>> From the Cube studios in Palo Alto and Boston, bringing you data driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> While the precise direction of VMware's future is unknown, given the plan Broadcom acquisition, one thing is clear. The topic of what Broadcom plans will not be the main focus of the agenda at the upcoming VMware Explore event next week in San Francisco. We believe that despite any uncertainty, VMware will lay out for its customers what it sees as its future. And that future is multi-cloud or cross-cloud services, what we call Supercloud. Hello, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we drill into the latest survey data on VMware from ETR. And we'll share with you the next iteration of the Supercloud definition based on feedback from dozens of contributors. And we'll give you our take on what to expect next week at VMware Explorer 2022. Well, VMware is maturing. You can see it in the numbers. VMware had a solid quarter just this week, which was announced beating earnings and growing the top line by 6%. But it's clear from its financials and the ETR data that we're showing here that VMware's Halcion glory days are behind it. This chart shows the spending profile from ETR's July survey of nearly 1500 IT buyers and CIOs. The survey included 722 VMware customers with the green bars showing elevated spending momentum, ie: growth, either new or growing at more than 6%. And the red bars show lower spending, either down 6% or worse or defections. The gray bars, that's the flat spending crowd, and it really tells a story. Look, nobody's throwing away their VMware platforms. They're just not investing as rapidly as in previous years. The blue line shows net score or spending momentum and subtracts the reds from the greens. The yellow line shows market penetration or pervasiveness in the survey. So the data is pretty clear. It's steady, but it's not remarkable. Now, the timing of the acquisition, quite rightly, is quite good, I would say. Now, this next chart shows the net score and pervasiveness juxtaposed on an XY graph and breaks down the VMware portfolio in those dimensions, the product portfolio. And you can see the dominance of respondents citing VMware as the platform. They might not know exactly which services they use, but they just respond VMware. That's on the X axis. You can see it way to the right. And the spending momentum or the net score is on the Y axis. That red dotted line at 4%, that indicates elevated levels and only VMware cloud on AWS is above that line. Notably, Tanzu has jumped up significantly from previous quarters, with the rest of the portfolio showing steady, as you would expect from a maturing platform. Only carbon black is hovering in the red zone, kind of ironic given the name. We believe that VMware is going to be a major player in cross cloud services, what we refer to as Supercloud. For months, we've been refining the concept and the definition. At Supercloud '22, we had discussions with more than 30 technology and business experts, and we've gathered input from many more. Based on that feedback, here's the definition we've landed on. It's somewhat refined from our earlier definition that we published a couple weeks ago. Supercloud is an emerging computing architecture that comprises a set of services abstracted from the underlying primitives of hyperscale clouds, e.g. compute, storage, networking, security, and other native resources, to create a global system spanning more than one cloud. Supercloud is three essential properties, three deployment models, and three service models. So what are those essential elements, those properties? We've simplified the picture from our last report. We show them here. I'll review them briefly. We're not going to go super in depth here because we've covered this topic a lot. But supercloud, it runs on more than one cloud. It creates that common or identical experience across clouds. It contains a necessary capability that we call a superPaaS that acts as a cloud interpreter, and it has metadata intelligence to optimize for a specific purpose. We'll publish this definition in detail. So again, we're not going to spend a ton of time here today. Now, we've identified three deployment models for Supercloud. The first is a single instantiation, where a control plane runs on one cloud but supports interactions with multiple other clouds. An example we use is Kubernetes cluster management service that runs on one cloud but can deploy and manage clusters on other clouds. The second model is a multi-cloud, multi-region instantiation where a full stack of services is instantiated on multiple clouds and multiple cloud regions with a common interface across them. We've used cohesity as one example of this. And then a single global instance that spans multiple cloud providers. That's our snowflake example. Again, we'll publish this in detail. So we're not going to spend a ton of time here today. Finally, the service models. The feedback we've had is IaaS, PaaS, and SaaS work fine to describe the service models for Supercloud. NetApp's Cloud Volume is a good example in IaaS. VMware cloud foundation and what we expect at VMware Explore is a good PaaS example. And SAP HANA Cloud is a good example of SaaS running as a Supercloud service. That's the SAP HANA multi-cloud. So what is it that we expect from VMware Explore 2022? Well, along with what will be an exciting and speculation filled gathering of the VMware community at the Moscone Center, we believe VMware will lay out its future architectural direction. And we expect it will fit the Supercloud definition that we just described. We think VMware will show its hand on a set of cross-cloud services and will promise a common experience for users and developers alike. As we talked about at Supercloud '22, VMware kind of wants to have its cake, eat it too, and lose weight. And by that, we mean that it will not only abstract the underlying primitives of each of the individual clouds, but if developers want access to them, they will allow that and actually facilitate that. Now, we don't expect VMware to use the term Supercloud, but it will be a cross-cloud multi-cloud services model that they put forth, we think, at VMworld Explore. With IaaS comprising compute, storage, and networking, a very strong emphasis, we believe, on security, of course, a governance and a comprehensive set of data protection services. Now, very importantly, we believe Tanzu will play a leading role in any announcements this coming week, as a purpose-built PaaS layer, specifically designed to create a common experience for cross clouds for data and application services. This, we believe, will be VMware's most significant offering to date in cross-cloud services. And it will position VMware to be a leader in what we call Supercloud. Now, while it remains to be seen what Broadcom exactly intends to do with VMware, we've speculated, others have speculated. We think this Supercloud is a substantial market opportunity generally and for VMware specifically. Look, if you don't own a public cloud, and very few companies do, in the tech business, we believe you better be supporting the build out of superclouds or building a supercloud yourself on top of hyperscale infrastructure. And we believe that as cloud matures, hyperscalers will increasingly I cross cloud services as an opportunity. We asked David Floyer to take a stab at a market model for super cloud. He's really good at these types of things. What he did is he took the known players in cloud and estimated their IaaS and PaaS cloud services, their total revenue, and then took a percentage. So this is super set of just the public cloud and the hyperscalers. And then what he did is he took a percentage to fit the Supercloud definition, as we just shared above. He then added another 20% on top to cover the long tail of Other. Other over time is most likely going to grow to let's say 30%. That's kind of how these markets work. Okay, so this is obviously an estimate, but it's an informed estimate by an individual who has done this many, many times and is pretty well respected in these types of forecasts, these long term forecasts. Now, by the definition we just shared, Supercloud revenue was estimated at about $3 billion in 2022 worldwide, growing to nearly $80 billion by 2030. Now remember, there's not one Supercloud market. It comprises a bunch of purpose-built superclouds that solve a specific problem. But the common attribute is it's built on top of