>> Announcer: Live from Berlin, Germany. It's theCube, covering NetApp Insight 2017. Brought to you by, NetApp. >> Welcome back to theCube's live coverage of NetApp Insight 2017, I'm your host Rebecca Knight along with my co-host Peter Burris. We are joined by Jean English. She is the Senior Vice President and Chief Marketing Officer of NetApp, thanks so much for comin' on the show. >> Thank you for having me, we're glad you're here with us to join us at Insight Berlin. >> We're always excited to do anything with NetApp. So, talk a little bit about NetApp's digital transformation. You're now at a year's long transformation from storage, your legacy, to data. Talk a little bit about your positioning in the market. >> Sure, so I think people have previously thought of NetApp as storage, and what we're so focused on now is data. And why data? Because that's what we hear from our customers, our partners, the analysts, is what is really topping their needs right now. If we think about how companies are transforming, they're having to think about digital transformation is topping the list. It's topping the most strategic agendas of most CEOs. But what happens is they have to think about the data. It has become a life blood of their business, and as it seamlessly flows through that business, and what does it mean to either optimize their operations, if they've gotta increase their customer touch points, do they have to create new product services, and even businesses. So we feel like right now that is where our focus is on data, and it's so much a part of our heritage that we look to the future as well. >> One of the things that you're working on now is helping customers use data in new, exciting, innovative, creative ways, can you talk broadly about your approach to that, and how you're drawing inspiration on customers and then empowering them? >> Absolutely, so we really try to think about, what is our purpose? And our purpose could be true to our heritage from 25 years ago, we just celebrated our 25 year anniversary this past spring, and it is to empower our customers to change the world with data. Just a few of those, we've seen now, especially in hybrid cloud environments, customers have to think about how are they gonna simplify to integrate data across on-prem, cloud environments, to accelerate digital transformation. One example of that is EidosMedia. We love their story, because their talking about how to get news stories, real time, through a cloud platform, into the hands of journalists that can publish real time live insights. Real time journalism, and so when you think about the speed that has to happen with creating stories, getting 'em published, getting 'em out to news networks, that's data. And it's a good data story. >> When you think about the data story though, a lot of people talk about how data is a fuel, or data is. And we tend to think, at least at SiliconANGLE Wikibon, that that's probably not the best analogy, because data's different from other resources. Most resources share the economics of scarcity, you can do this, or you can do that, but data's different because data could be copied, data can be shared. But data also can be appropriated inappropriately. Could you talk a little bit about the relationship or the direction that NetApp's taking to on the one hand, facilitate the sharing of data strategically while at the same time, ensuring that proper security and IP controls are placed on it. >> So I think people are looking to make sure that they can share freely data, and seamlessly integrate data across multiple sources. Right now what we find is that whether it's because you've had data that's been on-prem, and maybe that's more structured. Now we're startin' to see more unstructured data. So data's becoming a lot more diverse. People are constantly looking for the latest source of truth of data, so dynamic, and because it's so distributed across environments, people are trying to figure out, how do you integrate data, how do you share data, but it's all about simplicity, 'cause they need it to be efficient. They need to make sure that it's protected, so security is top of minds, so data protection is the upmost of importance. They're looking for ways to embrace future technologies. And whether that's thinking about different cloud environments, SAS applications, and then how do they create the most open opportunities. A lot of people aren't just putting their data in one cloud, what we're finding is, is it's a multi-cloud world, and they're looking for a holistic solution to more easily and seamlessly manage their data through those environments. >> But the infrastructure has to move from as you said, a storage orientation towards something that's going to facilitate the appropriate sharing and integration of data. Like a fabric. >> Yes. >> Can you talk a little bit about that. >> So we started the conversation around data fabric, it was one of the first people to really talk about data fabric in the market back in 2013. And this vision was about how do you seamlessly be able to share and integrate data across cloud and on-prem environments. That has become so true in how we've been building out that data fabric today. We just launched a few weeks ago that we are the first industry leading storage data service in the Microsoft Azure console, so that people can easily be able to, can do complete storage capabilities in cloud storage, in Microsoft, we've also been developing solutions to make sure that, maybe if you're not wanting to do everything in Office365 and Azure, you wanna back it up to AWS, so how do you have better backup capabilities? Sharing of data across clouds. We're also seeing that you may wanna sync data, so maybe once you put data into the cloud, and you run analytics or even machine learning, how do you then get data back? Because you wanna make sure that you're constantly being able to look holistically at your customers. This notion of one cloud, to back to on-prem, multi-cloud environments, has been critical as we think about customers and where they're going. >> One of the things we're also hearing about at this conference is, this is the day of the data visionary, and this is where people who are thinking about how to store data, use data, extract data, find value in the data. The demands on them, the pressures on them, are so intense. How is NetApp helping those people, sort of understanding where they are, not only in their businesses, but also in their trajectories of their careers, and then helping them move forward. >> We've been really thinking about who is really using data to disrupt, and are this disruptive use of data to really drive business results. It's not just about having the data, it's about how are you gonna have an impact on the business. So we start to think about this notion of who is a data thriver? Who's thriving with data versus who's just surviving and in fact, some are even resisting. So we actually partner with IDC to launch a study on data thrivers to look at who is truly looking at driving new revenue streams, attracting new customers, how are they able to use data as correlistic part of their business. Not some one off or side project to help do the digital transformation, but what was gonna drive really good business results. Data as an asset. Data across business and IT. And we see new roles are emerging from this. We're seeing that, Chief Data Officers, there's Chief Digital Officers, Chief Data Scientists, Chief Transformation Officers. All new roles that have been emerging in the last couple of years, but these data thrivers are seeing tremendous business impact. >> So, what is it that separates those people, I mean I think that, those really, those companies and those business models, and what are sort of the worst case scenarios for those companies that are just surviving and not necessarily thriving, in this new environment. >> Yeah, I think it's interesting, we're seeing that companies that actually put data at the center of what they do. So we think of it as a data-centric organization, are seeing 6x in what they're seeing in terms of being able to drive real customer acquisition. When we think about what it means to drive operational efficiency, when we think about 2x times in terms of profitability, real bottom line results, compared to people that are simply just surviving with data. What's interesting is that when we start to think about what are the attributes of these people, so business and IT working together in unison. These roles in fact that are emerging are starting to become those catalysts and change agents that are bringing IT and the business more together. We're also seeing that when you think of data as an asset, even to the bottom line, how does data become more critical in terms of what they see in terms of being a differentiated advantage for the company. Also, thinking through quality, quality, quality. You've gotta make sure that the data is of highest quality and it's constantly being cleansed. Then in terms of how do we think of it being used across the business, it's not just about holding data and locking it away behind a firewall. Data more today is so dynamic, distributed and diverse, that you have to let it be utilized and activated across the business. And then to think through, it's starts not just in terms of what customers are using and seeing from data, what they can actually see in terms of customer touch points and having a better customer experience, but then how do you make sure it even comes back to the development to create new products, create new services, maybe even eliminate waste. Stop doing product lines based on what they're seeing from actual usage. So it's a pretty fascinating space right now, but the data thriver is the new thought we're thinking in terms of getting that out in the market and really sharing more so with our clients, so that they can benchmark themselves as well. >> So, you're a CMO. >> Yes. >> You're telling a story, but you also have operational responsibilities. How would you tell your peers to use data differently? >> Well, I think there's a couple things. I mean, for me data's the life blood of how we think about how we actually create a better customer experience. We're using data constantly to better understand what are our customer's needs, and those customers are evolving. Before, in the loyalist that we love was storage architects and admins, we're starting to see that people are thinking about how to use more hybrid cloud data services with CIOs. How are they gonna look at a cloud strategy? With DevOps, how are they gonna create, deploy, and, applications at speed? How are they gonna be able to help to really think through, what are they gonna do to drive more analytics and better workload usage, and efficiencies? Our clients are evolving, and when we think about how do you reach those clients differently, we have to know who they are. We have to use data to understand them. We have to be more personalized. We just relaunched our entire digital experience, so that when we try to look at how do you bring people into something that's more customized, more personalized, what does it mean to be a cloud architect that's thinking about a data backup and protection plan. What does it mean for someone in DevOps who's thinking about how do I actually create and deploy an application at speed? How do you think about someone that's gonna look at the needs from a CIO, so much differently than before. But, using data, using customization, thinking about an engaging experience, bringing 'em through that experience so that we solve their business challenges. We use data and analytics every day. I think of us as being the new data scientists. People say, is it art or is it science and marketing? I'm like, it's a little bit of the storytelling, absolutely, we have to lead with stories, but the data and the analytics is where we really understand our customers best. So using analytic models, using predictive models, using more ways in which we can actually reach customers in new ways we never have before through social. But bring them into a new conversation. Analytics, analytics, storytelling, and understanding, getting closer to new clients like we never have before, and then thinking through how do we use that full-circle loop of learning to get better and better in how we engage our customers in ways they want to engage with us. >> I wanna switch gears just a second, and I know that you've just been nominated as an International Board Member. You were a Board Member before, of Athena of the Triangle, which is about supporting and inspiring women in the technology industry. As we know that this is the dearth of women, technologists, is a big problem in the US and globally. Can you tell us a little more about the organization and what you're doing? >> So, Athena International is really about, how do you promote women's leadership? It's across the world, in fact we just launched some very exciting initiatives in China where I lived for a year, and the President of Athena International is a friend of mine, and she was really looking at how do you foster growth, especially in emerging markets and countries where women's leadership can be so profound in terms of how do you impact the business, government, and market, and really overall global success. Athena is focused on, is technology, but it's also with women in many industries. But really, how do you gain the powerful mentorships, how do you gain powerful access to programs, to having more access to expertise that can help them to think through business models, business cases. How do they grow their business, it might be from financial to career counseling, to mentoring on marketing, but it's really thinking through women's leadership as a whole. >> And is NetApp also working on behalf of those, of that cause too? >> We're really focused on, today in fact we're gonna be hosting the, the annual Women in Technology Summit. So we're so focused on how do we think about developing women in technology, how to think about that across not only our employees, but our partners and our customers, and it's not just about women, this is men and women working together to determine how do we stop the fact that we've got to get more access to women in mentorships and sponsorships, and really really driving how we have leadership as we grow, really grow into our careers, and can drive more business impact. >> Great. Well Jean, thanks so much for coming on theCUBE, >> Thank you. >> It was really fun talking to you. >> Absolutely, thank you both. >> I'm Rebecca Knight for Peter Burris, we will have more from NetApp Insight, here in Berlin, Germany in just a little bit.

Live from Berlin Germany, it's the queue Covering NetApp insight 2017 brought to you by Neda Welcome back to the cubes live coverage of net app insight here in Berlin Germany I'm your host Rebecca Knight along with my co-host Peter Burris. We are joined by Mark Bregman. He is the CTO of net app Thanks so much for coming on the cube Thanks for taking the time so you have been recently looking into your crystal ball to predict the future and you have some some fun sometimes counterintuitive Predictions about what we're going to be seeing in the next Year and decade to come right so so your first pitch in you said data will become Self-aware right what do you mean by that? Well the title is kind of provocative really the idea is that? Data is going to carry with it much more of its metadata Metadata becomes almost more important than the data in many cases and we can anticipate Sort of architectures in which the data drives the processing whereas today? We always have data is sort of a pile of data over here And then we have a process that we execute against the data that's our been our tradition in the computing world for a long long time as data becomes more self-aware the data as it passes through Will determine what processes get executed on it? So let me give you a simple analogy from a different field from the past in The communications world we used to have circuit switched systems There was some central authority that understood the whole network If you and I wanted to communicate it would figure out the circuit set up the circuit And then we would communicate and that's sort of similar to traditional Processing of data the process knows everything it wants to do it knows where to find the data. It does that it puts It somewhere else But in the communications world we move to packets which data, so now the packet the data Carries with it the information about what should happen to it And I no longer have to know everything about the network nobody has to know everything about the network I pass it to the nearest neighbor who says well I don't know where it's ultimately going, but I know it's going generally in that direction and eventually it gets there now Why is that better? It's very robust it's much more scalable and Particularly in a world where the rules might be changing. I don't have to necessarily redo the program I can change the the markup if you will the tagging of the data You can think of different examples imagine the data That's sitting in a autonomous vehicle and there's an accident now There are many people who want access to that data the insurance company the authorities the manufacturer the data has contained within it the Knowledge of who can do what would that data? So I don't have to now have a separate program that can determine Can I use that data or not the data says sorry you're not allowed to see this. This is private data You can't see this part of it Maybe the identify our data for the obviously the insurance company needs to know who the car owner is But maybe they don't need to know something else like where I came from The authorities might need both well he came from a bar So you can imagine that as an example if you the implications, yes marker are important for example if I Wanted to develop an application. That would be enhanced by having access to data I had to do programming to get to that data because some other application control that data and that data was defined contextually by that application right and so everything was handled by the application by moving the metadata into the data now I can bring that data to my Application more easily less overhead and that's crucial because the value of data accretes It grows as you can combine it in new and interesting ways so by putting the metadata end of the data I can envision a world where it becomes much faster much more Fasil to combine data and new and Exactly it. Also is easier to move the Processing through the data to the data because the processing is no longer a monolithic program It's some large set of micro services and the data organizes which ones to execute So I think we'll see I mean this is not a near-term prediction This is not one for next year because it requires rethinking How we think about data and processing, but I think we'll see it with the emergence of micro services compositional programming Metadata together with the data will see more functional programs little programs well That's your quick rush before we go on to the next one. It's almost like in the early night or the late 1970s It was networks of devices ARPANET the became the Internet and then the web was networks of pages And then we moved into networks of application services Do you foresee a day where it's going to be literally networks of data? Yes, and in fact That's a great example because if you think about what happened in the evolution of the web through what we called web 2.0 That the pages were static data They came alive in the web 2.0, and there was a much less of a distinction between the data and the program In the web layer right so that's what we're saying we see that emerging even further Next prediction was about virtual machines becoming rideshare machines well this is somewhat complementary to the first one they all kind of fit together and Here the idea is you know if we go back in the earlier days of IT it wasn't that long ago that if you needed? Something you ordered the server, and you installed it you owned it and then we got to the model of the public cloud, which is like a rental and by the same analogy if in the past if I wanted a vehicle I had to buy it and Then the rental car agencies came up, and I said well, you know when I go to Berlin I'm not gonna buy a car for three days I'll rent a car, but I can choose which car I want do I want the BMW, or do I want you know of Volkswagen That's very similar to the way the cloud works today. I pick what instances I want and They they meet my needs And if I make the right choice great and by the way I pay for it while I have it not for the work It's getting done so if I forget to return that instance. I'm still getting charged But the rideshare is kind of like uber and we're starting to see that with things like serverless computing In the model that I say I want to get this work done The infrastructure decides what shows up in the same way that when I call uber I don't get to pick what car shows up they send me the one that's most convenient for them and me and I get charged for the work going from point A to point B. Not for the amount of time There's some differentiation if there is so cool Ah, they come to that and and so that's more like a rideshare But as you point out even in the rideshare world. I have some choices. I can't choose if I want a large SUV I might get a BMW SUV or I might get a