now you're kind of having that fly-wheel effect where you're able to throw off different innovations in different areas. Can you talk more about the data hub and the relevance to what you're doing with Veritas and data protection? Let's unpack that a little bit. >> Sure, sure, the data hub, we had a great keynote this morning with Jyothi the VP of Marketing for Veritas and he had an interesting customer tidbit. He had some sort of unnamed government agency customer that actually gets penalized when they're unable to retrieve data fast enough. That's not something that many of our customers have, but they do get penalized in terms of opportunity costs. The reason why is 'cause customers just have their data siloed into all these different split-up locations and that prevents them from being able to get insight out of that data. If you look at AI luminaries like Andrew Ng or even people like Dominique Brezinski at Apple, they all agree that you have to, in order to be successful with your data strategy, you have to unify these data silos. And that's what the data hub does. For the first time we're able to unify everything from data warehousing, to data lakes, to streaming analytics, to AI and now even backup all onto a single platform with multidimensional performance. That's FlashBlade and that is our data hub, we think it's revolutionary and we're challenging the rest of the storage industry to follow suit. Let's make less silos, let's unify the data into a data hub so that our customers can get real actionable information out of their data. >> I was on a crowd chat the other day, you guys put out an open letter to the storage community, an open challenge, so that was kind of both a little controversial but also some fun. That's a very important point you're making about sort of putting data at the core. I make an observation, it's not so much true about Facebook anymore 'cause after the whole fake news thing their market value dropped. But if you look at the top five companies in terms of market value, include Facebook in there, they and Berkshire keep doing this, but let's assume for a second that Facebook's up there. Apple, Google, Facebook, Microsoft, and Amazon, top five in terms of US market value. Of course markets ebb and they flow, but it's no coincidence that those are data companies. They all have a lot of hard assets at those companies. They've got data at their core so it's interesting to hear you talk about data hub because one of the challenges that we see for traditional companies, call them incumbents, is they have data in stovepipes. For them to compete they've got to put it in the digital world, they've got to put data at their core. It's not just for start-ups and people doing Greenfield, it's for folks that are established and don't want to get disrupted. Long-winded question, how do they get, let's think of traditional company, an incumbent company, how do they get from point A to point B with the data hub? >> I think Andrew Ng has a great talked-point on this. He basically talks about your data strategy and you need to think about, as a company, how do you acquire data and then how do you unify into a single data hub? It's not just around putting it on a single platform, such as FlashBlade. A valuable byproduct of that is if you have all the stove-piped data, though you probably in terms of your data scientist trying to get access to it, now have to, they have 10 different stovepipes you've got 10 different VPs that you have to go talk to in order to get access to that data. So it really starts with stopping the bleeding and starting to have a data strategy around how do we acquire and how do we make certain or storing data in the same place and have a single unified data hub in order to maximize the value we are able to get out of that data. >> You know when I talked to, I'll throw my two cents in, I talk to a lot of chief data officers. To me, the ones that are most insightful talk about their five imperatives. First of all, is they got to understand how data contributes to monetization. Whether it's saving money or making money, it's not necessarily selling your data. I think a lot of people make that mistake, oh I'm going to monetize my data, I mean I'm going to sell my data, no, it's all about how it contributes to value. The second is, what about data sources? And then how do I get access to data sources? There's a lot implied there in terms of governance and security and who has access to that. And in the same time, how do I scale up my business so that I get the right people who can act on that data? Then how do I form relationships with a line of business so that I can maximize that monetization? Those are, I think, sensible steps that aren't trivial. They require a lot of thought and a lot of cultural change and I would imagine that's what a lot of your customers are going through right now. >> I think they are and I think as IT practitioners out there, I think that we have a duty to get closer to our business and be able to kind of educate them around these data strategies. To give them the same level of insight that you're talking about, you see in some chief data officers. But if I looked out at the, there's a recent study on the Fortune 50, the CXOs, and these aren't even CIOs, they're actually, we think as IT practitioners that the cloud is the most disruptive thing that we see, but the CEOs and the CFOs are actually five times more likely to talk about AI and data as being more disruptive to their business. But most of them have no data strategy, most of them don't know how