>>The Cube Presents UI Path Forward five. Brought to you by UI Path, >>Everybody. We're back in Las Vegas. The cube's coverage we're day one at UI Path forward. Five. Pascal Borne is here. He's an expert and bestselling author in the topic of AI and automation and the book Intelligent Automation. Welcome to the world of Hyper Automation, the first book on the topic. And of course, Ray Wong is back on the cube. He's the founder, chairman and principal analyst, Constellation Reese, also bestselling author of Everybody Wants To Rule the World. Guys, thanks so much for coming on The Cubes. Always a pleasure. Ray Pascal, First time on the Cube, I believe. >>Yes, thank you. Thanks for the invitation. Thank you. >>So what is artificial about artificial intelligence, >>For sure, not people. >>So, okay, so you guys are both speaking at the conference, Ray today. I think you're interviewing the co CEOs. What do you make of that? What's, what are you gonna, what are you gonna probe with these guys? Like, how they're gonna divide their divide and conquer, and why do you think the, the company Danielle in particular, decided to bring in Rob Sland? >>Well, you know what I mean, Like, you know, these companies are now at a different stage of growth, right? There's that early battle between RPA vendors. Now we're actually talking something different, right? We're talking about where does automation go? How do we get the decisioning? What's the next best action? That's gonna be the next step. And to take where UI path is today to somewhere else, You really want someone with that enterprise cred and experience the sales motions, the packages, the partnership capabilities, and who else better than Roblin? He, that's, he's done, he can do that in his sleep, but now he's gotta do that in a new space, taking whole category to another level. Now, Daniel on the other hand, right, I mean, he's the visionary founder. He put this thing from nothing to where he is today, right? I mean, at that point you want your founder thinking about the next set of ideas, right? So you get this interesting dynamic that we've seen for a while with co CEOs, those that are doing the operations, getting the stuff out the door, and then letting the founders get a chance to go back and rethink, take a look at the perspective, and hopefully get a chance to build the next idea or take the next idea back into the organization. >>Right? Very well said. Pascal, why did you write your book on intelligent automation and, and hyper automation, and what's changed since you've written that book? >>So, I, I wrote this book, An Intelligent Automation, two years ago. At that time, it was really a new topic. It was really about the key, the, the key, the key content of the, of the book is really about combining different technologies to automate the most complex end to end business processes in companies. And when I say capabilities, it's, we, we hear a lot about up here, especially here, robotic process automation. But up here alone, if you just trying to transform a company with only up here, you just fall short. Okay? A lot of those processes need more than execution. They need language, they need the capacity to view, to see, they need the capacity to understand and to, and to create insights. So by combining process automation with ai, natural language processing, computer vision, you give this capability to create impact by automating end to end processes in companies. >>I, I like the test, what I hear in the keynote with independent experts like yourself. So we're hearing that that intelligent automation or automation is a fundamental component of digital transformation. Is it? Or is it more sort of a back office sort of hidden in inside plumbing Ray? What do you think? >>Well, you start by understanding what's going on in the process phase. And that's where you see discover become very important in that keynote, right? And that's where process mining's playing a role. Then you gotta automate stuff. But when you get to operations, that's really where the change is going to happen, right? We actually think that, you know, when you're doing the digital transformation pieces, right? Analytics, automation and AI are coming together to create a concept we call decision velocity. You and I make a quick decision, boom, how long does it take to get out? Management committee could free forever, right? A week, two months, never. But if you're thinking about competing with the automation, right? These decisions are actually being done a hundred times per second by machine, even a thousand times per second. That asymmetry is really what people are facing at the moment. >>And the companies that are gonna be able to do that and start automating decisions are gonna be operating at another level. Back to what Pascal's book talking about, right? And there are four questions everyone has to ask you, like, when do you fully intelligently automate? And that happens right in the background when you augment the machine with a human. So we can find why did you make an exception? Why did you break a roll? Why didn't you follow this protocol so we can get it down to a higher level confidence? When do you augment the human with the machine so we can give you the information so you can act quickly. And the last one is, when do you wanna insert a human in the process? That's gonna be the biggest question. Order to cash, incident or resolution, Hire to retire, procure to pay. It doesn't matter. When do you want to put a human in the process? When do you want a man in the middle, person in the middle? And more importantly, when do you want insert friction? >>So Pascal, you wrote your book in the middle of the, the pandemic. Yes. And, and so, you know, pre pandemic digital transformation was kind of a buzzword. A lot of people gave it lip service, eh, not on my watch, I don't have to worry about that. But then it became sort of, you're not a digital business, you're out of business. So, so what have you seen as the catalyst for adoption of automation? Was it the, the pandemic? Was it sort of good runway before that? What's changed? You know, pre isolation, post isolation economy. >>You, you make me think about a joke. Who, who did your best digital transformation over the last years? The ceo, C H R O, the Covid. >>It's a big record ball, right? Yeah. >>Right. And that's exactly true. You know, before pandemic digital transformation was a competitive advantage. >>Companies that went into it had an opportunity to get a bit better than their, their competitors during the pandemic. Things have changed completely. Companies that were not digitalized and automated could not survive. And we've seen so many companies just burning out and, and, and those companies that have been able to capitalize on intelligent automation, digital transformations during the pandemic have been able not only to survive, but to, to thrive, to really create their place on the market. So that's, that has been a catalyst, definitely a catalyst for that. That explains the success of the book, basically. Yeah. >>Okay. Okay. >>So you're familiar with the concept of Stew the food, right? So Stew by definition is something that's delicious to eat. Stew isn't simply taking one of every ingredient from the pantry and throwing it in the pot and stirring it around. When we start talking about intelligent automation, artificial intelligence, augmented intelligence, it starts getting a bit overwhelming. My spy sense goes off and I start thinking, this sounds like mush. It doesn't sound like Stew. So I wanna hear from each of you, what is the methodical process that, that people need to go through when they're going through digital trans transmission, digital transformation, so that you get delicious stew instead of a mush that's just confused everything in your business. So you, Ray, you want, you want to, you wanna answer that first? >>Yeah. You know, I mean, we've been talking about digital transformation since 2010, right? And part of it was really getting the business model, right? What are you trying to achieve? Is that a new type of offering? Are you changing the way you monetize something? Are you taking existing process and applying it to a new set of technologies? And what do you wanna accomplish, right? Once you start there, then it becomes a whole lot of operational stuff. And it's more than st right? I mean, it, it could be like, well, I can't use those words there. But the point being is it could be a complete like, operational exercise. It could be a complete revenue exercise, it could be a regulatory exercise, it could be something about where you want to take growth into the next level. And each one of those processes, some of it is automation, right? There's a big component of it today. But most of it is really rethinking about what you want things to do, right? How do you actually make things to be successful, right? Do I reorganize a process? Do I insert a place to do monetization? Where do I put engagement in place? How do I collect data along the way so I can build better feedback loop? What can I do to build the business graph so that I have that knowledge for the future so I can go forward doing that so I can be successful. >>The Pascal should, should, should the directive be first ia, then ai? Or are these, are these things going to happen in parallel naturally? What's your position on that? Is it first, >>So it, so, >>So AI is part of IA because that's, it's, it's part of the big umbrella. And very often I got the question. So how do you differentiate AI in, I a, I like to say that AI is only the brain. So think of ai cuz I'm consider, I consider AI as machine learning, Okay? Think of AI in a, like a brain near jar that only can think, create, insight, learn, but doesn't do anything, doesn't have any arms, doesn't have any eyes, doesn't not have any mouth and ears can't talk, can't understand with ia, you, you give those capabilities to ai. You, you basically, you create a cap, the capability, technological capability that is able to do more than just thinking, learning and, and create insight, but also acting, speaking, understanding the environment, viewing it, interacting with it. So basically performing these, those end to end processes that are performed currently by people in companies. >>Yeah, we're gonna get to a point where we get to what we call a dynamic scenario generation. You're talking to me, you get excited, well, I changed the story because something else shows up, or you're talking to me and you're really upset. We're gonna have to actually ch, you know, address that issue right away. Well, we want the ability to have that sense and respond capability so that the next best action is served. So your data, your process, the journey, all the analytics on the top end, that's all gonna be served up and changed along the way. As we go from 2D journeys to 3D scenarios in the metaverse, if we think about what happens from a decentralized world to decentralized, and we think about what's happening from web two to web three, we're gonna make those types of shifts so that things are moving along. Everything's a choose your end venture journey. >>So I hope I remember this correctly from your book. You talked about disruption scenarios within industries and within companies. And I go back to the early days of, of our industry and East coast Prime, Wang, dg, they're all gone. And then, but, but you look at companies like Microsoft, you know, they were, they were able to, you know, get through that novel. Yeah. Ibm, you know, I call it survived. Intel is now going through their, you know, their challenge. So, so maybe it's inevitable, but how do you see the future in terms of disruption with an industry, Forget our industry for a second, all industry across, whether it's healthcare, financial services, manufacturing, automobiles, et cetera. How do you see the disruption scenario? I'm pretty sure you talked about this in your book, it's been a while since I read it, but I wonder if you could talk about that disruption scenario and, and the role that automation is going to play, either as the disruptor or as the protector of the incumbents. >>Let's take healthcare and auto as an example. Healthcare is a great example. If we think about what's going on, not enough nurses, massive shortage, right? What are we doing at the moment? We're setting five foot nine robots to do non-patient care. We're trying to capture enough information off, you know, patient analytics like this watch is gonna capture vitals from a going forward. We're doing a lot what we can do in the ambient level so that information and data is automatically captured and decisions are being rendered against that. Maybe you're gonna change your diet along the way, maybe you're gonna walk an extra 10 minutes. All those things are gonna be provided in that level of automation. Take the car business. It's not about selling cars. Tesla's a great example. We talk about this all the time. What Tesla's doing, they're basically gonna be an insurance company with all the data they have. They have better data than the insurance companies. They can do better underwriting, they've got better mapping information and insights they can actually suggest next best action do collision avoidance, right? Those are all the things that are actually happening today. And automation plays a big role, not just in the collection of that, that information insight, but also in the ability to make recommendations, to do predictions and to help you prevent things from going wrong. >>So, you know, it's interesting. It's like you talk about Tesla as the, the disrupting the insurance companies. It's almost like the over the top vendors have all the data relative to the telcos and mopped them up for lunch. Pascal, I wanna ask you, you know, the topic of future of work kind of was a bromide before, but, but now I feel like, you know, post pandemic, it, it actually has substance. How do you see the future of work? Can you even summarize what it's gonna look like? It's, it's, Or are we here? >>It's, yeah, it's, and definitely it's, it's more and more important topic currently. And you, you all heard about the great resignation and how employee experience is more and more important for companies according to have a business review. The companies that take care of their employee experience are four times more profitable that those that don't. So it's a, it's a, it's an issue for CEOs and, and shareholders. Now, how do we get there? How, how do we, how do we improve the, the quality of the employee experience, understanding the people, getting information from them, educating them. I'm talking about educating them on those new technologies and how they can benefit from those empowering them. And, and I think we've talked a lot about this, about the democratization local type of, of technologies that democratize the access to those technologies. Everyone can be empowered today to change their work, improve their work, and finally, incentivization. I think it's a very important point where companies that, yeah, I >>Give that. What's gonna be the key message of your talk tomorrow. Give us the bumper sticker, >>If you will. Oh, I'm gonna talk, It's a little bit different. I'm gonna talk for the IT community in this, in the context of the IT summit. And I'm gonna talk about the future of intelligent automation. So basically how new technologies will impact beyond what we see today, The future of work. >>Well, I always love having you on the cube, so articulate and, and and crisp. What's, what's exciting you these days, you know, in your world, I know you're traveling around a lot, but what's, what's hot? >>Yeah, I think one of the coolest thing that's going on right now is the fact that we're trying to figure out do we go to work or do we not go to work? Back to your other point, I mean, I don't know, work, work is, I mean, for me, work has been everywhere, right? And we're starting to figure out what that means. I think the second thing though is this notion around mission and purpose. And everyone's trying to figure out what does that mean for themselves? And that's really, I don't know if it's a great, great resignation. We call it great refactoring, right? Where you work, when you work, how we work, why you work, that's changing. But more importantly, the business models are changing. The monetization models are changing macro dynamics that are happening. Us versus China, G seven versus bricks, right? War on the dollar. All these things are happening around us at this moment and, and I think it's gonna really reshape us the way that we came out of the seventies into the eighties. >>Guys, always a pleasure having folks like yourself on, Thank you, Pascal. Been great to see you again. All right, Dave Nicholson, Dave Ante, keep it right there. Forward five from Las Vegas. You're watching the cue.

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Spark became a top level Apache project in 2014, and then shortly thereafter, burst onto the big data scene. Spark, along with the cloud, transformed and in many ways, disrupted the big data market. Databricks optimized its tech stack for Spark and took advantage of the cloud to really cleverly deliver a managed service that has become a leading AI and data platform among data scientists and data engineers. However, emerging customer data requirements are shifting into a direction that will cause modern data platform players generally and Databricks, specifically, we think, to make some key directional decisions and perhaps even reinvent themselves. Hello and welcome to this week's wikibon theCUBE Insights, powered by ETR. In this Breaking Analysis, we're going to do a deep dive into Databricks. We'll explore its current impressive market momentum. We're going to use some ETR survey data to show that, and then we'll lay out how customer data requirements are changing and what the ideal data platform will look like in the midterm future. We'll then evaluate core elements of the Databricks portfolio against that vision, and then we'll close with some strategic decisions that we think the company faces. And to do so, we welcome in our good friend, George Gilbert, former equities analyst, market analyst, and current Principal at TechAlpha Partners. George, good to see you. Thanks for coming on. >> Good to see you, Dave. >> All right, let me set this up. We're going to start by taking a look at where Databricks sits in the market in terms of how customers perceive the company and what it's momentum looks like. And this chart that we're showing here is data from ETS, the emerging technology survey of private companies. The N is 1,421. What we did is we cut the data on three sectors, analytics, database-data warehouse, and AI/ML. The vertical axis is a measure of customer sentiment, which evaluates an IT decision maker's awareness of the firm and the likelihood of engaging and/or purchase intent. The horizontal axis shows mindshare in the dataset, and we've highlighted Databricks, which has been a consistent high performer in this survey over the last several quarters. And as we, by the way, just as aside as we previously reported, OpenAI, which burst onto the scene this past quarter, leads all names, but Databricks is still prominent. You can see that the ETR shows some open source tools for reference, but as far as firms go, Databricks is very impressively positioned. Now, let's see how they stack up to some mainstream cohorts in the data space, against some bigger companies and sometimes public companies. This chart shows net score on the vertical axis, which is a measure of spending momentum and pervasiveness in the data set is on the horizontal axis. You can see that chart insert in the upper right, that informs how the dots are plotted, and net score against shared N. And that red dotted line at 40% indicates a highly elevated net score, anything above that we think is really, really impressive. And here we're just comparing Databricks with Snowflake, Cloudera, and Oracle. And that squiggly line leading to Databricks shows their path since 2021 by quarter. And you can see it's performing extremely well, maintaining an elevated net score and net range. Now it's comparable in the vertical axis to Snowflake, and it consistently is moving to the right and gaining share. Now, why did we choose to show Cloudera and Oracle? The reason is that Cloudera got the whole big data era started and was disrupted by Spark. And of course the cloud, Spark and Databricks and Oracle in many ways, was the target of early big data players like Cloudera. Take a listen to Cloudera CEO at the time, Mike Olson. This is back in 2010, first year of theCUBE, play the clip. >> Look, back in the day, if you had a data problem, if you needed to run business analytics, you wrote the biggest check you could to Sun Microsystems, and you bought a great big, single box, central server, and any money that was left over, you handed to Oracle for a database licenses and you installed that database on that box, and that was where you went for data. That was your temple of information. >> Okay? So Mike Olson implied that monolithic model was too expensive and inflexible, and Cloudera set out to fix that. But the best laid plans, as they say, George, what do you make of the data that we just shared? >> So where Databricks has really come up out of sort of Cloudera's tailpipe was they took big data processing, made it coherent, made it a managed service so it could run in the cloud. So it relieved customers of the operational burden. Where they're really strong and where their traditional meat and potatoes or bread and butter is the predictive and prescriptive analytics that building and training and serving machine learning models. They've tried to move into traditional business intelligence, the more traditional descriptive and diagnostic analytics, but they're less mature there. So what that means is, the reason you see Databricks and Snowflake kind of side by side is there are many, many accounts that have both Snowflake for business intelligence, Databricks for AI machine learning, where Snowflake, I'm sorry, where Databricks also did really well was in core data engineering, refining the data, the old ETL process, which kind of turned into ELT, where you loaded into the analytic repository in raw form and refine it. And so people have really used both, and each is trying to get into the other. >> Yeah, absolutely. We've reported on this quite a bit. Snowflake, kind of moving into the domain of Databricks and vice versa. And the last bit of ETR evidence that we want to share in terms of the company's momentum comes from ETR's Round Tables. They're run by Erik Bradley, and now former Gartner analyst and George, your colleague back at Gartner, Daren Brabham. And what we're going to show here is some direct quotes of IT pros in those Round Tables. There's a data science head and a CIO as well. Just make a few call outs here, we won't spend too much time on it, but starting at the top, like all of us, we can't talk about Databricks without mentioning Snowflake. Those two get us excited. Second comment zeros in on the flexibility and the robustness of Databricks from a data warehouse perspective. And then the last point is, despite competition from cloud players, Databricks has reinvented itself a couple of times over the year. And George, we're going to lay out today a scenario that perhaps calls for Databricks to do that once again. >> Their big opportunity and their big challenge for every tech company, it's managing a technology transition. The transition that we're talking about is something that's been bubbling up, but it's really epical. First time in 60 years, we're moving from an application-centric view of the world to a data-centric view, because decisions are becoming more important than automating processes. So let me let you sort of develop. >> Yeah, so let's talk about that here. We going to put up some bullets on precisely that point and the changing sort of customer environment. So you got IT stacks are shifting is George just said, from application centric silos to data centric stacks where the priority is shifting from automating processes to automating decision. You know how look at RPA and there's still a lot of automation going on, but from the focus of that application centricity and the data locked into those apps, that's changing. Data has historically been on the outskirts in silos, but organizations, you think of Amazon, think Uber, Airbnb, they're putting data at the core, and logic is increasingly being embedded in the data instead of the reverse. In other words, today, the data's locked inside the app, which is why you need to extract that data is sticking it to a data warehouse. The point, George, is we're putting forth this new vision for how data is going