Mercedes SUV I can't choose that I can't choose it the silver or black But I get a higher class and what we're seeing with the cloud Or these kind of instances virtual solutions is they are also becoming more specialized I might it might be that for a particular workload I want some instance that has have GPUs in them or some neural chip or something else In much the same way that The rental model would say go choose the exact one you want The rideshare model would say I need to get this work done and the infrastructure might decide this is best serviced by five instances with GPU or Because of availability and cost maybe it's 25 instances of standard processors because you don't care about how long it takes so It's this compromise and it's really very analogous to the rideshare model now coming back to the earlier discussion as The units of work gets smaller and smaller and smaller and become really micro services Now I can imagine the data driving that decision hailing the cab hailing the rideshare and driving What needs to be done? So that's why I see them in somewhat complementary and so what's the upshot though? For the employee and for the company I think there are two things one is you got to make the right decision? You know if I were to use uber to commute to Sunnyvale every day It'd break the bank, and it would be kind of stupid so for that particular task I own my vehicle But if I'm gonna go to Tahoe for the weekend, and I meet an SUV I'm not gonna buy one neither am I going to take an uber I'm in a rent one because that's the right vehicle on the other hand when I'm going from you know where I live to the marina within San Francisco, that's a 15 minute drive I On demand I take an uber and I don't really care now if I have 10 friends I might pick a big one or a small one But again that the distinction is there so I think for companies They need to understand the implications and a lot of times as with many people they make the wrong initial choice And then they have then they learn from it so You know there are people who take uber everywhere And I talked and I said I had a friend who was commuting to HP every day by uber from the city from San Francisco That just didn't make sense he kind of knew that but The next one is data will grow faster than the ability to transport it, but that's ok it doesn't sound ok it Doesn't sound ok and for a long time. We've worried about that. We've done compression, and we've done all kinds of things We've built bigger pipes And we've but we were fundamentally transporting data between data centers or more recently between the data center and the cloud big chunks of data What this really talks about is with the emergence of quality IOT in a broad sense? Telematics IOT digital health many different cases there's going to be more and more and more data both generated and ultimately stored at the edge and That will not be able to be shipped all of that will not be able to be shipped back to the core And it's okay not to do that because there's also Processing at the edge so in an autonomous vehicle where you may be generating 20 megabytes per hour or more You're not gonna ship that all back You're gonna store it you're gonna do some local processing you're gonna send the summary of it the appropriate summary back But you're also gonna keep it there for a while because maybe there's an accident and now I do need all that data I didn't ship it back from every vehicle But that one I care about and now I'm gonna bring it back or I'm gonna do some different processing than I originally Thought I would do so again the ability to Manage this is going to be important, but it's managed in a different way. It means we need to figure out ways to do overall Data lifecycle management all the way from the edge where historically that was a silo we didn't care about it Probably all the way through the archive or through the cloud where we're doing machine learning rules generation and so on but it also suggests that we're going to need to do a better job of Discriminating or demarcating different characteristic yen classes of data, and so that data at the edge Real-world data that has real-world implications right now is different from data that summarizes business events which is different from data that Summarized as things models that might be integrated something somewhere else And we have to do a better job of really understanding the relationships between data It's use its asset characteristics etcetera, would you agree with that absolutely and maybe you see the method in my madness now? Which is that data will have? Associated with it the metadata that describes that so that I don't misuse it you know think about The video data off of a vehicle I might want to have a sample of that every I don't know 30 seconds, but now if there's really a problem and it may be not an accident Maybe it's a performance problem. You skidded I'd like to go back and see why was there a Physical issue with the vehicle that I need to think about as an engineering problem was it Your driving ability was it a cat jumped in front of the car so But I need to be able to as you pointed out in a systematic way distinguish what data I'm looking at and where it belongs and where it came from The final prediction it concerns the evolution from Big Data to huge data so that is Really driven by the Increasing need we have to do machine learning AI Very large amounts of data being analyzed in near real time to meet new needs for business And there's again a little like many of these things There's a little bit of a feedback loop so that drives us to new architectures for example being able to do in memory analytics But in-memory analytics with all that important data. I want to have persistence technologies are coming along like Storage class memories that are allowing us to build persistent storage persistent memory We'll have to re our Kotak the applications, but at the same time that persistent memory data I don't want to lose it so it has to be thought of also as a part of the storage system Historically we've had systems the compute system, and there's a pipe and there's a storage system And they're separate they're kind of coming together, and so you're seeing the storage Impinge on the system the compute system our announcement of Plexus store acquisition is how we're getting there But at the same time you see what might have been thought of is the memory of the computer System really be an extended part of the storage system with all the things related to copy management backup and and And so on so that's really what that's talking about and you know it's being driven by another factor I think which is a higher level factor. We started in the first 50 years of the IT industry was all about automating processes That ran the business they didn't change the business. They made it more efficient accounting systems etc since probably 2000 there's been a little bit of a shift Because of the web and mobile to say oh I can use this to change the relationship with my customer Customer in density I can use mobile and and I can change the banking business Maybe you don't ever come to the bank for cash anymore even to an ATM because they've changed that The wave that's starting now which is driving This is the realization in many organizations, and I truly believe eventually in all organizations that They can have new data-driven businesses That are transforming their fundamental view of their business so an example I would use is imagine a shoe maker a shoe manufacturer well for 50 years. They made better shoes They had better distribution, and they could do better inventory management and get better cost and all of that with IT in the last Seven or ten years, they've started to be able to build a relationship with their client. Maybe they put some Sensors in the shoe, and they're doing you know Fitbit like stuff mostly for them That was about a better client relationship, so they could sell better shoes cuz I wrench eiated now The next step is what happens if they wake up and say wait a minute We could take all this data and sell it to the insurance companies or healthcare companies or the city planners Because we now know where everyone's walking all the time That's a completely different business But that requires new kind of lytx that we can't almost not imagine in the current storage model so it drives these new architectures And there is one more prediction, okay? Which is that and it comes back again? It kind of closed the whole cycle as we see these Intelligence coming to the data and new processing forms and so on we also need a way to change data management to give us really Understanding of data through its whole lifecycle one of the one example would be how can I ensure? That I understand the chain of custody of data the example of an automobile there's an accent well How do I know that data was an alter or? how can I know whose touch this data along the way because I might have an audit trail and So we see the emergence of a new Distributed and mutable management framework if when I say those two words together you probably think Blockchain which is the right thing to think but it's not the blockchain. We know today there may be something It's something like that But it will be a distributed and immutable ledger that will give us new ways to access and understand our data Once you open up the once you open up Trying to get the metaphor once you decide to put the metadata next to the data Then you're going to decide to put a lot more control information in that metadata Exactly, so this is just an extension said it kind of closes the loop exactly Mark well, thanks so much for coming on the show and for talking about the future with us It was really fun to have you on the show we should come back in a year and see if maybe you're right exactly exactly Thank you. I'm Rebecca night. We will have more from NetApp insight. Just after this

>> [Announcer] Live from Berlin, Germany, It's The Cube, covering NetApp Insight 2017, brought to you by NetApp. >> We are wrapping up a day of coverage at NetApp Insight on The Cube. I'm Rebecca Knight, along with My cohost, Peter Burris. So, we've had a lot of great interviews here today. We've heard from NetApp executives, customers, partners about this company's transformation, and about what it's doing now to help other companies have a similar transformation. What have been some of your impressions of where NetApp is right now, and what it's saying? >> I think it starts with the observation that NetApp realized a number of years ago that if it was just going to be a commodity storage company, it was gonna have a hard time, and so NetApp itself went through a digital transformation to try to improve its understanding of how customers really engaged with it, how it could improve its operational profile's financial footprint, and the result of that was a company that, first off, was more competitive, but also that had learned something about digital transformation, and realized the relationship between the products that they were selling, the services that they were providing, the ecosystem they had that they could tap, been working with customers, and said, what is we took this knowledge, applied it to those things, what would we end up with? And so we now have a company that is still talking about products, but very much it's also talking about what businesses could do in day to day differently to effect the type of transformation that NetApp itself has been going through, and it's a compelling story. >> And you're describing this introspection that the company did, as you said, if we can't survive with our old business model, what can we do differently, and now eating it's own dog food, but then telling other companies about its story, and how its made changes. I mean, do you think NetApp is where it should be today? Are you pleased with the progress you've seen? >> Well that's one of the great challenges in the tech industry today, is nobody's quite sure where they should be. >> [Rebecca] There are no benchmarks. >> Because nobody's sure what's going on underneath them. So many years ago, in response to a reporter's questions about IBM, they said, well what do you think? Is IBM going to be successful at moving the aircraft, turning the aircraft carrier? And I said, you don't get it. IBM's problem is not that they're trying to turn the aircraft carrier, it's that they're trying to rotate the ocean, so that they could go straight, and everybody else's position would change, and that's a lot of what's happening in the technology industry today, as the people are turning, the ocean's being rotated, and there are a couple of companies, like AWS, that seem to have their fingerprint, or their finger on some of those changes. I'm not sure NetApp has that kind of a presence in the industry, but what is clear is that the direction that NetApp has taken is generating improved financial results, a lot better customer satisfaction, and it's putting them into position to play in the next round, so to speak, of competition in this industry, and in an industry that's changing this fast, that, all by itself, is a pretty good position to be in. >> Well, you know, and you're talking about the changing industry, and then also the changing employment needs that this company has in terms of getting people in their workforce who really understand, not just that data in an asset, which is what we keep hearing today, too, but really understanding how to capture the data, tease out the right insights from the data, and then deploy a strategy based on those insights that actually will create value to the business, whether that's acquiring new customers, or saving money, or earning new lines of business, too. >> Well, for example, we had a great conversation with Sheila Fitzpatrick about GDPR, this phenomenal conversation. Sheila is in charge of privacy at NetApp, and the decision that she drove was to not just to GDPR, NetApp have to GDPR here in Europe, but to GDPR across the entire company. Now two years ago, I don't know that a NetApp person would have come onto The Cube and talked about GDPR, but that is a problem, that is a challenge that every business is facing, and bringing somebody on that has made some really consequential decisions for a company like NetApp to be able to say, here's how other businesses need to think about GDPR, think about data privacy, is a clear example of NetApp trying to establish itself as a thought leader about data, and not just a thought leader about commodity storage. So I think there's a lot of changes that NetApp's gonna go through. They still are talking about on tap, they still are talking about HCI, they're talking about all the various flash products that they have, so that's still part of their conversation, but increasingly they're positioning those products, not in terms of price performance, but in terms of applications to the business based on the practical realities of data. >> And I also think we've heard a number of executives talk about NetApp having a more consultative relationship with its clients and partners, and really learning from them, how they're doing things, and then sharing the learnings at events like NetApp Insight, here, and just really on the ground more, working in partnership with these companies, too. >> Data is a physical thing, and I think a lot of people forget that. A lot of people just look at data and say, oh it's this ephemeral thing, it's out there, and I don't much have to worry about it, but physics is an issue when you're working with data. Adam Steltzner, Dr. Adam, the gentleman from NASA, he talked about the role that data science is playing in NASA Mars exploration, talked about the need to worry about sparse data, because they have dial up speeds to send data back from a place like Mars. They're working on problems, but when you start thinking in those terms, the physical limitations, the physical realities, the physical constraints of data become very real. GDPR is not a physical constraint, but it's a legal constraint, and it might as well be physics. If a company does something, we heard, for example, that there are companies out there, based on their practices and how they were hacked, would have found themselves facing $160 billion liability. >> [Rebecca] Yeah. >> Now that may not be physics, you know, I can only move so much data back from Mars, but that is a very real legal constraint that would have put those companies out of business if GDPR governance rules had been in place. So what's happening today is companies, or enterprises are looking to work with people who understand the very physical, practical, legal, and intellectual property realities of data, and if NetApp is capable of demonstrating that, and showing how you could turn that into applications, and into infrastructure that works for the business, then that is a great partner for any enterprise. >> Well do you think that other companies get it? I mean, the sense of where we are today? You use this example of GDPR, and how it really could have sent companies out of business if those rules had been in place, and they had been hacked, or suffered some huge data breach. Do you think that NetApp is setting itself up as the thought leader, and in many ways is the thought leader? Are there companies on the same level? >> No, they're not, and certainly there are a lot of tech companies that are moving in that direction, and that they're comparable with NetApp, and working both close with NetApp, and in opposition to NetApp, at least competitively, but the reality is that most enterprises are, how best to put this? Well, what I like to say is William Gibson, the famous author who coined the term cyberspace, for example, once said, the future's here, it's just evenly distributed. So there are pockets of individuals in every company who are very cognizant of these challenges, the physical realities of data, what it means, what role data actually plays, what does it mean to actually call data an asset? What's the implications on the business of looking at data as a asset? That's in place in pockets, but it's not something that's broadly diffused within most businesses, certainly not our client base, not the Wikibon angle client base, is certainly not broadly aware of some of these challenges. A lot of things have to happen over the course of the next few years for executives, and rank and file folks to comprehend the characteristics, or the nature of these changes, start to internalize, start to act in concert with the possibilities of data, as opposed to in opposition to the impacts of data. >> And those are the people who, we had guests on today just talked about the data resisters, because there are those in companies, maybe they're just an individual in a company, but that can have a real impact on the company's strategy of moving forward, deploying its data smartly. >> Yeah, absolutely, and we also had the gentleman from The Economist who made the observation that concerns about artificial intelligence impacts employment might be a little overblown. >> [Rebecca] Right, right. >> So a lot of those data resisters might be sitting there asking the question, what will be the impact of additional data on my job? And it's a reasonable question to ask, because if your business, we also talked about physicians. A radiologist, for example, someone who looks at x-rays has historically not been a patient facing person. They would sit in the back and look at the x-rays, they would write up the results, and they would give them to the clinician, who would actually talk to the patient. I, not too long ago, saw this interesting television ad where radiologists presented themselves as being close to the patient. Why? Because radiology is one of those disciplines in medicine that's likely to be strongly impacted by AI, because AI can find those patterns better than, often, a physician can. Now the clinician may be a little less effected by AI, because the patient is a human being that needs to have their hand held. >> [Rebecca] And their life is on the line. >> Their life is on the line. The healing and treatment is about whether or not the person is able to step up and heal themselves. >> [Rebecca] Right. >> So there's going to be this kind of interesting observation over the next few years. Folks that work with other people will use data to inform. Folks that work with machines, folks that don't work with other people, are likely to find that other machines end up being really, really good at their job. >> [Rebecca] Right. >> Because of the speeds of data, at the compactness of data, human beings just cannot respond to data as fast as a machine, but machines still cannot respond to people as well as people can. >> And they don't have empathy. >> And they don't have empathy, so if I were to make a prediction, I would say that, in the future, if your job is more tied to using machines, yeah, you got a concern, but if your job is tied to working with people, your job is gonna be that much more important, and increasingly, the people that are working with machines are gonna have to find jobs that have them work with other people. >> Right, right. Well it's been a great day. It's fun to work with you. This is our first time together on The Cube. It was a great day. >> Well The Cube is a blast. >> The Cube is a blast. It's a constant party. I'm Rebecca Knight for Peter Burris, this has been NetApp Insight 2017 in Berlin. We will see you next time.