hyperscale infrastructure. So overall, cloud services, including Supercloud, peak by the end of the decade. But Supercloud continues to grow and will take a higher percentage of the cloud market. The reasoning here is that the market will change and compute, will increasingly become distributed and embedded into edge devices, such as automobiles and robots and factory equipment, et cetera, and not necessarily be a discreet... I mean, it still will be, of course, but it's not going to be as much of a discrete component that is consumed via services like EZ2, that will mature. And this will be a key shift to watch in spending dynamics and really importantly, computing economics, the things we've talked about around arm and edge and AI inferencing and new low cost computing architectures at the edge. We're talking not the near edge, like, Lowes and Home Depot, we're talking far edge and embedded devices. Now, whether this becomes a seamless part of Supercloud remains to be seen. Look, if that's how we see it, the current and the future state of Supercloud, and we're committed to keeping the discussion going with an inclusive model that gathers input from all parts of the industry. Okay, that's it for today. Thanks to Alex Morrison, who's on production, and he also manages the podcast. Ken Schiffman, as well, is on production in our Boston office. Kristin Martin and Cheryl Knight, they help us get the word out on social media and in our newsletters. And Rob Hoffe is our editor in chief over at Silicon Angle and does some helpful editing. Thank you, all. Remember these episodes, they're all available as podcasts, wherever you listen. All you got to do is search Breaking Analysis Podcast. I publish each week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com or DM me @Dvellante or comment on our LinkedIn posts. Please do check out etr.ai. They've got some great enterprise survey research. So please go there and poke around, And if you need any assistance, let them know. This is Dave Vellante for the Cube Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis. (lively music)

>>Yeah. Okay. Now we're going to dig deeper into HP es moral and try to better understand how it's going to impact customers. And with me to do that are Robert Christensen is the vice president strategy in the office of the C, T. O. And Kumar Srikanth is the chief technology officer and head of software both, of course, with Hewlett Packard Enterprise. Gentlemen, welcome to the program. Thanks for coming on. >>Good seeing you. Thanks for having us. >>Always. Great. Great to see you guys. So, Esmeralda, kind of a interesting name. Catchy name. But tomorrow, what exactly is H P E s bureau? >>Yeah. It's indeed a catchy name. Our branding team done a fantastic job. I believe it's actually a derivation from Esmeralda. The Spanish for Emerald Berlin. Supposed to have some very mystical powers. Um, and they derived as moral from there, and we all actually, initially that we heard it was interesting. Um, so as well was our effort to take all the software, the platform tools that HB has and provide these modern operating platform to the customers and put it under one brand. It has a modern container platform. It has a persistent stories distribute the date of February. It has been foresight, as many of our customers similar, So it's the think of it as a container platform offering for modernization of the civilization of the customers. >>Yeah, it's an interesting to talk about platform, so it's not a lot of times people think product, but you're positioning it as a platform, so it has a broader implications. >>That's very true. So as the customers are thinking of this civilization, modernization containers and microservices, as you know there has become, has become the stable whole. So it's actually a container orchestration platform. It offers open source proven. It is as well as the persistence always bolted to >>so by the way, s moral, I think emerald in Spain, I think in the culture it also has immunity powers as well. So immunity >>from >>lock in and all those other terrible diseases. Maybe it helps us with covid to rob Robert. When you talk to customers, what problems do you probe for that that is immoral. Can can do a good job solving. >>Yeah, they That's a really great question because a lot of times they don't even know what it is that they're trying to solve for, other than just a very narrow use case. But the idea here is to give them a platform by which they can bridge both the public and private environment for what to do an application development specifically in the data side. So when they're looking to bring Container Ization, which originally got started on the public cloud and has moved its way, I should say, become popular in the public cloud and has moved its way on premises. Now Esmeralda really opens the door to three fundamental things. But how do I maintain an open architecture like you're referring to some low or oh, no lock in of my applications And there were two. How do I gain a data fabric or data consistency of accessing the data so I don't have to rewrite those applications when I do move them around and then, lastly, where everybody is heading down, the real value is in the AI ML initiatives that companies are are really bringing that value of their data and locking the data at where the data is being generated and stored. And so the is moral platform is those multiple pieces that I was talking about stacked together to deliver those solutions for the client. >>So come on, what's the How does it work? What's the sort of I p or the secret sauce behind it all? What makes HP different? >>Continuing our team of medical force around, uh, it's a moral platform for optimizing the data Indians who were close. I think I would say there are three unique characteristics of this platform. Number one is actually provides you both an ability to run stable and stateless were close under the same platform, and number two is as we were thinking about. Unlike analogues, covenant is open source. It actually produce you all open source government as well as an orchestration behind you. So you can actually you can provide this hybrid, um, thing that drivers was talking about. And then actually we built the work flows into it. For example, we're actually announced along with Esmeralda MLS, but on their customers can actually do the work flow management. Our own specifically did the work force. So the magic is if you want to see the secrets of is all the efforts that have been gone into some of the I p acquisitions that HBs the more years we should be. Blue Data bar in the nimble emphasize, all these pieces are coming together and providing a modern digitalization platform for the customers. >>So these pieces, they all have a little bit of a machine intelligence in them. Yeah, People used to think of a I as the sort of separate thing, having the same thing with containers, right? But now it's getting embedded in into the stack. What? What is the role of machine intelligence or machine learning in Edinburgh? >>I would take a step back and say, You know this very well. They're the customer's data amount of data that is being generated, and 95% or 98% of data is machine generated, and it has a serious amount of gravity, and it is sitting at the edge, and we were the only the only one that edge to the cloud data fabric that's built. So the number one is that we are bringing computer or a cloud to the data. They're taking the data to the cloud like if you go, it's a cloud like experience that provides the customer. Yeah, is not much value to us if we don't harness the data. So I said this in one of the blood. Of course, we have gone from collecting the data era to the finding insights into the data so that people have used all sorts of analysis that we are to find data is the new oil to the air and the data. And then now you're applications have to be modernized. And nobody wants to write an obligation in a non microservices fashion because you want to build the modernization. So if you bring these three things, I want to have a data. Gravity have lots of data. I had to build an area applications and I want to have an idea those three things I think we bring together to the customs. >>So, Robert, let's stay on customers from it. I mean, you know, I want to understand the business impact, the business case. I mean, why should all the you know, the cloud developers have all the fun? You mentioned that you're bridging the cloud and on