to be used. And you've used this Uber example to underscore the future state. Please explain? >> Okay, so this is hopefully an example everyone can relate to. The idea is first, you're automating things that are happening in the real world and decisions that make those things happen autonomously without humans in the loop all the time. So to use the Uber example on your phone, you call a car, you call a driver. Automatically, the Uber app then looks at what drivers are in the vicinity, what drivers are free, matches one, calculates an ETA to you, calculates a price, calculates an ETA to your destination, and then directs the driver once they're there. The point of this is that that cannot happen in an application-centric world very easily because all these little apps, the drivers, the riders, the routes, the fares, those call on data locked up in many different apps, but they have to sit on a layer that makes it all coherent. >> But George, so if Uber's doing this, doesn't this tech already exist? Isn't there a tech platform that does this already? >> Yes, and the mission of the entire tech industry is to build services that make it possible to compose and operate similar platforms and tools, but with the skills of mainstream developers in mainstream corporations, not the rocket scientists at Uber and Amazon. >> Okay, so we're talking about horizontally scaling across the industry, and actually giving a lot more organizations access to this technology. So by way of review, let's summarize the trend that's going on today in terms of the modern data stack that is propelling the likes of Databricks and Snowflake, which we just showed you in the ETR data and is really is a tailwind form. So the trend is toward this common repository for analytic data, that could be multiple virtual data warehouses inside of Snowflake, but you're in that Snowflake environment or Lakehouses from Databricks or multiple data lakes. And we've talked about what JP Morgan Chase is doing with the data mesh and gluing data lakes together, you've got various public clouds playing in this game, and then the data is annotated to have a common meaning. In other words, there's a semantic layer that enables applications to talk to the data elements and know that they have common and coherent meaning. So George, the good news is this approach is more effective than the legacy monolithic models that Mike Olson was talking about, so what's the problem with this in your view? >> So today's data platforms added immense value 'cause they connected the data that was previously locked up in these monolithic apps or on all these different microservices, and that supported traditional BI and AI/ML use cases. But now if we want to build apps like Uber or Amazon.com, where they've got essentially an autonomously running supply chain and e-commerce app where humans only care and feed it. But the thing is figuring out what to buy, when to buy, where to deploy it, when to ship it. We needed a semantic layer on top of the data. So that, as you were saying, the data that's coming from all those apps, the different apps that's integrated, not just connected, but it means the same. And the issue is whenever you add a new layer to a stack to support new applications, there are implications for the already existing layers, like can they support the new layer and its use cases? So for instance, if you add a semantic layer that embeds app logic with the data rather than vice versa, which we been talking about and that's been the case for 60 years, then the new data layer faces challenges that the way you manage that data, the way you analyze that data, is not supported by today's tools. >> Okay, so actually Alex, bring me up that last slide if you would, I mean, you're basically saying at the bottom here, today's repositories don't really do joins at scale. The future is you're talking about hundreds or thousands or millions of data connections, and today's systems, we're talking about, I don't know, 6, 8, 10 joins and that is the fundamental problem you're saying, is a new data error coming and existing systems won't be able to handle it? >> Yeah, one way of thinking about it is that even though we call them relational databases, when we actually want to do lots of joins or when we want to analyze data from lots of different tables, we created a whole new industry for analytic databases where you sort of mung the data together into fewer tables. So you didn't have to do as many joins because the joins are difficult and slow. And when you're going to arbitrarily join thousands, hundreds of thousands or across millions of elements, you need a new type of database. We have them, they're called graph databases, but to query them, you go back to the prerelational era in terms of their usability. >> Okay, so we're going to come back to that and talk about how you get around that problem. But let's first lay out what the ideal data platform of the future we think looks like. And again, we're going to come back to use this Uber example. In this graphic that George put together, awesome. We got three layers. The application layer is where the data products reside. The example here is drivers, rides, maps, routes, ETA, et cetera. The digital version of what we were talking about in the previous slide, people, places and things. The next layer is the data layer, that breaks down the silos and connects the data elements through semantics and everything is coherent. And then the bottom layers, the legacy operational systems feed that data layer. George, explain what's different here, the graph database element, you talk about the relational query capabilities, and why can't I just throw memory at solving this problem? >> Some of the graph databases do throw memory at the problem and maybe without naming names, some of them live entirely in memory. And what you're dealing with is a prerelational in-memory database system where you navigate between elements, and the issue with that is we've had SQL for 50 years, so we don't have to navigate, we can say what we want without how to get it. That's the core of the problem. >> Okay. So if I may, I just want to drill into this a little bit. So you're talking about the expressiveness of a graph. Alex, if you'd bring that back out, the fourth bullet, expressiveness of a graph database with the relational ease of query. Can you explain what you mean by that? >> Yeah, so graphs are great because when you can describe anything with a graph, that's why they're becoming so popular. Expressive means you can represent anything easily. They're conducive to, you might say, in a world where we now want like the metaverse, like with a 3D world, and I don't mean the Facebook metaverse, I mean like the business metaverse when we want to capture data about everything, but we want it in context, we want to build a set of digital twins that represent everything going on in the world. And Uber is a tiny example of that. Uber built a graph to represent all the drivers and riders and maps and routes. But what you need out of a database isn't just a way to store stuff and update stuff. You need to be able to ask questions of it, you need to be able to query it. And if you go back to prerelational days, you had to know how to find your way to the data. It's sort of like when you give directions to someone and they didn't have a GPS system and a mapping system, you had to give them turn by turn directions. Whereas when you have a GPS and a mapping system, which is like the relational thing, you just say where you want to go, and it spits out the turn by turn directions, which let's say, the car might follow or whoever you're directing would follow. But the point is, it's much easier in a relational database to say, "I just want to get these results. You figure out how to get it." The graph database, they have not taken over the world because in some ways, it's taking a 50 year leap backwards. >> Alright, got it. Okay. Let's take a look at how the current Databricks offerings map to that ideal state that we just laid out. So to do that, we put together this chart that looks at the key elements of the Databricks portfolio, the core capability, the weakness, and the threat that may loom. Start with the Delta Lake, that's the storage layer, which is great for files and tables. It's got true separation of compute and storage, I want you to double click on that George, as independent elements, but it's weaker for the type of low latency ingest that we see coming in the future. And some of the threats highlighted here. AWS could add transactional tables to S3, Iceberg adoption is picking up and could accelerate, that could disrupt Databricks. George, add some color here please? >> Okay, so this is the sort of a classic competitive forces where you want to look at, so what are customers demanding? What's competitive pressure? What are substitutes? Even what your suppliers might be pushing. Here, Delta Lake is at its core, a set of transactional tables that sit on an object store. So think of it in a database system, this is the storage engine. So since S3 has been getting stronger for 15 years, you could see a scenario where they add transactional tables. We have an open source alternative in Iceberg, which Snowflake and others support. But at the same time, Databricks has built an ecosystem out of tools, their own and others, that read and write to Delta tables, that's what makes the Delta Lake and ecosystem. So they have a catalog, the whole machine learning tool chain talks directly to the data here. That was their great advantage because in the past with Snowflake, you had to pull all the data out of the database before the machine learning tools could work with it, that was a major shortcoming. They fixed that. But the point here is that even before we get to the semantic layer, the core foundation is under threat. >> Yep. Got it. Okay. We got a lot of ground to cover. So we're going to take a look at the Spark Execution Engine next. Think of that as the refinery that runs really efficient batch processing. That's kind of what disrupted the DOOp in a large way, but it's not Python friendly and that's an issue because the data science and the data engineering crowd are moving in that direction, and/or they're using DBT. George, we had Tristan Handy on at Supercloud, really interesting discussion that you and I did. Explain why this is an issue for Databricks? >> So once the data lake was in place, what people did was they refined their data batch, and Spark has always had streaming support and it's gotten better. The underlying storage as we've talked about is an issue. But basically they took raw data, then they refined it into tables that were like customers and products and partners. And then they refined that again into what was like gold artifacts, which might be business intelligence metrics or dashboards, which were collections of metrics. But they were running it on the Spark Execution Engine, which it's a Java-based engine or it's running on a Java-based virtual machine, which means all the data scientists and the data engineers who want to work with Python are really working in sort of oil and water. Like if you get an error in Python, you can't tell whether the problems in Python or where it's in Spark. There's just an impedance mismatch between the two. And then at the same time, the whole world is now gravitating towards DBT because it's a very nice and simple way to compose these data processing pipelines, and people are using either SQL in DBT or Python in DBT, and that kind of is a substitute for doing it all in Spark. So it's under threat even before we get to that semantic layer, it so happens that DBT itself is becoming the authoring environment for the semantic layer with business intelligent metrics. But that's again, this is the second element that's under direct substitution and competitive threat. >> Okay, let's now move down to the third element, which is the Photon. Photon is Databricks' BI Lakehouse, which has integration with the Databricks tooling, which is very rich, it's newer. And it's also not well suited for high concurrency and low latency use cases, which we think are going to increasingly become the norm over time. George, the call out threat here is customers want to connect everything to a semantic layer. Explain your thinking here and why this is a potential threat to Databricks? >> Okay, so two issues here. What you were touching on, which is the high concurrency, low latency, when people are running like thousands of dashboards and data is streaming in, that's a problem because SQL data warehouse, the query engine, something like that matures over five to 10 years. It's one of these things, the joke that Andy Jassy makes just in general, he's really talking about Azure, but there's no compression algorithm for experience. The Snowflake guy started more than five years earlier, and for a bunch of reasons, that lead is not something that Databricks can shrink. They'll always be behind. So that's why Snowflake has transactional tables now and we can get into that in another show. But the key point is, so near term, it's struggling to keep up with the use cases that are core to business intelligence, which is highly concurrent, lots of users doing interactive query. But then when you get to a semantic layer, that's when you need to be able to query data that might have thousands or tens of thousands or hundreds of thousands of joins. And that's a SQL query engine, traditional SQL query engine is just not built for that. That's the core problem of traditional relational databases. >> Now this is a quick aside. We always talk about Snowflake and Databricks in sort of the same context. We're not necessarily saying that Snowflake is in a position to tackle all these problems. We'll deal with that separately. So we don't mean to imply that, but we're just sort of laying out some of the things that Snowflake or rather Databricks customers we think, need to be thinking about and having conversations with Databricks about and we hope to have them as well. We'll come back to that in terms of sort of strategic options. But finally, when come back to the table, we have Databricks' AI/ML Tool Chain, which has been an awesome capability for the data science crowd. It's comprehensive, it's a one-stop shop solution, but the kicker here is that it's optimized for supervised model building. And the concern is that foundational models like GPT could cannibalize the current Databricks tooling, but George, can't Databricks, like other software companies, integrate foundation model capabilities into its platform? >> Okay, so the sound bite answer to that is sure, IBM 3270 terminals could call out to a graphical user interface when they're running on the XT terminal, but they're not exactly good citizens in that world. The core issue is Databricks has this wonderful end-to-end tool chain for training, deploying, monitoring, running inference on supervised models. But the paradigm there is the customer builds and trains and deploys each model for each feature or application. In a world of foundation models which are pre-trained and unsupervised, the entire tool chain is different. So it's not like Databricks can junk everything they've done and start over with all their engineers. They have to keep maintaining what they've done in the old world, but they have to build something new that's optimized for the new world. It's a classic technology transition and their mentality appears to be, "Oh, we'll support the new stuff from our old stuff." Which is suboptimal, and as we'll talk about, their biggest patron and the company that put them on the map, Microsoft, really stopped working on their old stuff three years ago so that they could build a new tool chain optimized for this new world. >> Yeah, and so let's sort of close with what we think the options are and decisions that Databricks has for its future architecture. They're smart people. I mean we've had Ali Ghodsi on many times, super impressive. I think they've got to be keenly aware of the limitations, what's going on with foundation models. But at any rate, here in this chart, we lay out sort of three scenarios. One is re-architect the platform by incrementally adopting new technologies. And example might be to layer a graph query engine on top of its stack. They could license key technologies like graph database, they could get aggressive on M&A and buy-in, relational knowledge graphs, semantic technologies, vector database technologies. George, as David Floyer always says, "A lot of ways to skin a cat." We've seen companies like, even think about EMC maintained its relevance through M&A for many, many years. George, give us your thought on each of these strategic options? >> Okay, I find this question the most challenging 'cause remember, I used to be an equity research analyst. I worked for Frank Quattrone, we were one of the top tech shops in the banking industry, although this is 20 years ago. But the M&A team was the top team in the industry and everyone wanted them on their side. And I remember going to meetings with these CEOs, where Frank and the bankers would say, "You want us for your M&A work because we can do better." And they really could do better. But in software, it's not like with EMC in hardware because with hardware, it's easier to connect different boxes. With software, the whole point of a software company is to integrate and architect the components so they fit together and reinforce each other, and that makes M&A harder. You can do it, but it takes a long time to fit the pieces together. Let me give you examples. If they put a graph query engine, let's say something like TinkerPop, on top of, I don't even know if it's possible, but let's say they put it on top of Delta Lake, then you have this graph query engine talking to their storage layer, Delta Lake. But if you want to do analysis, you got to put the data in Photon, which is not really ideal for highly connected data. If you license a graph database, then most of your data is in the Delta Lake and how do you sync it with the graph database? If you do sync it, you've got data in two places, which kind of defeats the purpose of having a unified repository. I find this semantic layer option in number three actually more promising, because that's something that you can layer on top of the storage layer that you have already. You just have to figure out then how to have your query engines talk to that. What I'm trying to highlight is, it's easy as an analyst to say, "You can buy this company or license that technology." But the really hard work is making it all work together and that is where the challenge is. >> Yeah, and well look, I thank you for laying that out. We've seen it, certainly Microsoft and Oracle. I guess you might argue that well, Microsoft had a monopoly in its desktop software and was able to throw off cash for a decade plus while it's stock was going sideways. Oracle had won the database wars and had amazing margins and cash flow to be able to do that. Databricks isn't even gone public yet, but I want to close with some of the players to watch. Alex, if you'd bring that back up, number four here. AWS, we talked about some of their options with S3 and it's not just AWS, it's blob storage, object storage. Microsoft, as you sort of alluded to, was an early go-to market channel for Databricks. We didn't address that really. So maybe in the closing comments we can. Google obviously, Snowflake of course, we're going to dissect their options in future Breaking Analysis. Dbt labs, where do they fit? Bob Muglia's company, Relational.ai, why are these players to watch George, in your opinion? >> So everyone is trying to assemble and integrate the pieces that would make building data applications, data products easy. And the critical part isn't just assembling a bunch of pieces, which is traditionally what AWS did. It's a Unix ethos, which is we give you the tools, you put 'em together, 'cause you then have the maximum choice and maximum power. So what the hyperscalers are doing is they're taking their key value stores, in the case of ASW it's DynamoDB, in the case of Azure it's Cosmos DB, and each are putting a graph query engine on top of those. So they have a unified storage and graph database engine, like all the data would be collected in the key value store. Then you have a graph database, that's how they're going to be presenting a foundation for building these data apps. Dbt labs is putting a semantic layer on top of data lakes and data warehouses and as we'll talk about, I'm sure in the future, that makes it easier to swap out the underlying data platform or swap in new ones for specialized use cases. Snowflake, what they're doing, they're so strong in data management and with their transactional tables, what they're trying to do is take in the operational data that used to be in the province of many state stores like MongoDB and say, "If you manage that data with us, it'll be connected to your analytic data without having to send it through a pipeline." And that's hugely valuable. Relational.ai is the wildcard, 'cause what they're trying to do, it's almost like a holy grail where you're trying to take the expressiveness of connecting all your data in a graph but making it as easy to query as you've always had it in a SQL database or I should say, in a relational database. And if they do that, it's sort of like, it'll be as easy to program these data apps as a spreadsheet was compared to procedural languages, like BASIC or Pascal. That's the implications of Relational.ai. >> Yeah, and again, we talked before, why can't you just throw this all in memory? We're talking in that example of really getting down to differences in how you lay the data out on disk in really, new database architecture, correct? >> Yes. And that's why it's not clear that you could take a data lake or even a Snowflake and why you can't put a relational knowledge graph on those. You could potentially put a graph database, but it'll be compromised because to really do what Relational.ai has done, which is the ease of Relational on top of the power of graph, you actually need to change how you're storing your data on disk or even in memory. So you can't, in other words, it's not like, oh we can add graph support to Snowflake, 'cause if you did that, you'd have to change, or in your data lake, you'd have to change how the data is physically laid out. And then that would break all the tools that talk to that currently. >> What in your estimation, is the timeframe where this becomes critical for a Databricks and potentially Snowflake and others? I mentioned earlier midterm, are we talking three to five years here? Are we talking end of decade? What's your radar say? >> I think something surprising is going on that's going to sort of come up the tailpipe and take everyone by storm. All the hype around business intelligence metrics, which is what we used to put in our dashboards where bookings, billings, revenue, customer, those things, those were the key artifacts that used to live in definitions in your BI tools, and DBT has basically created a standard for defining those so they live in your data pipeline or they're defined in their data pipeline and executed in the data warehouse or data lake in a shared way, so that all tools can use them. This sounds like a digression, it's not. All this stuff about data mesh, data fabric, all that's going on is we need a semantic layer and the business intelligence metrics are defining common semantics for your data. And I think we're going to find by the end of this year, that metrics are how we annotate all our analytic data to start adding common semantics to it. And we're going to find this semantic layer, it's not three to five years off, it's going to be staring us in the face by the end of this year. >> Interesting. And of course SVB today was shut down. We're seeing serious tech headwinds, and oftentimes in these sort of downturns or flat turns, which feels like this could be going on for a while, we emerge with a lot of new players and a lot of new technology. George, we got to leave it there. Thank you to George Gilbert for excellent insights and input for today's episode. I want to thank Alex Myerson who's on production and manages the podcast, of course Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Siliconangle.com, he does some great editing. Remember all these episodes, they're available as podcasts. Wherever you listen, all you got to do is search Breaking Analysis Podcast, we publish each week on wikibon.com and siliconangle.com, or you can email me at David.Vellante@siliconangle.com, or DM me @DVellante. Comment on our LinkedIn post, and please do check out ETR.ai, great survey data, enterprise tech focus, phenomenal. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis.