>> Announcer: Live from Berlin, Germany, it's The Cube covering NetApp Insight 2017. Brought to you by NetApp. >> Welcome back to The Cube's live coverage of NetApp Insight here in Berlin, Germany. I'm your host Rebecca Knight along with my co-host Peter Burris. We are join by Alfred Manhart. He is the Senior Direct Channel and System Integrator Ischemia for NetApp, and Lars Gobel, who is the Head of Strategy and Innovation for DARZ. Thanks so much for joining us. >> Thank you. >> Thank you for the invitation. >> So Manfred, I mean Alfred, before the cameras were rolling, you were talking a little bit about key partnerships and why they are so critical to helping NetApp manage the data and help it flow freely. Can you tell our viewers a little bit more about the partnerships aspect? >> So we have, of course, partnering with NetApp is a base of our strategy. It's not just a initiative. So partnering is key for us. And what we currently see is that the partner landscape has to change. The existing partner that what we are trying to help them to transform to the digital world change the world with data on one side and on the other side we need additional new partner that make the complex customer-oriented offering become reality. This is an example probably DARZ's staff anyhow, but they build up this kind of multiple partnerships to offer the customer-related offering and solution for the end customers. >> Great, great. So tell us how you fit in here Lars? I mean, as important of partnerships. >> So, we are in a situation that IT is getting more and more complex. And we also get into the position that the understand is now clear that not the company can internally are the best at every part. So, for example, Global Innovation Index makes analyzes with the outcome that everywhere where partnerships exists, the innovation is much higher. And today we talk over new business model, we talk over innovation, scalability, flexibility, and for these topics and all the for the new size of environments and also of the challenges the customers have. They need the best for every part of the solutions and we at DARZ, a full IT service provider, try to bring that together. So we offer from co-location housing over private co-hosting up to a public cloud and hyper cloud scenarios complete bandwidth. So we bring together Amazon Web Service and Microsoft Azure to realize one solution for the customer. >> So, every large enterprise is gonna have multiple relationships like the one that they have with you. And while you are helping to bring Amazon and Azure and others under the DARZ umbrella of services, there is gonna have to be something that connects them a little bit more deeply, right? That's probably gonna be data. >> Lars: Yeah. >> So tell us a little bit about that underlying fabric that's going to be required to ensure that data can be rendered in all of these different environments and sourced from all of these different environments according to the needs of business. What do you think? What will NetApp's role in that be? >> That's an interesting one. I think the world from a partnership perspective is even getting more complex, yeah? Instead of making everything as a single one st-- One initial shot, more technical, it's more outcome-based, longer-term based. So if you're not thinking that way, what should be my desired outcome of what-- How my world should look like in a year, in two years from now, you probably choose the wrong partner from the beginning. So this kind of being relevant and being prepared for the future, for all the challenges that are coming up, is very, very important. And data is a short-term issue and of course you have to consider what you want to do with data long term. That is the challenge to balance out the short-term benefits with the long-term objective you have. And thus makes the world more complex. >> So what do you look for in a partner? As you said, you could realize too late you chose the wrong partner from the beginning. But what are sort of the key characteristics and attributes that you want? >> OK, from our perspective we also, we do two things. On the one side, we concentrate on the existing partners and support them on their way to the new world. Yeah? Not all of them will make it. Yeah? And on the other side, we have an acquisition program in place, that we address the partner that are needed for the future and also expand the ecosystem with partners, which are probably we are not even aware of. Talking about coder partners, alliance partners, cloud partners we currently have not in our portfolio. So it's both, driving the existing channel ecosystem to the digital world and acquiring partners that are needed for the future. >> Great. Well Alfred, Lars, thank you so much for coming on the show. It's been great having you. >> Thank you >> Thank you very much for inviting us. >> I'm Rebecca Knight for Peter Burris, we will have more from NetApp Insight just after this. (upbeat music)

>> Narrator: Live from Berlin, Germany, it's theCUBE. Covering NetApp Insight 2017. Brought to you by NetApp. (techno music) Welcome back to theCUBE's live coverage of NetApp Insight here in Berlin, Germany. I'm your host, Rebecca Knight, along with my cohost Peter Burris. We have two guests for this segment. We have Matt Watts, he is the director and data strategist and director of technology at NetApp, and Kenneth Cukier, a senior editor at The Economist, and author of the best-selling book Big Data, and author of a soon to be best-selling book on AI. Welcome. Thank you. Thank you much for coming on the show. Pleasure to be here. So, this is the, we keep hearing NetApp saying this is the day of the data visionary. I'd love to hear both of you talk about what a data visionary is, and why companies, why this is a necessary role in today's companies. Okay, so I think if you look at the generations that we've been through in the late nineties, early 2000's, it was all about infrastructure with a little bit of application and some data associated to it. And then as we kind of rolled forward to the next decade the infrastructure discussion became less. It became more about the applications and increasingly more about the data. And if we look at the current decade that we're in right now, the infrastructure discussions have become less, and less, and less. We're still talking about applications, but the focus is on data. And what we haven't seen so much of during that time is the roles changing. We still have a lot of infrastructure people doing infrastructure roles, a lot of application people doing application roles. But the real value in this explosion of data that we're seeing is in the data. And it's time now that companies really look to put data visionaries, people like that in place to understand how do we exploit it, how do we use it, what should we gather, what could we do with the information that we do gather. And so I think the timing is just right now for people to be really considering that. Yeah, I would build on what Matt just said. That, functionally in the business and the enterprise we have the user of data, and we have the professional who collected the data. And sometimes we had a statistician who would analyze it. But pass it along to the user who is an executive, who is an MBA, who is the person who thinks with data and is going to present it to the board or to make a decision based on it. But that person isn't a specialist on data. That person probably doesn't, maybe doesn't even know math. And the person is thinking about the broader issues related to the company. The strategic imperatives. Maybe he speaks some languages, maybe he's a very good salesperson. There's no one in the middle, at least up until now, who can actually play that role of taking the data from the level of the bits and the bytes and in the weeds and the level of the infrastructure, and teasing out the value, and then translating it into the business strategy that can actually move the company along. Now, sometimes those people are going to actually move up the hierarchy themselves and become the executive. But they need not. Right now, there's so much data that's untapped you can still have this function of a person who bridges the world of being in the weeds with the infrastructure and with the data itself, and the larger broader executives suite that need to actually use that data. We've never had that function before, but we need to have it now. So, let me test you guys. Test something in you guys. So what I like to say is, we're at the middle of a significant break in the history of computing. The first 50 years or so it was known process, unknown technology. And so we threw all our time and attention at understanding the technology. >> Matt: Yeah. We knew accounting, we knew HR, we even knew supply-chain, because case law allowed us to decide where a title was when. [Matt] Yep. But today, we're unknown process, known technology. It's going to look like the cloud. Now, the details are always got to be worked out, but increasingly we are, we don't know the process. And so we're on a road map of discovery that is provided by data. Do you guys agree with that? So I would agree, but I'd make a nuance which is I think that's a very nice way of conceptualizing, and I don't disagree. But I would actually say that at the frontier the technology is still unknown as well. The algorithms are changing, the use cases, which you're pointing out, the processes are still, are now unknown, and I think that's a really important way to think about it, because suddenly a lot of possibility opens up when you admit that the processes are unknown because it's not going to look like the way it looked in the past. But I think for most people the technology's unknown because the frontier is changing so quickly. What we're doing with image recognition and voice recognition today is so different than it was just three years ago. Deep learning and reinforcement learning. Well it's going to require armies of people to understand that. Well, tell me about it. This is the full-- Is it? For the most, yes it's a full employment act for data scientists today, and I don't see that changing for a generation. So, everyone says oh what are we going to teach our kids? Well teach them math, teach them stats, teach them some coding. There's going to be a huge need. All you have to do is look at the society. Look at the world and think about what share of it is actually done well, optimized for outcomes that we all agree with. I would say it's probably between, it's in single percents. Probably between 1% and 5% of the world is optimized. One small example: medical science. We collect a lot of data in medicine. Do we use it? No. It's the biggest scandal going on in the world. If patients and citizens really understood the degree to which medical science is still trial and error based on the gumption of the human mind of a doctor and a nurse rather than the data that they actually already collect but don't reuse. There would be Congressional hearings everyday. People, there would be revolutions in the street because, here it is the duty of care of medical practitioners is simply not being upheld. Yeah, I'd take exception to that. Just, not to spend too much time on this, but at the end of the day, the fundamental role of the doctor is to reduce the uncertainty and the fear and the consequences of the patient. >> Kenneth: By any means necessary and they are not doing that. Hold on. You're absolutely right that the process of diagnosing and the process of treatment from a technical standpoint would be better. But there's still the human aspect of actually taking care of somebody. Yeah, I think that's true, and think there is something of the hand of the healer, but I think we're practicing a form of medicine that looks closer to black magic than it does today to science. Bring me the data scientist. >> Peter: Alright. And I think an interesting kind of parallel to that is when you jump on a plane, how often do you think the pilot actually lands that plane? He doesn't. No. Thank you. So, you still need somebody there. Yeah. But still need somebody as the oversight, as that kind of to make a judgment on. So I'm going to unify your story, my father was a cardiologist who was also a flight surgeon in the Air Force in the U.S., and was one of the few people that was empowered by the airline pilots association to determine whether or not someone was fit to fly. >> Matt: Right. And so my dad used to say that he is more worried about the health of a bus driver than he is of an airline pilot. That's great. So, in other words we've been gah-zumped by someone who's father was both a doctor and a pilot. You can't do better than that. So it turns out that we do want Sully on the Hudson, when things go awry. But in most cases I think we need this blend of the data on one side and the human on the other. The idea that the data just because we're going to go in the world of artificial intelligence machine learning is going to mean jobs will be eradicated left and right. I think that's a simplification. I think that the nuance that's much more real is that we're going to live in a hybrid world in which we're going to have human beings using data in much more impressive ways than they've ever done it before. So, talk about that. I mean I think you have made this compelling case that we have this huge need for data and this explosion of data plus the human judgment that is needed to either diagnose an illness or whether or not someone is fit to fly a plane. So then where are we going in terms of this data visionary and in terms of say more of a need for AI? Yeah. Well if you take a look at medicine, what we would have is, the diagnosis would probably be done say for a pathology exam by the algorithm. But then, the health care coach, the doctor will intervene and will have to both interpret this for, first of what it means, translate it to the patient, and then discuss with the patient the trade-offs in terms of their lifestyle choices. For some people, surgery is the right answer. For others, you might not want to do that. And, it's always different with all of the patients in terms of their age, in terms of whether they have children or not, whether they want the potential of complications. It's never so obvious. Just as we do that, or we will do that in medicine, we're going to do that in business as well. Because we're going to take data that we never had about decisions should we go into this market or that market. Should we take a risk and gamble with this product a little bit further, even though we're not having a lot of sales because the profit margins are so good on it. There's no algorithm that can tell you that. And in fact you really want the intellectual ambition and the thirst for risk taking of the human being that defies the data with an instinct that I think it's the right thing to do. And even if we're going to have failures with that, and we will, we'll have out-performance. And that's what we want as well. Because society advances by individual passions, not by whatever the spreadsheet says. Okay. Well there is this issue of agency right? So at the end of the day a human being can get fired, a machine cannot. A machine, in the U.S. anyway, software is covered under the legal strictures of copywriting. Which means it's a speech act. So, what do you do in circumstances where you need to point a finger at something for making a stupid mistake. You keep coming back to the human being. So there is going to be an interesting interplay over the next few years of how this is going to play out. So how is this working, or what's the impact on NetApp as you work with your customers on this stuff? So I think you've got the AI, ML, that's kind of one kind of discussion. And that can lead you into all sorts of rat holes or other discussions around well how do we make decisions, how do we trust it to make decisions, there's a whole aspect that you have to discuss around that. I think if you just bring it back to businesses in general, all the businesses that we look at are looking at new ways of creating new opportunities, new business models, and they're all collecting data. I mean we know the story about General Electric. Used to sell jet engines and now it's much more about what can we do with the data that we collect from the jet engines. So that's finding a new business model. And then you vote with a human role in that as well, is well is there a business model there? We can gather all of this information. We can collect it, we can refine it, we can sort it, but is there actually a new business model there? And I think it's those kind of things that are inspiring us as a company to say well we could uncover something incredible here. If we could unlock that data, we could make sure it's where it needs to be when it needs to be there. You have the resources to bring to bed to be able to extract value from it, you might find a new business model. And I think that's the aspect that I think is of real interest to us going forward, and kind of inspires a lot of what we're doing. Great. Kenneth, Matt, thank you so much for coming on the show. It was a really fun conversation. Thank you. Thank you for having us. We will have more from NetApp Insight just after this. (techno music)

>> Announcer: Live from Berlin, Germany, it's theCUBE, covering NetApp Insight 2017. Brought to you by NetApp. (upbeat music) >> Welcome back to theCUBE's live coverage of NetApp Insight 2017, here in Berlin, Germany. I'm your host, Rebecca Knight, along with my co-host, Peter Burris. We are joined by Shelia Fitzpatrick, she is the Chief Privacy Officer of NetApp, and Paul Stringfellow who is a Technical Director at Gardner Systems. Shelia, Paul, thanks so much for joining us. >> Thank you. >> Thank you for inviting us. >> So, I want to talk about data privacy. The general data protection regulation, the EU's forthcoming laws, GDPR, are going to take effect in May of next year. They represent a huge fundamental change about the way that companies use data. Can you just set the scene for our viewers and explain what these changes mean? >> Sure, happy to. As you said, GDPR is the newest regulation, it will replace the current EU directive, goes into effect May 25th of 2018. It has some fundamental changes that are massively different than any other data privacy laws you've ever seen. First and foremost, it is a legal, compliance and business issue as opposed to a technology issue. It's also the first extra-territorial regulation, meaning, it will apply to any organization anywhere in the world, regardless of whether or not they have a presence in Europe. But if they provide goods and services to an EU resident, or they have a website that EU residents would go to to enter data, they are going to have to comply with GDPR, and that is a massive change for companies. Not to mention the sanctions, the sanctions can be equal to 20 million Euro or 4% of a company's annual global turnover, pretty phenomenal sanctions. There are a lot of fundamental changes, but those are probably the biggest right there. >> What are some of the biggest challenges that companies are... I mean, you talked about the threat of sanctions and just the massive implications of what companies need to do to prepare? >> To really prepare, as I'm talking to customers, they really need, unfortunately a lot of companies are just thinking about security. And they're thinking, well as long as we have encryption, as long as we have tokenization, as long as we're locking down that data, we're going to be okay. I'm saying, no. It first and foremost starts with building that legal compliance program. What does your data privacy program look like? What personal data are you collecting? Why are you collecting it? Do you have the legal right to collect it? Part of GDPR requires unambiguous, explicit, freely-given consent. Companies can no longer force or imply consent. A lot of times when you go on to websites the terms and conditions are so impossible to understand that people just tick the box (laughs). Well, under GDPR, that will no longer be valid because it has to be very transparent, very easily understandable, very readable. And people have to know what organizations are doing with their data. And it puts ownership and more control of data back into the hands of the data subject, as opposed to the organizations that are collecting data. SO those are some of the fundamental changes. For the Cloud environment, for instance, for a lot of big hyperscalers, GDPR now puts obligations on data processors which is very different from the current regulation. SO that's going to be a fundamental change of business for a lot of organizations. >> Now, is it just customers or is it customers and employees as well? >> It's customers, employees, suppliers, it's any personal data that an organization collects, regardless of the relationship. >> SO what does it mean? Does it mean