Prem, uh, they talk about when you talk to customers and what they are seeing is the business impact. What's the real drivers for them. >>That's a great question because at the end of the day I think the reason survey that was that cost and performance is still the number one requirement for the real close. Second is agility, the speed of which they want to move. And so those two are the top of mind every time. But the thing we find in as moral, which is so impactful, is that nobody brings together the silicon, the hardware, the platform and all that stacked together work and combined, like as moral does with the platforms that we have and specifically, you know, when we start getting 90 92 93% utilization out of ai ml workloads on very expensive hardware, it really, really is a competitive advantage over a public cloud offering which does not offer those kind of services. And the cost models are so significantly different. So we do that by collapsing the stack. We take out as much intellectual property, give me, um, as much software pieces that are necessary. So we are closest to the silicon closest to the applications bring into the hardware itself, meaning that we can inter leave the applications, meaning that you can get to true multi tendency on a particular platform that allows you to deliver a cost optimized solution. So when you talk about the money side, absolutely. There's just nothing out there and then on the second side, which is agility. Um, one of the things that we know is today is that applications need to be built in pipelines. Right? This is something that has been established now for quite some time now. That's really making its way on premises. And what Kumar was talking about was, how do we modernize? How do we do that? Well, there's going to be something that you want to break into Microservices and containers. There's something you don't now the ones that they're going to do that they're gonna get that speed and motion etcetera out of the gate. And they can put that on premises, which is relatively new these days to the on premises world. So we think both will be the advantage. >>Okay, I want to unpack that a little bit. So the cost is clearly really 90 plus percent utilization. I mean, come on. You know, even even a pre virtualization. We know what it was like even with virtualization, you never really got that high. I mean, people would talk about it, but are you really able to sustain that in real world workloads? >>Yeah, I think when you I think when you when you make your exchangeable currency into small pieces, you can insert them into many areas. And we have one customer was running 18 containers on a single server and each of those containers, as you know, early days of data. You actually modernized what we consider we won containers of micro B. Um, so if you actually build these microservices and you have all anti affinity rules and you have rationing formulas all correctly, you can pack being part of these things extremely violent. We have seen this again. It's not a guarantee. It all depends on your application and your I mean, as an engineer, we want to always understand how this can be that sport. But it is a very modern utilization of the platform with the data and once you know where the data is, and then it becomes very easy to match those >>now. The other piece of the value proposition that I heard Robert is it's basically an integrated stack, so I don't have to cobble together a bunch of open source components. It's there. There's legal implications. There's obviously performance implications that I would imagine that resonates is particularly with the enterprise buyer, because they have the time to do all this integration. >>That's a very good point. So there is an interesting, uh, interesting question that enterprise they want to have an open source, so there is no lock in. But they also need help to implement and deploy and manage it because they don't have expertise. And we all know that Katie has actually brought that AP the past layer standardization. So what we have done is we've given the open source and you write to the covenant is happy, but at the same time orchestration, persistent stories, the data fabric, the ai algorithms, all of them are bolted into it. And on the top of that, it's available both as a licensed software and run on Prem. And the same software runs on the Green Lake so you can actually pay as you go and you don't we run it for them in in a collar or or in their own data center. >>Oh, good. I was one of my latter questions, so I can get this as a service paid by the drink. Essentially, I don't have to install a bunch of stuff on Prem and pay >>a perpetual license container at the service and the service in the last Discover. And now it's gone production. So both MLRS is available. You can run it on friends on the top of Admiral Container platform or you can run inside of the Green Bay. >>Robert, are there any specific use case patterns that you see emerging amongst customers? >>Yeah, absolutely. So there's a couple of them. So we have a really nice relationship that we see with any of the Splunk operators that were out there today. Right? So Splunk containerized their operator. That operator is the number one operator, for example, for Splunk, um, in the i t operation side or notifications as well as on the security operation side. So we found that that runs highly effective on top of his moral on top of our platforms that we just talked about what, uh, Kumar just talked about, but I want to also give a little bit of backgrounds to that same operator platform. The way that the Admiral platform has done is that we've been able to make highly active, active with a check availability at 95 nines for that same spark operator on premises on the kubernetes open source, which is, as far as I'm concerned. Very, very high end computer science work. You understand how difficult that is? Uh, that's number one. Number two, you'll see spark just a spark. Workloads as a whole. All right. Nobody handles spark workloads like we do. So we put a container around them, and we put them inside the pipeline of moving people through that basic, uh uh, ml ai pipeline of getting a model through its system through its train and then actually deployed to our MLS pipeline. This is a key fundamental for delivering value in the data space as well. And then, lastly, this is This is really important. When you think about the data fabric that we offer, um, the data fabric itself, it doesn't necessarily have to be bolted with the container platform to container at the actual data. Fabric itself can be deployed underneath a number of our for competitive platforms who don't handle data. Well, we know that we know that they don't handle it very well at all. And we get lots and lots of calls for people say, Hey, can you take your as Merrill data for every and solve my large scale, highly challenging data problems, we say yes. And then when you're ready for a real world full time but enterprise already, container platform would be happy to privilege. >>So you're saying if I'm inferring correctly, you're one of the values? Is your simplifying that whole data pipeline and the whole data science science project? Unintended, I guess. >>Okay, >>that's so so >>absolutely So where does the customer start? I mean, what what are the engagements like? Um, what's the starting point? >>It's being is probably one of the most trusted enterprise supplier for many, many years, and we have a phenomenal workforce of the both. The PowerPoint next is one of the leading world leading support organization. There are many places to start with. The right one is Obviously all these services are available on the green leg as we just start apart and they can start on a pay as you go basis. We have many customers that. Actually, some of the grandfather from the early days of pleaded and map are and they're already running, and they actually improvised on when, as they move into their next generation modernization, um, you can start with simple as metal container platform with persist with the story compared to this operation and can implement as as little as $10 and to start working. Um, and finally, there is a a big company like HP E. As an enterprise company defined next services. It's very easy for the customers to be able to get that support on the day to operation. >>Thank you for watching everybody's day volonte for the Cube. Keep it right there for more great content from Esmeralda. >>A mhm, okay.