from around the globe it's thecube with digital coverage of exascale day made possible by hewlett packard enterprise welcome everyone to the cube celebration of exascale day ben bennett is here he's an hpc strategist and evangelist at hewlett-packard enterprise ben welcome good to see you good to see you too son hey well let's evangelize exascale a little bit you know what's exciting you uh in regards to the coming of exoskilled computing um well there's a couple of things really uh for me historically i've worked in super computing for many years and i have seen the coming of several milestones from you know actually i'm old enough to remember gigaflops uh coming through and teraflops and petaflops exascale is has been harder than many of us anticipated many years ago the sheer amount of technology that has been required to deliver machines of this performance has been has been us utterly staggering but the exascale era brings with it real solutions it gives us opportunities to do things that we've not been able to do before if you look at some of the the most powerful computers around today they've they've really helped with um the pandemic kovid but we're still you know orders of magnitude away from being able to design drugs in situ test them in memory and release them to the public you know we still have lots and lots of lab work to do and exascale machines are going to help with that we are going to be able to to do more um which ultimately will will aid humanity and they used to be called the grand challenges and i still think of them as that i still think of these challenges for scientists that exascale class machines will be able to help but also i'm a realist is that in 10 20 30 years time you know i should be able to look back at this hopefully touch wood look back at it and look at much faster machines and say do you remember the days when we thought exascale was faster yeah well you mentioned the pandemic and you know the present united states was tweeting this morning that he was upset that you know the the fda in the u.s is not allowing the the vaccine to proceed as fast as you'd like it in fact it the fda is loosening some of its uh restrictions and i wonder if you know high performance computing in part is helping with the simulations and maybe predicting because a lot of this is about probabilities um and concerns is is is that work that is going on today or are you saying that that exascale actually you know would be what we need to accelerate that what's the role of hpc that you see today in regards to sort of solving for that vaccine and any other sort of pandemic related drugs so so first a disclaimer i am not a geneticist i am not a biochemist um my son is he tries to explain it to me and it tends to go in one ear and out the other um um i just merely build the machines he uses so we're sort of even on that front um if you read if you had read the press there was a lot of people offering up systems and computational resources for scientists a lot of the work that has been done understanding the mechanisms of covid19 um have been you know uncovered by the use of very very powerful computers would exascale have helped well clearly the faster the computers the more simulations we can do i think if you look back historically no vaccine has come to fruition as fast ever under modern rules okay admittedly the first vaccine was you know edward jenner sat quietly um you know smearing a few people and hoping it worked um i think we're slightly beyond that the fda has rules and regulations for a reason and we you don't have to go back far in our history to understand the nature of uh drugs that work for 99 of the population you know and i think exascale widely available exoscale and much faster computers are going to assist with that imagine having a genetic map of very large numbers of people on the earth and being able to test your drug against that breadth of person and you know that 99 of the time it works fine under fda rules you could never sell it you could never do that but if you're confident in your testing if you can demonstrate that you can keep the one percent away for whom that drug doesn't work bingo you now have a drug for the majority of the people and so many drugs that have so many benefits are not released and drugs are expensive because they fail at the last few moments you know the more testing you can do the more testing in memory the better it's going to be for everybody uh personally are we at a point where we still need human trials yes do we still need due diligence yes um we're not there yet exascale is you know it's coming it's not there yet yeah well to your point the faster the computer the more simulations and the higher the the chance that we're actually going to going to going to get it right and maybe compress that time to market but talk about some of the problems that you're working on uh and and the challenges for you know for example with the uk government and maybe maybe others that you can you can share with us help us understand kind of what you're hoping to accomplish so um within the united kingdom there was a report published um for the um for the uk research institute i think it's the uk research institute it might be epsrc however it's the body of people responsible for funding um science and there was a case a science case done for exascale i'm not a scientist um a lot of the work that was in this documentation said that a number of things that can be done today aren't good enough that we need to look further out we need to look at machines that will do much more there's been a program funded called asimov and this is a sort of a commercial problem that the uk government is working with rolls royce and they're trying to research how you build a full engine model and by full engine model i mean one that takes into account both the flow of gases through it and how those flow of gases and temperatures change the physical dynamics of the engine and of course as you change the physical dynamics of the engine you change the flow so you need a closely coupled model as air travel becomes more and more under the microscope we need to make sure that the air travel we do is as efficient as possible and currently there aren't supercomputers that have the performance one of the things i'm going to be doing as part of this sequence of conversations is i'm going to be having an in detailed uh sorry an in-depth but it will be very detailed an in-depth conversation with professor mark parsons from the edinburgh parallel computing center he's the director there and the dean of research at edinburgh university and i'm going to be talking to him about the azimoth program and and mark's experience as the person responsible for looking at exascale within the uk to try and determine what are the sort of science problems that we can solve as we move into the exoscale era and what that means for humanity what are the benefits for humans yeah and that's what i wanted to ask you about the the rolls-royce example that you gave it wasn't i if i understood it wasn't so much safety as it was you said efficiency and so that's that's what fuel consumption um it's it's partly fuel consumption it is of course safety there is a um there is a very specific test called an extreme event or the fan blade off what happens is they build an engine and they put it in a cowling and then they run the engine at full speed and then they literally explode uh they fire off a little explosive and they fire a fan belt uh a fan blade off to make sure that it doesn't go through the cowling and the reason they do that is there has been in the past uh a uh a failure of a fan blade and it came through the cowling and came into the aircraft depressurized the aircraft i think somebody was killed as a result of that and the aircraft went down i don't think it was a total loss one death being one too many but as a result you now have to build a jet engine instrument it balance the blades put an explosive in it and then blow the fan blade off now you only really want to do that once it's like car crash testing you want to build a model of the car you want to demonstrate with the dummy that it is safe you don't want to have to build lots of cars and keep going back to the drawing board so you do it in computers memory right we're okay with cars we have computational power to resolve to the level to determine whether or not the accident would hurt a human being still a long way to go to make them more efficient uh new materials how you can get away with lighter structures but we haven't got there with aircraft yet i mean we can build a simulation and we can do that and we can be pretty sure we're right um we still need to build an engine which costs in excess of 10 million dollars and blow the fan blade off it so okay so you're talking about some pretty complex simulations obviously what are some of the the barriers and and the breakthroughs that are kind of required you know to to do some of these things that you're talking about that exascale is going to enable i mean presumably there are obviously technical barriers but maybe you can shed some light on that well some of them are very prosaic so for example power exoscale machines consume a lot of power um so you have to be able to design systems that consume less power and that goes into making sure they're cooled efficiently if you use water can you reuse the water i mean the if you take a laptop and sit it on your lap and you type away for four hours you'll notice it gets quite warm um an exascale computer is going to generate a lot more heat several megawatts actually um and it sounds prosaic but it's actually very important to people you've got to make sure that the systems can be cooled and that we can power them yeah so there's that another issue is the software the software models how do you take a software model and distribute the data over many tens of thousands of nodes how do you do that efficiently if you look at you know gigaflop machines they had hundreds of nodes and each node had effectively a processor a core a thread of application we're looking at many many tens of thousands of nodes cores parallel threads running how do you make that efficient so is the software ready i think the majority of people will tell you that it's the software that's the problem not the hardware of course my friends in hardware would tell you ah software is easy it's the hardware that's the problem i think for the universities and the users the challenge is going to be the software i think um it's going to have to evolve you you're just you want to look at your machine and you just want to be able to dump work onto it easily we're not there yet not by a long stretch of the imagination yeah consequently you know we one of the things that we're doing is that we have a lot of centers of excellence is we will provide well i hate say the word provide we we sell super computers and once the machine has gone in we work very closely with the establishments create centers of excellence to get the best out of the machines to improve the software um and if a machine's expensive you want to get the most out of it that you can you don't just want to run a synthetic benchmark and say look i'm the fastest supercomputer on the planet you know your users who want access to it are the people that really decide how useful it is and the work they get out of it yeah the economics is definitely a factor in fact the fastest supercomputer in the planet but you can't if you can't afford to use it what good is it uh you mentioned power uh and then the flip side of that coin is of course cooling you can reduce the power consumption but but how challenging is it to cool these systems um it's an engineering problem yeah we we have you know uh data centers in iceland where it gets um you know it doesn't get too warm we have a big air cooled data center in in the united kingdom where it never gets above 30 degrees centigrade so if you put in water at 40 degrees centigrade and it comes out at 50 degrees centigrade you can cool it by just pumping it round the air you know just putting it outside the building because the building will you know never gets above 30 so it'll easily drop it back to 40 to enable you to put it back into the machine um right other ways to do it um you know is to take the heat and use it commercially there's a there's a lovely story of they take the hot water out of the supercomputer in the nordics um and then they pump it into a brewery to keep the mash tuns warm you know that's that's the sort of engineering i can get behind yeah indeed that's a great application talk a little bit more about your conversation with professor parsons maybe we could double click into that what are some of the things that you're going to you're going to probe there what are you hoping to learn so i think some of the things that that are going to be interesting to uncover is just the breadth of science that can be uh that could take advantage of exascale you know there are there are many things going on that uh that people hear about you know we people are interested in um you know the nobel prize they might have no idea what it means but the nobel prize for physics was awarded um to do with research into black holes you know fascinating and truly insightful physics um could it benefit from exascale i have no idea uh i i really don't um you know one of the most profound pieces of knowledge in in the last few hundred years has been the theory of relativity you know an austrian patent clerk wrote e equals m c squared on the back of an envelope and and voila i i don't believe any form of exascale computing would have helped him get there any faster right that's maybe flippant but i think the point is is that there are areas in terms of weather prediction climate prediction drug discovery um material knowledge engineering uh problems that are going to be unlocked with the use of exascale class systems we are going to be able to provide more tools more insight [Music] and that's the purpose of computing you know it's not that it's not the data that that comes out and it's the insight we get from it yeah i often say data is plentiful insights are not um ben you're a bit of an industry historian so i've got to ask you you mentioned you mentioned mentioned gigaflop gigaflops before which i think goes back to the early 1970s uh but the history actually the 80s is it the 80s okay well the history of computing goes back even before that you know yes i thought i thought seymour cray was you know kind of father of super computing but perhaps you have another point of view as to the origination of high performance computing [Music] oh yes this is um this is this is one for all my colleagues globally um you know arguably he says getting ready to be attacked from all sides arguably you know um computing uh the parallel work and the research done during the war by alan turing is the father of high performance computing i think one of the problems we have is that so much of that work was classified so much of that work was kept away from commercial people that commercial computing evolved without that knowledge i uh i have done in in in a previous life i have done some work for the british science museum and i have had the great pleasure in walking through the the british science museum archives um to look at how computing has evolved from things like the the pascaline from blaise pascal you know napier's bones the babbage's machines uh to to look all the way through the analog machines you know what conrad zeus was doing on a desktop um i think i think what's important is it doesn't matter where you are is that it is the problem that drives the technology and it's having the problems that requires the you know the human race to look at solutions and be these kicks started by you know the terrible problem that the us has with its nuclear stockpile stewardship now you've invented them how do you keep them safe originally done through the ascii program that's driven a lot of computational advances ultimately it's our quest for knowledge that drives these machines and i think as long as we are interested as long as we want to find things out there will always be advances in computing to meet that need yeah and you know it was a great conversation uh you're a brilliant guest i i love this this this talk and uh and of course as the saying goes success has many fathers so there's probably a few polish mathematicians that would stake a claim in the uh the original enigma project as well i think i think they drove the algorithm i think the problem is is that the work of tommy flowers is the person who took the algorithms and the work that um that was being done and actually had to build the poor machine he's the guy that actually had to sit there and go how do i turn this into a machine that does that and and so you know people always remember touring very few people remember tommy flowers who actually had to turn the great work um into a working machine yeah super computer team sport well ben it's great to have you on thanks so much for your perspectives best of luck with your conversation with professor parsons we'll be looking forward to that and uh and thanks so much for coming on thecube a complete pleasure thank you and thank you everybody for watching this is dave vellante we're celebrating exascale day you're watching the cube [Music]

>>from around the globe. It's the Cube with digital coverage of data automated and event. Siri's Brought to You by Iot Tahoe Welcome, everyone to the second episode in our data automated Siri's made possible with support from Iot Tahoe. Today we're gonna drill into the data lifecycle, meaning the sequence of stages that data travels through from creation to consumption to archive. The problem, as we discussed in our last episode, is that data pipelines, they're complicated, They're cumbersome, that disjointed, and they involve highly manual processes. Ah, smart data lifecycle uses automation and metadata to approve agility, performance, data quality and governance and ultimately reduce costs and time to outcomes. Now, in today's session will define the data lifecycle in detail and provide perspectives on what makes a data lifecycle smart and importantly, how to build smarts into your processes. In a moment, we'll be back with Adam Worthington from ethos to kick things off, and then we'll go into an export power panel to dig into the tech behind smart data life cycles, and it will hop into the crowdchat and give you a chance to ask questions. So stay right there. You're watching the cube innovation impact influence. Welcome >>to the Cube disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader. >>High tech digital coverage. Okay, we're back with Adam Worthington. Adam, good to see you. How are things across the pond? >>Thank you, I'm sure. >>Okay, so let's let's set it up. Tell us about yourself. What? Your role is a CTO and >>automatically. As you said, we found a way to have a pretty in company ourselves that we're in our third year on. Do we specialize in emerging disruptive technologies within the infrastructure? That's the kind of cloud space on my phone is the technical lead. So I kind of my job to be an expert in all of the technologies that we work with, which can be a bit of a challenge if you have a huge for phone is one of the reasons, like deliberately focusing on on also kind of pieces a successful validation and evaluation of new technologies. >>So you guys really technology experts, data experts and probably also expert in process and delivering customer outcomes. Right? >>That's a great word there, Dave Outcomes. That's a lot of what I like to speak to customers about. >>Let's talk about smart data, you know, when you when you throw in terms like this is it kind of can feel buzz, wordy. But what are the critical aspects of so called smart data? >>Help to step back a little bit, seen a little bit more in terms of kind of where I can see the types of problems I saw. I'm really an infrastructure solution architect trace on and what I kind of benefit we organically. But over time my personal framework, I focused on three core design principal simplicity, flexibility, inefficient, whatever it was designing. And obviously they need different things, depending on what the technology area is working with. But that's a pretty good. So they're the kind of areas that a smart approach to data will directly address. Reducing silos that comes from simplifying, so moving away from conflict of infrastructure, reducing the amount of copies of data that we have across the infrastructure and reducing the amount of application environments that need different areas so smarter get with data in my eyes anyway, the further we moved away from this. >>But how does it work? I mean, how do you know what's what's involved in injecting smarts into your data lifecycle? >>I think one of my I actually did not ready, but generally one of my favorite quotes from the French lost a mathematician, Blaise Pascal. He said, If I get this right, I have written a short letter, but I didn't have time. But Israel, I love that quite for lots of reasons >>why >>direct application in terms of what we're talking about, it is actually really complicated. These developers technology capabilities to make things simple, more directly meet the needs of the business. So you provide self service capabilities that they just need to stop driving. I mean, making data on infrastructure makes the business users using >>your job. Correct me. If I'm wrong is to kind of put that all together in a solution and then help the customer realize that we talked about earlier that business out. >>Yeah, enough if they said in understanding both sides so that it keeps us on our ability to deliver on exactly what you just said is big experts in the capabilities and new a better way to do things but also having the kind of the business understanding to be able to ask the right questions. That's how new a better price is. Positions another area that I really like his stuff with their platforms. You can do more with less. And that's not just about using data redundancy. That's about creating application environments, that conservative and then the infrastructure to service different requirements that are able to use the random Io thing without getting too kind of low level as well as the sequential. So what that means is you don't necessarily have to move data from application environment a do one thing related, and then move it to the application environment. Be that environment free terms of an analytics on the left Right works. Both keep the data where it is, use it or different different requirements within the infrastructure and again do more with less. And what that does is not just about simplicity and efficiency. It significantly reduces the time to value of that as well. >>Do you have examples that you can share with us even if they're anonymous customers that you work with that are maybe a little further down on the journey. Or maybe not >>looking at the you mentioned data protection earlier. So another organization This is a project which is just kind of hearing confessions moment, huge organization. They're literally petabytes of data that was servicing their back up in archive. And what they have is not just this realization they have combined. I think I different that they have dependent on the what area of infrastructure they were backing up, whether it was virtualization, that was different because they were backing up PC's June 6th. They're backing up another database environment, using something else in the cloud knowledge bases approach that we recommended to work with them on. They were able to significantly reduce complexity and reduce the amount of time that it systems of what they were able to achieve and what this is again. One of the clients have They've gone above the threshold of being able to back up for that. >>Adam, give us the final thoughts, bring us home. In this segment, >>the family built something we didn't particularly such on, that I think it is really barely hidden. It is spoken about as much as I think it is, that agile approaches to infrastructure we're going to be touched on there could be complicated on the lack of it efficient, the impact, a user's ability to be agile. But what you find with traditional approaches and you already touched on some of the kind of benefits new approaches there. It's often very prescriptive, designed for a particular as the infrastructure environment, the way that it served up the users in kind of a packaged. Either way, it means that they need to use it in that whatever wave in data bases, that kind of service of as it comes in from a flexibility standpoint. But for this platform approach, which is the right way to address technology in my eyes enables, it's the infrastructure to be used. Flexible piece of it, the business users of the data users what we find this capability into their innovating in the way they use that on the White House. I bring benefits. This is a platform to prescriptive, and they are able to do that. What you're doing with these new approaches is all of the metrics that we touched on and pass it from a cost standpoint from a visibility standpoint, but what it means is that the innovators in the business want really, is to really understand what they're looking to achieve and now have to to innovate with us. Now, I think I've started to see that with projects season places. If you do it in the right way, you articulate the capability and empower the business users in the right ways. Very significantly. Better position. The advantages on really matching significantly bigger than their competition. Yeah, >>Super Adam in a really exciting space. And we spent the last 10 years gathering all this data, you know, trying to slog through it and figure it out. And now, with the tools that we have and the automation capabilities, it really is a new era of innovation and insights. So, Adam or they didn't thanks so much for coming on the Cube and participating in this program. >>Exciting times with that. Thank you very much Today. >>Now we're going to go into the power panel and go deeper into the technologies that enable smart data life cycles. Stay right there. You're watching the cube. Are >>you interested in test driving? The i o ta ho platform Kickstart the benefits of data automation for your business through the Iot Labs program. Ah, flexible, scalable sandbox environment on the cloud of your choice with set up a service and support provided by Iot. Top. Click on the Link and connect with the data engineer to learn more and see Iot Tahoe in action. >>Welcome back, everybody to the power panel driving business performance with smart data life cycles. Leicester Waters is here. He's the chief technology officer from Iot Tahoe. He's joined by Patrick Smith, who was field CTO from pure storage. And is that data? Who's a system engineering manager at KohI City? Gentlemen, good to see you. Thanks so much for coming on this panel. >>Thank you. >>Let's start with Lester. I wonder if each of you could just give us a quick overview of your role. And what's the number one problem that you're focused on solving for your customers? Let's start with Lester Fleet. >>Yes, I'm Lost Waters, chief technology officer for Iot Tahoe and really the number one problem that we're trying to solve for our customers is to understand, help them understand what they have, because if they don't understand what they have in terms of their data. They can't manage it. They can't control it. The cap monitor. They can't ensure compliance. So really, that's finding all you can about your data that you have. And building a catalog that could be readily consumed by the entire business is what we do. >>Patrick Field, CTO in your title That says to me, You're talking to customers all the time, so you got a good perspective on it. Give us your take on things here. >>Yeah, absolutely. So