that I'm renting your data? Does it mean that I, 'cause you now own it, it's not me owning it. >> I own it, that's right. >> What are some of the implications of how folks are going to monetize some of these resources? >> SO what it actually means is, as an organization that's collecting data, you have to have a legal and valid business reason for needing that data. SO part of GDPR requires what's called, data minimization. You should only be collecting the minimal amount of data you need in order to provide the service you're going to provide, or manage the relationship you're going to manage. And you are never, as an organization, the owner of that data, you're the data steward. I am giving you permission to use my data for a very specific reason. You can't take liberties with that data. You can't do, what I call, scope-creep which is, once you have the data, "Oh, I can do whatever I want "with that data," no you can't. Unless I have consented to it, you cannot use that data. And so, that is going to be a major change for organizations to deal with and it doesn't matter if it's your employee data, your customer data, your partner data, your alternative worker data, your supplier data. Whose ever data you have, you better be transparent about that data. >> Shelia, you haven't once mentioned technology. Paul, what does this mean from a technology perspective? >> I suppose it's my job to mention technology? >> As Shelia will tell you, the GDPR, it should not be driven by IT. Because it's not an IT problem, it's absolutely a legal and compliance issue. However, I think there's a technology problem in there. So for lots of things that Shelia is talking about, in terms of understanding your data, in terms of being able to find data, being able to remove data when you no longer need to use it, that's absolutely a technology problem. And I think, actually, maybe something you won't hear said very often, I'm a real fan of GDPR, I think a it's long overdue it's probably because Shelia's been beating me round the head for the last 12 months >> I have. >> about it. But, I think it's one of those things that's long overdue to all of us within enterprises, within business, who hold and look after data. Because what we've done, traditionally, is that we just collected tons and tons of data and we bought storage 'cause storage could be relatively cheap, we're moving things to the Cloud. And, we've got absolutely no control, no management, no understanding of what the data is, where it is, who has access to it? Does anybody even access it, I'm paying for it, does anybody even use it? And I think what this is, for me, if GDPR wasn't a regulatory thing that we had to do, I think it's a set of really good practices that, as organizations, we should be looking to follow anyway. And technology plays a small part in that, it will enable organizations to understand the data better, it will enable those organizations to be able to find information as and when they need it. When somebody makes a subject access request, how are you going to find that data without appropriate technology? And I think, first and foremost, it's something that is forcing organizations to look at the way they culturally look after data within their business. This is no longer about, "Let me just keep things forever and I won't worry about it." This is a cultural shift that says data is actually an asset in your business. And as Shelia actually mentioned before, and something I'll pinch in future, the data is not mine, I'm just the custodian of that data while you allow me to be so. So I should treat that like anything else I'm looking after on your behalf. SO I think it's those kind of fundamental shifts that will drive technology adoption, no doubt, to allow you to do that, but actually, it's much more of a cultural shift in the way that we think of data and the way that we manage data in our businesses. >> Well you're talking about it as this regulation that is long overdue, and it will cause this cultural shift. So what will be different in the way that companies do business and the way that they treat their customer data, and their customer's privacy? And their employee's privacy, too, as you pointed out? >> Well, and part of the difference is going to be that need for transparency. So companies are going to have to be very upfront about what they're doing with the data, as Paul said. You know, why are they collecting that data, and they need to think differently about the need for data. Instead of collecting massive amounts of data that you really don't need, they need to take a step back and say, "This is the type of relationship "I'm trying to manage." Whether it's an employment relationship, whether it's a customer relationship, whether it's a partner relationship. What is the minimum amount of information I need in order to manage that relationship? So if I have an employee, for instance, I don't need to know what my employee does on their day off. Maybe that's a nice thing to know because I think well, maybe we can offer them a membership to a gym because they like to work out? That's not a must-have, that's a nice-to-have. And GDPR is going to force must-haves. In order to manage the employment relationship I have to be able to pay you, I have to be able to give you a job, I have to be able to provide benefits, I have to be able to provide performance evaluations and other requirements, but if it's not legally required, I don't need that data. And so it's going to change the way companies think about developing programs, policies, even technology. As they start to think about how they're developing new technology, what data do they need to make this technology work? And technology has actually driven the need for more privacy laws. If you think about IoT, artificial intelligence, Cloud. >> Mobile. >> Absolutely. Great technology, but from a privacy perspective, the privacy was never a part of the planning process. >> In fact, in many respects it was the exact opposite. There were a whole bunch of business models, I mean if you think about it in the technology industry, there's two fundamental business models. There's the ad-based business model, which is, "Give us all your data "and we'll figure out a way to monetize it." >> Absolutely. >> And there's a transaction-based business model which says, "We'll provide you a service "and you pay us, and we promise to do something "and only something with your data." >> Absolutely. >> It's the difference between the way Google and Facebook work, and say, Apple and Microsoft work. SO how is this going to impact these business models in ways of thinking about engaging customers at least where GDPR is the governing model? >> Well, it is going to force a fundamental change in their business model. SO the companies that you mentioned, that their entire business model is based on the collection and aggregation of data, and in some cases, the selling of personal data. >> Some might say screwing you. >> Some might definitely say that, especially if you're a privacy attorney, you might say that. They offer fabulous services and people willingly give up their privacy, that's part of the problem, is that they're ticking the box to say, "I want to use Facebook, I want to use Twitter, "I want to use LinkedIn "because these are great technologies." But, it's the scope-creep. It's what you're doing behind the scenes that I don't know how you're using my data. SO transparency is going to become more and more critical in the business model and that's going to be a cultural, as Paul said, a cultural shift for companies that their entire business model's based on personal data. They're struggling because they're the companies that, no matter what they do, they're going to have to change. They can't just make a simple, change their policy or procedure, they have to change their entire business model to meet the GDPR obligations. >> And I think from, like Shelia says there, and obviously GDPR's very much around, kind of, private data. Well, the conversation we're having with our customers is, is a much wider scope than that, it is all of the data that you own. And it's important, I think, organizations need to stop being fast and loose with the information that they hold because not only is the private information about those people there that, you know, me and you, and that we don't want that necessarily leaked across the well to somebody who might look to exploit that for some other reason. But, that might be, business confidential information, that might be price list, it might be your customer list. And, at the moment, I think in lots of organizations we have a culture where people from top to bottom in an organization don't necessarily understand that. SO they might be doing something where, we had a case in UK recently where some records, security arrangements for Heathrow Airport were found on a bus. So somebody copied them to a USB stick, no encryption, somebody copied it to a USB stick, thought it was okay to take home and leave in the back of, probably didn't think it was okay to leave in the back of the taxi, but certainly thought it was okay to take that information home. And you look at that and think, well, what other business asset that that organization held would they have treated with such disdain, almost to say "I just don't care, this is just ones and zeroes, "why would I care about it?" It's that shift that I think we're starting to see. And I think it's that shift that organizations should have taken a long time ago. We talk to customers, and you hear of events like this all the time, data is the new gold, data is the new precious material of your choice. >> Which it really isn't. It really isn't, here's why I say that because this is the important thing and leads to the next question I was going to ask you. Every asset that's ever been conceived follows the basic laws in economic scarcity. Take gold, you can apply to that purpose, you can make connectors for a chip, or you can use it as a basis for making jewelry or some other purpose. But, data is fungible in so many ways. You can connect it and in many respects, we talked about it a little bit earlier, the act of making it private is, in many respects, the act of turning it into an asset. SO one of the things I want to ask you about, if you think about it, is that, there will still be a lot of net new ways to capture data that's associated with a product or service in a relationship. SO we're not saying that GDPR is going to restrict the role that data plays, it's just going to make it more specific. We're still going to see more IoT, we're still going to see more mobile services, as long as the data that's being collected is in service to the relationship or the product that's being offered. >> Yeah, you're absolutely right. I mean, one of the things that I always say is that, GDPR's intent is not stop organizations from collecting data, data is your greatest asset, you need data to manage any kind of relationship. But, you're absolutely right in what it's going to do is force transparency, so instead of doing things behind the scenes where nobody has any idea what you're doing with my data, companies are going to have to be extremely transparent about it and think about how it's being used. You talked about data monetization, healthcare data today is ten times more valuable than financial data. It is the data that all hackers want. And the reason is, is because you take even aggregate and statistical information through, say trial clinics, information that you think there's no way to tie it back to a person, and by adding just little elements to it, you have now turned that data into greater value and you can now connect it back to a person. SO data that you think does not have value, the more we add to it and the more, sort of, profiling we do, the more valuable that data is going to become. >> But it's even more than that, right? Because not only are you connecting it back to a person, you're connecting it back to a human being. Whereas financial data is highly stylized, it's defined, it's like this transaction defining, and there's nothing necessarily real about it other than that's the convention that we used to for example, do accounting. But, healthcare data is real. It ties back to, what am I doing, what drugs am I taking, why am I taking them, when am I visiting somebody? This is real, real data that provides deep visibility into the human being, who they are, what they face, and any number of other issues. >> Well, if you think about GDPR, too, they expanded the definition of personal data under GDPR. SO it now includes data, like biometric and genetic information that is heavily used in the healthcare industry. It also includes location data, IP information, unique identifiers. SO a lot of companies say, "Well, we don't collect personal data "but we have the unique identifiers." Well, if you can go through any kind of process to tie that back to a person, that's now personal data. SO GDPR has actually the first entry into the digital age as opposed to the old fashioned processing. Where you can now take different aspects of data and combine it to identify a human being, as you say. >> So, I got one more question. This is something of a paradox, sorry for jumping in, but I'm fascinated by this subject. Something of a paradox. Because the act of making data private, at least to the corporation, is an act of creating an asset, and because the rules of GDPR are so much more specific and well thought through than most rules regarding data, does it mean that companies that follow GDPR are likely, in the long run, to be better at understanding, taking advantage of, and utilizing their data assets? That's the paradox. Most people say, "I need all the data." Well, GDPR says, "Maybe you need to be more specific "about how you handle your data assets." What do you think, is this going to create advantages for certain kinds of companies? >> I think it absolutely is going to create advantages in two ways. One, I see organizations that comply with GDPR as having a competitive advantage. Because, number one it goes down to trust. If I'm going to do business with Company A or Company B, I'm going to do business with the company that actually takes my personal data seriously. But, looking' at it from your point of view, absolutely. As companies become more savvy when it comes to data privacy compliance, not just GDPR, but data privacy laws around the world, they're also going to see more of that value in the data, be more transparent about it. But, that's also going to allow them to use the data for other purposes, because they're going to get very creative in how having your data is actually going to benefit you as an individual. SO they're going to have better ways of saying, "But, by having your data I can offer you these services." >> GDPR may be a catalyst for increased data maturity. >> Absolutely. >> Well, I wanna ask you about the cultural shift. We've been talking so much about it from the corporate standpoint, but will it actually force a cultural shift from the customer standpoint, too? I mean, this idea of forcing transparency and having the customer understand why do you need this from me, what do you want? I mean, famously, Europeans are more private than Americans. >> Oh much so. As you've said, "Just click accept, okay, fine, "tell me what I need to know, "or how can I use this website?" >> Well, the thing is that, it's not necessarily from a consumer point of view, but I do think it's from a personal point of view from everybody. SO whether you work inside an organization that keeps data, that's starting to understand just how valuable that data might be. And just to pick up on something, that just to pop at something you were saying before, I think one of the other areas where this has business benefit is that that better and increased management and maturity, actually I think is actually a great way, that better maturity around how we look after our data, has huge impact. Because, it has huge impact in the cost of storing' it, if we want to use Cloud services why am I putting things there that nobody looks at? And then, looking at maintaining this kind of cultural shift that says, "If I'm going to have data in my organization, "I'm no longer going to have it on a USB stick "and leave it in the back of a cab "when it's got security information "of a global major airport on it. "I'm going to think about that "because I'm now starting to understand." And this big drive about, people starting to understand how the information that people keep about you has a potential bigger impact, and it has a potential bigger impact if that data, yeah, we've seen data breach, after data breach after data breach. You can't look at the news any day of the week without some other data breach and that's partly because, a bit like health and safety legislation, GDPR's there because you can't trust all those organizations to be mature enough with the way that we look after our data to do these things. SO legislation and regulations come across and said, "Well, actually this stuff's really important "to me and you as individuals, "so stop being fast and loose with it, "stop leaving it in the back of taxis, "stop letting it leak out your organization "because nobody cares." And that's driving a two-way thing, here, it's partly we're having to think more about that because actually, we're not trusting organizations who are looking after our data. But, as Shelia said, if you become an organization that has a reputation for being good with the way they lock their data, and look after data, that will give you a competitive edge alongside, actually I'm being much more mature, I'm being much more controlled and efficient with how I look after my data. That's got big impact in how I deliver technology and certainly, within a company. Which is why I'm enthusiastic about GDPR, I think it's forcing lots and lots of long-overdue shift in the way that we, as people, look after data, architect technology, start to think about the kind of solutions and the kind of things that we do in the way that we deliver IT into business and enterprise across the globe. >> I think one of the things, too, and Paul brought it up, is he mentioned security several times. And, as Paul knows, one of my pet peeves is when companies say, "We have world-class security, "therefore we're compliant with GDPR." And I go, "Really, so you're basically locking down data "you're not legally allowed to have? That's "what you're telling me." >> Like you said earlier, it's not just about having encryption everywhere. >> Exactly, and it's funny how many companies say "Well, we're compliant with GDPR "because we encrypt the data." And I go, "Well, if you're not legally allowed "to have that data, that's not going to help you at all." And, unfortunately, I think that's what a lot of companies think, that as long as we're looking at the security side of the house, we're good. And they're missing the whole boat on GDPR. >> It's got to be secure. >> It's got to be secure. >> But-- >> You got to legally have it first. >> Exactly. The chicken and the egg. >> But, what's always an issue with security, around data and the stuff that Shelia talked about is quite a lot, is that one of the risks you have, is you can have all the great security in the world but, if the right person with the right access to the right data has all the things that they should have, that doesn't mean that they can't steal that data, lose that data, do something with that data that they shouldn't be doing, just because we've got it secured. SO we need to have policies and procedures in place that allow us to manage that better, a culture that understands the risk of doing those kinds of things, and maybe, alongside technologies that identify, unusual use of data are important within that. >> Well, Paul, Shelia, thank you so much for coming on the show, it's been a fascinating conversation. >> Thank you very much, appreciate it. >> Yeah, thanks for having us on, appreciate it. >> I'm Rebecca Knight for Peter Burris, we will have more from NetApp Insight here in Berlin in just a little bit. (upbeat music)