AI works. It's up to us as IT practitioners to educate the business. To say here's what's possible, here's what we have to do in order to maximize the value out of data, so that you can get a business advantage out of this. It's incumbent on us as IT leaders. >> So Nathan, I think again, that's really insightful because let's face it, if you're moving at the speed of the CIO, which is what many companies want to do, because that's the so called, fat middle and that's where the money is. But you're behind, I mean we're moving into a new era, the cloud era, no pun intended, is here, it's solid but we're entering that data of machine intelligence and we built the foundation with the dupe even, there's a lot of data now what do we do with it? We see, and I wonder if you could comment on this, is the innovation engine of the future changing it? It use to be Moore's Law, we marched to the cadence of Moore's Law for years. Now it's data applying machine intelligence and then, of course, using the cloud for scale and attracting start-ups and innovation. That's fine because we want to program infrastructure, we don't want to deploy infrastructure. If you think about Pure, you got data for sure. You're going hard after machine intelligence. And cloud, if I understand your cloud play, you sell to cloud providers whether they're on-prem or in the public cloud but what do you think about those? That innovation sandwich that I just described and how do you guys play? >> Well, cloud is where we get over 30% of our revenue so we're actually selling to the cloud, cloud service providers, et cetera. For example, one of the biggest cloud service providers out there that I think today's announcement helps them out a lot from a policy perspective actually used FlashBlade to reduce their SLAs, to reduce their restore time from, I think, it was 30 hours down to 38 minutes. They were paying money before to their customers. What we see in our cloud strategy is one of empowering cloud providers, but also we think that cloud is increasingly, at the infrastructure layer, going to be commoditized and it's going to be about how do we enable multicloud? So how do we enable customers to get around data gravity problems? I've got this big, weighty database that I want to see if I can move it up to the cloud but that takes me forever. So how do we help customers be able to move to one cloud or even exit a cloud to another or back to on-prem? We think there's a lot of value in applying our, for example deduplication technology, et cetera, to helping customers with those data gravity problems, to making a more open world in terms of sharing data to and from the cloud. >> Great, well we looked at Pure and Veritas getting together, do some hard core engineering, going to market, solving some real problems. Thanks Nathan for hanging out, this iconic beautiful Tavern on the Green in the heart of New York City. Appreciate you coming on theCUBE. >> Thanks Dave. >> All right, keep it right there everybody, Dave Vallante. We'll be right back right after this short break. You're watching theCUBE from Veritas Solutions Day, #VeritasVision, be right back. (digital music)

>> From Tavern On The Green, in Central Park, New York, it's theCUBE. Covering Veritas Vision Solution Day. Brought to you by Veritas. >> Hello everybody welcome back to the Tavern On The Green. We're here in the heart of Central Park in New York City you're watching theCUBE the leader in live tech coverage. We go out to the events, we extract the signal from the noise, big events, small events. We're here at the Veritas Solution Days, #VtasVision. Veritas Vision used to be a very large, big tent conference. They've changed the format now and they go out, they're going out to 20 cities this year belly to belly with the customers and we've got one here. Vishal Kadakia who is the data protection manager at NBC Universal. Vishal thanks so much for coming on theCUBE. >> No problem thank you for having me. >> So as I say we love to get the customer perspectives, but let me start with this event. Why, you're a busy person, you're managing a lot of data, why do you take time out to come to event like this? What do you learn? >> You always get to learn new stuff, new products that you don't necessarily get to learn, 'cause you're always just zoned into your day-to-day work that you're doing so you don't always get to see what the new features may be or you miss it. These type of events are generally good to come see that. >> So what's the day in the life like these days for data protection manager and really I'm interested in how it's changed over the last five or six years, as you see things like, the buzzwords, digital transformation, big data, cloud, multi cloud, all the vendor buzzwords, but you actually have to live that. So how has that changed the role of data protection and data protection managers specifically? >> It's definitely a lot more complicated. Before you were just backing up om prem, you had tape, pretty much made it simple. Now you have all these different workloads, you're sending out to clouds, multi tenant as they keep calling it, the hybrid, which is another buzzword. Trying to manage the different workloads is a lot more complex than it was five years ago. You have various cloud vendors, you have various storage vendors, so managing all of that, obviously the data growth from the smaller backups to now, big data which could be terabytes, petabytes, to try to back that up has been a bit of a challenge. >> But that's a challenge for someone like you who's, you know, RPO and