my patches in here on day talkto customers and prospects in lots of different verticals across the region. And as they look at their environments and their data landscape, they're faced with massive growth in the data that they're trying to analyze and demands to be able to get insight our stuff and to deliver better business value faster than they've ever had to do in the past. So >>got it. And is that of course, Kohi City. You're like the new kid on the block. You guys were really growing rapidly created this whole notion of data management, backup and and beyond. But I'm assistant system engineering manager. What are you seeing from from from customers your role and the number one problem that you're solving. >>Yeah, sure. So the number one problem I see time and again speaking with customers. It's around data fragmentation. So do two things like organic growth, even maybe budgetary limitations. Infrastructure has grown over time very piecemeal, and it's highly distributed internally. And just to be clear, you know, when I say internally, that >>could be >>that it's on multiple platforms or silos within an on Prem infrastructure that it also does extend to the cloud as well. >>Right Cloud is cool. Everybody wants to be in the cloud, right? So you're right, It creates, Ah, maybe unintended consequences. So let's start with the business outcome and kind of try to work backwards to people you know. They want to get more insights from data they want to have. Ah, Mawr efficient data lifecycle. But so let's let me start with you were thinking about like the North Star for creating data driven cultures. You know, what is the North Star or customers >>here? I think the North Star, in a nutshell, is driving value from your data. Without question, I mean way, differentiate ourselves these days by even nuances in our data now, underpinning that, there's a lot of things that have to happen to make that work out. Well, you know, for example, making sure you adequately protect your data, you know? Do you have a good You have a good storage sub system? Do you have a good backup and recovery point objectives? Recovery time objective. How do you Ah, are you fully compliant? Are you ensuring that you're taking all the boxes? There's a lot of regulations these days in terms with respect to compliance, data retention, data, privacy and so forth. Are you taking those boxes? Are you being efficient with your, uh, your your your data? You know, In other words, I think there's a statistic that someone mentioned me the other day that 53% of all businesses have between three and 15 copies of the same data. So you know, finding and eliminating does is it is part of the part of the problem is when you do a chase, >>um, I I like to think of you're right, no doubt, business value and and a lot of that comes from reducing the end in cycle times. But anything that you guys would would add to that. Patrick, Maybe start with Patrick. >>Yeah, I think I think in value from your data really hits on tips on what everyone wants to achieve. But I think there are a couple of key steps in doing that. First of all, is getting access to the data and asked that, Really, it's three big problems, firstly, working out what you've got. Secondly, looking at what? After working on what you've got, how to get access to it? Because it's all very well knowing that you've got some data. But if you can't get access to it either because of privacy reasons, security reasons, then that's a big challenge. And then finally, once you've got access to the data making sure that you can process that data in a timely manner >>for me, you know it would be that an organization has got a really good global view of all of its data. It understands the data flow and dependencies within their infrastructure, understands that precise legal and compliance requirements, and you had the ability to action changes or initiatives within their environment to give the fun. But with a cloud like agility. Um, you know, and that's no easy feat, right? That is hard work. >>Okay, so we've we've talked about. The challenge is in some of the objectives, but there's a lot of blockers out there, and I want to understand how you guys are helping remove them. So So, Lester. But what do you see as some of the big blockers in terms of people really leaning in? So this smart data lifecycle >>yeah, Silos is is probably one of the biggest one I see in business is yes, it's it's my data, not your data. Lots of lots of compartmentalization. Breaking that down is one of the one of the challenges. And having the right tools to help you do that is only part of the solution. There's obviously a lot of cultural things that need to take place Teoh to break down those silos and work together. If you can identify where you have redundant data across your enterprise, you might be able to consolidate those. >>So, Patrick, so one of the blockers that I see is legacy infrastructure, technical debt, sucking all the budget you got. You know, too many people have having to look after, >>as you look at the infrastructure that supports people's data landscapes today for primarily legacy reasons. The infrastructure itself is siloed. So you have different technologies with different underlying hardware and different management methodologies that they're there for good reason, because historically you have to have specific fitness, the purpose for different data requirements. And that's one of the challenges that we tackled head on a pure with with the flash blade technology and the concept of the data, a platform that can deliver in different characteristics for the different workloads. But from a consistent data platform >>now is that I want to go to you because, you know, in the world in your world, which to me goes beyond backup. And one of the challenges is, you know, they say backup is one thing. Recovery is everything, but as well. The the CFO doesn't want to pay for just protection, and one of things that I like about what you guys have done is you. You broadened the perspective to get more value out of your what was once seen as an insurance policy. >>I do see one of the one of the biggest blockers as the fact that the task at hand can, you know, can be overwhelming for customers. But the key here is to remember that it's not an overnight change. It's not, you know, a flick of a switch. It's something that can be tackled in a very piecemeal manner on. Absolutely. Like you said, You know, reduction in TCO and being able to leverage the data for other purposes is a key driver for this. So, you know, this can be this can be resolved. It would be very, you know, pretty straightforward. It can be quite painless as well. Same goes for unstructured data, which is very complex to manage. And, you know, we've all heard the stats from the the analysts. You know, data obviously is growing at an extremely rapid rate, but actually, when you look at that, you know how is actually growing. 80% of that growth is actually in unstructured data, and only 20% of that growth is in unstructured data. S o. You know, these are quick win areas that customers can realize immediate tco improvement and increased agility as well >>paint a picture of this guy that you could bring up the life cycle. You know what you can see here is you've got this this cycle, the data lifecycle and what we're wanting to do is inject intelligence or smarts into this, like like life cycles. You see, you start with ingestion or creation of data. You're you're storing it. You got to put it somewhere, right? You gotta classify it. You got to protect it. And then, of course, you want to reduce the copies, make it, you know, efficient on. And then you want to prepare it so that businesses can actually sumit. And then you've got clients and governance and privacy issues, and I wonder if we could start with you. Lester, this is, you know, the picture of the life cycle. What role does automation play in terms of injecting smarts into the lifecycle? >>Automation is key here, especially from the discover it catalog and classify perspective. I've seen companies where they geo and will take and dump their all of their database scheme is into a spreadsheet so that they can sit down and manually figure out what attributes 37 means for a column names, Uh, and that's that's only the tip of the iceberg. So being able to do automatically detect what you have automatically deduced where what's consuming the data, you know, upstream and downstream. Being able to understand all of the things related to the lifecycle of your data. Back up archive deletion. It is key. And so we're having having good tool. IShares is very >>important. So, Patrick, obviously you participate in the store piece of this picture s I wonder if you could talk more specifically about that. But I'm also interested in how you effect the whole system view the the end end cycle time. >>Yeah, I think Leicester kind of hit the nail on the head in terms of the importance of automation because the data volumes are just just so massive. Now that you can, you can you can effectively manage or understand or catalog your data without automation. Once you understand the data and the value of the data, then that's where you can work out where the data needs to be at any point in >>time, right? So pure and kohi city obviously partner to do that and of course, is that you guys were part of the protect you certainly part of the retain. But Also, you provide data management capabilities and analytics. I wonder if you could add some color there. >>Yeah, absolutely. So, like you said, you know, we focused pretty heavily on data protection. Is just one of our one of our areas on that infrastructure. It is just sitting there, really? Can, you know, with the legacy infrastructure, It's just sitting there, you know, consuming power, space cooling and pretty inefficient. And what, if anything, that protest is a key part of that. If I If I have a modern data platform such as, you know, the cohesive data platform, I can actually do a lot of analytics on that through application. So we have a marketplace for APS. >>I wonder if we could talk about metadata. It's It's increasingly important. Metadata is data about the data, but Leicester maybe explain why it's so important and what role it plays in terms of creating smart data lifecycle. A >>lot of people think it's just about the data itself, but there's a lot of extended characteristics about your data. So so imagine if or my data life cycle I can communicate with the backup system from Kohi City and find out when the last time that data was backed up or where is backed up to. I can communicate exchange data with pure storage and find out what two years? And is the data at the right tier commensurate with its use level pointed out and being able to share that metadata across systems? I think that's the direction that we're going in right now. We're at the stage where just identifying the metadata and trying to bring it together and catalog the next stage will be OK using the AP eyes it that that we have between our systems can't communicate and share that data and build good solutions for customers to use. >>It's a huge point that you just made. I mean, you know, 10 years ago, automating classification was the big problem, and it was machine intelligence, you know, obviously attacking that, But your point about as machines start communicating to each other and you start, it's cloud to cloud. There's all kinds of metadata, uh, kind of new meta data that's being created. I often joke that someday there's gonna be more metadata than data, so that brings us to cloud and that I'd like to start with you. >>You know, I do think, you know, having the cloud is a great thing. And it has got its role to play, and you can have many different permutations and iterations of how you use it on. Um, you know, I may have sort of mentioned previously. You know, I've seen customers go into the cloud very, very quickly, and actually recently, they're starting to remove workloads from the cloud. And the reason why this happens is that, you know, Cloud has got its role to play, but it's not right for absolutely everything, especially in their current form as well. A good analogy I like to use on this may sound a little bit cliche, but you know, when you compare clouds versus on premises data centers, you can use the analogy of houses and hotels. So to give you an idea so you know, when we look at hotels, that's like the equivalent of a cloud, right? I can get everything I need from there. I can get my food, my water, my outdoor facilities. If I need to accommodate more people, I can rent some more rooms. I don't have to maintain the hotel. It's all done for me. When you look at houses the equivalent to on premises infrastructure, I pretty much have to do everything myself, right. So I have to purchase the house. I have to maintain it. I have to buy my own food and water. Eat it. You have to make improvements myself. But then why do we all live in houses? No, in hotels. And the simple answer that I can I can only think of is, is that it's cheaper, right. It's cheaper to do it myself. But that's not to say that hotels haven't got their role to play. Um, you know? So, for example, if I've got loads of visitors coming over for the weekend, I'm not going to go build an extension to my house just for them. I will burst into my hotel into the cloud, um, and use it for, you know, for for things like that. So what I'm really saying is the cloud is great for many things, but it can work out costlier for certain applications, while others are a perfect >>It's an interesting analogy. I hadn't thought of that before, but you're right because I was going to say Well, part of it is you want the cloud experience everywhere, but you don't always want the cloud experience especially, you know, when you're with your family, you want certain privacy that I've not heard that before. He's out. So that's the new perspective s Oh, thank you, but but But Patrick, I do want to come back to that cloud experience because, in fact, that's what's happening. In a lot of cases, organizations are extending the cloud properties of automation on Prem. >>Yeah, I thought, as I thought, a really interesting point and a great analogy for the use of the public cloud. And it really reinforces the importance of the hybrid and multi cloud environment because it gives you the flexibility to choose where is the optimal environment to run your business workloads? And that's what it's all about and the flexibility to change which environment you're running in, either for more months to the next or from one year to the next. Because workloads change and the characteristics that are available in the cloud change, the hybrid cloud is something that we've we've lived with ourselves of pure, So our pure one management technology actually sits in hybrid cloud and what we we started off entirely cloud native. But now we use public cloud for compute. We use our own technology at the end of a high performance network link to support our data platform. So we get the best of both worlds and I think that's where a lot of our customers are trying to get to. >>Alright, I want to come back in a moment there. But before we do, let's see, I wonder if we could talk a little bit about compliance, governance and privacy. I think the Brits hung on. This panel is still in the EU for now, but the you are looking at new rules. New regulations going beyond GDP are where does sort of privacy governance, compliance fit in the data lifecycle, then, is that I want your thoughts on this as well. >>Yeah, this is this is a very important point because the landscape for for compliance, around data privacy and data retention is changing very rapidly. And being able to keep up with those changing regulations in an automated fashion is the only way you're gonna be able to do it. Even I think there's a some sort of Ah, maybe ruling coming out today or tomorrow with the changed in the r. So this is things are all very key points and being able to codify those rules into some software. Whether you know, Iot Tahoe or or your storage system or kohi city, it will help you be compliant is crucial. >>Yeah. Is that anything you can add there? I mean, it's really is your wheelhouse. >>Yeah, absolutely. So, you know, I think anybody who's watching this probably has gotten the message that, you know, less silos is better. And it absolutely it also applies to data in the cloud is where as well. So you know, my aiming Teoh consolidate into fewer platforms, customers can realize a lot better control over their data. And the natural effect of this is that it makes meeting compliance and governance a lot easier. So when it's consolidated, you can start to confidently understand who's accessing your data. How frequently are they accessing the data? You can also do things like, you know, detecting anomalous file access activities and quickly identify potential threats. >>Okay, Patrick, we were talking. You talked earlier about storage optimization. We talked to Adam Worthington about the business case, the numerator, which is the business value, and then the denominator, which is the cost and what's unique about pure in this regard. >>Yeah, and I think there are. There are multiple time dimensions to that. Firstly, if you look at the difference between legacy storage platforms that used to take up racks or aisles of space in the data center, the flash technology that underpins flash blade way effectively switch out racks rack units on. It has a big play in terms of data center footprint, and the environmental is associated with the data center. If you look at extending out storage efficiencies and the benefits it brings, just the performance has a direct effect on start we whether that's, you know, the start from the simplicity that platform so that it's easy and efficient to manage, whether it's the efficiency you get from your data. Scientists who are using the outcomes from the platform, making them more efficient to new. If you look at some of our customers in the financial space there, their time to results are improved by 10 or 20 x by switching to our technology from legacy technologies for their analytics, platforms. >>The guys we've been running, you know, Cube interviews in our studios remotely for the last 120 days is probably the first interview I've done where haven't started off talking about Cove it, Lester. I wonder if you could talk about smart data lifecycle and how it fits into this isolation economy. And hopefully, what will soon be a post isolation economy? >>Yeah, Come. It has dramatically accelerated the data economy. I think. You know, first and foremost, we've all learned to work at home. You know, we've all had that experience where, you know, people would have been all about being able to work at home just a couple days a week. And here we are working five days. That's how to knock on impact to infrastructure, to be able to support that. But going further than that, you know, the data economy is all about how a business can leverage their data to compete in this New World order that we are now in code has really been a forcing function to, you know, it's probably one of the few good things that have come out of government is that we've been forced to adapt and It's a zoo. Been an interesting journey and it continues to be so >>like Lester said, you know, we've We're seeing huge impact here. Working from home has pretty much become the norm. Now, you know, companies have been forced into basically making it work. If you look online retail, that's accelerated dramatically as well. Unified communications and videoconferencing. So really, you know the point here, is that Yes, absolutely. We're you know, we've compressed, you know, in the past, maybe four months. What already would have taken maybe even five years, maybe 10 years or so >>We got to wrap. But Celester Louis, let me ask you to sort of get paint. A picture of the sort of journey the maturity model that people have to take. You know, if they want to get into it, where did they start? And where are they going to give us that view, >>I think, versus knowing what you have. You don't know what you have. You can't manage it. You can't control that. You can't secure what you can't ensure. It's a compliant s so that that's first and foremost. Uh, the second is really, you know, ensuring that your compliance once, once you know what you have. Are you securing it? Are you following the regulatory? The applicable regulations? Are you able to evidence that, uh, how are you storing your data? Are you archiving it? Are you storing it effectively and efficiently? Um, you know, have you Nirvana from my perspective, is really getting to a point where you you've consolidated your data, you've broken down the silos and you have a virtually self service environment by which the business can consume and build upon their data. And really, at the end of the day, as we said at the beginning, it's all about driving value out of your data. And ah, the automation is is key to this, sir. This journey >>that's awesome and you just described is sort of a winning data culture. Lester, Patrick, thanks so much for participating in this power panel. >>Thank you, David. >>Alright, So great overview of the steps in the data lifecycle and how to inject smarts into the process is really to drive business outcomes. Now it's your turn. Hop into the crowd chat, please log in with Twitter or linked in or Facebook. Ask questions, answer questions and engage with the community. Let's crowdchat, right. Yeah, yeah, yeah.

>>from around the globe. It's the Cube with digital coverage of data automated and event. Siri's brought to you by Iot. Tahoe. Okay, we're back with Adam Worthington. Who's the CTO and co founder of Ethos Adam. Good to see you. How are things across the pond? >>Thank you. I'm sure that a little bit on your side. >>Okay, so let's let's set it up. Tell us about yourself. What your role is a CTO and give us the low down on those. >>Sure, So we get automatic. As you said CTO and co founder of A were pretty young company ourselves that we're in our sixth year and we specialize in emerging disruptive technologies within the infrastructure Data center kind of cloud space. And my role is the technical lead. So it's kind of my job to be an expert in all of the technologies that we work with, which can be a bit of a challenge if you have a huge portfolio, is one of the reasons we deliberately focusing on on also kind of a validation and evaluation of new technologies. Yeah, >>so you guys are really technology experts, data experts and probably also expert in process and delivering customer outcomes. Right? >>That's a great word there, Dave Outcomes. That's a lot of what I like to speak to customers about on. Sometimes I get that gets lost, particularly with within highly technical field. I like the virtualization guy or a network like very quickly start talking about the nuts and bolts of technology on I'm a techie. I'm absolutely a nerd, like the best tech guitar but fundamentally reporting in technologies to meet. This is outcomes to solve business problems on on to enable a better way. >>Love it. We love tech, too, but really, it's all about the customer. So let's talk about smart data. You know, when you when you throw in terms like this is it kind of Canfield Buzz Wordy. But let's let's get into the meat on it. What does that mean to you? One of the critical aspects of so called smart data >>cool probably hoped to step back a little bit and set the scene a little bit more in in terms of kind of where I came from, the types of problems that I'm really an infrastructure solution architect trace on what I kind of benefits. We organically But over time my personal framework, I focused on three core design principles whatever it was I was designing. And obviously they need different things. Depending on what technology area is that we're working with. That's pretty good on. And what I realized that we realized we started with those principles could be it could be used more broadly in the the absolute best of breed of technologies. And those really disrupt, uh, significantly improve upon the status quo in one or more of those three areas. Ideally or more simple, more on if we look at the data of the challenges that organizations, enterprises organizations have criticized around data and smart fail over the best way. Maybe it's good to reflect on what the opposite end of the story is kind of why data is often quite dumb. The traditional approaches. We have limited visibility into the data that we're up to the story using within our infrastructure as what we kind of ended up with over time, through no fault of the organizations that have happened silos, everyone silos of expertise. So whether that be, that's going out. Specialized teams, socialization, networking. They have been, for example, silos of infrastructure, which trade state of fragmentation copies of data in different areas of the infrastructure on copies of replication in that data set or reputation in terms of application environments. I think that that's kind of what we tend to focus on, what it's becoming, um, resonating with more organizations. There's a survey that one of the vendors that we work with actually are launched vendor 5.5 years ago, a medical be gone. They work with any company called Phantom Born a first of a kind of global market, 900 respondents, all different vectors, a little different countries, the U. S. And Germany. And what they found was shocking. It was a recent survey so focused on secondary data, but the lessons learned the information taken out a survey applies right across the gamut of infrastructure data organizations. Just some stats just pull out the five minutes 85% off the organization surveyed store between two and five stores data in 3 to 5 clouds. 