>> Narrator: Live form Berlin, Germany, it's the Cube, covering NetApp insight 2017, brought to you by NetApp. Welcome back to the Cube's live coverage of NetApp insight 2017, we're here in Berlin, Germany, I'm your host, Rebecca Night along with my cohost Peter Burris. We have two guests on the program now, we have Rory McBride, who is the technical account manager at Aero and Bryan Mclosky, who is the vice president world wide for hyper converge infrastructure at NetApp. Bryan, Rory, thanks so much for coming on the show. >> Thanks. >> Let me start with you, Bryan, talk a little bit, tell our viewers a little bit about the value, that HCI delivers to customers, especially in terms of simplifying the data. >> In a nutshell, what NetApp HCI does is it takes what wold normally be hours and hours to implement a solution and 100s of inputs, generally, over 400 inputs and it simplifies it down to under 30 inputs in an installation, that will be done within 45 minutes. Traditionally HCI solutions have similar implementation characteristics, but you lose some of the enterprise flexibility and scale, that customers of NetApp have come to expect over the years. What we've done is we've provided that simplicity, while allowing customers to have the enterprise capabilities and flexibility, that they've grown accustomed to. >> Is this something, that you are talking with customers, in terms of the simplicity, what were you hearing from customers? >> Most customers these days are challenged of, everybody has to find a way to do more with less or to do minimally a lot more with the same. If you think of NetApp, we've always been wonderful about giving customers a great production experience. When you buy a typical NetApp product, you're gonna own it for three, four or five years and it will continue. NetApp has always been great for that three, four and five year time frame and what we've done with HCI is we really simplified the beginning part of that curve of how do you get it from the time it lands on your dock to implement it and usable by our users in a short manner, that's what HCI has brought to the NetApp portfolio, that's incremental to what was there before. >> One of the advantages to third parties, that work closely with NetApp is, that by having a simpler approach of doing things, you can do more of them, but on the other hand, you want to ensure, that you're also focused on the value add. In the field, when you're sitting down with a customer and working with them to ensure, that they get the value, that they want from these products, how do you affect that balance? As the product becomes simpler to the customer now being able to focus more on other things, other than configuration of limitation. >> We've been able to get to doing something with your data is the key. You needed a little bar of entry, which a lot of the software and hardware providers are trying to do today. I think HCI just has to pull all of that together, which is great. We're hearing from third party vendors, that it's great, that from day one, they've been integrated into the overall portfolio message and I think customers are just gonna be pretty excited with what they can do from zero with this hardware. >> When you think about ultimately how they're gonna spend their time, what are they going to be doing instead of now all this all configuration work? What is Aero gonna be doing now, that you're not doing that value added configuration work? >> Hopefully, we'll be helping to realize the full potential of what they bought, rather than spending a lot of time trying to make the hardware work, they're concentrating more on delivering a service or an application back to the business, it's gonna generate some revenue. In Aero we're talking a lot to people about IOT and it's gonna be the next wave of information, that people are gonna have to deal with and having a stable product, that can support and provide value, you have information back to business, it's gonna be key. >> Bryan, HCI, as you noted, dramatically reduces the time to get to value, not only now, but it also sustains that level of simplicity over the life of the utilization of the product. How does it fit into the rest of the NetApp product set, the rest of the NetApp portfolio? What does it make better, what makes it better in addition to just the HCI product? >> NetApp has a really robust portfolio of offerings, that we, at a high level categorize into our next generation offerings, which are Solid Fire, Flexpod Solid Fire, storage grid and hyper converge and then the traditional NetApp on tap based offerings. What the glue between the whole portfolio is the data fabric and HCI is very tightly integrated into the data fabric, one of the innovations we are delivering is snap mirror integration of the RHCI platform into the traditional on tap family of products. You can seamlessly move data from our hyper converge system to a traditional on tap base system and it also gives you seamless mobility to either your own private cloud or to public cloud platforms. As a company with a wide portfolio, it gives us the ability to be consultative with our partners and our customers. What we want is and we feel customers are best served on NetApp and we want them to use NetApp, and if an on tap base system is a better solution for them than hyper converge, then that's absolutely what we will recommend for them. Into your earlier question about the partners, one of the interesting things with HCI is it's the first time as NetAPP were delivering an integrated system with compute and with a hyperviser, it comes preconfigured with the emware and it's a wonderful opportunity for our partners to add incremental value through the sale cycle to what they've brought to NetApp in the past. Because as NetApp, we're really storage experts, where our partners have a much wider and deeper understanding of the whole ecosystem than we do. It's been interesting for us to have discussions with partners, cuz we're learning a lot, because we're now involved in layers and we're deeply involved at higher levels of the stack, than we have been. >> I'm really interested in that, because you say, that you have this consultative relationship with these customers, how are you able to learn from them, their best practices and then do you transfer what you've learned to other partners and other customers? >> From the customer and we try and disseminate the learning as much as we can, but we're a huge organization with many account teams, but it all starts with what the customers wants to accomplish, minimally they need a solution, that's gonna plug in and do what they expect it to do today. What's the more important part is where what their vision is for where they wanna be three years down the road, five years down the road, 10 years down the road. It's that vision piece, that tends to drive more towards one part of the portfolio, than the other. >> Take us through how this works. You walk into an account, presumably Aero ECS has a customer. The Aero ECS customer says, "Well, we have an issue, that's going to require some specialized capabilities and how we use our data". You can look at a lot of different options, but you immediately think NetApp, what is it, that leads you to NetApp HCI versus on tap, versus Solid Fire, is there immediate characteristic, that you say, "That's HCI"? >> I would say, that the driving factor was the fact, that they wanted something that's simple and easy to manage, they want to get a mango data base up and running or they've got some other application, that really depends on their business. The underlying hardware needs to function. Bryan was saying, that it's got element OS sitting underneath it, which is in its 10th iteration and you've got VM version six, which is the most adopted virtualization platform out there. These are two best breed partnerships coming together and people are happy with that, and can move, and manage it from a single pane of glass moving forward from day one right the way through when they need to transition to a new platform, which is seamless for them. That's great from any application point, because you don't wanna worry about the health of things, you wanna be able to give an application back to the business. We talked about education, this event is gauged towards bringing customers together with NetApp and understanding the messaging around HCI, which is great. >> What are the things, that you keep hearing form customers, does this need for data simplicity, this need for huge time saving products and services? What do you think, if you can think three to five years down the road, what will the next generation of concerns be and how are you, I'm gonna use the word, that we're hearing a lot, future proof, what you're doing now to serve those customers needs of the future? >> Three to five years down the road. I can't predict three to five years out very reliably. >> But you can predict, that they're gonna have more data, they're going to merge it in new and unseen ways and they need to do it more cheaply. >> The future proofing really comes in from the data fabric. With the integration into the data fabric, you could have information, that started on a NetApp system, that was announced eight years ago, seamlessly moves into a solid fire or flash array, which seamlessly moves to a hyperconverge system, which seamlessly moves to your private cloud, which eventually moves off to a public cloud and you can bring it back into any tiers and wherever you want that data in six, seven, eight years, the data fabric will extend to it. Within each individual product, there are investment protection technologies within each one, but it's the data fabric, that should make customers feel comfortable, that no matter where they're gonna end up, taking their first step with NetApp is a step in the right direction. >> The value added ecosystem, that NetApp and others use and Aero ECS has a big play around that, has historically been tied back into hardware assets, how does it feel to be moving more into worrying about your customers data assets? >> I think it's an exciting time to be bringing those things together. At the end of the day, it's what the customer wants, they want a solution, that integrates seamlessly from whether that be the rack right the way up to the application, they want something, that they can get on their phone, they want something they can get on their tablet, they want the same experience regardless whether they're in an airplane or right next to the data center. The demand on data is huge and will only get bigger over the next five years. I was looking at a recent cover of forest magazine, it was from a number of years ago about Nokia and how can anybody ever catch them and where are they now? I think you need to be able to spot the changes and adapt quickly and to steal one of the comments from the key note yesterday, is moving from a survivor to a thriver with your data, it's gonna be key to those companies. >> In talking about the demands on data growing, it's also true, that the demands on data professionals are growing too. How is that changing the way you recruit and retain top talent? >> For us, as NetApp, if you were to look at what we wanted in the CV five years ago, we wanted people, that understood storage, we wanted people, that knew about volumes, that knew about data layouts, that knew how to maximize performance by physical placement of data and now what we're looking for is people, that really understand the whole stack and that can talk to customers about their application needs their business problems, can talk to developers. Because what we've done is we've taken those people, that were good in all those other things I mentioned, when you ask them what did you love about this product, none of them ever came back and said I love the first week I spent installing it. We've taken that away and we've let them do more interesting work. A challenge for us is, us is a collective society, is to make sure we bring people forward from an education perspective skills enablement, so they're capable of rising to that next level of demand, but we're taking a lot of the busy work out. >> Making sure, that they have the skills to be able to take what they're seeing in the data and then take action. >> We want our customers to look at NetApp as data expert, that can work with them on their business problem, not a storage expert, that can explain how an array works. >> Bryan, Rory, thank you so much for coming on the show, it's been a great conversation. >> Thank you. >> Thank you very much. >> You are watching the Cube, we will have more from NetApp insight, I'm Rebecca Night for Peter Burris in just a little bit.

>> Announcer: Live from Berlin, Germany, it's theCUBE! Covering NetApp Insight 2017. Brought to you by NetApp. (rippling music) Welcome back to theCUBE's live coverage of NetApp Insight. I'm Rebecca Knight, your host, along with my cohost Peter Burris. We are joined by Brett Roscoe. He is the Vice President for Solutions and Service Marketing at NetApp, and Laura Dubois, who is a Group Vice President at IDC. Thanks so much for coming on the show. Yeah, thanks for having us. Thank you for having us. So, NetApp and IDC partner together and worked on this big research project, as you were calling it, a thought leadership project, to really tease out what the companies that are thriving and being successful with their data strategies are doing, and what separates those from those that are merely just surviving. Do you want to just lay the scene for our viewers and explain why you embarked on this? Well, you know, it's interesting. NetApp has embarked on its own journey, right, its own transformation. If you look at where the company's been really over the past few years in terms of becoming a traditional storage company to a truly software, cloud-focused, data-focused company, right? And that means a whole different set of capabilities that we provide to our customers. It's a different, our customers are looking at data in a different way. So what we did was look at that and say we know that we're going through a transformation, so we know our customers are going through a journey themselves. And whatever their business model is, it's being disrupted by this digital economy. And we wanted a way to work with IDC and really help our customers understand what that journey might look like, where they might be on that path, and what are the tools and what are the engagement models for us to help them along that journey? So that was really the goal, was really, it's engagement with our customers, it's looking and being curious about where they are on their journey on digital, and how do they move forward in that, in doing all kinds of new things like new customer opportunities and new business and cost optimization, all that kind of stuff. So that's really what got us interested in the project to begin with. Yeah, and I would just add to that. Revenue's at risk of disruption across pretty much every industry, and what's different is the amount of revenue that's at risk within one industry to the next. And all of this revenue that's at risk, is really as a consequence of new kinds of business models, new kinds of products and services that are getting launched new ways of engaging with customers. And these are some of the things that we see thrivers doing and outperforming merely just survivors, or even just data resisters. And so we want to understand the characteristics of data thrivers, and what are they doing that's uniquely different, what are their attributes versus companies that are just surviving. So let's tease that out a little bit. What are these data thrivers doing differently? What are some of the best practices that have emerged from this study? Well I mean, I think if you look at there's a lot of great information that came out of the study for us in terms of what they're doing. I think in a nutshell, it's really they put a focus on their data and they look at it as an asset to their business. Which means a lot of different things in terms of how is the data able to drive opportunities for them. I mean, there's so many companies now that are getting insights from their data, and they're able to push that back to their customer. I mean, NetApp is a perfect example of that. We actually do that with our customers. All the telemetry data we collect from our own systems, we provide that information back to our customers so they can help plan and optimize their own environments. So I think data is certainly, it's validated our theory, our message of where we're going with data, but I think the data focus, I mean, there's lot of other attributes, there's the focus of hiring chief data officers within the company, there's certainly lots of other attributes, Laura, that you can comment on. Yeah, I mean, we see new roles emerging around data, right, and so we see the rise of the data management office. We see the emergence of a Chief Data Officer, we see data architects, certainly data scientists, and this data role that's increasingly integrated into sort of the traditional IT organization, enterprise, architecture. And so enterprise, architecture and these data roles very, very closely aligned is one, I would say, example of a best practice in terms of the thriver organizations, is having these data champions, if you will, or data visionaries. And certainly there's a lot of things that need to be done to have a successful execution, and a data strategy as a first place, but then a successful execution around data. And there's a lot of challenges that exist around data as well. So the survey highlighted that obviously data's distributed, it's dynamic and it's diverse, it's not only in your private cloud but in the public cloud, I think it's at 34% on average of data is in a public cloud. So, how to deal with these challenges is, I think, also one of the things that you guys wanted to highlight. Yeah, and I think the other big revelation was the thrivers, one of the aspects, so not their data focus but also they're making business decisions with their data. They tend to use that data in terms of their operations and how they drive their business. They tend to look for new ways to engage with their customers through a digital or data-driven experience. Look at the number of mobile apps coming out of consumer, really B to C kind of businesses. So there's more and more digital focus, there's more and more data focus, and there's business decisions made around that data. So, I want to push you guys on this a little bit. 'Cause we've always used data in business, so that's not new. There's always been increasing amounts of data being used. So while the volume's certainly new, it's very interesting, it's by itself not that new. What is new about this? What is really new about it that's catalyzing this change right now? Have you got some insights into that? Well, I would just say if you look at some of the largest companies that are no longer here, so you've got Blockbuster, you've got Borders Books and Music, you've got RadioShack, look at what Amazon has done to the retail industry. You look at what Uber is doing to the transportation industry. Look at every single industry, there's disruption. And there's the success of this new innovative company, and I think that's why now. Yes, data has always been an important attribute of any kind of business operation. As more data gets digital, combine that with innovation and APIs that allow you to, and the public cloud, allow you to use that as a launch pad for innovation. I think those are some of the things about why now. I mean, that would be my take, I don't know-- Yeah, I think there's a couple things. Number one, I think yes, businesses have been storing data for years and using data for years, but what you're seeing is new ways to use the data. There's analytics now, it is so easy to run analytics compared to what it was just years ago, that you can now use data that you've been storing for years and run historical patterns on that, and figure out trends and new ways to do business. I think the other piece that is very interesting is the machine learning, the artificial intelligence, right? So much of the industry now, I mean, look at the automotive industry. They are collecting more information than I bet they ever thought they would, because the autonomous driving effort, all of that, is all about collecting information, doing analytics on information, and creating AI capabilities within their products. So there's a whole new business that's all new, there's whole new revenue streams that are coming up as a result of leveraging insights from data. So let me run something by ya, 'cause I was looking for something different. It used to be that the data we were working was what I call stylized data. You can't go out here in Berlin and wander the streets and find Accounting. It doesn't exist, it's human-made, it's contrived. HR is contrived. We have historically built these systems based on transactions, highly stylized types of data. There's only so much you can do with it. But because of technology, mobile, IOT, others, we now are utilizing real world data. So we're collecting an entirely new class of data that has a dramatic impact in how we think about business and operations. Does that comport with what the study said, that study respondents focusing on new types of data as opposed to just traditional sources of data? We certainly looked at correlations of what data thrivers are doing by different types of data. I would say, in terms of the new types of data that are emerging, you've got time series data, stream data, that's increasingly important. You've got machine-generated data from sensors. And