>> Well, quick introductions for everybody kind of out there watching in the Data Summit. I'm Ali Tierney. I am the GVP. I run EMEA Sales for Snowflake, and I'm joined today with Michael Gillespie. Quick, just to introduce himself, what he does, and the DPE come structure as it goes. Go ahead, Micheal. >> Thanks, Ali. So as you said, I'm CDTO at Domino's Pizza Enterprises. So the company that I work for, we have the franchise rights and run Australia and New Zealand, France, Belgium, Netherlands, Germany, Japan, Luxembourg, and Denmark. And that's obviously Domino's Pizza for those markets. I look after four different verticals within the business. IT for the group, Strategy and Insights where our BI team resides and has a lot to do with Snowflake. Our Store Innovations Team, our Store Innovation Operations team which look at everything from robotics in store, how to use data better in store to be working at optimum level, and our digital team which is where I started in, actually, 13 years ago. And they're guiding our digital platform at a global level and how we localize it with the local marketing teams. >> Brilliant, I'm American and I grew up with Domino's Pizza, so help me understand, kind of, from a high structure. You've been there 13 years. My growing up experience was picking up a phone and pushing buttons and calling Domino's, and clearly a ton of modernization has come in the last 20 years, and you've been with the company for 13. What have you seen as you've grown into the DPE digital kind of space and you're driving that market? How are you guys using data? What have you seen happen over the last 13 years? >> Domino is itself, or at least DPE as well, has always been a data-driven business. What we've seen, though, as we've become more of a business that utilizes digital and technology to enhance, whether the customer experience or our store operations or our enterprise team. Is the availability of data to make decisions or to actually find insights. And if I look back, I've been lucky to go on a journey of 13 years with DPE. The power of analytics and data was apparent in a digital space. And it gave us a level of insight over a purchase that we never had before. So a great example of our first use of real data in a customer experience outside callbacks people are late, where we could give real-time feedback to a customer around their progression of their order through something called Pizza Tracker, which is shared across all and used across most Domino's in the world. And they're most common for most purchasing processes. Since then, we've gone from, I could count, very easily in between this call, how many orders would make in a day online, to now over 70% of our businesses online. We have a huge amount of data coming in from different, different areas of business. And now the challenge for myself and my team is how do we make this data readily available? To the local marketing teams, local operations teams. To really get better insights on the local market. So we've just gone from having a small pool of data to a tremendous quantum of data. >> So as you look to kind of localize your markets, right? I think you just mentioned seven or eight different markets that you're in. And I would assume then you have some data sharing that goes on within DPE, right? So Belgium wants something that's different than the Netherlands that was different than Japan, right? So how are you right now democratizing that data and giving it to your customers so that your end users can see how to use that, right? In local marketing in local, kind of, business uses. >> Correct. So, we have, we have nine markets now within DPE and all those markets, every market has unique needs and wants and challenges that they're trying to solve for. So our goal is to really try to simplify the access. And that's what we talk about democratizing data. We have a series of reports so we can build customized reports so that we don't have to do as many ad hoc requests. Then when giving those dashboards having the ability to customize and benchmark where you need to. And then when it comes down to a unique customer experience that's obviously going to be a localized marketing on them because different customers bought certain, certain volumes of pizza or sides and different market that's different. So we need to make the tools that each of them and or allow our marketing teams around the world to get access to the data that they can really help them make the most informed decisions to support their franchisees and stores. >> How much of your technology has moved in general to the cloud? And then secondarily to that question, as you've moved there, and I assume significant multi-clouds because you've got so many different regions and locations, how are using Snowflake to help move data into the cloud? >> I would say from a cloud perspective we're well advanced in being clouded for a majority of our platforms or at least moving in that direction. And we're being cloud friendly economic solution and some of that data solutions for quite a while. We still have some on-premise data, like most companies, and we're in the process of migrating. And we have to be aware that we operate within markets like Europe, where GDPR is there. And we have to, we have to be well across requirements from that ability that perspective. But regardless of GDPR or not with any form of customer data or employee data or any personal data we have, we know it's a privilege to hold. So anytime we are working with data we always want to make sure that we're storing it and accessing it in the most secure way. And then beyond that, we want to make sure that, as I talked about, we want to democratize data and make it more accessible. So, you know, I'm really looking forward to seeing as we build out and continue to build out our data strategy, how we continue to work with the likes of Snowflake to just bring faster and more insightful, you know, visibility into each particular market and at a global level as well. So that our global leaders can understand how the business is performing but also get micro where they need to. >> How, as you go through your cloud journey and then and with Snowflake specifically, how did you guys look to governance and how did you look to ensure your security around data? >> Yeah. So know for us, it's all about making sure we've got the right governance and controls and processes. So working with our security team, working with the right architects on data flow and processes, working with our legal team and representatives in each market and that's vital. You know, having policies and governance around any form of activity whether it be data or around changes on the website or changes even in any operational processes is important. So. >> Yeah >> And the greatest thing is if he can, you know, through, if you're making dashboards that are unquantifiable non-personal data, you know that's a lot easier to manage, as well. Because that's giving you a representation of groups not actually down to the particular customer. >> That makes perfect sense. How have you found migrating to Snowflake? Talk through that journey a little bit and I know you're relatively early in the journeys but talk at your experience has its been so far. >> You know, the BI team, my BI team and Strategy and Insights Team have definitely been huge fans of Snowflake and the support from the team there and and the partners we're using for integration. You know, one thing that I know that, that excites me from a strategic level, it's Snowflake's