63% of organizations have between four and 16 coffees of exactly the same data. Nearly nine out of 10 respondents believe that organizations, secondly, data's fragmented across silos are touched on is would become nearly impossible to manage over the long term on. And 91% of the vast majority of organizations leadership were concerned about the level of visibility their teams. So they're the kind of areas that a smart approach to data will directly address. So reducing silos that comes from simplifying so moving away from complexity of infrastructure, reducing the amount of copies of data that we have across the infrastructure and reducing the amount of application environment. I mean, Harry, so smarter we get with data is in my eyes. Anyway, the further we moved away from this, >>there was a lot in that answer, but I want to kind of summarize it if I can talk. You started with simplicity, flexibility, efficiency. Of course, that's what customers want. And then I was gonna ask you about you know, what challenges customers are facing, and I think you laid it out here. But I want to I want to pick on a couple of some of the data that you talked about the public cloud treat that adds complexity and diversity in skill requirements. The copies of data is so true, like data is just like like if rebels, If you Star Trek franchise, they just expand and replicate. So that's an expense, and it adds complexity. Silo data means you spend a lot of time trying to figure out who's got the right data. What's the real truth with a lot of manual processes involved in the visibility is obviously critical. So those are the problems on. But course you talked about how you address those, But But how does it work? I mean, how do you know what's what's involved in injecting smarts into your data? Lifecycle >>that plane, Think about it. So insurance of the infrastructure and say they were very good reasons why customers are in situations they have been in this situation because of the limits are traditional prices. So you look at something is fundamental. So a great example, um on applications that utilize the biggest fundamentally back ups are now often what that typically required is completely separate infrastructure to everything else. But when we're talking about the data set, so what would be a perfect is if we could back up data on use it for other things, and that's where a, uh, a technology provider like So So although it better technology is incredibly simple, it's also incredibly powerful and allows identification, consolidation. And then, if you look at just getting insight out of that fundamentally tradition approaches to infrastructure, they're put in a point of putting a requirement. And therefore it wasn't really incumbent exposed any information out of the data that's stored within the division, which makes it really tricky to do anything else outside of the application. That that's where something like Iot how come in in terms of abstracting away the complexity more directly, I So these are the kind of the area. So I think one of my I did not ready, but generally one of my favorite quotes from the French philosopher and a mathematician, Blaise Pascal, he says, I get this right. I have written a short letter, but I didn't have time. But Israel. I love that quite for lots of reasons, that computation of what we're talking about, it is actually really complicated to develop a technology capability to make things simple, more directly meet the needs of the business. So you provide self service capabilities that they just need to stop driving. I mean making data on infrastructure makes sense for the business users. Music. It's My belief is that the technology shouldn't mean that the users of the technology has to be a technology expert what we really want them to be. And they should be a business experts in any technology that you should enable on demand for the types of technologies to get me excited. They're not necessarily from a ftt complicated technology perspective, but those are really focused on impressive the capability. >>Yeah. Okay, so you talked about back up, We're gonna hear from Kohi City a little bit later and beyond backup data protection, Data Management, That insight piece you talked earlier about visibility, and that's what the Iot Tahoe's bringing table with its software. So that's another component of the tech stack, if you will, Um, and then you talk about simplicity. We're gonna hear from pure storage. They're all about simple storage. They call it the modern data experience. I think so. So those are some of the aspects and your job. Correct me. If I'm wrong is to kind of put that all together in a solution and then help the customer realize that we talked about earlier that business out. >>Yeah, it's that they said, in understanding both sides so that it keeps us on our ability to be able to deliver on exactly what you just said. It's being experts in the capabilities and new and better ways to do things but also having the kind of business under. I found it to be able to ask the right questions, identify how new a better price is positions and you touched on. Yet three vendors that we work with that you have on the panel are very genuinely of. I think of the most exciting around storage and pure is a great one. So yes, a lot of the way that they've made their way. The market is through impressive C and through producing data redundancy. But another area that I really like is with that platform, you can do more with less. And that's not just about using data redundancy. That's about creating application environment, that conservative, then the infrastructure to service different requirements are able to do that the random Io thing without getting too kind of low level as well as a sequential. So what that means is that you don't necessarily have to move data from application environment a do one thing. They disseminate it and then move it to the application environment. Be that based environment three in terms of an analytics on the left to right work. So keep the data where it is, use it for different requirements within the infrastructure and again do more with less. And what that does is not just about simplicity and efficiency. It significantly reduces the time to value. Well at that again resonates that I want to pick up a soundbite that resonates with all of the vendors we have on the panel later. This is the way that they're able todo a better a better TCO better our alliance significantly reduce the value of data. But to answer your question, yeah, you're exactly right. So it's key to us to kind of position, understand? Customer climbs, position the right technology. >>Adam. I wonder if you could give us your insights based on your experience with customers in terms of what success looks like. I'm interested in what they're measuring. I'm big on and end cycle times and taking a systems view, but of course you know customers. They want to measure everything, whether it's the productivity of developers or, you know, time to insights, etcetera. What >>are >>they? One of the KP eyes that are driving success and outcomes? >>Those capabilities on historically in our space have always been a bit really. When you talk about total cost of ownership, talk about return on investment, you talk about time to value on. I've worked in many different companies, many different infrastructure, often quite complicated environments and infrastructure. I'm being able to put together anything Security realistic gets proven out. One solution gets turned around our alliance TCO is challenging. But now with these new, a better approach is that more efficient, enables you to really build a true story and on replicate whatever you want. Obviously ran kind of our life, and the key thing is to say from data, But now it's time to value. So what we what? We help in terms of the scoping on in terms of the understanding what the requirements are, we specifically called out business outcomes what organizations are looking to achieve and then back on those metrics, uh, to those outcomes. What that does is a few different things, but it provides a certain success criteria. Whether that's success criteria within a proof of concept of the mobile solutions on being able to speak that language on before, more directly meet the needs of the business kind of crystallized defined way is we're only really be able to do that. Now we work with >>Yeah, So when you think about the business case, they are a why benefit over cost benefit obviously lower tco you lower the denominator, you're going to increase the output in the value. And then I would I would really stress that I think the numerator, ultimately especially in a world of data, is the most important. And I think the TCO is fundamental. It's really becoming table stakes. You gotta have simple. You've gotta have efficient. You've got to be agile. But it enables that that numerator, whether that's new customer revenue, maybe, you know, maybe cost savings across the business. And again that comes from taking that systems view. Do you >>have >>examples that you can share with us even if they're anonymous, eyes the customers that you work with that or maybe a little further down on the journey, or maybe not things that you can share with us that are proof points here. >>Sure, it's quite easy and very gratifying when you've spoken to a customer. We know you've been doing this for 20 years, and this is the way that your infrastructure if you think about it like this, if we implemented that technology or this new approach, then we will enable you to get simple, often ready, populous. Reduce your back. I worked on a project where a customer accused that back book from I think it was. It was nine. Just under 10. It was nine fully loaded. Wraps back. We should just for the it you're providing the fundamental underlying storage architectures. And they were able to consolidate that that down on, provide additional capacity. Great performance. The less than half Uh huh. Looking at the you mentioned data protection earlier. So another organization. This is a project which is just kind of nearing completion of the moment. Huge organization. They're literally petabytes of data that was servicing their back up in archive. And what they have is not just the reams of data, they have the combined thing. I different backup. Yeah, that they have dependent on the what area of infrastructure they were backing up. So whether it was virtualization that was different, they were backing up. Pretty soon they're backing up another database environment using something else in the cloud. So a consolidated approach that we recommended to work with them on they were able to significantly reduce complexity and reduce the amount of time that it system what they were able to achieve. And this is again one of the clients have they've gone above the threshold of being able to back up. When they tried to do a CR, you been everything back up into in a second. They want people to achieve it. Within the timescales is a disaster recovery, business continuity. So with this, we're able to prove them with a proof up. Just before they went into production and the our test using the new approach. And they were able to recover everything the entire interest in minutes instead of a production production, workloads that this was in comparison to hours and that was those hours is just a handful of workloads. They were able to get up and running with the entire estate, and I think it was something like an hour on the core production systems. They were up and running practically instantaneously. So if you look at really stepping back what the customers are looking to the chief, they want to be able to if there is any issues recover from those issues, understand what they're dealing with. Yeah, On another, we have customers that we work with recently what they had huge challenges around and they were understandably very scared about GDP are. But this is a little while ago, actually, a bit still no up. A conversation has gone away. Just everybody are still speaks to issues and concerns around GDP are applying understanding whether they so put in them in us in a position to be able to effectively react. Subject That was something that was a key metric. A target for on infrastructure solution that we work with and we were able to provide them with the insight into their data on day enables them to react to compliance. And they're here to get a subject access request way created in significantly. I'm >>awesome. Thank you for that. I want to pick up on a little bit. So the first example you get your infrastructure in order to bust down those silos and what I've when I talk to customers. And I've talked to a number of banks, insurance companies, other financial services of manufacturers when they're able to sort of streamline that data lifecycle and bring in automation and intelligence, if you will. What they tell me is now they're able to obviously compress the time to value, but also they're loading up on way more initiatives and projects that they can deliver for the business. And you talk for about about the line of business having self served. The businesses feel like they actually are really invested in the data, that it's their data that it's not, you know, confusing and a lot of finger pointing. So so that's that's huge on. And I think that your other example is right on as well of really clear business value that organizations are seeing. So thanks for those you know. Now is the time really, t get these houses in order, if you will, because it really drives competitive advantage, especially take your second example in this isolation economy, you know, being able to respond things like privacy are just increasingly critical. Adam, give us the final thoughts. Bring us home in this segment, >>not the farm of built, something we didn't particularly touch on that I think it's It's fairly fairly hidden. It isn't spoken about as much as I think it is that digital approaches to infrastructure we've already touched on there could be complicated on lack of efficiency, impact, a user's ability to be agile, what you find with traditional approaches. And you already touched on some of the kind of benefits and new approaches that they're often very prescriptive, designed for a particular as the infrastructure environment, the way that it served up to the users in a kind of A packaged either way means that they need to use it in that whatever way, in places. So that kind of self service aspect that comes in from a flexibility standpoint that for me in this platform approach, which is the right way to address technology in my eyes enables it's the infrastructure to be used effectively so that the business uses of the data users what we find in this capability into their hand and start innovating in the way that they use that on the way that they bring benefits a platform to prescriptive, and they are able to do that. So what you're doing with these new approaches is all of the metrics that we touched on fantastic from a cost standpoint, from a visibility standpoint. But what it means is that the innovators in the business want to really, really understand what they're looking to achieve and now tools to innovate with us. Now, I think I've started to see that with projects that were completed, you could do it in the right way. You articulate the capability and empower the business users in the right way. Then very significantly better position. Take advantage of this on really match and significantly bigger than their competition. >>Super Adam in a really exciting space. And we spent the last 10 years gathering all this data, you know, trying to slog through it and figure it out. And now, with the tools that we have and the automation capabilities, it really is a new era of innovation and insights. So, Adam or they didn't thanks so much for coming on the Cube and participating in this program >>Exciting times. And thank you very much today. >>Alright, Stay safe and thank you. Everybody, this is Dave Volante for the Cube. Yeah, yeah, yeah, yeah

(upbeat music) >> Voiceover: From around the globe, it's theCUBE with digital coverage of Data Automated. An event series brought to you by Io-Tahoe. >> Welcome everyone to the second episode in our Data Automated series made possible with support from Io-Tahoe. Today, we're going to drill into the data lifecycle. Meaning the sequence of stages that data travels through from creation to consumption to archive. The problem as we discussed in our last episode is that data pipelines are complicated, they're cumbersome, they're disjointed and they involve highly manual processes. A smart data lifecycle uses automation and metadata to improve agility, performance, data quality and governance. And ultimately, reduce costs and time to outcomes. Now, in today's session we'll define the data lifecycle in detail and provide perspectives on what makes a data lifecycle smart? And importantly, how to build smarts into your processes. In a moment we'll be back with Adam Worthington from Ethos to kick things off. And then, we'll go into an expert power panel to dig into the tech behind smart data lifecyles. And, then we'll hop into the crowd chat and give you a chance to ask questions. So, stay right there, you're watching theCUBE. (upbeat music) >> Voiceover: Innovation. Impact. Influence. Welcome to theCUBE. Disruptors. Developers. And, practitioners. Learn from the voices of leaders, who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on theCUBE. Your global leader in high tech digital coverage. >> Okay, we're back with Adam Worthington. Adam, good to see you, how are things across the pond? >> Good thank you, I'm sure our weather's a little bit worse than yours is over the other side, but good. >> Hey, so let's set it up, tell us about yourself, what your role is as CTO and--- >> Yeah, Adam Worthington as you said, CTO and co-founder of Ethos. But, we're a pretty young company ourselves, so we're in our sixth year. And, we specialize in emerging disruptive technology. So, within the infrastructure data center kind of cloud space. And, my role is a technical lead, so I, it's kind of my job to be an expert in all of the technologies that we work with. Which can be a bit of a challenge if you have a huge portfolio. One of the reasons we got to deliberately focus on. And also, kind of pieces of technical validation and evaluation of new technologies. >> So, you guys are really technology experts, data experts, and probably also expert in process and delivering customer outcomes, right? >> That's a great word there Dave, outcomes. I mean, that's a lot of what I like to speak to customers about. >> Let's talk about smart data you know, when you throw out terms like this it kind of can feel buzz wordy but what are the critical aspects of so-called smart data? >> Cool, well typically I had to step back a little bit and set the scene a little bit more in terms of kind of where I came from. So, and the types of problems I've sorted out. So, I'm really an infrastructure or solution architect by trade. And, what I kind of, relatively organically, but over time my personal framework and approach. I focused on three core design principles. So, simplicity, flexibility and efficiency. So, whatever it was I was designing and obviously they need different things depending on what the technology area is that we're working with. So, that's for me a pretty good step. So, they're the kind of areas that a smart approach in data will directly address both reducing silos. So, that comes from simplifying. So, moving away from complexity of infrastructure. Reducing the amount of copies of data that we have across the infrastructure. And, reducing the amount of application environment for the need for different areas. So, the smarter we get with data it's in my eyes anyway, the further we move away from those traditional legacy. >> But, how does it work? I mean, how, in other words, what's involved in injecting smarts into your data lifecycle? >> I think one of my, well actually I didn't have this quote ready, but genuinely one of my favorite quotes is from the French philosopher and mathematician, Blaise Pascal and he says, if I get this right, "I'd have written you a shorter letter, but I didn't have the time." So, there's real, I love that quote for lots of reasons. >> Dave: Alright. >> That's direct applications in terms of what we're talking about. In terms of, it's actually really complicated to develop a technology capability to make things simple. Be more directly meeting the needs of the business through tech. So, you provide self-service capability. And, I don't just mean self-driving, I mean making data and infrastructure make sense to the business users that are using it. >> Your job, correct me if I'm wrong, is to kind of put that all together in a solution. And then, help the customer you know, realize what we talked about earlier that business out. >> Yeah, and that's, it's sitting at both sides and understanding both sides. So, kind of key to us in our abilities to be able to deliver on exactly what you've just said, is being experts in the capabilities and new and better ways of doing things. But also, having the kind of, better business understanding to be able to ask the right questions to identify how can you better approach this 'cause it helps solve these issues. But, another area that I really like is the, with the platforms you can do more with less. And, that's not just about reducing data redundancy, that's about creating application environments that can service, an infrastructure to service different requirements that are able to do the random IO thing without getting too kind of low level tech. As well as the sequential. So, what that means is, that you don't necessarily have to move data from application environment A, do one thing with it, collate it and then move it to the application environment B, to application environment C, in terms of an analytics kind of left to right workload, you keep your data where it is, use it for different requirements within the infrastructure and again, do more with less. And, what that does, it's not just about simplicity and efficiency, it significantly reduces the times of value that that faces, as well. >> Do you have examples that you can share with us, even if they're anonymized of customers that you've worked with, that are maybe a little further down on the journey. Or, maybe not and--- >> Looking at the, you mentioned data protection earlier. So, another organization this is a project which is just coming nearing completion at the moment. Huge organization, that literally petabytes of data that was servicing their backup and archive. And, what they had is not just this reams of data. They had, I think I'm right in saying, five different backup applications that they had depending on the, what area of infrastructure they were backing up. So, whether it was virtualization, that was different to if they were backing up, different if they were backing up another data base environment they were using something else in the cloud. So, a consolidated approach that we recommended to work with them on. They were able to significantly reduce complexity and reduce the amount of time that it took them. So, what they were able to achieve and this was again, one of the key departments they had. They'd gone above the threshold of being able to backup all of them. >> Adam, give us the final thoughts, bring us home in this segment. >> Well, the final thoughts, so this is something, yeah we didn't particularly touch on. But, I think it's kind of slightly hidden, it isn't spoken about as much as I think it could be. Is the traditional approaches to infrastructure. We've already touched on that they can be complicated and there's a lack of efficiency. It impacts a user's ability to be agile. But, what you find with traditional approaches and we've already touched on some of the kind of benefits to new approaches there, is that they're often very prescriptive. They're designed for a particular firm. The infrastructure environment, the way that it's served up to the users in a kind of a packaged kind of way, means that they need to use it in that, whatever way it's been dictated. So, that kind of self-service aspect, as it comes in from a flexibility standpoint. But, these platforms and these platform approaches is the right way to address technology in my eyes. Enables the infrastructure to be used flexibly. So, the business users and the data users, what we find is that if we put in this capability into their hands. They start innovating the way that they use that data. And, the way that they bring benefits. And, if a platform is too prescriptive and they aren't able to do that, then what you're doing with these new approaches is get all of the metrics that we've touched on. It's fantastic from a cost standpoint, from an agility standpoint. But, what it means is that the innovators in the business, the ones that really understand what they're looking to achieve, they now have the tools to innovate with that. And, I think, and I've started to see that with projects that we've completed, if you do it in the right way, if you articulate the capability and you empower the business users in the right way. Then, they're in a significantly better position, these businesses to take advantages and really sort of match and significantly beat off their competition environment spaces. >> Super Adam, I mean a really exciting space. I mean we spent the last 10 years gathering all this data. You know, trying to slog through it and figure it out and now, with the tools that we have and the automation capabilities, it really is a new era of innovation and insight. So, Adam Worthington, thanks so much for coming in theCUBE and participating in this program. >> Yeah, exciting times and thank you very much Dave for inviting me, and yeah big pleasure. >> Now, we're going to go into the power panel and go deeper into the technologies that enable smart data lifecyles. And, stay right there, you're watching theCUBE. (light music) >> Voiceover: Are you interested in test-driving the Io-Tahoe platform? Kickstart the benefits of Data Automation for your business through the IoLabs program. A flexible, scalable, sandbox environment on the cloud of your choice. With setup, service and support provided by Io-Tahoe. Click on the link and connect with a data engineer to learn more and see Io-Tahoe in action. >> Welcome back everybody to the power panel, driving business performance with smart data lifecyles. Lester Waters is here, he's the Chief Technology Officer from Io-Tahoe. He's joined by Patrick Smith, who is field CTO from Pure Storage. And, Ezat Dayeh who is Assistant Engineering Manager at Cohesity. Gentlemen, good to see you, thanks so much for coming on this panel. >> Thank you, Dave. >> Yes. >> Thank you, Dave. >> Let's start with Lester, I wonder if each of you could just give us a quick overview of your role and what's the number one problem that you're focused on solving for your customers? Let's start with Lester, please. >> Ah yes, I'm Lester Waters, Chief Technology Officer for Io-Tahoe. And really, the number one problem that we are trying to solve for our customers is to help them understand what they have. 