I would say that one thing that the thrivers do better than merely just survivors, is have processes and procedures in place to action the data. To collect it and analyze it, as Brett pointed out, is accessible, and it's easy. But what's not easy to is to action results out of that data to drive change and business processes, to drive change in how things are brought to market, for example. So, those are things that data thrivers are doing that maybe data survivors aren't. I don't know if you have anything to add to that. Yeah, no, I think that's exactly right. I think, yes, traditional data, but it's interesting because even those traditional data sets that have been sitting there for years have untapped value. >> Peter: Wikibon knew types of data. That's right. But we've also been doing data warehousing, analytics for a long time. So it seems as though, I would guess, that the companies that are leading, many that you mentioned, are capturing data differently, they're using analytics and turning data into value differently, and then they are taking action based on that data differently. And I'm wondering if across the continuum that you guys have identified, of thrivers all the way down to survivors, and you mentioned one other, data-- >> Laura: resisters. resisters, and there was, anyways. So there's some continuum of data companies. Do they fall into that pattern, where I'm good at capturing data, I'm good at generating analytics, but I'm not good at taking action on it? Is that what a data resister is? So a data resister is sort of the one extreme. Companies that don't have well-aligned processes where they're doing digital transformation on a very ad hoc basis, it's not repeatable. They're somewhat resistant to change. They're really not embracing that there's disruption going on that data can be a source of enablement to do the disrupting, not being disrupted. So they're kind of resisting those fundamental constructs, I would say. They typically tend to be very siloed. Their IT's in a very siloed architecture where they're not looking for ways to take advantage of new opportunities across the data they're generating, or the data they're collecting, rather. So that would be they're either not as good at creating business value out of the data they have access to. Yes, that's right, that's right. And then I think the whole thing with thrivers is that they are purposeful. They set a high level objective, a business-level objective that says we're going to leverage data and we're going to use digital to help drive our business forward. We are going to look to disrupt our own business before somebody disrupts it for us. So how do you help those data resistors? What's your message to them, particularly if they may not even operate with the belief that data is this asset? I mean, that's the whole premise of the study. I think the data that comes out, like you know, hey data thrivers, you're two times more likely to draw two times more profitability to there's lots of great statistics that we pulled out of this to say thrivers have a lot more going for them. There is a direct corelation that says if you are taking a high business value of your data, and high business value of the digital transformation that you are going to be more profitable, you're going to generate more revenue, and you're going to be more relevant in the next 10 to 20 years. And that's what we want to use that, to say okay where are you on this journey? We're actually giving them tools to measure themselves by taking assessments. They can take an assessment of their own situation and say okay, we are a survivor Okay, how do we move closer to being a thriver? And that's where NetApp would love to come in and engage and say let us show you best practices, let us show you tools and capabilities that we can bring to bear to your environment to help you go a little bit further on that journey, or help you on a path that's going to lead you to a data thriver. Yeah, that's right, I agree with that. (laughs) What is the thing that keeps you up at night for the data resister, though, in the sense of someone who is not, does not have, maybe not even capturing and storing the data but really has no strategy to take whatever insights the data might be giving them to create value? I don't know, that's a hard question. I don't know, what keeps you up at night? Well, I think if I were looking at a data resister, I think the stats, the data's against them. I mean, right? If you look at a Fortune 500 company in the 1950s, their average lifespan was something like 40 years. And by the year 2020, the average lifespan of an S&P 500 company is going to be seven years, and that's because of disruption. Now, historically that may have been industrial disruption, but now it's digital disruption, and that right there is, if you're feeling like you're just a survivor, that ought to keep a survivor up at night. If I can ask too. It's, for example, one of the reasons why so many executives say you have to hire millennials, because there's this presumption that millennials have a more natural affinity with data, than older people like me. Now, there's not necessarily a lot of stats that definitely prove that, but I think that's one of the, the misperceptions, or one of the perceptions, that I have to get more young people in because they'll be more likely to help me move forward in an empirical style of management than some older people who are used to a very, very different type of management practice. But still there are a lot of things that companies, I would presume, would need to be able to do to move from one who's resisting these kinds of changes to actually taking advantage of it. Can I ask one more question? Is it that, did the research discover that data is the cause of some of these, or just is correlated with success? In other words, you take a company like Amazon, who did not have to build stores like traditional retailers, didn't have to carry that financial burden, didn't have to worry so much about those things, so that may be starting to change, interestingly enough. Is that, so they found a way to use data to alter that business, but they also didn't have to deal with the financial structure of a lot of the companies they were competing with. They were able to say our business is data, whereas others had said our business is serving the customer with these places in place. So, which is it? Do you think it's a combination of cause and effect, or is it just that it's correlated? Hmm. I would say it's probably both. We do see a correlation, but I would say the study included companies whose business was data, as well as companies that were across a variety of industries where they're just leveraging data in new ways. I would say there's probably some aspects of both of that, but that wasn't like a central tenent of the study per se, but maybe that will be phase two. Maybe we'll mine the data and try and find some insights there. Yeah, there's a lot more information that we can glean from this data. We think this'll be an ongoing effort for us to kind of be a thought leader in this area. I mean, the data proved that there was 11% of those 800 respondents that are thrivers, which means most people are not in that place yet. So I think it's going to be a journey for everyone. Yes, I agree that some companies may have some laws of physics or some previous disruptions like brick and mortar versus online retail, but it doesn't mean there's not ways that traditional companies can't use technology. I mean, you look at, in the white paper, we used examples like General Electric and John Deere. These are very traditional companies that are using technology to collect data to provide insights into how customers are using their products. So that's kind of the thought leadership that any company has to have, is how do I leverage digital capabilities, online capabilities, to my advantage and keep being disruptive in the digital age? I think that's kind of the message that we want them to hear. Right, and I would just add to that. It's not only their data, but it's third-party data. So it's enriching their data, say in the case of Starbucks. So Starbucks is a company that certainly has many physical assets. They're taking their customer data, they're taking partner data, whether that be music data, or content from the New York Times, and they're combining that all to provide a customer experience on their mobile app that gives them an experience on the digital platform that they might have experienced in the physical store. So when they go to order their coffee in their mobile pay app, they don't have to wait in line for their coffee, it's already paid for and ready when they go to pick it up. But while they're in their app, they can listen to music or they can read the New York Times. So there's a company that is using their own data plus third party data to really provide a more enriched experience for their company, and that's a traditional, physical company. And they're learning about their customers through that process too. Exactly, exactly, right. Are there any industries that you think are struggling more with this than others? Or is it really a company-specific thing? Well, the research shows that companies in ever industry are facing disruption, and the research shows that companies in every industry are reacting to that disruption. There are some industries that tend to have, obviously by industry they might have more thrivers or more resisters, but nothing I can per se call out by industry. I think retail is the one that you can point to and say there's an industry that's really struggling to really keep up with the disruption that the large, people like Amazon and others have really leveraged digital well advanced of them, well in advance of their thought process. So I think the white paper actually breaks down the data by industry, so you can kind of look at that, I think that will provide some details. But I think every, there is no industry immune, we'll just put it that way. And the whole concept of industry is undergoing change as well. That's true, that is true, everything's been disrupted. Great, well, Brett and Laura thank you so much for coming on our show. We had a great conversation. Thank you. Enjoy your time. You're watching theCUBE, we'll have more from NetApp Insight after this. (rippling music)

(upbeat electronic music) >> Announcer: Live, from Berlin, Germany it's theCUBE. Covering NetApp Insight 2017. Brought to you by NetApp. Welcome back to theCUBE's live coverage of NetApp Insight here in Berlin, Germany. I'm your host, Rebecca Knight, along with my co-host Peter Burris. We are joined by Deepak Visweswaraiah. He is the senior vice president for data fabric manageability at NetApp. Thanks so much for coming on the show, Deepak. Thank you. So let's talk about the data fabric, and why modern IT needs it to do what it needs to do. For acceleration. I think anyone attending the conference, I thought the keynote that happened yesterday Kenneth Corky from Economist actually talked about how data actually is growing. And then how much of that is becoming more and more important to companies. Not only just from an ability to be able to actually handle data, but how they make their decisions based on the amount of data that they have today. The fact that we have that technology, and we have the mindset to be able to actually handle that data, I think gives that unique power to customers who actually have that data. And within their capacity. So, if you look at it in terms of the amount of data growing and what companies are trying to do with that, the fact is that data is not all in one place, it's not all in one format, it's not all just sitting in some place. Right, in terms of the fact that we call it, you know, data being diverse, data being dynamic and then what have you. So, this data, for any CIO, if you talk to an IT organization and ask them in terms of do you even really know where all your data lives, they probably, you know, 80% of the time they don't know where it is all. And they do not know who is accessing what data. Do they actually really have the access or the right people accessing the right data? And then what have you. So, being able to look at all of this data in different silos that is there, to be able to have visibility across these, to be able to actually handle the diversity of that data, whether it is structured, unstructured, comes from, you know, the edges of the network, whether it is streaming, and different types of, you know, media for that matter, whether it is streaming, video, audios, what have you. With that kind of diversity in the data, and the fact that it lives in multiple places, how do you handle all of that in a seamless fashion? Having a ability to view all of that and making decisions on leveraging the value of that data. So, number one, is really to be able to handle that diversity. What you need is a data fabric that can actually see multiple end points and kind of bring that together in one way and one form with one view for a customer. That's the number one thing, if you will. The second thing is in terms of being able to take this data and do something that's valuable in terms of their decision making. How do I decide to do something with it? I think one of the examples you might have seen today for example, is that, we have 36 billion data points coming from our own customer base, that we bring back to NetApp, and help our customers to understand in the universe of the storage end points with all the data collected, we can actually tell them what may proactively tell them, what maybe going wrong what can actually they do better. And then how can they do this. This is really what that decision making capability is to be able to analyze. It's about being able to provide that data, for analytics to happen. And that analytics may happen whether it happens in the cloud, whether it happens where the data is, it shouldn't really matter, and it's our responsibility to provide or serve that data in the most optimized way to the applications that are analyzing that data. And that analysis actually helps make significant amount of decisions that the customers are actually looking to. The third is, with all of this that is underlying infrastructure that provides the capability to handle this large amount of data, not only, and also that diversity that I talked about. How do you provide that capability for our customers, to be able to go from today's infrastructure in their data center, to be able to have and handle a hybrid way of doing things in terms of their infrastructure that they use within their data center, whether they might actually have infrastructure in the cloud, and leveraging the cloud economics to be able to do what they do best, and, or have service providers and call locators, in terms of having infrastructure that may be. Ability to be able to seamlessly look all of that providing that technology to be able to modernize their data center or in the cloud seamlessly. To be able to handle that with our technology is really the primary purpose of data fabric. And then that's what I believe we provide to our customers. So, people talk about data as an asset. And folks talk about what you need to ensure the data becomes an asset. When we talk about materials we talk about inventory we talk about supply chain, which says there's a linear progression, one of the things that I find fascinating about the term fabric even though there's a technical connotation to it, is it does suggest that in fact what businesses need to do is literally weave a data tapestry that supports what the business is going to do. Because you cannot tell with any certainty it's certainly not a linear progression, but data is going to be connected in a lot of different ways >> Deepak: Yeah To achieve the goals of the business. Tell us a little bit about the processes the underlying technologies and how that informs the way businesses are starting to think about how data does connect? >> Deepak: Can you repeat the last part? How data connects, how businesses are connecting data from multiple sources? And turning it into a real tapestry for the business. Yeah, so as you said, data comes in from various different sources for that matter, in terms of we use mobile devices so much more in the modern era, you actually have data coming in from these kind of sources, or for example in terms of let's say IoT, in terms of sensors, that are all over the place in terms of how that data actually comes along. Now, let's say, in terms of if there is a customer or if there is an organization that is looking at this kind of data that is coming from multiple different sources all coming in to play the one thing is just the sheer magnitude of the data. What typically we have seen is that there is infrastructure at the edge, even if you take the example of internet of things. You try and process the data at the edge as much as you can, and bring back only what is aggregated and what is required back to you know, your data center or a cloud infrastructure or what have you. At the same time, just that data is not good enough because you have to connect that data with the internal data that you have about-- Okay, who is this data coming from and what kind of data, what is that meta-data that connects my customers to the data that is coming in? I can give you a couple of examples in terms of let's say there is an organization that provides weather data to farmers in the corners of a country that is densely populated, but you really can never get into with a data center infrastructure to those kind of remote areas. There are at the edge, where you have these sensors in terms of being able to sample the weather data. And sample also the data of the ground in itself, it terms of being able to, the ultimate goals is to be able to help the farmer in terms of when is the right time to be able to water his field. When is the right time to be able to sow the seeds. When is the right time for him to really cut the crops, when is the most optimized time. So, when this data actually comes back from each of these locations, it's all about being able to understand where this data is coming from, from the location, and being able to connect that to the weather data that is actually coming from the satellites and relating that and collating that to be able to determine and tell a farmer on his mobile device, to be able to say okay, here is the right time, and if you don't actually cut the crops in the next week, you may actually lose the window because of the weather patterns that they see and what have you. That's an example of what I could talk about as far as how do you connect that data that is coming in from various sources. And as a great example, I think, was at the keynote yesterday about a Stanford professor talking about the race track, it's really about that race track and not just about any race track that where the cars are actually making those laps, to be able to understand and predict correctly in terms of when to make that pit stop in a race. You really need the data from that particular race track because it has characteristics that have an impact on the wear and tear of the tires. For example. That's really all about being able to correlate that data. So it's having the understanding of the greater context but the specific context too. >> Deepak: Absolutely, absolutely. Great. You also talked about you talked about the technology that's necessary, but you also mentioned the right mindset. Can you unpack that a little bit for our viewers? The mindset I talked about earlier, was really more in terms of can we actually if you think some time before, we couldn't have attacked some of the problems that we can afford to today. It's really having the mindset of being able to from the data I can do things that I could never do before. We could solve, we can solve things in the nature of being able to being able to impact lives if you will. One of our customers leads a Mercy technology. Has built a out care platform, that provides that has a number of healthcare providers coming together. Where they were actually able to make a significant impact where they could actually determine 40% of the patients coming into their facilities, really were prevented from coming back into with a sepsis kind of diagnosis. Before then, they reduce that sepsis happening in 40% of the time. Which is a significant, significant impact, if you will, for the human. Just having that mindset in terms of you have all the data and you can actually change the world with that data, and you can actually find solutions to problems that you could never have before because you have the technology and you have that data. Which was never there before. So you can actually make those kinds of improvements. It's all about extracting those insights. >> Deepak: Absolutely. Thank you so much for coming on the show, Deepak. It was a pleasure having you Thank you for having me. Thank you very much. I'm Rebecca Knight, for Peter Burris, we will have more from NetApp Insight in just a little bit. (dramatic electronic music)