ability to be cloud agnostic and for us everything we build in the future we have chosen partners that we work with in the cloud space. We shouldn't be, we should always be having that ability to be flexible or we're always going to have some fragmented data sets and the ability to utilize a solution that can stretch out into those is very important. So you know, from a strategic level that's a great level of flexibility and from a micro level, and to look at how the team operate when they're coming with stories around greater efficiency, greater flexibility, reduced processing time, reduce, reduce time, reduction in costs and certain activities. That's a great story to be told. That's what I like about this story is that they were all wins. You know, I'm getting from the team that I can run more intensive workloads now. You know, that they can they can do more immediate action. You know, they are cutting down time, as I said, something down from hours to minutes down getting some early results and that's so important. >> So, tell me what kind of business insights you're delivering back to your stakeholders when you get through this process? The quick wins. >> Yeah, well I guess it's just us being able to get reports out faster. Get information out faster, Get access to any acts, build, build bespoke things quicker. It's all about Domino's as a business that's quite an entrepreneurial fast moving. So if you can find efficiencies that, like any business, that's, that's the point. But if we can find efficiencies within our team what it means is we've got a quantum of work the team can do or a service can do, or a bucket of costs can do. If we can reduce that quantum of whether it be cost or time and human effort, that means we can output more. One thing that we're also looking at is we talked about democratizing data earlier, but how can we empower, empower teams to get insights faster? Or to go, I always think there should be no one key holder. There should be key holders of obviously the security of the data and the, and the safety and the and the rules around it. But, in regards to broad insight data or in visibility of results, we should be trying to make that as accessible as possible so that teams can find the reading sites. You've got then thousands or hundreds of people that are looking. Whether it be franchisees at store or team members that had offices in different departments. If they can get greater visibility at a top level data and drill in micro and performance, imagine the insights you continue to do or if you can get reports in their hands faster. Time in a fast moving business a day or two of lost opportunity is huge. So how do you get to make those decisions faster? And how do you stay ahead of your game? >> So as you think of data cloud and as you think of how you're going to build out a DPE specific data cloud, where do you see that going? How, where do you get where's your nirvana and end goal from your data club? >> How do we make better use of that data? So, how do we win? We know that our data repositories are only going to continue to grow. You know, we're a business that was growing at a relatively strong rate. If you look at our previous results, we have a multitude of countries. We have 2,600 stores around the world pumping out pieces every night. And that's creating different forms of data. We have 70% of our customers online. When you're capturing a continuous amount of data. One thing that we want to do is not only manage it efficiently We know that capturing data is a privilege as well, so that we're capturing the right data. And then when you're capturing the right data we still know that the quantum of that will increase. So then how we are storing it and making sure that as we add more data to our repositories we are not actually making its harder to access or it's slower to access. So it's bringing down our reports that we're continuing to optimize and what we're seeing and I touched on when you're bringing time down from hours to minutes with a tool. We're doing that. We're bringing down those solutions. So being able to manage the increasing volume of data we're getting in a more efficient way. Being able to democratize the access of it in a safe, secure, but insightful way. But, you know, having the backing of a service like Snowflake in the background, supporting access and functioning about data. Hopefully, this just means that it will give us more ability to be nimble and do more in the future. >> As you've broken down data silos with using Snowflake and started to democratize data and put it all in one spot your ML becomes richer and more able to make better decisions because you got it all out of silos at this point. >> Yeah.We've got a better floral collection about data. And we can make those data repositories more accessible or no more efficient in accessing them. It's only going to enrich our models and it's going to challenge us. I can challenge and the business can challenge the strategy and insights and BI team to look at a multitude of ways as part of supporting the business. Because they've always got a backlog of reports or solutions they want to deliver. So, we had started a journey of being a data driven company. We have started the journey of a digital company many, many years ago. >> So as we leave today Michael and we wrap up. Last question I have for you is, as you know, everybody's coming and saying do the next bread is coolest next thing. What would you recommend the users of our conference? What would you say? Like how would you, how would you say to go to market and do it the right way? >> Yeah. Let's say the main thing is for those people to reflect upon their own business and understand the challenges at hand. it's very easy to be asked, why aren't we doing AI? Why aren't we doing machine learning? Why aren't we? But those are just solutions. You should be trying to take time to say okay, but what are some of that challenges? And then can we apply those technologies to it? or could a rudimentary approach, approach of just a simple report or a very basic algorithm solve for that. But if you could take your system to the next level with ML, don't do it for ML's sake or if you could take it with a complex data extract. Make sure you've got an angle inside of what you want to deliver. And then know, once you go down the path of anything more complicated, especially with things like machine learning, that it's a never-ending story. And you're probably not going to get the result you like in the first couple of weeks or month because that's what it is. It's a learning solution. It's a ever evolving beast and you can't just throw it out there and say, "Oh, everyone will be happy." So make sure you've got a fair commitment to getting into that game. And that you've got an envision in hand, and that envision will, I can tell you, usually move once you achieve it. Because you're only going to unlock more realities or more alternative solutions that'll grow from it. >> Absolutely. >> So be strong and want the challenges. >> I love that, and it's how we like to think about the data cloud in general, right? Is we are delivering to the business. At the end of the day, data is useless if you're not giving insights and ability for your business to make decisions and move forward. So I completely agree and I really appreciate the time you took today to sit down with me and educate me on Domino's and educate the world on how you're using data to make better decisions in the business. Thanks, Michael. >> Thanks for your time.