'Cause if they don't understand what they have in terms of their data, they can't manage it, they can't control it, they can't monitor it, they can't ensure compliance. So, really that's finding all that you can about your data that you have and building a catalog that can be readily consumed by the entire business is what we do. >> Patrick, field CTO in your title, that says to me you're talking to customers all the time so you've got a good perspective on it. Give us you know, your take on things here. >> Yeah absolutely, so my patch is in the air and talk to customers and prospects in lots of different verticals across the region. And, as they look at their environments and their data landscape, they're faced with massive growth in the data that they're trying to analyze. And, demands to be able to get inside are faster. And, to deliver business value faster than they've ever had to do in the past, so. >> Got it and then Ezat at Cohesity, you're like the new kid on the block. You guys are really growing rapidly. You created this whole notion of data management, backup and beyond, but from Assistant Engineering Manager what are you seeing from customers, your role and the number one problem that you're solving? >> Yeah sure, so the number one problem I see you know, time and again speaking with customers it's all around data fragmentation. So, due to things like organic growth you know, even maybe budgetary limitations, infrastructure has grown you know, over time, very piecemeal. And, it's highly distributed internally. And, just to be clear you know, when I say internally you know, that could be that it's on multiple platforms or silos within an on-prem infrastructure. But, that it also does extend to the cloud, as well. >> Right hey, cloud is cool, everybody wants to be in the cloud, right? So, you're right it creates maybe unattended consequences. So, let's start with the business outcome and kind of try to work backwards. I mean people you know, they want to get more insights from data, they want to have a more efficient data lifecyle. But, so Lester let me start with you, in thinking about like, the North Star, creating data driven cultures you know, what is the North Star for customers here? >> I think the North Star in a nutshell is driving value from your data. Without question, I mean we differentiate ourselves these days by even the nuances in our data. Now, underpinning that there's a lot of things that have to happen to make that work out well. You know for example, making sure you adequately protect your data. You know, do you have a good storage system? Do you have a good backup and recovery point objectives, recovering time objectives? Do you, are you fully compliant? Are you ensuring that you're ticking all the boxes? There's a lot of regulations these days in terms, with respect to compliance, data retention, data privacy and so fourth. Are you ticking those boxes? Are you being efficient with your data? You know, in other words I think there's a statistic that someone mentioned to me the other day that 53% of all businesses have between three and 15 copies of the same data. So you know, finding and eliminating those is part of the problems you need to chase. >> I like to think of you know, you're right. Lester, no doubt, business value and a lot of that comes from reducing the end to end cycle times. But, anything that you guys would add to that, Patrick and Ezat, maybe start with Patrick. >> Yeah, I think getting value from data really hits on, it hits on what everyone wants to achieve. But, I think there are a couple of key steps in doing that. First of all is getting access to the data. And that's, that really hits three big problems. Firstly, working out what you've got. Secondly, after working out what you've got, how to get access to it. Because, it's all very well knowing that you've got some data but if you can't get access to it. Either, because of privacy reasons, security reasons. Then, that's a big challenge. And then finally, once you've got access to the data, making sure that you can process that data in a timely manner. >> For me you know, it would be that an organization has got a really good global view of all of its data. It understands the data flow and dependencies within their infrastructure. Understands the precise legal and compliance requirements. And, has the ability to action changes or initiatives within their environment. Forgive the pun, but with a cloud like agility. You know, and that's no easy feat, right? That is hard work. >> Okay, so we've talked about the challenges and some of the objectives, but there's a lot of blockers out there and I want to understand how you guys are helping remove them? So, Lester what do you see as some of the big blockers in terms of people really leaning in to this smart data lifecycle. >> Yeah silos, is probably one of the biggest one I see in businesses. Yes, it's my data not your data. Lots of compartmentalization. And, breaking that down is one of the challenges. And, having the right tools to help you do that is only part of the solution. There's obviously a lot of cultural things that need to take place to break down those silos and work together. If you can identify where you have redundant data across your enterprise, you might be able to consolidate those. >> Yeah so, over to Patrick, so you know, one of the blockers that I see is legacy infrastructure, technical debt sucking all the budget. You got you know, too many people having to look after. >> As you look at the infrastructure that supports peoples data landscapes today. For primarily legacy reasons, the infrastructure itself is siloed. So, you have different technologies with different underlying hardware, different management methodologies that are there for good reason. Because, historically you had to have specific fitness for purpose for different data requirements. >> Dave: Ah-hm. >> And, that's one of the challenges that we tackled head on at Pure. With the flash plate technology and the concept of the data hub. A platform that can deliver in different characteristics for the different workloads. But, from a consistent data platform. >> Now, Ezat I want to go to you because you know, in the world, in your world which to me goes beyond backup and one of the challenges is you know, they say backup is one thing, recovery is everything. But as well, the CFO doesn't want to pay for just protection. Now, one of the things that I like about what you guys have done is you've broadened the perspective to get more value out of your what was once seen as an insurance policy. >> I do see one of the biggest blockers as the fact that the task at hand can you know, be overwhelming for customers. But, the key here is to remember that it's not an overnight change, it's not you know, the flick of the switch. It's something that can be tackled in a very piecemeal manner. And, absolutely like you've said you know, reduction in TCO and being able to leverage the data for other purposes is a key driver for this. So you know, this can be resolved. It can be very you know, pretty straightforward. It can be quite painless, as well. Same goes for unstructured data, which is very complex to manage. And you know, we've all heard the stats from the analysts, you know data obviously is growing at an extremely rapid rate. But, actually when you look at that you know, how is it actually growing? 80% of that growth is actually in unstructured data and only 20% of that growth is in structured data. So you know, these are quick win areas that the customers can realize immediate TCO improvement and increased agility, as well. >> Let's paint a picture of this guys, if I can bring up the lifecyle. You know what you can see here is you've got this cycle, the data lifecycle and what we're wanting to do is inject intelligence or smarts into this lifecyle. So, you can see you start with ingestion or creation of data. You're storing it, you've got to put it somewhere, right? You've got to classify it, you've got to protect it. And then, of course you want to you know, reduce the copies, make it you know, efficient. And then, you want to prepare it so that businesses can actually consume it and then you've got compliance and governance and privacy issues. And, I wonder if we could start with you Lester, this is you know, the picture of the lifecycle. What role does automation play in terms of injecting smarts into the lifecycle? >> Automation is key here, you know. Especially from the discover, catalog and classify perspective. I've seen companies where they go and we'll take and dump all of their data base schemes into a spreadsheet. So, that they can sit down and manually figure out what attribute 37 means for a column name. And, that's only the tip of the iceberg. So, being able to automatically detect what you have, automatically deduce where, what's consuming the data, you know upstream and downstream, being able to understand all of the things related to the lifecycle of your data backup, archive, deletion, it is key. And so, having good toolage areas is very important. >> So Patrick, obviously you participate in the store piece of this picture. So, I wondered if you could just talk more specifically about that, but I'm also interested in how you affect the whole system view, the end-to-end cycle time. >> Yeah, I think Lester kind of hit the nail on the head in terms of the importance of automation. Because, the data volumes are just so massive now that you can't effectively manage or understand or catalog your data without automation. Once you understand the data and the value of the data, then that's where you can work out where the data needs to be at any point in time. >> Right, so Pure and Cohesity obviously partnered to do that and of course, Ezat you guys are part of the protect, you're certainly part of the retain. But also, you provide data management capabilities and analytics, I wonder if you could add some color there? >> Yeah absolutely, so like you said you know, we focus pretty heavily on data protection as just one of our areas. And, that infrastructure it is just sitting there really can you know, the legacy infrastructure it's just sitting there you know, consuming power, space, cooling and pretty inefficient. And, automating that process is a key part of that. If I have a modern day platform such as you know, the Cohesity data platform I can actually do a lot of analytics on that through applications. So, we have a marketplace for apps. >> I wonder if we could talk about metadata. It's increasingly important you know, metadata is data about the data. But, Lester maybe explain why it's so important and what role it plays in terms of creating smart data lifecycle. >> A lot of people think it's just about the data itself. But, there's a lot of extended characteristics about your data. So, imagine if for my data lifecycle I can communicate with the backup system from Cohesity. And, find out when the last time that data was backed up or where it's backed up to. I can communicate, exchange data with Pure Storage and find out what tier it's on. Is the data at the right tier commencer with it's use level? If I could point it out. And, being able to share that metadata across systems. I think that's the direction that we're going in. Right now, we're at the stage we're just identifying the metadata and trying to bring it together and catalog it. The next stage will be okay, using the APIs and that we have between our systems. Can we communicate and share that data and build good solutions for customers to use? >> I think it's a huge point that you just made, I mean you know 10 years ago, automating classification was the big problem. And you know, with machine intelligence you know, we're obviously attacking that. But, your point about as machines start communicating to each other and you start you know, it's cloud to cloud. There's all kinds of metadata, kind of new metadata that's being created. I often joke that some day there's going to be more metadata than data. So, that brings us to cloud and Ezat, I'd like to start with you. >> You know, I do think that you know, having the cloud is a great thing. And, it has got its role to play and you can have many different you know, permutations and iterations of how you use it. And, you know, as I've may have sort of mentioned previously you know, I've seen customers go into the cloud very, very quickly and actually recently they're starting to remove workloads from the cloud. And, the reason why this happens is that you know, cloud has got its role to play but it's not right for absolutely everything. Especially in their current form, as well. A good analogy I like to use and this may sound a little bit clique but you know, when you compare clouds versus on premises data centers. You can use the analogies of houses and hotels. So, to give you an idea, so you know, when we look at hotels that's like the equivalent of a cloud, right? I can get everything I need from there. I can get my food, my water, my outdoor facilities, if I need to accommodate more people, I can rent some more rooms. I don't have to maintain the hotel, it's all done for me. When you look at houses the equivalent to you know, on premises infrastructure. I pretty much have to do everything myself, right? So, I have to purchase the house, I have to maintain it, I have buy my own food and water, eat it, I have to make improvements myself. But, then why do we all live in houses, not in hotels? And, the simple answer that I can only think of is, is that it's cheaper, right? It's cheaper to do it myself, but that's not to say that hotels haven't got their role to play. You know, so for example if I've got loads of visitors coming over for the weekend, I'm not going to go and build an extension to my house, just for them. I will burst into my hotel, into the cloud. And, you use it for you know, for things like that. So, what I'm really saying is the cloud is great for many things, but it can work out costlier for certain applications, while others are a perfect fit. >> That's an interesting analogy, I hadn't thought of that before. But, you're right, 'cause I was going to say well part of it is you want the cloud experience everywhere. But, you don't always want the cloud experience, especially you know, when you're with your family, you want certain privacy. I've not heard that before, Ezat. So, that's a new perspective, so thank you. But, Patrick I do want to come back to that cloud experience because in fact that's what's happening in a lot of cases. Organizations are extending the cloud properties of automation on-prem. >> Yeah, I thought Ezat brought up a really interesting point and a great analogy for the use of the public cloud. And, it really reinforces the importance of the Hybrid and the multicloud environment. Because, it gives you that flexibility to choose where is the optimal environment to run your business workloads. And, that's what it's all about. And, the flexibility to change which environment you're running in, either from one month to the next or from one year to the next. Because, workloads change and the characteristics that are available in the cloud change. The Hybrid cloud is something that we've lived with ourselves at Pure. So, our Pure management technology actually sits in a Hybrid cloud environment. We started off entirely cloud native but now, we use the public cloud for compute and we use our own technology at the end of a high performance network link to support our data platform. So, we're getting the best of both worlds. I think that's where a lot of our customers are trying to get to. >> All right, I want to come back in a moment there. But before we do, Lester I wonder if we could talk a little bit about compliance and governance and privacy. I think the Brits on this panel, we're still in the EU for now but the EU are looking at new rules, new regulations going beyond GDPR. Where does sort of privacy, governance, compliance fit in for the data lifecycle. And Ezat, I want your thought on this as well? >> Ah yeah, this is a very important point because the landscape for compliance around data privacy and data retention is changing very rapidly. And, being able to keep up with those changing regulations in an automated fashion is the only way you're going to be able to do it. Even, I think there's a some sort of a maybe ruling coming out today or tomorrow with a change to GDPR. So, this is, these are all very key points and being able to codify those rules into some software whether you know, Io-Tahoe or your storage system or Cohesity, it'll help you be compliant is crucial. >> Yeah, Ezat anything you can add there, I mean this really is your wheel house? >> Yeah, absolutely, so you know, I think anybody who's watching this probably has gotten the message that you know, less silos is better. And, it absolutely it also applies to data in the cloud, as well. So you know, by aiming to consolidate into you know, fewer platforms customers can realize a lot better control over their data. And, the natural affect of this is that it makes meeting compliance and governance a lot easier. So, when it's consolidated you can start to confidently understand who's accessing your data, how frequently are they accessing the data. You can also do things like you know, detecting an ominous file access activities and quickly identify potential threats. >> Okay Patrick, we were talking, you talked earlier about storage optimization. We talked to Adam Worthington about the business case, you've got the sort numerator which is the business value and then a denominator which is the cost. And, what's unique about Pure in this regard? >> Yeah, and I think there are multiple dimensions to that. Firstly, if you look at the difference between legacy storage platforms, they used to take up racks or aisles of space in a data center. With flash technology that underpins flash played we effectively switch out racks for rack units. And, it has a big play in terms of data center footprint and the environmentals associated with a data center. If you look at extending out storage efficiencies and the benefits it brings. Just the performance has a direct effect on staff. Whether that's you know, the staff and the simplicity of the platform so that it's easy and efficient to manage. Or, whether it's the efficiency you get from your data scientists who are using the outcomes from the platform and making them more efficient. If you look at some of our customers in the financial space their time to results are improved by 10 or 20 x by switching to our technology. From legacy technologies for their analytics platforms. >> So guys, we've been running you know, CUBE interviews in our studios remotely for the last 120 days. This is probably the first interview I've done where I haven't started off talking about COVID. Lester, I wondered if you could talk about smart data lifecycle and how it fits into this isolation economy and hopefully what will soon be a post-isolation economy? >> Yeah, COVID has dramatically accelerated the data economy. I think you know, first and foremost we've all learned to work at home. I you know, we've all had that experience where you know, people would hum and har about being able to work at home just a couple of days a week. And, here we are working five days a week. That's had a knock on impact to infrastructure to be able to support that. But, going further than that you know, the data economy is all about how a business can leverage their data to compete in this new world order that we are now in. COVID has really been a forcing function to you know, it's probably one of the few good things that have come out of COVID is that we've been forced to adapt. And, it's been an interesting journey and it continues to be so. >> Like Lester said you know, we're seeing huge impact here. You know, working from home has pretty much become the norm now. You know, companies have been forced into making it work. If you look at online retail, that's accelerated dramatically, as well. Unified communications and video conferencing. So, really you know, that the point here is that, yes absolutely we've compressed you know, in the past maybe four months what probably would have taken maybe even five years, maybe 10 years or so. >> We've got to wrap, but so Lester let me ask you, sort of paint a picture of the sort of journey the maturity model that people have to take. You know, if they want to get into it, where do they start and where are they going? Give us that view. >> Yeah, I think first is knowing what you have. If you don't know what you have you can't manage it, you can't control it, you can't secure it, you can't ensure it's compliant. So, that's first and foremost. The second is really you know, ensuring that you're compliant once you know what you have, are you securing it? Are you following the regulatory, the regulations? Are you able to evidence that? How are you storing your data? Are you archiving it? Are you storing it effectively and efficiently? You know, have you, nirvana from my perspective is really getting to a point where you've consolidated your data, you've broken down the silos and you have a virtually self-service environment by which the business can consume and build upon their data. And, really at the end of the day as we said at the beginning, it's all about driving value out of your data. And, automation is key to this journey. >> That's awesome and you've just described like sort of a winning data culture. Lester, Patrick, Ezat, thanks so much for participating in this power panel. >> Thank you, David. >> Thank you. >> All right, so great overview of the steps in the data lifecyle and how to inject smarts into the processes, really to drive business outcomes. Now, it's your turn, hop into the crowd chat. Please log in with Twitter or LinkedIn or Facebook, ask questions, answer questions and engage with the community. Let's crowd chat! (bright music)

>>from around the globe. It's the queue with digital coverage of Docker Con Live 2020 brought to you by Docker and its ecosystem partners. Welcome to the Special Cube coverage of Docker Con 2020. It's a virtual digital event co produced by Docker and the Cube. Thanks for joining us. We have great segment here. Precision cancer medicine really is evolving where the personalization of the data are really going to be important to personalize those treatments based upon unique characteristics of the tumors. This is something that's been a really hot topic, talking point and focus area in the industry. And technology is here to help with two great guests who are using technology. Docker Docker containers a variety of other things to help the process go further along. And we got here spring and who's the bioinformatics research assistant and Camille took Who's a student and in turn, you guys done some compelling work. Thanks for joining this docker con virtualized. Thanks for coming on. >>Thanks for having me. >>So first tell us about yourself and what you guys doing at the Children's Cancer Institute? That's where you're located. What's going on there? Tell us what you guys are doing there? >>Sure, So I built into Cancer Institute. As it sounds, we do a lot of research when it comes to specifically the Children's cancer, though Children a unique in the sense that a lot of the typical treatment we use for adult may or may not work or will have adverse side effects. So what we do is we do all kinds of research. But what lab and I love, which we call a dry love What we do research in silica, using computers at the develop pipelines in order to improve outcomes for Children. >>And what are some of the things you get some to deal with us on the tech side, but also there's the workflow of the patients survival rates, capacity, those constraints that you guys are dealing with. And what are some of the some of the things going on there that you have to deal with and you're trying to improve the outcomes? What specific outcomes were you trying to work through? >>Well, at the moment off of the past decade and all the work you've done in the past decade, we've made a substantial impact on the supply of ability off several high risk cancers in Pediatrics on and we've Got a certain Program, which spent I'll talk about in more depth called the Zero Childhood Cancer Program and essentially that aims to reduce childhood cancer in Children uh, zero. So that, in other words, with the previous five ability 100% on hopefully, no lives will be lost. But that's >>and what do you guys doing specifically? What's your your job? What's your focus? >>Yes, so part of our lab Old computational biology. Uh, we run a processing pipeline, the whole genome and our next guest that, given the sequencing information for the kids, though, we sequence the healthy cells and we sequence there. Two missiles. We analyze them together, and what we do is we find mutations that are causing the cancel that help us determine what treatment. So what? Clinical trials might be most effective for the kids and so specifically Allah books on that pipeline where we run a whole bunch of bioinformatics tools, that area buying thematic basically biology, informatics, and we use the data generated sequel thing in order to extract those mutations that will be the cancer driving mutations that hopefully we can target in order to treat the kids. >>You know, you hear about an attack and you hear Facebook personalization recommendation engines. What the click on you guys are really doing Really? Mawr personalization around treatment recommendations. These kinds of things come into it. Can you share a little bit about what goes on there and and tell us what's happening? >>Well, as you mentioned when you first, some brought us into this, which we're looking at, the the profile of the team itself and that allows us to specialize the medication on the young treatment for that patient on. Essentially, that lets us improve the efficiency and the effectiveness off the treatment, which in turn has an impact on this probability off. >>What are some of the technical things? How did you guys get involved with Docker with Docker fit into all this? >>Yeah, I'm sure Camille will have plenty to bring up on this as well. But, um, yes, it's been quite a project to the the pipeline that we have. Um, we have built on a specific platforms and is looking great. But as with most tools in a lot of things that you develop when your engineers eyes pretty easy for them to become platform specific. And then that kind of stuck there. And you have to re engineer the whole thing kind of of a black hole. That's such a pain to there. So, um, the project that Mikhail in my field working on was actually taking it to the individual's pools we used in the pipeline and Docker rising them individually containing them with the dependencies they need so that we could hook them up anyway. We want So we can configure the pipeline, not just customized based off of the data like we're on the same pipeline and every it even being able to change the pipeline of different things to different kids. Be able to do that easily, um, to be able to run it on different platforms. You know, the fact that we have the choice not only means that we could save money, but if there's a cloud instance that will run an app costal. If there's a platform that you know wanted to collaborate with us and they say, Oh, we have this wholesome data we'd love for you to analyze. It's over hell, like a lot of you know, >>use my tool. It's really great. >>Yeah. And so having portability is a big thing as well. And so I'm sure people can go on about, uh, some of the pain point you having to do authorize all of the different, But, you know, even though they Austin challenges associated with doing it, I think the payoff is massive. >>Dig into this because this is one of the things where you've got a problem statement. You got a real world example. Cancer patients, life or death gets a serious things going on here. You're a tech. You get in here. What's going on? You're like, Okay, this is going to be easy. Just wrangle the data. I throw some compute at it. It's over, right? You know what? How did you take us through the life? They're, you know, living >>right. So a supreme I mentioned before, first and foremost well, in the scale of several 100 terabytes worth of data for every single patient. So obviously we can start to understand just how beneficial it is to move the pipeline to the data, rather the other way around. Um, so much time would be saved. The money costs as well, in terms of actually Docker rising the but the programs that analyze the data, it was quite difficult. And I think Sabrina would agree mate would agree with me on this point. The primary issue was that almost all of the apps we encountered within the pipeline we're very, very heavily dependent on very specific versions off some dependencies, but that they were just build upon so many other different APS on and they were very heavily fined tuned. So docker rising. It was quite difficult because we have to preserve every single version of every single dependency in one instance just to ensure that that was working. And these apps get updated quite Simpson my regularly. So we have to ensure that our doctors would survive. >>So what does it really take? The doc arise your pipeline. >>I mean, it was a whole project. Well, um, myself, Camille, we had a whole bunch of, um, automatic guns doing us over the summer, which was fantastic as well. And we basically have a whole team of lost words like, Okay, here's another automatic pull in the pipeline. You get enterprise, you get to go for a special you get enterprise, they each who individually and then you've been days awake on it, depending on the app. Easier than others. Um, but particularly when it comes to things a lot by a dramatic pools, some of them are very memory hungry. Some of them are very finicky. Some of the, um ah, little stable than others. And so you could spend one day characterizing a tool. And it's done, you know, in a handful of Allah's old. Sometimes it could make a week, and he's just getting this one tool done. And the idea behind the whole team working on it was eventually use. Look through this process, and then you have, um, a docker file set up. Well, anyone to run it on any system. And we know we have an identical set up, which was not sure before, because I remember when I started and I was trying to get the pipeline running on my own machine. Ah, lot of things just didn't look like Oh, you don't have the very specific version of ah that this developer has. 00 that's not working because you don't have this specific girl file that actually has a bug fixes in it. Just for us like, Well, >>he had a lot of limitations before the doctor and doctor analyzing docker container