>> Announcer: From Berlin, Germany, it's the Cube. Covering NetApp Insight 2017, brought to you by NetApp. Welcome back to the Cube's live coverage of NetApp Insight here in Berlin, Germany, I'm your host Rebecca Knight along with my cohost Peter Burris. We are joined by Manfred Buchanan, he is the VP systems engineering IMIA for NetApp and Mark Carlton who is an independent IT consultant. Manfred, Mark, thanks so much for coming on the show. Thank you. Thank you for having us. So Manfred, I want to start with you, you're a company veteran, you've been with NetApp for a long time, lets talk about the data management innovations that make IT modernization possible. It's a big question. That's a great question, you know, as a veteran talking about AI and the future and data management, things make it capable, but just coming off the general session, it takes something like our object store and think about, I put an object, a picture from you, I just put it into the storage and you know, it gets handed over into Amazon analytics and Amazon analytics, oh, you are smiling. And think about this without any coding and just few things to pluck it together and it works and if you take it further it works at scale so it's not only your face, it's the two thousand, four thousand, ten thousand faces here. You just put it in in parallel at scale Amazon at scale does the analytics on top and you get the results back just as a blocking in architecture, this data management at scale is this innovation. Is this the next gen data centers, all of them. But it's not magic, something allows that to happen. So what are those kind of two or three technologies that are so crucial to ensuring that that change in system actually is possible? I will put it pretty simple, the core technology we provide connect the non premise data center with the public cloud and make this whole thing seamless happen. And make it happen for all different protocols. You have it in the send space and then an ice class in the cloud, you have it on files on premise move the file over, and you have it with an object, and an object even we go further we integrate it into message pass. Maybe it's too technical but a message pass is just I got an event and I tell someone else this event coming to something and that's what we do with the picture analyzers. I got an event, which is, I get the picture, and with this event, I tell Amazon please do something with the picture and I give you the picture to analyze. So it's a fabric, there's object storage and there's AI and related technologies that allow you to do something as long as the data is ready for that to be done. Yeah and even move to data with it basically that's what we do. And if you think about it's unbelievable magic. Mark I want to ask you, you are, you're an independent IT consultant, you've been following NetApp for a long time, you have your own blog what are some of the biggest trends that you're seeing, what are some of the biggest concerns you hear from customers? Really from customers it's more around what steps to take the markets changing as we can see what we were saying there with data sprawling and it's spreading so fast, it's growing so fast. What we were storing a few years ago a few years ago when I first started someone talked about a terabyte and you thought that's a big system or you got 50 terabytes and you were huge. Now we're talking about 500 terabytes, 100 terabytes and the difference is is what sort of data that is. Is it stored in the right place? And I think that's one of the biggest challenges is knowing what data you have, how to use it and how to get the most out of the data that, and in the right place so we talked about the on prem, on process whether it be in the cloud, whether it be an object and I think that's key from where we're moving with the data fabric within NetApp and how NetApp's creating their data management suite as such for on tap, for the solufy suite and how they're joining the products up so it makes it seamless that we can move this data about from these different platforms. And I think one of the biggest things, biggest thing for me, especially when I'm talking to customers is it's the strategy of what you can do with data. It's the, it's there's no complications, as Manfred said, it's as if it's magic, it's that type of thing, it will go, you can do whatever you want with it. And I think from a customer point of view because they don't have to make that choice and say that's what I want to do today they've got scale, they've got flexibility, they can control where their data sits, they can move it back and forth and the sprawl out into AWS this year and then Google and with a cloud that size and being able to use those three different cloud platforms, even IBM cloud and how they can plug into theirs. It's, it's really starting to open those doors and really argue the point around the challenges. You've got a lot of answers to a lot of different things. So how do you help customers make sense of all of this, I mean as you said, there are a lot of options, they can go a lot of different ways, they know that they need to use their data as an asset, they need to, they need to deploy it find that value, what's your advice? You know let me just also take a step back, we talk about we get more and more data. We talk about connecting the different clouds, but at the same time we also talked about basics I move from fresh into search class memory and I make everything faster. If you think about more data, to process more data in the same time everything needs to go faster and I give you a simple example or just challenge you, how many have you sitting before a business application in your company and you sit, you press an enter button and it takes, takes a minute, takes another and you go, uh, sorry. Thinking about it. Why does it take so long? As a veteran in the old days, what we said is basically, we press the enter button and we said we need to go for a coffee and come back and after the coffee the transaction is done. Now we talked about one stage about microseconds and milliseconds and all these things but put it into relation, take a transaction I press the enter button and it would have taken let me say 10 minutes until I got a result out of it. And this was in times of when storage response times were 10 milliseconds. Take this one into response time is now one millisecond and you do the same amount of data, you press the enter button and it's not 10 minutes, it's a minute. Now you say the next generation technology we showed, it's even a thousand times faster. You go now from a minute, to a thousand of a minute, a millisecond, you know what a millisecond means for you? You press the enter button, result is there. And now you think you get more and more data petabytes of data, how can I make sure and process it as fast as possible? So that's one character you look into and I believe the future is also for AI and all these things is how fast can you process, maybe we get a measurement which called petabytes per second or petabytes per millisecond can you process to get information out of it. And then at the same time you said which solution, which choices? I believe in the current world, as it's so fast moving, all the solutions evolve at a high speed so at a certain time you just make a decision, I just go with this one and even if you go with the public cloud, you choose the public cloud, one is price but also choose it on capabilities, if you go to the IBM side, what an IBM Watson is doing in terms of AI, incredible and that's what we use for actify queue in the support side so it's not only the system, the speed of the system, where do you ploy the data, but at the same time I give you all the information, what are you doing with your data on the support side? You're connecting this and customers will choose like we do it internally the best solution and what we give them, we give them the choice, we give them reference architectures, how it works with this one, how it works with this one, we may give them some kind of guidance but to be frank and as a veteran and sometimes as the guys know me, I'm straightforward, the decision is something the customer needs to make or the partner with the customer together because you have the knowledge basically on the implementation side, need to make, I'm the best one in this one, I know how it works, I know how I can do it, but that's a choice which is more under customer together with their implementation partners. Great, well Manfred, Mark, thanks so much for coming on the Cube, this was great, great having you on. Thank you very much. I'm Rebecca Knight, for Peter Burris, we will have more from NetApp Insight just after this.

>> Narrator: Live from Berlin, Germany It's theCUBE, covering NetApp Insight 2017 Brought to you by NetApp. Welcome back to theCUBE's live coverage of NetApp Insight 2017, here in Berlin, Germany. I'm your host Rebecca Knight along with my co-host Peter Burris. We are joined by Tim Pitcher, he is the Vice President, Next-Generation Data Centre for NetApp. Thanks so much for coming on the program. It's an absolute pleasure, it's a pleasure to be here. So let's start just defining for our viewers the Next-Generation Data Centre, how it's built, how it's founded. Yeah so, if you think about NetApp today we think about our customers really consuming technology in three ways. We've got sort of more, we're modernizing traditional data centers and architectures using data management and flash storage and these sorts of things and this really is our back yard, this is what we've been doing for years and years, been incredibly successful at it. And the big disrupter in many ways is Cloud and so our partnerships with the major hyperscalers are critically important to us as well. But there's a third piece to the jigsaw which is the Next-Generation Data Centre and the way we think about that is that if you imagine that you want to use Cloud services but you want to do a lot of that yourself, you want to take advantage of the sort of simple, scalable, automated nature of Cloud then that's really what we're delivering in the Next-Generation Data Centre for our customers. So the Next-Generation Data Centre is being driven by technology advances, business requirements, the realities of data, what are the practical things that are driving, or indicating, the steps that people should take as they think about new technology and new business practices? I mean, the big driver is really to remove a lot of complexity from their business so if you think about going to the Cloud, you're making a really very simple consumption choice. You're saying I'm going to consume data and services from the public Cloud environment and that drives a similar behavior inside large organizations as well, organizations of all sizes. So they're thinking about how do they build private Cloud, take advantage of both with a hybrid Cloud environment, or they can have multiple public Cloud instances as well. So they're thinking about it all very differently and they're thinking about the most appropriate services that they're trying to deliver or the most appropriate way to deliver that application or that data set, if you will, to their customers. So it's not like everything needs to be in one place, and also critically customers very often want to change that as well so they'll make a decision to put something in a public cloud, it might not be the best fit over time for whatever reason, so they want to bring it back in house and deliver that on their own infrastructure and when they do that they want to take advantage, they like what they've had in the Cloud so they want to put that on premise. So the real drive is they really want simplicity, they're really focused on a much more performant outcome that's focused on simplicity focused on how you scale your business and being able to have truly multi-tenant environments that give you the predictability of your traditional architectures if you will, the architectures you know well and have been using for a long time. You want to be able to do that in a Cloud like environment because you the economics of Cloud but you get the predictability of dedicated environments. So which of the customers that you work with are in fact executing this Next-Generation Data Centre strategy most beautifully in your opinion? Well so, if you think about the strategy that NetApp has for our Next-Gen Data Centre is really based on two companies that they acquired. One is Object Storage platform called StorageGRID Webscale the other one is SolidFire. Which, SolidFire was a young, emerging, hot technology company that was focused on delivering what I've just articulated, simple technologies, simple storage platform operated at scale, completely automated and SolidFire was born out of a service provider, born out of a service provider at the same time as OpenStack so it's kind of unique in that perspective. The company was formed to solve a problem and the problem that Rackspace really were looking to solve was how do they take their managed service clients and move them into the Cloud, what's stopping them doing that? And the answer is obviously customers worry about security and things like that but the key thing that was really stopping them was their concern about performance. So if I'm going to share, put all my stuff in with everybody else's, in a shared environment, how do I know I'm going to get what I'm paying for how do I know that I'm not going to have somebody else's applications consume all the services that are going to be given to me? So as a consequence, this was the thing that prevented people going to the Cloud so this is what the company formed to fix so SolidFire came out of that and that's our background and that's why NetApp acquired us because very different way of looking at things so as a consequence service providers are really at the forefront of how they deliver services to their customers and they leveraged SolidFire and we were very successful as an independent company selling to service providers and have been increasingly successful now that we're part of NetApp. Our very first customer for example is in Jersey and is still a very happy NetApp customer, a company called Calligo and they offer tiered services all on SolidFire, trusted Cloud services in and off-shore kind of environment they're focused on the financial services community and things like that. And now we have also major services providers like 1and1 in Germany, which is one of the largest services providers in Europe, long time NetApp customer and they're a SolidFire customer for their public Cloud services as well for the Cloud that they offer. And in the UK as well, Interoute, major service provider. What I like about them is one, they deal with a massive amount of traffic, they've got a huge network so very traffic intensive, but also they really take advantage of NetApp being, sorry, SolidFire being part of NetApp now so they use the on-tap base products in their manage services which those products are optimized for that kind of environment but for their Cloud environment where they're offering tiered services they use SolidFire so they've got us on both sides of the house if you will and so its a great example of SolidFire being part of NetApp, why that's so powerful, why that's so successful. And companies like Internet Solutions in South Africa is one major service provider in South Africa, big consumer of SolidFire and now is part of NetApp, it's a much better place for them because we've got a big business in South Africa, we're very successful there, so we're part of that team now and they go from strength to strength. So now the next challenge is taking some of the best practices that have emerged from what you've learned from working with these service providers and transferring them to other industries. Yeah so, we're seeing a lot in Fin-tech right now, Farmer is a good market for us, Astrozeneca uses SolidFire so a great example of one of NetApps long-term and major customers that's now consuming products and services from other business units and other offerings that we have across a much broader portfolio so they're very happy customers now. That's part of our global account business. Business Wire in the UAE is another example of a successful business transformation that they're doing as well. We've seen a lot of activity in Dev-ops, these products are perfect for Dev-ops because they're so simple, they don't require management they're completely automated, you're not building those large infrastructures of people to support these environments. And it's much quicker to be able to launch applications because of the simple nature of the technology you can launch applications, new products, new services so your time to market is an awful lot quicker as well. Great, well thanks so much for coming on the show Tim, it's been really fun talking to you. It's been a pleasure, thanks very much. I'm Rebecca Knight for Peter Burris, we will have more from NetApp Insight just after this. (electronic music)

>> Announcer: Live from Berlin, Germany. It's The Cube, covering NetApp Insight 2017. Brought to you by NetApp. Welcome back to The Cube's live coverage of NetApp Insight 2017. I'm your host, Rebecca Knight, along with my cohost, Peter Burris. We are joined by Jean English. She is the senior vice president and chief marketing officer of NetApp. Thanks so much for coming on the show. >> Thank you for having me. >> We're glad you're here with us to join us at Insight Berlin. We're always excited to do anything with NetApp. So talk a little bit about NetApp's digital transformation. You're now at a years long transformation from storage, your legacy, to data. Talk a little bit about your positioning in the market. >> Sure. I think people have previously thought of NetApp as storage. And what we are so focused on now is data. And why data? Because that's what we hear from our customers, our partners' analysts, is what is really topping their needs right now. And when we think about how companies are transforming, they're having to think about digital transformation is topping the list, is topping the most strategic agendas of most CEOs. But what happens is they have to think about the data and how does it become a lifeblood of their business? How does it seamlessly flow through that business? And what does it mean to either optimize their operations, if they've got to increase their customer in touch points, if they have to create new products, services, and even businesses. So we feel like right now that is why our focus is on data. And it's so much a part of our heritage that we look to the future as well. >> So, one of the thing's that you're working on now is helping customers use data in new, exciting, innovative, creative ways. Can you talk broadly about your approach to that and how you're drawing inspiration from customers and then empowering them? >> Absolutely. We really try to think about what is our purpose. And our purpose could be true to our heritage from 25 years ago, we've just celebrated our 25 year anniversary this past spring. And it is to empower our customers to change the world with data. And just a few of those we see now, especially is hybrid cloud environments, customers have to think about how are they going to simplify and integrate data across on-prem, cloud environments, to accelerate digital transformation. One example of that is EidosMedia. We love their story, because they're talking about how to get new stories, real time, through a cloud platform into the hands of journalists that can publish real-time live insights, real-time journalism. And so, when you think about the speed that has to happen with creating stories, getting them published, getting them out to news networks... That's data, and it's a good data story. >> When you think about the data story, though, a lot of people talk about how data is a fuel or data is... And we tend to think, at least it's looking like a Wikibon, but that's probably not the best analogy. Because data's different from other resources. Most resources share the economics of scarcity. You can do this or you can do that. But data's different, because data could be copied, data could be shared, but data also could be appropriated inappropriately. Could you talk a little bit about the relationship, or the direction that NetApp's taking to, on the one hand, facilitate the sharing of data strategically while at the same time ensuring that proper security and IP controls are placed on it? >> Absolutely. I think people are looking to make sure they can share freely, data, and seamlessly integrate data across multiple sources. Right now what we find, whether it's because