>> So, hi, my name is Daniel Terry, I work as Lead Designer at SEB. So, today we will go through why we are why we are Mirantis' customer, why we choose Docker Enterprise, and mainly what challenges we were facing before we chose to work with Docker, and where we are today, and our keys to success. >> Hi, my name is Johan, I'm a senior developer and a Tech Lead at SEB. I was in the beginning with Docker for like, four years ago. And as Daniel was saying here, we are going to present to you our journey with Docker and the answers. >> Yeah, who are we? We are SEB group. So we are a classic, financial large institutions. So, classic and traditional banking services. In Sweden, we are quite a big bank, one of the largest. And we are on a journey of transforming the bank so it has to be online 24-seven. People can do their banking business every day, whenever they want, nothing should stop them to be online. So this is putting a lot of pressure on us on infrastructure to be able to give them that service. (drum fill) >> So our timeline here. Is look, we started out with how to facilitate the container technology it has to be. 2016. And, in 2018, we had the first Docker running in SEB in a standalone mode. You need that. We didn't have any swarm, or given up this cluster since a while. For 2019, we have our first Docker-prise enterprise cluster at SEB. And today, 2020, we have the latest and greatest version of Docker installed. We are running around approximately two and a fifth at 450 specs. Around a thousand services and around 1500 containers. So, developer challenges. As for me as a developer, previous to Docker was really, really hard to get things in production. Times. It took big things and ordering services and infrastructures was a pain in the... yeah, you know what I mean? So for me, it was all about processes. We use natural processes and meaning that I wasn't able to, to see maintaining my system in production. I was handing that over to our operations teams and operation teams in that time, they didn't know how the application works. They didn't know how to troubleshoot it and see, well, what's going wrong. They were experts on the infrastructure and the platforms, but not on our applications. We were working in silos, meaning that I as a developer, only did developing things. The operations side did their things, and the security side did their things. But we didn't work as a team. I mean, today we have a completely different way of working. We will not see shapes. I mean, we have persons that were really good in maybe MQ technologies, or in some programming language and so on, but we didn't have the knowledge in the team techs to solve things, as we should have. Long lead times. I mean, everything we were trying to do had to follow the processes as we had. I mean that we should fill in some forms, send it away, hopefully someone was getting, getting back to us and saying, yeah guys, we can help you out with these services or this infrastructure, but it takes a really long time to do that. I mean, ordering infrastructure is when you're not an expert on that really hard to do. And often the orders we made or placed were wrong. When we have forms to fill in, it wasn't possible for us to do things automatically. Meaning that we didn't have the code, or the infrastructure as code. Meaning that if we didn't get the right persons into the meetings the first time, we didn't have the possibility to do it the right way, meaning that we had to redo and redo, and hopefully sometimes we got the right. We didn't have consistent set ups between the environments. When we order, as for example, a test environment, we could maybe order it with some minor resources, less CPU, so less memory, less disc or whatever. Or actually less performance on the hardware, but then we moved up to production. We realized that we have different hardware, different discs, different memories, and that could actually cause some serious problems in applications, access-wise. I mean, everyone likes to have exercise, especially if you are the maintainer of the system. That was really, really hard to get. I mean, every system has their own services, their own service, and therefore they need to apply for access to those other services. But today there's a complete difference since we only have one class to produce. Since we don't have infrastructure as a code back then, there were really lots of human errors. I mean, everyone was doing things manually. When you're coming from the Windows perspective, everything is a UI. You tend to prefer that way of working, meaning that if you used to click something in between the environments, the environments will not look the same. Life cycles. I mean, just imagine. When we have the server installed, it's like a pet. You have everything configured all from certificates to port openings, cartels, install patches, you name it. And then imagine that Windows are terminating a version and you need to reinstall that. Everything needs to be redone from the beginning. So there was a really long time taking to, to do the LCM activities, General lack of support of Microservice architecture was really also, a thing that are driving us forward with the containers technology, since we can't scale our applications in the same way as for containers. We, for example, couldn't have two applications or two processes using the same TCP port. For example, if you'd like to scale a web server, you can't do that on the same hardware. You need to have two different servers. And just imagine replacing all the excesses, replacing all the orders again for more hardware, and then manually a setting up there. The low balancer in front is a really huge task to do. And necessarily if you don't have the knowledge how the infrastructure is where you're working, then it's also really hard for you as a developer to do things right. Traditionalist. I mean, the services for us are like pets. They were really, really hard to set up. It'd take maybe a week or so. And if something was wrong with them, we will try to fix them as a pet. I mean, we couldn't just kill them and throw them away. It will actually destroy the application as this, our, like a unit box where all our things are installed. >> So, coming in from the infrastructure part of this, we've also seen challenges. For my team, we're coming from a Windows environment. So doing like a DevOps journey, which we want to do, makes it harder due to our nature in our environments. We are not used to, maybe use API, so we are not used to giving open APIs to our developers to do changes on the servers. Since we are a bank, we don't allow users to log into the servers, which means we have to do things for them all the time. This was very time consuming. And a lot of the challenges we actually still are seeing is the existing infrastructure. You can't just put that container platform on it, and thinking you're sold and everything. One of the biggest issues for us is, has been to getting servers. Windows servers usually takes like 15 minutes, Linux servers can takes up to two week in a bad day. So we really lack like, infrastructure as code. If we want a low balancer, that is also an order form. If we want the firewall opening, that's an order form. Hopefully they will not deny it. So it will go faster. So it's a lot of old processes that we need to go through. So what we wanted to do is that we want to move all of these things to the developers, so they can do it. They can own up their problems, but with our old infrastructure, that wasn't possible. We are a heavily ITIL-based organization, meaning that everything went from a cab. Still does in some way, we have one major service window every month where we take everything down. There is a lot of people involved in everything. So it's quite hard to know what will be done during the maintenance window. We lack supporting tools, or we lacked supporting tools, like log-in, good log-in tools. We have a bunch of CI/CD tools, but the maturity level of the infrastructure team wasn't that good. Again, order form and processes. If we want to, like, procure, do our procurement on a new like, storage system, or a backup system, we talk about here. So to do it is, for us, with containers, it would solve a lot of problems, because we cause we would then move the problems, not maybe move the problems to the developer, but we would make it able for them to own their own problems. So everything that we have talked about up till now boils down to business drivers. So the management's gave- gave us some policies to, or what they, how they want to change the company, so we can be this agile and fast moving bank. So one of the biggest drivers are cloud readiness, where Containers comes in perfectly. So we can build it on premises, and then we can move it to the cloud when we are ready but we can't, but we also need an exit strategy to move it back on premises if we need, due to hard regulations. Maybe you can throw it in the air. >> Absolutely. I definitely can. You're absolutely right. We need to develop things in a certain way. So we can move from infrastructure to infrastructure depending, or regardless of the vendor. Meaning that if we are able to run it on-prem, we should be able to run it in cloud or vice versa. We should