izing it. It was tough. What was it like before and after? >>And we'll probably speak more people full. It was basically, uh, yeah, days or weeks trying to set up on in. Stole everything needed around the whole pipeline. Yeah, it took a long time. And even then, a lot of things, But how you got to set up this? You know, I think speculation of pipeline, all the units, these are the three of the different programs. Will you need this version of obligation? This new upgrade of the tools that work with that version of Oz The old, all kinds of issues that you run into when they schools depend on entirely different things and to install, like, four different versions of python. Three different versions of our or different versions of job on the one machine, you know, just to run it is a bit of >>what has. It's a hassle. Basically, it's a nightmare. And now, after you're >>probably familiar with that, >>Yeah. So what's it like after >>it's a zoo? It supports ridiculously efficient. Like it. It's It's incredible what Michael mentioned before, as soon as we did in stone. Those at the versions of the dependencies. Dhaka keeps them naturally, and we can specify the versions within a docker container. So we can. We can absolutely guarantee that that application will run successfully and effectively every single time. >>Share with me how complicated these pipelines are. Sounds like that's a key piece here for you guys. And you had all the hassles that you do. Your get Docker rised up and things work smoothly. Got that? But tell >>me about >>the pipelines. What's what's so complicated about them? >>Honestly, the biggest complication is all of the connection. It's not a simple as, um, run a from the sea, and then you don't That would be nice, but that know how these things work if you have a network of programs with the output of this, input for another, and you have to run this program before this little this one. But some of the output become input for multiple programs, and by the time you hook the whole thing up, it looks like a gigantic web of applications. The way all the connections, so it's a massive Well, it almost looks like a massive met when you look at it. But having each of the individual tools contained and working means that we can look them all up. And even though it looks complicated, it would be far more complicated if we had that entire pipeline. You know, in a single program like having to code, that whole thing in a single group would be an absolute nightmare. Where is being able to have each of the tools as individual doctors means we just have the link, the input on that book, which is the top. But once you've done that, it means that you know each of the individual pools will run. And if an individual fails, or whatever raised in memory or other issues run into, you can rerun that one individual school re hooks the output into whatever the next program is going without having one massive you know, program will file what it fails midway through, and there's nothing you can do. >>Yeah, you unpack. It really says, Basically, you get the goodness to the work up front, and a lot of goodness come out of it. So this lets comes to the future of health. What are the key takeaways that you guys have from this process? And how does it apply to things that might be helpful to you right around the corner? Or today, like deep learning as you get more tools out there with machine learning and deep learning? Um, we hope there's gonna be some cool things coming out. What do you guys see here? And the insights? >>Well, we have a section of how the computational biologist team that is looking into doing more predictive talks working out, um, basically the risk of people developing can't the risks of kids developing cancel. And that's something you can do when you have all of this data. But that requires a lot of analysis as well. And so one of the benefits of you know being able to have these very moveable pipelines and tools makes it easier to run them on. The cloud makes it easier to shale. You're processing with about researches to the hospitals, just making collaboration easier. Mainz that data sharing becomes a possibility or is before if you have three different organizations. But the daughter in three different places. Um, how do you share that with moving the daughter really feasible. Pascal, can you analyze it in a way that practical and so I don't want one of the benefits of Docker? Is all of these advanced tools coming out? You know, if there's some amazing predicted that comes out that uses some kind of regression little deep learning, whatever. If we wanted to add that being able to dock arise a complex school into a single docker ice makes it less complicated that highlighted the pipeline in the future, if that's something we'd like to do, >>Camille, any thoughts on your end on this? >>Actually, I was Sabrina in my mind for the last point. I was just thinking about scalability definitely is very. It's a huge point because the part about the girls as a technology does any kind of technology that we've got to inspect into the pipeline. As of now, it be significantly easier with the use of Docker. You could just docker rise that technology and then implant that straight into the pipeline. Minimal stress. >>So productivity agility doesn't come home for you guys. Is that resonate? >>Yeah, definitely. >>And you got the collaboration. So there's business benefits, the outcomes. Are there any proof points you could share on some results that you guys are seeing some fruit from the tree, if you will, from all this Goodness. >>Well, one of the things we've been working on is actually a collaboration with those Bio Commons and Katica. They built a platform, specifically the development pipelines. We wanted to go out, and they have support for Docker containers built into the platform, which makes it very easy to push a lot of containers of the platform, look them up and be able to collaborate with them not only to try a new platform without that, but also help them look like a platform to be able to shoot action access data that's been uploaded there as well. But a lot of people we wouldn't have been able to do that if we hadn't. Guys, they're up. It just wouldn't have. Actually, it wouldn't be possible. And now that we have, we've been able to collaborate with them in terms of improving the platform. But also to be able to share and run our pipelines on other data will just pretty good, >>awesome. Well, It's great to have you on the Cube here on Docker Con 2020 from down under. Great Internet connections get great Internet down. They're keeping us remote were sheltering in place here. Stay safe and you guys final question. Could you eat? Share in your own words from a developer? From a tech standpoint, as you're in this core role, super important role, the outcomes are significant and have real impact. What has the technology? What is docker ization done for you guys and for your work environment and for the business share in your own words what it means. A lot of other developers are watching What's your opinion? >>But yeah, I mean, the really practical point is we've massively increased capacity of the pipeline. One thing that been quite fantastic years. We've got a lot of increased. The Port zero child who can program, which means going into the schedule will actually be able to open a program. Every child in Australia that, uh, has cancel will be ableto add them to the program. Where is currently we're only able to enroll kids who are low survivability, right? So about 30% the lowest 30% of the viability we're able to roll over program currently, but having a pipeline where we can just double the memory like that double the amount of battle. Uh, and the fact that we can change the instance is really to just double the capacity trip. The capacity means that now that we have the support to be able to enroll potentially every kid, Mr Leo, um, once we've upgraded the whole pipeline, it means will actually be a code with the amount of Children being enrolled, whereas on the existing pipeline, we're currently that capacity. So doing the upgrade in a really practical way means that we're actually going to be a triple the number of kids in Australia. We can add onto the program which wouldn't have been possible otherwise >>unleashing the limitations and making it totally scalable. Your thoughts as developers watching you're in there, Your hand in your hands, dirty. You built it. It's showing some traction. What's what's your what's your take? What's your view? >>Well, I mean first and foremost locks events. It just feels fantastic knowing that what we're doing is as a substantial and quantify who impact on the on a subset of the population and we're literally saving lives. Analyze with the work that we're doing in terms off developing with With that technology, such a breeze especially compared Teoh I've had minimal contact with what it was like without docker and from the horror stories I've heard, it's It's It's a godsend. It's It's it's really improved The quality of developing. >>Well, you guys have a great mission. And congratulations on the success. Really impact right there. You guys are doing great work and it must feel great. I'm happy for you and great to connect with you guys and continue, you know, using technology to get the outcomes, not just using technology. So Fantastic story. Thank you for sharing. Appreciate >>you having me. >>Thank you. >>Okay, I'm John for we here for Docker Con 2020 Docker con virtual docker con digital. It's a digital event This year we were all shale three in place that we're in the Palo Alto studios for Docker con 2020. I'm John furrier. Stay with us for more coverage digitally go to docker con dot com from or check out all these different sessions And of course, stay with us for this feat. Thank you very much. Yeah, yeah, yeah, yeah, yeah, yeah

>> From our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Welcome to the special. Keep conversation here in Palo Alto, California. I'm John for a co host of the Cube. Were Arsenal was a C I S O. C. So for Microsoft also corporate vice President, Chief information security. Thanks for joining me today. >> Thank you. >> Appreciate it. Thanks. So you have a really big job. You're a warrior in the industry, security is the hardest job on the planet. >> And hang in sight >> of every skirt. Officer is so hard. Tell us about the role of Microsoft. You have overlooked the entire thing. You report to the board, give us an overview of what >> happens. Yeah. I >> mean, it's you know, obviously we're pretty busy. Ah, in this world we have today with a lot of adversaries going on, an operational issues happening. And so I have responsibility. Accountability for obviously protecting Microsoft assets are customer assets. And then ah, And for me, with the trend also responsibility for business continuity Disaster recovery company >> on the sea. So job has been evolving. We're talking before the camera came on that it's coming to CEO CF roll years ago involved to a business leader. Where is the sea? So roll now in your industry is our is a formal title is it establishes their clear lines of reporting. How's it evolved? What's the current state of the market in terms of the sea? So it's roll? >> Yeah, the role is involved. A lot. Like you said, I think like the CIA or twenty years ago, you know, start from the back room of the front room and I think the, you know, one of things I look at in the role is it's really made it before things. There's technical architecture, there's business enablement. There's operational expert excellence. And then there's risk management and the older ah, what does find the right word? But the early see so model was really about the technical architecture. Today. It's really a blend of those four things. How do you enable your business to move forward? How do you take calculated risks or manage risks? And then how do you do it really effectively and efficiently, which is really a new suit and you look at them. You'LL see people evolving to those four functions. >> And who's your boss? Would you report to >> I report to a gentleman by the name of a curtain. Little Benny on DH. He is the chief digital officer, which would be a combination of Seo did officer and transformation as well as all of Microsoft corporate strategy >> and this broad board visibility, actually in security. >> Yeah, you >> guys, how is Microsoft evolved? You've been with the company for a long time >> in the >> old days ahead perimeters, and we talk about on the Cube all the time. When a criminalist environment. Now there's no perimeter. Yeah, the world's changed. How is Microsoft evolved? Its its view on security Has it evolved from central groups to decentralize? How is it how how was it managed? What's the what's the current state of the art for security organization? >> Well, I think that, you know, you raise a good point, though things have changed. And so in this idea, where there is this, you know, perimeter and you demanded everything through the network that was great. But in a client to cloak cloud world, we have today with mobile devices and proliferation or cloud services, and I ot the model just doesn't work anymore. So we sort of simplified it down into Well, we should go with this, you know, people calls your trust, I refer to It is just don't talk to strangers. But the idea being is this really so simplified, which is you've got to have a good identity, strong identity to participate. You have to have managed in healthy device to participate, to talk to, ah, Microsoft Asset. And then you have to have data in telemetry that surrounds that all the time. And so you basically have a trust, trust and then verify model between those three things. And that's really the fundamental. It's really that simple. >> David Lava as Pascal senior with twenty twelve when he was M. C before he was the C E O. V M. Where he said, You know his security do over and he was like, Yes, it's going to be a do over its opportunity. What's your thoughts on that perspective? Has there been a do over? Is it to do over our people looking at security and a whole new way? What's your thoughts? >> Yeah, I mean, I've been around security for a long time, and it's there's obviously changes in Massa nations that happened obviously, at Microsoft. At one point we had a security division. I was the CTO in that division, and we really thought the better way to do it was make security baked in all the products that we do. Everything has security baked in. And so we step back and really change the way we thought about it. To make it easier for developers for end users for admin, that is just a holistic part of the experience. So again, the technology really should disappear. If you really want to be affected, I think >> don't make it a happy thought. Make it baked in from Day one on new product development and new opportunity. >> Yeah, basically, shift the whole thing left. Put it right in from the beginning. And so then, therefore, it's a better experience for everyone using it. >> So one of things we've observed over the past ten years of doing the Cube when do first rolled up with scene, you know, big data role of date has been critical, and I think one of the things that's interesting is, as you get data into the system, you can use day that contextually and look at the contextual behavioral data. It's really is create some visibility into things you, Meyer may not have seen before. Your thoughts and reaction to the concept of leveraging data because you guys get a lot of data. How do you leverage the data? What's the view of data? New data will make things different. Different perspectives creates more visibility. Is that the right view? What's your thoughts on the role of Data World Data plays? >> Well, they're gonna say, You know, we had this idea. There's identity, there's device. And then there's the data telemetry. That platform becomes everything we do, what there's just security and are anomalous behavior like you were talking about. It is how do we improve the user experience all the way through? And so we use it to the service health indicator as well. I think the one thing we've learned, though, is I was building where the biggest data repositories your head for some time. Like we look at about a six point five trillion different security events a day in any given day, and so sort of. How do you filter through that? Manage? That's pretty amazing, says six point five trillion >> per day >> events per day as >> coming into Microsoft's >> that we run through the >> ecosystem your systems. Your computers? >> Yeah. About thirty five hundred people. Reason over that. So you can Certainly the math. You need us. Um, pretty good. Pretty good technology to make it work effectively for you and efficiently >> at RC A Heard a quote on the floor and on the q kind of echoing the same sentiment is you can't hire your way to success in this market is just not enough people qualified and jobs available to handle the volume and the velocity of the data coming in. Automation plays a critical role. Your reaction to that comment thoughts on? >> Well, I think I think the cure there, John, those when you talk about the volume of the data because there's what we used to call speeds and feeds, right? How big is it? And I used to get great network data so I can share a little because we've talked, like from the nineties or whatever period that were there. Like the network was everything, but it turns out much like a diverse workforce creates the best products. It turns out diverse data is more important than speeds and feeds. So, for example, authentication data map to, you know, email data map to end point data map. TEO SERVICE DATA Soon you're hosting, you know, the number of customers. We are like financial sector data vs Healthcare Data. And so it's the ability Teo actually do correlation across that diverse set of data that really differentiates it. So X is an example. We update one point two billion devices every single month. We do six hundred thirty billion authentications every single month. And so the ability to start correlating those things and movement give us a set of insights to protect people like we never had before. >> That's interesting telemetry you're getting in the marketplace. Plus, you have the systems to bring it in >> a pressure pressure coming just realized. And this all with this consent we don't do without consent, we would never do without consent. >> Of course, you guys have the terms of service. You guys do a good job on that, But I think the point that I'm seeing there is that you guys are Microsoft. Microsoft got a lot of access. Get a lot of stuff out there. How does an enterprise move to that divers model because they will have email, obviously. But they have devices. So you guys are kind of operating? I would say tear one of the level of that environment cause you're Microsoft. I'm sure the big scale players to that. I'm just an enterprising I'm a bank or I'm an insurance company or I'm in oil and gas, Whatever the vertical. Maybe. What do I do if I'm the sea? So they're So what does that mean, Diversity? How should they? >> Well, I think they have a diverse set of data as well. Also, if they participate, you know, even in our platform today, we you know, we have this thing called the security graph, which is an FBI people can tap into and tap into the same graph that I use and so they can use that same graph particular for them. They can use our security experts to help them with that if they don't have the all the resource and staff to go do that. So we provide both both models for that to happen, and I think that's why a unique perspective I should think should remind myself of which is we should have these three things. We have a really good security operations group we have. I think that makes us pretty unique that people can leverage. We build this stuff into the product, which I think is good. But then the partnership, the other partners who play in the graph, it's not just us. So there's lots of people who play on that as well. >> So like to ask you two lines of questions. Wanting on the internal complex is that organizations will have on the external complexity and realities of threats and coming in. How do they? How do you balance that out? What's your vision on that? Because, you know, actually, there's technology, his culture and people, you know in those gaps and capabilities on on all three. Yeah, internally just getting the culture right and then dealing with the external. How does a C so about his company's balance? Those realities? >> Well, I think you raised a really good point, which is how do you move the culture for? That's a big conversation We always have. And that was sort of, you know, it's interesting because the the one side we have thirty five hundred people who have security title in their job, But there's over one hundred thousand people who every day part of their job is doing security, making sure they'LL understand that and know that is a key part we should reinforce everyday on DSO. But I think balancing it is, is for me. It's actually simplifying just a set of priorities because there's no shortage of, you know, vendors who play in the space. There's no shortage of things you can read about. And so for us it was just simplifying it down and getting it. That simplifies simplified view of these are the three things we're going to go do we build onerous platform to prioritize relative to threat, and then and then we ensure we're building quality products. Those five things make it happen. >> I'd like to get your thoughts on common You have again Before I came on camera around how you guys view simplification terminal. You know, you guys have a lot of countries, the board level, and then also you made a common around trust of security and you an analogy around putting that drops in a bucket. So first talk about the simplification, how you guys simplifying it and why? Why is that important? >> You think we supply two things one was just supplying the message to people understood the identity of the device and making sure everything is emitting the right telemetry. The second part that was like for us but a Z to be illustrative security passwords like we started with this technology thing and we're going to do to FAA. We had cards and we had readers and oh, my God, we go talk to a user. We say we're going to put two FAA everywhere and you could just see recoil and please, >> no. And then >> just a simple change of being vision letters. And how about this? We're just going to get rid of passwords then People loved like they're super excited about it. And so, you know, we moved to this idea of, you know, we always said this know something, know something new, how something have something like a card And they said, What about just be something and be done with it? And so, you know, we built a lot of the capability natively into the product into windows, obviously, but I supported energies environment. So I you know, I support a lot of Mac clinics and IOS and Android as well So you've read it. Both models you could use by or you could use your device. >> That's that. That's that seems to be a trend. Actually, See that with phones as well as this. Who you are is the password and why is the support? Because Is it because of these abuses? Just easy to program? What's the thought process? >> I think there's two things that make it super helpful for us. One is when you do the biometric model. Well, first of all, to your point, the the user experience is so much better. Like we walk up to a device and it just comes on. So there's no typing this in No miss typing my password. And, you know, we talked earlier, and that was the most popular passwords in Seattle with Seahawks two thousand seventeen. You can guess why, but it would meet the complexity requirements. And so the idea is, just eliminate all that altogether. You walk up machine, recognize you, and you're often running s o. The user experience is great, but plus it's Actually the entropy is harder in the biometric, which makes it harder for people to break it, but also more importantly, it's bound locally to the device. You can't run it from somewhere else. And that's the big thing that I think people misunderstanding that scenario, which is you have to be local to that. To me, that's a >> great example of rethinking the security paradigm. Exactly. Let's talk about trust and security. You you have an opinion on this. I want to get your thoughts, the difference between trust and security so they go hand in hand at the same time. They could be confused. Your thoughts on this >> well being. You can have great trust. You can, so you can have great security. But you generally and you would hope that would equate like a direct correlation to trust. But it's not. You need to you build trust. I think our CEO said it best a long time ago. You put one bucket of water, one bucket. Sorry, one truffle water in the bucket every time. And that's how you build trust. Over time, my teenager will tell you that, and then you kick it over and you put it on the floor. So you have to. It's always this ratcheting up bar that builds trust. >> They doing great you got a bucket of water, you got a lot of trust, that one breach. It's over right, >> and you've got to go rebuild it and you've got to start all over again. And so key, obviously, is not to have that happen. But then, that's why we make sure you have operational rigor and >> great example that just totally is looking Facebook. Great. They have massive great security. What really went down this past week, but still the trust factor on just some of the other or societal questions? >> Yeah, >> and that something Do it. >> Security. Yeah, I think that's a large part of making sure you know you're being true. That's what I said before about, you know, we make sure we have consent. We're transparent about how we do the things we do, and that's probably the best ways to build trust. >> Okay, so you guys have been successful in Microsoft, just kind of tight the company for second to your role. It's pretty well documented that the stock prices at an all time high. So if Donatella Cube alumni, by the way, has been on the cue before he he took over and clear he didn't pivot. He just said we'd go in the cloud. And so the great moves, he don't eat a lot of great stuff. Open source from open compute to over the source. And this ship has turned and everything's going great. But that cheering the cloud has been great for the company. So I gotta ask you, as you guys move to the cloud, the impact to your businesses multi fold one products, ecosystem suppliers. All these things are changing. How has security role in the sea? So position been impact that what have you guys done? How does that impact security in general? Thoughts? >> Yeah, I think we obviously were like any other enterprise we had thousands of online are thousands of line of business applications, and we did a transformation, and we took a method logical approach with risk management. And we said, Okay, well, this thirty percent we should just get rid of and decommission these. We should, you know, optimize and just lifting shifting application. That cloud was okay, but it turns out there's massive benefit there, like for elasticity. Think of things that quarterly reporting or and you'll surveys or things like that where you could just dynamically grow and shrink your platform, which was awesome linear scale that we never had Cause those events I talk about would require re architectures. Separate function now becomes linear. And so I think there is a lot of things from a security perspective I could do in a much more efficient must wear a fish. In fact, they're then I had to have done it before, but also much more effective. I just have compute capability. Didn't have I have signal I didn't have. And so we had to wrap her head around that right and and figure out how to really leverage