RTO is not getting relaxed. >> Right. >> Right. And you know people always talk about getting my weekends back so, but now you have to keep up with all of these other technologies so what is it? Is it a lot of reading, is it just going to sessions like this, having vendors come in, how do you keep up with it all? >> I think it's a big mix of both. It's going out to these events, but also having vendors come to you. Doing your own research, so it's a combination of just constantly keeping up. So, I would say it's a combination of all. >> One of the things that I would be concerned about in your roll is to have just more stove pipes. Are you able to just conceptually, not technical, deep technical anyway, I love tech, but are you to create, let's call it a abstraction layer for your data protection. Is that kind of your vision or where you're headed, so that you don't have to have 10 different formats and methodologies and processes around data protection? >> Yeah, I think that's the goal that I think every company's trying to go to, is consolidate, simplify. Whether that's vendor, whether that's hardware. I think that's really the goal of any organization now. And that's kind of where we're headed also. >> So if it's a baseball game analogy, and you're nine inning game, where are you in terms of that journey? Is it early days, kind of first inning, are you kind of warmin' up in the bullpen, are you sort of well into the game? >> I think we're well into the game. We're probably into the middle innings I would say. >> Okay. So you can see sort of that vision becoming a reality. And what are the priorities then in terms of getting to that point? Is it skill sets, is it technology, is it people? >> I would say it's technology. I would say that consolidation is probably the big word. We're all trying to consolidate while trying back up the large data sets. And I think that's where we are right now. That's where we're starting to get to, and see the plan forming, seeing where our methodologies, our strategies on how we're going to go forward. >> As you move toward the cloud, Vishal, whether or not it's even pushing data to the cloud, a lot of times you just can't. But it seems like that cloud operating model is something that's alluring to folks. Simplifying, agility, self service, are those initiatives that you guys have enacted? >> In terms of that, yeah we're I think in that phase, I think we're in our beginning to form that plan, because once you get to a cloud, you have to really have a good plan. Otherwise, your data is going to be all over the place. You're not going to know where it is, and managing that's just going to become that much harder. So I think in terms of that, we're trying to really come out with a good plan of how you migrate to the cloud. 'Cause once you get to the cloud, there's a whole different set of complexities that you have in managing it. >> Like what? Maybe tick off a few, so we can paint a picture. >> So once you get to the cloud, migrating, so you've formulated your plan how to get to, what cloud to use, what vendor you're using. How do you migrate from your on prem to the cloud is I think one of the big complexities, which I think kind of stumps a lot of people. You know you want to go to the cloud, just don't know how to get there. >> Is that just because the volume of data and you got to move data and it just takes so long? I mean to back up your iPhone takes forever and it fails left and right. >> Yeah, absolutely. >> So okay. It's the amount of data and the time it takes? >> Right, and you also have legacy applications, which may not be cloud ready and how do you deal with that? So you have that hybrid model you still want to keep some stuff om prem but you want to go the cloud. What goes to the cloud, which cloud do you go to? All of that is where I think we're really at and I don't think it's any different than any other organization, so that's kind of where. >> And how about this notion of multi cloud? I mean is that something that is real in your business? >> Yeah, I think it definitely is. I think our end users are trying to take advantage of where to go best? Some places Azure might work best. Some places AWS might work. There's also Google now that's coming up, so I think you have to kind of consider where the workload would be best to go to. >> Is Shadow sort of IT and cloud creep problematic for you and in other words, you know, lines of businesses saying, it's easy, I can swipe a credit card and I'm up and running in minutes. And then, oh I got to protect this data, it's got to be compliant. Has that been a challenge for you, do you feel like you have that under control? >> No, that has definitely been a challenge area. Different groups that have kind of tried to do their own thing and then found out, oh wait, this is way harder than we thought. Let us go back to our central team. But by then it's kind of all over the place, right so that's definitely been interesting. >> Yeah it's hard, because thinking about that you probably might have done it differently. You might have put in processes and procedures in place and now you've got to clean up the mess so to speak. But okay, so I want to get into Veritas, and you're a Veritas customer? >> I am. >> So how does Veritas help you with all these solutions? I mean a lot of the things I've just asked you, I think are part of either their road map or they're making claims that they can currently help solve some of these problems. Can they, what do you do with Veritas, and how legitimate is their ability in terms of being able to