also be able to move between clouds, and not be forced into one cloud provider. So that's really important for us at SEB. Short time to market is also a thing here. I mean, we are working with the huge customers. I can't name them, but they're really huge. And they need to have us being moving forward. I mean, able to really fast switch from one technology, maybe to another, we are here for them. And it's really important to us to be really fast for us to get new things out in production. All right, maybe. Nothing else? >> I don't, don't really. From the upside, we are in a huge staff DevOps transition. So, or a forced DevOps transition, which means we need to start looking at new infrastructure solutions, maybe deploy our infrastructure parts inside of containers to be able to use it the same way in the cloud. That's what we do prior, do here on premises, we have private clouds which are built on techno- technology, container technology today. So this fits quite good to have the Docker platform being one part of that one. >> Yeah. And this is solid, we are also working really, really actively on open source platforms and open source drivers. We can see that we have a huge amount of vendors in SEB, really huge ones, but we can also see that we can, facilitate open source platforms, and open source technology as well. So container technology will bring that for us. I mean, instead of having a SaaS platform and SaaS services, we can actually instantiate our own with containers and stuff. >> Also we are, since we are quite heavily regulated, the process of going through to you as like a SaaS service can take up to two years for us to go through, and then maybe the SaaS service, is it, is it what we want to use anymore? So, also we want to develop the things in our own premises and maybe, and scale it to the cloud if we need. And also we want to be an attractive employer, where maybe it's not that, the coolest thing for a young student to work in mainframe, we have a mainframe it's, it's not going anywhere, but it's hard to get people, and we want to be an attractive employer, and everyone is talking Kubernetics and containers or, and clouds. So we need to transition into those technologies. >> Yeah, we need to be open minded and necessarily facilitate the new technologies. So we can actually attract new employees. So it's really important to us to have an open mind. Our experience with Docker Containers. I mean, as I said before, scalability is a really important thing for us today. When we are using a more microservice architecture, we need to be able to Skype. We need to be scaling horizontally instead of vertically. So for that, containers are perfect storage. As we said before, we have a huge problem with environments being differently set up, since it was often manually done. Today, as we have a infrastructure as a code, it's really, really nice to have the same things exactly configured, the same in all environments. And we also have the same tooling, meaning that if I can run it on my machine, it's the same tooling I will be using to run it for test purposes or in production. That's a huge benefit for us as a developer. Time to market. I mean, today, we don't have to order service, we are using the service approach here. So we have a container cluster that are actually just sitting there waiting for our services to be hosted. So no more forms, no more calls, no more meetings before we can set up anything. We also own our problems. I mean, before, as I said, we have the processes, meaning that we ship our applications to any server. And then the operation sites take over. That's not the case now. We are actually using this as we should in DevOps. Meaning the other teams are actually responsible for all their errors as well. Even if it's on the infrastructure part, it's completely different if it's a platform's problem, because then it's the platform's team, and we can use different windows. We can try stuff out, we have an open mind. And that says that I can download and try any container image I would like on my developer machine. It's not maybe, okay to run it in production without having the security people look at it. But normally it's really, really much faster instead of waiting maybe six months, we can maybe wait one week or so. And of course less to none LCM activities. I mean, as I said before, it will take months, maybe, to do an LCM activity on multiple servers. Today, our LCM activities more or less are just switching to a new version of the image from Docker hub. That's all we have to do. So that's actually maintained during the processes we have in CI/CD pipelines. >> And the last one. So our keys to success: you should get demanded from the managers and management that everything should be a container. All the new development has to go through a container before you start ordering servers. Everything shall go through a CI/CD pipeline. We don't actually, here at SEB. Our developers build their own CI/CD pipeline. We just provide a platform for them to use it against, and the CI/CD to systems, but they build everything for themselves. Cause they know how their application works, how it should be deployed, with what tools. We just provide them with a tool set. Build a Cross Team. So you should incorporate all the processes that you need, but you should focus on the developer part, because you are building a platform for the developers, not for operations or security. >> And then maybe >> A lot of... >> you'll be able to take flight >> Yeah. Luck has nothing to do with it? Yes, it has. Of course, luck has something to do with it, even if you're really passionate, even if you're really good at some things. I mean, we got some really nice help from Dr. Inc. We were really... Came in with the technology in the right time for us to be, and we had really engaged people with these projects and that's a really luck for us to have. >> Yeah. And also we... I want to thank our colleagues, because we have another container team who started before us. And they have actually run into a lot of organizational problems, which they have sold, so we could piggyback on that, on those solutions. Also, start small and scale it. This is where Docker swarm comes, fits perfectly. So we have actually, we started with swarm. We are moving into Kubernetics in this platform. We will not force-move anything. The developers just should show us, what their- fits their needs. Thank you! >> Thank you very much.

>> From San Francisco, it's theCUBE, covering Day2IQ, brought to you by Day2IQ. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the Day2IQ headquarters. They used to be called Mesosphere. They rebranded the company. They've got a much bigger focus than just Mesos and supporting Mesos. So we're here to get the story, really talk about enterprise's journey to cloud native, and we're excited to have our first guest. He's Brian Kenyon, the chief strategy officer. Brian, great to see you. >> Thanks for having me. >> Absolutely. So DayQI, Day2IQ. >> Correct. >> I'm going to get it eventually, by the end of the day. Interesting name. What does Day2IQ mean? Why did you guys rebrand the company that? >> Yeah, absolutely. So we were formerly known as Mesosphere, and the technology that we founded the company on was an open source package called Mesos, so the name naturally had a very close tie with Mesos and Mesosphere. So as we looked to rebrand the company and really enter the market with some of the changes we've seen in the evolution of cloud native, we focused on where customers were having trouble, where they were focused on operations, how they were going to take these concepts and these great ideas that were pervasive in the concept of cloud native and make them institutionalized and operationalized inside their companies. And what we found was, you know, day zero is when you played around and tested things, and day one is when you got it installed and stood up, but day two is when you really focused on the operations. How do I make this enterprise-ready? How do I make this fit my business? All of that happened on day two and after. So we saw that as a pretty natural way to focus our energy and focus our market penetration on day two. >> Right. And you also expanded beyond just kind of the Mesos ecosystem into some other areas, in containers, in Kubernetes, also data. So you guys are taking a little broader approach than maybe the company had at the original launch. >> Yeah, absolutely. And you've heard from one of our founders already and you spoke to our head of engineering. So I'm the newest of those, right? I joined in February, so I'm just, you know, almost 10 months in. So when I joined, I spent a lot of time meeting with our customers, talking to partners, talking to other folks and vendors in the space, and what we saw was there was a massive shift happening from where cloud native started maybe three, four, five years ago to where it is today, and one of the biggest changes has been around the emergence of Kubernetes, which has turned into a de facto standard for containers in cloud native. And so as we've evolved and moved into this D2IQ name, as we've started focusing on meeting our customer, we've obviously taken on a bigger stance inside the Kubernetes community and the