that. And to be honest, get the point. We're exploited because you were the MySpace. I have disaster and continent and business. This is processed stuff. And so, you know, everyone build dark fiber, big data centers, storage, active, active. And now when you use a platform is a service like on that kind of azure. You could just click a Bach and say, I want this thing to replicate. It also feeds your >> most diverse data and getting the data into the system that you throw a bunch of computer at that scale. So What diverse data? How does that impact the good guys and the bad guys? That doesn't tip the scales? Because if you have divers date and you have his ability, it's a race for who has the most data because more data diversity increases the aperture and our visibility into events. >> Yeah, I you >> know, I should be careful. I feel like I always This's a job. You always feel like you're treading water and trying to trying to stay ahead. But I think that, um, I think for the first time in my tenure do this. I feel there's an asymmetry that benefits. They're good guys in this case because of the fact that your ability to reason over large sets of data like that and is computed data intensive and it will be much harder for them like they could generally use encryption were effectively than some organization because the one the many relationship that happens in that scenario. But in the data center you can't. So at least for now, I feel like there's a tip This. The scales have tipped a bit for the >> guy that you're right on that one. I think it's good observation I think that industry inside look at the activity around, from new fund adventures to overall activity on the analytics side. Clearly, the data edge is going to be an advantage. I think that's a great point. Okay, that's how about the explosion of devices we're seeing now. An explosion of pipe enabled devices, Internet of things to the edge. Operational technologies are out there that in factory floors, everything being I P enables, kind of reminds me of the old days. Were Internet population you'd never uses on the Internet is growing, and >> that costs a lot >> of change in value, creation and opportunities devices. Air coming on both physical and software enabled at a massive rate is causing a lot of change in the industry. Certainly from a security posture standpoint, you have more surface area, but they're still in opportunity to either help on the do over, but also create value your thoughts on this exploding device a landscape, >> I think your Boston background. So Metcalfe's law was the value the net because the number of the nodes on the network squared right, and so it was a tense to still be true, and it continues to grow. I think there's a huge value and the device is there. I mean, if you look at the things we could do today, whether it's this watch or you know your smartphone or your smart home or whatever it is, it's just it's pretty unprecedented the capabilities and not just in those, but even in emerging markets where you see the things people are doing with, you know, with phones and Lauren phones that you just didn't have access to from information, you know, democratization of information and analysis. I think it's fantastic. I do think, though, on the devices there's a set of devices that don't have the same capabilities as some of the more markets, so they don't have encryption capability. They don't have some of those things. And, you know, one of Microsoft's responses to that was everything. Has an M see you in it, right? And so we, you know, without your spirit, we created our own emcee. That did give you the ability to update it, to secure, to run it and manage it. And I think that's one of the things we're doing to try to help, which is to start making these I, O. T or Smart devices, but at a very low cost point that still gives you the ability because the farm would not be healed Update, which we learn an O. T. Is that over time new techniques happen And you I can't update the system >> from That's getting down to the product level with security and also having the data great threats. So final final talk Tracking one today with you on this, your warrior in the industry, I said earlier. See, so is a hard job you're constantly dealing with compliance to, you know, current attacks, new vector, new strains of malware. And it's all over the map. You got it. You got got the inbound coming in and you got to deal with all that the blocking and tackling of the organization. >> What do you What do >> you finding as best practice? What's the what if some of the things on the cso's checklist that you're constantly worried about and or investing in what some of >> the yeah, >> the day to day take us through the day to day life >> of visited a lot? Yeah, it >> starts with not a Leslie. That's the first thing you have to get used to, but I think the you know again, like I said, there's risk Manager. Just prioritize your center. This is different for every company like for us. You know, hackers don't break and they just log in. And so identity still is one of the top things. People have to go work on him. You know, get rid of passwords is good for the user, but good for the system. We see a lot in supply chain going on right now. Obviously, you mentioned in the Cambridge Analytical Analytics where we had that issue. It's just down the supply chain. And when you look at not just third party but forthe party fifth party supply and just the time it takes to respond is longer. So that's something that we need to continue to work on. And then I think you know that those are some of the other big thing that was again about this. How do you become effective and efficient and how you managed that supply chain like, You know, I've been on a mission for three years to reduce my number of suppliers by about fifty percent, and there's still lots of work to do there, but it's just getting better leverage from the supplier I have, as well as taking on new capability or things that we maybe providing natively. But at the end of the day, if you have one system that could do what four systems going Teo going back to the war for talent, having people, no forces and versus one system, it's just way better for official use of talent. And and obviously, simplicity is the is the friend of security. Where is entropy is not, >> and also you mentioned quality data diversity it is you're into. But also there's also quality date of you have quality and diverse data. You could have a nice, nice mechanism to get machine learning going well, but that's kind of complex, because in the thie modes of security breaches, you got pre breached in breech post breach. All have different data characteristics all flowing together, so you can't just throw that answer across as a prism across the problem sets correct. This is super important, kind of fundamentally, >> yeah, but I think I >> would I would. The way I would characterize those is it's honestly, well, better lessons. I think I learned was living how to understand. Talk with CFO, and I really think we're just two things. There's technical debt that we're all working on. Everybody has. And then there's future proofing the company. And so we have a set of efforts that go onto like Red Team. Another actually think like bad people break them before they break you, you know, break it yourself and then go work on it. And so we're always balancing how much we're spending on the technical, that cleanup, you know, modernizing systems and things that are more capable. And then also the future proofing. If you're seeing things coming around the corner like cryptography and and other other element >> by chain blockchain, my supply chain is another good, great mechanism. So you constantly testing and R and D also practical mechanisms. >> And there in the red team's, which are the teams that attacking pen everything, which is again, break yourself first on this super super helpful for us >> well bred. You've seen a lot of ways of innovation have been involved in multiple ways computer industry client server all through the through the days, so feel. No, I feel good about this you know, because it reminds me and put me for broken the business together. But this is the interesting point I want to get to is there's a lot of younger Si SOS coming in, and a lot of young talent is being attractive. Security has kind of a game revived to it. You know, most people, my friends, at a security expert, they're all gamers. They love game, and now the thrill of it. It's exciting, but it's also challenging. Young people coming might not have experience. You have lessons you've learned. Share some thoughts over the years that scar either scar tissue or best practices share some advice. Some of the younger folks coming in breaking into the business of, you know, current situation. What you learned over the years it's Apple Apple. But now the industry. >> Yeah, sadly, I'd probably say it's no different than a lot of the general advice I would have in the space, which is there's you value experience. But it turns out I value enthusiasm and passion more here so you can teach about anybody whose passion enthusiastic and smart anything they want. So we get great data people and make them great security people, and we have people of a passion like you know, this person. It's his mission is to limit all passwords everywhere and like that passion. Take your passion and driver wherever you need to go do. And I >> think the nice >> thing about security is it is something that is technically complex. Human sociology complex, right? Like you said, changing culture. And it affects everything we do, whether it's enterprise, small, medium business, large international, it's actually a pretty It's a fasten, if you like hard problem. If you're a puzzle person, it's a great It's a great profession >> to me. I like how you said Puzzle. That's I think that's exactly it. They also bring up a good point. I want to get your thoughts on quickly. Is the talent gap is is really not about getting just computer science majors? It's bigger than that. In fact, I've heard many experts say, and you don't have to be a computer scientist. You could be a lot of cross disciplines. So is there a formula or industry or profession, a college degree? Or is it doesn't matter. It's just smart person >> again. It depends if your job's a hundred percent. Security is one thing, but like what we're trying to do is make not we don't have security for developers you want have developed to understand oppa security and what they build is an example on DSO. Same with administrators and other components. I do think again I would say the passion thing is a key piece for us, but But there's all aspects of the profession, like the risk managers air, you know, on the actuarial side. Then there's math people I had one of my favorite people was working on his phD and maladaptive behavior, and he was super valuable for helping us understand what actually makes things stick when you're trying to train their educate people. And what doesn't make that stick anthropologist or super helpful in this field like anthropologist, Really? Yeah, anthropologist are great in this field. So yeah, >> and sociology, too, you mentioned. That would think that's a big fact because you've got human aspect interests, human piece of it. You have society impact, so that's really not really one thing. It's really cross section, depending upon where you want to sit in the spectrum of opportunity, >> knowing it gives us a chance to really hire like we hire a big thing for us has been hard earlier in career and building time because it's just not all available. But then also you, well, you know, hire from military from law enforcement from people returning back. It's been actually, it's been a really fascinating thing from a management perspective that I didn't expect when I did. The role on has been fantastic. >> The mission. Personal question. Final question. What's getting you excited these days? I mean, honestly, you had a very challenging job and you have got attend all the big board meetings, but the risk management compliance. There's a lot of stuff going on, but it's a lot >> of >> technology fund in here to a lot of hard problems to solve. What's getting you excited? What what trends or things in the industry gets you excited? >> Well, I'm hopeful we're making progress on the bad guys, which I think is exciting. But honestly, this idea the you know, a long history of studying safety when I did this and I would love to see security become the air bags of the technology industry, right? It's just always there on new president. But you don't even know it's there until you need it. And I think that getting to that vision would be awesome. >> And then really kind of helping move the trust equation to a whole other level reputation. New data sets so data, bits of data business. >> It's total data business >> breath. Thanks for coming on the Q. Appreciate your insights, but also no see. So the chief information security officer at Microsoft, also corporate vice president here inside the Cuban Palo Alto. This is cute conversations. I'm John Career. Thanks for watching. >> Thank you.

live from Las Vegas it's the cute cuddly emc world 2016 brought to you by emc now here are your hosts John furrier and Dave vellante okay welcome back everyone we are here live at emc world 2016 SiliconANGLE media's the cube it's our flagship program we go out to the events and extract the signal from the noise i'm john for it my coast gave a lot there next is Robin Matlock was the CMO of VMware here on the cube cube alumni great to see you Robin thanks for joining us thanks happy to be here as always say no we just thought the jeremy bird news now the seam of gel technologies which he was illuminating the challenges of his branding challenge it's gonna be interesting to watch that happen but as a CMO you got to be plugged into all the themes so when i get your to get your thoughts on the show here and then you got the big show come up with vmworld what's your take on this because looking at the landscape there's a lot of change it's a challenge for marketers to try to make that message relevant what your thoughts of this show certainly the looming acquisition what's your thoughts on the show and how they're doing and and yeah so I think you have two big things there what's my thoughts on this show I think they've done a fabulous job Jeremy you know I go way back with Jeremy he's a fabulous market here one of the best in the industry and I mean this place is alive you know I think he has done some amazing creative things on stage on of you saw the keynote today I thought the James Bond thing was exceptional very entertaining keeps people engaged you know but also delivering really interesting content so I thought today's focus on cloud native was particularly interesting so I think he's doing a really good job of focusing on what people need to run their businesses today but also giving a nod out to the future and where the industry is going and the other thing that big discussion here I want to get your thoughts on this and is the first time pad Dell singers not here at emc world certainly a lot of hallway conversations it even surprised Joe Tucci who was Dave asked in the analyst session you know where's Pat guess was even on the cube every time so we had we miss you do not if you're watching this why isn't he here and just clear the air on the speculation of why he's not here there's a conspiracy theories are everywhere just let's clear the air on that first of all you guys crack me up if we run things the same every year you get bored you start coming up with all kinds of theories and rationale as to what's going on behind the scenes let me just put these rumors to rest Pascal singer is is fired up and excited about vmware and our future and the role we play in the dell technologies family as he ever has been when you do these events you think first and foremost what are the big messages or stories that i need to tell the marketplace it's no different than at vmworld then the second thing is who's the most appropriate person to come and tell these stories well the bottom line is the daily MC merger is probably one of the biggest most important messages that had to get covered here at emc world who's better to tell that story than Michael Dell and Joe Tucci right then there was a whole lot of great product information lots of new products being announced the best people to tell that are your CTOs your technical people we brought that you know some of the top talent from VMware ray o'farrell a longtime veteran of VMware was on stage yesterday talking about V realized in the control plane for a multi cloud world today KITT Kolbert you know one of the favorite VMware CTOs talking about cloud native so look there's nothing more to it than that Pat's alive and well trust me he's very engaged joe said that to the analyst he said look basically we only give Michael some time and we have all this product stuff to Paley's and that's a huge I mean they have a slew of announcements so it really took to summarize this is time slot issues they have been limited time on stage I mean Chad had to Russia's demo at the end so that seems to be the issue Michael needed to be out there up front obviously I don't even see it as an issue to be honest john i don't think it's an issue i think it's an opportunity at the end of the day what were the right things to cover what were the right speakers to cover those and you know I'm the one that called a shot for Pat I didn't think it was the right place i think really a rail farrell and kick Kolbert work yeah option kit was on the queue yesterday I'm all saurian given some great great commentary as well okay great so get that out of the way it's one of the clearly as a pat is our number one guest on the cube you know I don't you do that well there's number one Michael Dell I think it's given him a run for his money he's trying we're trying to see you at vmworld the corners let's talk about what's coming up because i see that pat will be on stage at vmworld so so he is going to have to put that together last year he delivered a really epic King no I thought it was very well done really talk about the future of the industry and vmware's role in it what's changed since then for you guys what can you share with us without you know tipping tipping the hand on the show theme because now we're gonna we were some almost there for vmworld last year to this year what's going on what's what's happening yeah I think there's gonna be a personal it'll be a lot of exciting things at vmworld and you have to be there delivered experience it firsthand we've laid out a vision for the industry and a lot of what we're doing is delivering on that vision I think there's things rapidly changing in our world that we know that for example cloud is changing every year there's kind of a new dimension to what's happening and how people are using clouds we think there's tremendous opportunities as we think about multiple clouds on how our customers are thinking about their workloads in a multi cloud world so I think you'll find a lot of interesting things we're doing in that front the whole roll of business mobility continues to evolve and change how does that relate to how I'm running my business on-premise or in the cloud I think you'll find a lot of neat things in that area and then this big wave of modern applications at the end of the day we're running our business on these big mission critical applications but the rapid iterative development process is really fundamentally changing the kind of value we can deliver back to the business and what we need to support that and do that as IT organizations to our line of business to people like me yeah CMOS who consume applications like nobody else in the business they don't excuse me you'll find a lot of focus on those areas well VMware has become such a strategic part of customers you know roadmaps and it's not just VMware it's the entire ecosystem that's what makes vmworld the best show this is the best enterprise show because everybody's there it's usually in San Francisco hey yeah is an awesome place to be I've got some additions on that we're in Vegas this year we I love it in our home turf in San Francisco we do to bottom line is mosconi's going through a lot of construction right now is there don't maybe the experience if you know is right for our great it's still the best under pressure because it is such a community and so you've got it you know you've got to keep elevating that right so you got the core technical content have some fun we saw some fun you know today so can you tell us kind of you know generally what we can expect this year yeah well first of all I think the audiences are evolving and you know our core traditional VI admin you know your virtual infrastructure admin of course that is the essence of the participation at vmworld but trust me new audience types are joining and coming to this event the networking side of the house you're seeing a lot more engagement participation their storage frankly there's overlap people come here they also go to vmworld your DevOps community is starting to find great value in a program like a vm world um some business executives but I'd say it is foundation it's a technical conference and it's the architects the CTOs and the class he updated the digital transformation I know that the air wash purchase was one that was a really good deal Sanjay poonen lead senior leader over there that the company has been doing very very well I've been seeing some updates on that what's going on with that cuz that's gonna bring in a whole nother IOT / application global peace any updates there from digital transformation conversations because at the end of the day as a CMO I feel like I'm at the tip of the spear of digital transformation you know I'm pushing the envelope about how we look at analytics and business intelligence and how we change the experience with our engagement with customers and partners how do I serve content more dynamically more relevant based on digital profiles that people who come and engage with us so i love this conversation and you know i think at the heart of all that we're doing is to accelerate digital transformation and make sure that I t plays the right critical role in that because the end of the day line of business has options and they are driving sometimes around IT but this is a really fantastic went for IT to be the experts in software software agility and really building apps for the business that are more relevant and you know really helpful and that I think is what VMware can really accelerate you mentioned the analytics I have a question for you around can you or how can you operationalize those analytics so you know traditionally the analytics have been insights for a few you gotta line up bill the cube takes forever how are you able to or are you able to operationalize those endings put those tool those tools in the hands of the people that can actually affect digital engagement in the front lines I think there's two dimensions to that I mean first of all you have to build your analytics environment on top of an agile infrastructure because at the end of the day the foundation has to be agile enough to serve a variety of different requirements changing requirements so you know obviously we have a big play on infrastructure infrastructure as a service and the foundations of that and the kind of root challenges their networking big bottleneck right so i might have this great infrastructure to compute on demand but I can't get my networking put you know protocols in place security risk things like that but then on the other hand you have to be able to consume these applications analytics is just one of many how do we ensure that i can get that out to my user community in the device form factor that they choose all controlled and governed effectively by me as an IT i think that really plays to both ends of the vmware strategy what we're doing in business mobility to allow you to transform experiences in engagement with customers and partners and employees but that also what we're doing kind of at the foundational level to ensure that the foundation can support these high demand applications that are distributed that micro services are a very different architecture from you know yesterday's are you doing that with your your team I mean you're gonna dogfooding that capability I don't know yeah vmware is one of the largest you know we are one of the biggest customers were the first customer for our technologies if i had my phone with you i could show you workspace one how i have access to my apps one button one push all completely under governance and control that's really the future of vmware it's the really the new form of user consumption of technology you guys are trying to make it easy your stand-up apps like workspaces and what not workspace one is breakthrough it's really break through and it you're right we're usually not engaging at the consumer level of enterprise right we're usually the back office were in that data center we're kind of in the bowels of IT but workspace one puts us forefront we're on the device now the user knows who vmware is now they're engaging with our applications and think it's really streamlined their experience to give them access to any app with one thumb print yeah and you know religious thing you've always been an enabling technology for innovation now it's moving up the stack so it's very interest to see that progress final question is that on that front I get that that's great news on the ecosystem what's change with the ecosystem because you know as you said vm was a very technical community yeah very engaging you don't have you have your shin you haven't they don't have your share fair of people who like to raise their hand and telling what do you think so a great active community so what are they saying what's the feedback from the community what are people raising their hands and and and saying and to you guys and and what's the conversation like right now I mean first of all um feedback from our ecosystem is fabulous i mean vmworld is really great case that he go look at the solutions exchange at vmworld it's just buzzing i can tell you we've pretty much are almost not quite but almost sold out of all the real estate that we have to offer in Vegas when we come here in late August I think that you could system that was changing evolving but you have really great evidence of new things happening I mean look at the X rail that got announced here between VMware and EMC look at the new pivotal cloud foundry photon platform bundle that we just announced last week you know so some real solutions orientation coming together in these partnerships and of course the broad ecosystem relative to cloud I think sis and SOS are getting very engaged with VMware in new ways we have a rich channel program I definitely think cloud providers service providers that's a kind of evolving and definitely growing part of our ecosystem and then I think even some of the traditional partners that we've had in the past you're seeing more solution oriented focus from those types of partnership Robin thanks so much for taking the time out of your busy schedule to come share your insights on the cube anytime great Robin Matlock the CMO of VMware here sharing her thoughts about the industry in the show and also the upcoming vmworld 2016 which will be in Mandalay Bay this year not San Francisco because Moscone is going to be half under construction so got to do a little you know interim step here should be a great show it'll be our seventh VMware world this year like EMC we all started there so I want to thank you for all the support and appreciate enabling us to be successful thank you so much always a pleasure Robin Matlock on the cube I'm John Faraday volante you're watching the cube looking back at the history of Dell