solve some of these problems? >> So we've been able to use Veritas to kind of, as a central location, management of everything. One of their tools as such is CloudPoint. So our biggest thing is if you don't have a central management tool like CloudPoint, which can manage your various cloud backups, then you're left with managing each cloud on its own. So as an operations standpoint, that's like a nightmare. So having a tool such as CloudPoint, right, and then that getting integrated back into NetBackup, which now gives us a central location for all my backups, for reporting, for audit purposes, any of that has been great. And I've been using Veritas since 3.1 so I've been a Veritas customer for a long time. I've seen the evolution of when it was 3.1, a lot of it was manually operated, a lot of scripts, where now a lot of it is automated. So that's helped a lot. We're automating VM policies, we're automating SQL backup policies, all of that has been great. >> Where are you today in terms of these. >> I'm sorry? >> Where are you at today in terms of the release? >> We're, I know they just released eight one two, we're on eight one one. >> Okay so close to current. Yeah I've seen some videos on eight one two. It looks like they've really put a lot of time and effort in to refreshing it. It looks like a microservices architecture, they're talking about containers and certainly you know, saying all the right things. From your perspective have you dug into it yet or is it still early? >> It's still early. I did deploy it on a test environment. Haven't fully played around with it but some of the cool concepts obviously are, you're going away from that Java console eventually, getting to that web based, able to access it from anywhere, the manageability, like a central tool to manage all of that. That I think they're finally gearing towards that and. >> And you guys are a VMware shop? >> We are a VMware shop. >> So when we were at VM World last August, this past year, and even the year before. Data protection was one of the hottest topics, you know, on the show floor. Were you there, I don't know if you were there. >> I was not there. >> I mean it was really a lot of buzz there, sort of a lot of new entrance in that space, and would I imagine a lot of people coming after you for your business, because that's a very large install base. So when you look at the vendor landscape, how do you look at it? Where do you position Veritas, relative to some of the other upstarts? Your thoughts on the competitive landscape, why Veritas? >> Well, my point of view has always been, if it's not broke you don't fix it. There may be other that may be doing something better, but at the end of the day if it's not drastically different, it's a lot of work to move away from one product to another. They'll always come to you and say, hey, we do this better, we do this better. But then when you compare it, to me, Veritas is that all encompassing. It doesn't only do virtual, it does physical well also. It doesn't only do big data, it does all the traditional databases as well. They're always constantly evolving and adding new workloads that it can also be compatible with. >> Yeah so, I would imagine it would be a little difficult to go to your CFO and try to justify a huge migration project given the other priorities that you have. Give me some insight there. I mean what kinds of things do you want to focus on, I mean obviously nobody wants to migrate anything, it's like moving a house. >> Yeah. >> You really don't want to do it, I mean sometimes you get a bigger house or a nicer house or a smaller house, but it's, moving is always a pain. So you'd rather put your effort in your shop somewhere else. Where are you putting that effort? What are some of the priorities that you have either personally or professionally? >> I would say in this sense I think it's I don't want to work the weekends, right. So how do we automate? How do we make operations easier for everybody, the engineering, the solution, the operations. I want to make it simple. I think Veritas allows us to do that 'cause they're an open source, they work with many vendors which makes it nice. So you can, such as VMware, it works with vRealize. All those plugins with VMware and you can eventually just automate and make it simple. >> And kind of get rid of a lot of the scripts which tend to be fragile, they take a lot of maintenance, they tend to be error prone, so if you can through a set of APIs automate programmatically move towards sort of infrastructurous code or a DevOps environment. I'm sure you guys do that internally. And what a difference it makes, from the sort of classic waterfall in terms of speed, agility, quality. I presume that you're seeing that in your shop? >> Yeah, we definitely are and something like a flex appliance would allow us to move towards that. It simplifies, gets us to where we are, but also helps us with our goals of simplifying, reducing our footprint, but still being able to be agile enough to go to cloud, to keep a hybrid model. So something like that is I think where we're seeing. >> Well Vishal, we love the customer perspective, Thank you for coming on. We like to hear the truth, Vertias, truth in Latin, of course. And really appreciate your time. >> Thank you very much. >> You're welcome. All right keep it right there everybody. We're here at Vtas Vision, #VtasVision, Veritas Vision Days in New York City, Central Park, Tavern on the Green, beautiful location. My name's Dave Vallante. We'll be right back, right after this short break. (upbeat music)