you've had data that's been on-prem, and maybe that's more structured. Now we're starting to see more unstructured data. So data's becoming a lot more diverse. People are constantly looking for the latest source of truth of data. It's so dynamic, and because it's so distributive across environments, people are trying to figure out how do you integrate data, how do you share data. But it's all about simplicity because they need it to be efficient. They need to make sure that it's protected. So security is top of minds, data protection is utmost of importance. They're looking different ways to embrace future technologies. And whether that's thinking about different cloud environments, Sass applications, and then how do they create the most open opportunities. A lot of people aren't just putting their data in one cloud. What we're finding is it's a multi-cloud world and they're looking for a wholistic solution to more easily and seamlessly manage their data through those environments. >> The infrastructure has to move from a storage orientation towards something that's going to facilitate the appropriate sharing and integration of data. Like a fabric. You could talk a little bit about that. >> Yes. We started the conversation around data fabric. Was one of the first people to really talk about data fabric in the market back in 2013. And this vision was about how do you seamlessly be able to share and integrate data across cloud and on-prem environments. That has become so true in how we've been building out that data fabric today. We just launched a few weeks ago that we are the first industry leading storage data service in the Microsoft Azure console. So that people can easily be able to do complete storage capabilities in cloud storage in Microsoft. We've also been developing solutions to make sure that maybe if you're not wanting to do everything in Office 365 and Azure, you want to back it up to AWS. So how do you have better backup capabilities? Sharing of data across clouds. We're also seeing that you my want to sync data. So maybe once you put data into the cloud and you run analytics or even machine learning, how do you get data back? Because you want to make sure that you're constantly being able to look wholistically at your customers. So this notion of one cloud to back to on-prem, multi-cloud environments has been critical as we've been thinking about customers and where they're going. >> One of the things we're also hearing about at this conference is that this is the day of the data visionary, and this is where people who are thinking about how to store data, use data, extract data, find value in the data... The demands on them, the pressures on them are so intense. How is NetApp helping those people? Understanding where they are, not only in their businesses, but also in their trajectories of their careers. And then helping them move forward. >> Absolutely. We've been really thinking about who is really using data to disrupt. And are this disruptive use of data to really drive business results. It's not just about having the data. It's about how are you going to have it impact on the business. So we started to think about this notion of who is a data thriver. And who's thriving with data versus who's just surviving. And in fact, some are even resisting. So we actually partner with IDC to launch a study on data thrivers. To look at who is truly looking at driving new revenue streams, attracting new customers. How are they able to use data as a corlistic part of their business? Not some one off or side project to help through the digital transformation, but what was going to drive really good business results, data as an asset, data across business and IT. And we see new roles are emerging from this. So we're seeing chief data officers, chief digital officers, chief data scientists, chief transformation officers. All new roles that have been emerging in the last couple of years. But these data thrivers are seeing tremendous business impact. >> So what is it that separates those people? I think of those companies and those business models. And what are some of the worst case scenarios for those companies that are just surviving and not necessarily thriving in this new environment? >> It's interesting. We're seeing that companies that actually put data at the center of what they do, so we think of it as a data-centered organization, are seeing 6x in what they're seeing in terms of being able to drive real customer acquisition. And we think about what it means to drive operational efficiency. When think about 2x times in terms of profitability, real bottom line results, compared to people that are simply just surviving with data. What's interesting is that when we started to think about what are the attributes of these people. So, business and IT working together in unison. These roles, in fact, that are emerging are starting to become those catalyst and change agents that are bringing IT and the business more together. We're also seeing that, when you think of data as an asset, even to the bottom line, how does data become more critical in terms of what they see, in terms of being a difference and an advantage for the company. Also, thinking through quality, quality, quality. So you've got to make sure that the data is of highest quality and it's constantly being cleansed. Then, in terms of how do we think of it being used across the business. It's not just about holding data and locking it away behind a firewall. Data, more today, is so dynamic, distributed, and diverse that you have to let it be utilized and activated across the business. And then to think through, it starts not just in terms of what customers are using and seeing from data, but they can actually see, in terms of customer touch points and having a better customer experience. But then how do you make sure it even comes back to development to create new products, great new services, maybe even eliminate waste? Stop doing product lines based on what they're seeing from actual usage. So it's a pretty fascinating space right now. But the data thriver is the new thought we're thinking in terms of getting that out in the market and really sharing that more so with our clients. So that they can benchmark themselves as well. >> Peter Burris: So, you're a CMO? Yes. You're telling a story, but you also have operational responsibilities. How would you tell your peers to use data differently? >> Well, I think there's a couple things. For me, data is the lifeblood of how we think about how we actually create a better customer experience. We're using data constantly to better understand what are our customers' needs? And those customers are evolving. Before, and the royalists that we love with storage architects and admins. We're starting to see that people are thinking about how to use more hybrid cloud data services. With CIOs, how are they going to look at a cloud strategy? With DevOps, how are they going to create deploying and applications at speed? How are they going to be able to help to really think through? What are they going to do to drive more analytics and better workload usage and efficiencies? So our clients are evolving. And when we think about how do you reach those clients differently? We have to know who they are. We have to use data to understand them. We have to be more personalized. We just relaunched our entire digital experience so that when we try to look at how do you bring people into something that's more customized, more personalized? What does it mean to be a cloud architect that's thinking about a data backup and protection plan? What does it mean to someone at DevOps that's thinking about how do I actually create and deploy an application at speed? How do you think about someone that's going to look at the needs from a CIO so much differently than before? But using data, using customization, thinking about an engaging experience, bringing them through that experience so that we solve their business challenges. We use data in analytics everyday. I think of us as being the new data scientists. People say, is it art or is it science and marketing? And I'm like, well it's a little bit of story telling. Absolutely we have to leave the stories. But the data, the analytics is where we really understand our customers best. And so using analytic models, using predictive models. Using more ways in which we can actually reach customers in new ways we never have before through social. But bring them into a new conversation. So, analytics, analytics, story telling and understanding, getting closer to new clients like we never have before, and then thinking through how do we use that full circle loop of learning to get better and better at how we engage our customers in ways they want to engage with us. >> I want to switch gears just a second. And I know that you've just been nominated as an international board member. You were a board member before of Athena of the Triangle, which is about supporting and inspiring women in the technology industry. As we know, the dearth of women technologists is a big problem in the U.S. and globally. Can you tell us a little bit more about the organization and what you're doing? >> Sure. So, Athena International is really about how do you promote women's leadership? And it's across the world. In fact, we just launched some very exciting initiatives in China, where I lived for a year. And the president of Athena International is a friend of mine and she was really looking at how do you foster growth, especially in emerging markets in countries where women's leadership can be so profound in terms of how do impact a business, government, and market and really overall global success. Athena is focused on its technology. But it's also women in many industries. But really, how do you gain the powerful mentorships? How do you gain powerful access to programs? To having more access to expertise that can help them to think through business models, business cases. How do they grow their business? It might be from financial, to career counseling, to mentoring on marketing, but it's really thinking through women's leadership as a whole. >> And is NetApp also working on behalf of that cause too? >> Today, in fact, we're going to be hosting the annual women in technology summit. And so we're so focused on how do we think about developing women in technology. How to think about that across not only our employees, but our partners and our customers. And it's not just about women. This is men and women working together to determine how do we stop the fact that we've got to get more access to women in mentorships and sponsorships. And really really driving how we have leadership as we grow into our careers and can drive more business impact. >> Great. Well Jean, thanks so much for coming on The Cube. It was really fun talking to you. Absolutely. Thank you both. I'm Rebecca Knight for Peter Burris, we will have more from NetApp Insight here in Berlin, Germany in just a little bit.

>> Narrator: Live from Berlin, Germany, it's The Cube! Covering NetApp Insight 2017. Brought to you by NetApp. Hello, everyone. We are kicking off day one, actually it's a one day show of NetApp Insight here in Berlin, Germany. I'm your host, Rebecca Knight, along with my co-host, Peter Burris. We're going to be talking about NetApp's digital transformation. It's amidst a year's long digital transformation. Set the scene for our viewers, Peter, a little bit about where NetApp is today and its evolution. Well, NetApp, like many companies in the technology industry, is trying to move from a focus where the asset's been on the hardware to an assets, to a focus where the asset's more on the data that the business is using. That's an industry-wide shift. NetApp, in particular, has been especially aggressive about putting forward this proposition that increasingly companies are data driven and that, therefore, they have to take care of the data. They have to treat it differently. That has an enormous implication for how businesses operate and certainly how technology companies are going to serve. So NetApp is not only leading the charge on its own transformation internally, but it's also helping other companies with their digital transformations. Well, it has to be. I mean, the whole notion of digital transformation is something that's very frequently misunderstood. The way we look at it at Wikibon, and I don't think that this is in at all an opposition to anything that NetApp would say, the way we look at it, is that data is an asset that the business uses. A digital business uses data assets differently than a non-digital business. In fact, we think it's a strong enough proposition, that we think the difference between a business and a digital business is the digital business's use of data. So, if you start from that proposition and you think about what does it mean to use data differently, then it has enormous implications in how the business institutionalizes its work, the types of people that it hires, the type of initiatives that it goes after, the way it engages its customers, et cetera. All of these are impacted by the simple proposition that if you use data as an asset, your business is going to have significant operational features that are going to transform. Well, I think that that's really what we're getting at. We heard in the keynote today, this is a real seminal moment for NetApp and really, for all businesses today. We're at a point in time with this explosion of data and it can mean really big things for companies. If you are storing that data well, managing that data, extracting value from that data. So I think that that's what we're going to hear a lot about today. Well, there are three things. If you're going to be a data-driven business, if you're going to be a business that uses data as an asset, and therefore, you institutionalize your work differently as a consequence, you're going to have to do three things really well. You're going to have to capture data well, you'll have to turn that data into value well and then, you're going to have to act on that data back in the marketplace. Increasing that involves a degree of automation, so when we start thinking about AI or machine learning or deep learning or a lot of the other buzzwords, what that really, what those buzzwords really are about is, how do we take data and then do something of consequence back in the marketplace? So every business is trying to better understand how it invests in those capabilities of capturing data, turning it into value and then acting on it in the marketplace. NetApp, as a company, is trying to provide the software and the underlying tooling, as well, obviously, as a lot of the infrastructure, to ensure that companies can do that more successfully. So it's the infrastructure and the products, but it's also this idea of best practices because we're going to hear today about a survey that NetApp executed with IDC about what the difference between the data thrivers, the companies that are using data, as you described, and then just the ones who are just surviving. We're really going to learn from them what it takes to do this well. Well, every company uses data, to some degree, and we used to spend a lot of time in the industry talking about the differences between data and information and insight. While those debates continue to go on, they really are just a bunch of analysts and consultants talking to each other. What's really important is to better understand the role that data plays within decision making, the sources of the data and the differences in those sources. Then, very importantly, the physical realities, the legal realities, and the intellectual property realities of data because those are the three things that are going to determine how your infrastructure actually gets set up, what role your applications play in business, how you can automate it or not. Ultimately, it's going to have an enormous impact on how your, the composition of the business, from a people standpoint as well. Well, I want to get into that a little bit because it really does have huge implications for your workforce. There's so many different demands and pressures on companies, but then, in particular, on the people who's job it is to execute these strategies and they are being asked to do so much and not being given the budget, perhaps, that they need to do it. I think that that's also putting a huge pressure on companies. There's a lot of pressure because of budgets and, but that has, there's a lot of reasons for that. I think the fundamental issue is, do people trust their data or not? We've certainly seen, on many levels, that people are reticent to take on a more data-oriented approach to living their lives. That's true in a social setting, it's also true inside a company as well. One of the big transformations that has to take place inside a company is a recognition that data is crucial to informing decisions and informing actions. But that it's not enough. At least not in just its raw form. There's a lot of other work that has to go on to ensure that data is presented in a way that's useful to human beings. We talk a lot about artificial intelligence and how artificial intelligence is going to disrupt a whole bunch of industries and dislocate a bunch of jobs. While there's definitely truth to that, what we've also seen is that, with each successive move forward with the tooling of information, we can go back a few hundred years in talking about this, that people have found ways to adjust. They found ways to incorporate that into their lives in a way that business is conducted. This particular transformation is going to be especially tricky because of the intensity of the depth of the, the, uh, the, the completeness of the data and what it promises to do. When you start introducing new types of automation, driven by data, that's going to have an enormous impact in how people see themselves in the workplace. Well, I also want to unpack a little bit about what you said. You described a real reluctance, a real reticence to incorporate data, to believe the data, trust the data and then make actionable decisions based on that data. What accounts for this, do you think? Well, I think that, partly, I think it's just human nature. That human beings are, uh, are, very tactile, we're very tactile. Our sources of information tends to be visible light, touch, listening. Data is inert until it's put into a form that impacts our senses. This is going to get very, very philosophical very quickly and I don't want to bore everybody (laughs) but what it means, ultimately, is that data presents models that have a consequential impact on the way of the world's work. We go through our lives with models. So, for example, we can look at this impressive show floor, and very quickly, we have a model of how we're going to get from point A to point B. If we were looking at that, just in data terms, it would remain very confusing. Almost like, you know, The Matrix. So, people need help in ensuring that data becomes complimentary to the normal, cognitive models of the way that we work and not positioned as a substitute or, worse, antitheical to how we generally live our lives. That's what, that's where some of the challenge is. Now, there's other challenges as well. For example, um, when you, we are, we are, we are, kind of, presuming that computers are a lot smarter than they are. In fact, computers are very, very stupid things. Now, that doesn't say anything about the technology or the quality of the technology, it says something about what computers actually are. So, if we give it great software, if you give a computer or a computer system great software, it's going to behave better than if we don't. But there's a difference between a computer and a human being. A computer can be told exactly what to do and it will do it, as long as the software is good. Not so with humans, particularly small humans. Not so with human beings. Yes. Yes. (laughs) Exactly. For those of you that who have kids. But human beings need different types of incentives. That's going to be one of the tensions, is the degree to which we can build systems, utilizing tooling, that is set up for technology, which is precise and says, "Do it this way." Human beings, which still need incentives, and still need to be included in the process, and still need to feel like they're being actuated. These are kind of high highfalutin words but they're very real words. When we talk about significant system complexity and change, and the designers of everything we're talking about, have to consider that. Well, we are going to be discussing all of these things, all these new products and software systems, as well as the change management issues today, here at the NetApp summit. Excellent. Looking forward to it. This is Rebecca Knight for Peter Burris. We will have more from NetApp 2017 in just a little bit. >> Narrator: Calling all barrier breakers, status.