Kubernetes product lines. >> Right. So what did you see? I mean, you're a long-time security executive. You've been in strategy and security for years and years and years. What did you see in this opportunity with a small start-up to get you to leave kind of the safe, comfortable, pretty standard corporate job into jumping back into this-- >> Nobody's ever said security's safe, so that's awesome. >> Well, safe certainly in terms of job security. (mumbles) my goodness, a big shill out there these days. >> It is, it is. >> But what did you see? >> I saw the future, is really what I saw. When you really took a step back and you looked at where compute was going and how organizations were starting to adopt new application methodologies, new application architectures, it was very clear that cloud had taken on a big portion of that and the concept of cloud native and open source technologies was becoming more and more prominent. And so as we looked at this, not only did we see a unique opportunity with the cloud native space, but if you fast forward a couple years, customers are going to be coming back around and starting to have conversations around security. How do I secure this? What, how do my CISOs and my operational folks in security understand this and how do they really start to apply the same controls and visibility to it? So it was a unique opportunity to get in and focus on where the future of our industry's going. >> Right. So it's an interesting thing with open source, and open source specifically in the enterprise. I think my favorite open source quote is, yeah, it's free like a puppy. You know, it's not free. You need support and you need training and you need a lot of help. So when you guys work with enterprises and they're incorporating more and more open source into their technology stack, what are some of the challenges that you guys are coming in to help them to actually get beyond a simple free download and the latest cool version to actually running in production, heavy duty loads, really important workloads. >> Absolutely. Yeah, one of the biggest shortfalls we see is obviously expertise, right? So there's a massive amount of innovation and capability that can be, can really be captured through open source software. The challenge is, it's all community-based. So folks contribute code, they sign it in, it's available for everybody to use, but how long is that code updated for? How long is it maintained? How do new features get added? What you see is you see a huge spike in interest and enthusiasm, and then just like every other hype cycle, you get to a trough of disillusionment where people move on to the next thing and the next thing in the open source community. And so organizations who want to leverage that innovation, want to focus their operations around open source, either for cost savings or time to market, find themselves a couple years later looking at code that's been abandoned, projects that aren't maintained anymore. We saw this in security with things like OpenSSL, right? One of the largest SSL libraries used across the entire security landscape. There were two people in the world maintaining that code. And so when a massive security vulnerability hit, organizations were scrambling. We want to stop that now for organizations that want to use open source. We, Day2IQ, want to bring our innovation, our expertise, to bring that open source to the customers and make sure that it's enterprise-ready, it's enterprise-supported, and it's enterprise-scalable. >> Right. So you guys have basically three market offerings, if I understand right. You've got a solution set where you're taking the core software and building solutions around it. You've got services, professional services, to get it in, get it up, and probably supported, so I have a 1-800 somebody to call, please, which, you couldn't call those two people in that case. >> Exactly. >> And then training, is that right? So those are how you're basically enterprise-hardening an open source kernel to get to a great solution for the customer. >> Yeah, what I'd also add in there is services. So whether it's advisory services, implementation services, or just kind of more traditional, our focus is really about meeting the customer where they need us. If you look at cloud and cloud native today, almost every customer across the globe is at a different evolution or a different maturity in that journey, and so some are at the very beginning where they're learning. Others are more towards the end where they're focused on operations and how do I streamline this, how do I hire the right folks. So we've taken a product, services, support, and training strategy that allows us to meet our customers where they are in their cloud native journey and assures us that we can provide the right level of expertise regardless of where they are. >> Right. What's been the biggest, of all the challenges that you see when people are getting started, what's some of the biggest challenges that you just see over and over and over again that you know you're going to get walking in the door? >> Over and over, you see training is just a constant, across the entire industry. No matter where a customer is in their evolution or their journey, they're constantly having to train, whether they're hiring and then training folks on the new way of developing or they're taking developers who have been building code and building applications in virtual machines or old monoliths for years that they want to train to this new paradigm. Training is a huge constant. The other piece is people are looking to rationalize their infrastructure. So services, we are in a very services-led industry right now where we can come in and help customers get stock of where are we today and where do we want to go long-term, and then put them on a plan, put them on a program or a path where they can achieve those outcomes, but do it in a way that's not disruptive or adds (mumbles). >> Right, 'cause the complexity just continues to increase. It's funny, you know, both Amazon introduced a piece of Amazon Cloud you can stick in your data center, and Google introduced a piece of Google Cloud that you can stick in your data center, and Microsoft recently introduced a piece of Azure that you can stick in your data center. So kind of this, you know, kind of real aggressive embracing of hybrid and this real embracing of complex setups where you can partition your workload based on where you think that workload should run today is really gaining hold. So the complexity is only going up, not going down. >> It is, you're absolutely right. And I will tell you, what you just brought up is a great example of why the complexity's going up. On-prem is a massively different, materially different environment than the clouds. The clouds are built on a margin, right? They're built on, if I take the same server and do this over and over again, I get repeatability, I get consistency, I get a very finite platform. If you look at how on-prem is, the traditional data center, you buy some servers from Dell, some servers from HP, storage from EMC, storage from HP. You've got all different types of hardware and software in there. So fixing that on-prem cloud is hard, and the clouds are struggling with this because the concept of taking their very clean, vanilla infrastructure and bringing that to the traditional on-premise is failing. That's where we shine. That's where we've built. That's where Mesosphere got their initial start was taking the cloud concept and bring it to the traditional data center. So we're helping clouds extend now by being that on-prem piece that speaks seamlessly with the clouds that our customers choose to use. >> Right. So I think, too, initially, the cloud was seen as a way to save money, and I've seen that evolve over time. It's really much more about speed and agility in your development cycles and getting new products to market. Do customers grok that? Are they still kind of wrestling with the cost savings and this is kind of an alternative way to buy compute and networking and capacity, or are they really moving fast because of the speed and the competitive threats? >> So I think it's interesting, and it varies, but I will tell you just from my lens, I'll say that a lot of customers are confused. They went to the cloud initially because they believe they wanted to be out of the data center game. It was easier for Amazon or Microsoft or Google to manage the data center than it was for their own IT teams. And so they shifted infrastructure up there, and then what they saw was the promises of hyperscaling, the promises of this elasticity. Your application grows as more users show up. They never realized that because those applications were built under a different premise, under a different architecture, and don't leverage the cloud native capabilities. So you're seeing a shift of people who've moved infrastructure or applications to the cloud to get out of the data center are now saying, okay, I'm kind of locked in, but where do I get my operational efficiency? Where do I get my hyperscaling? How do I get that? And now you're staring to see that shift from just using the clouds as infrastructure to more moving towards microservices, containers, and some of the things that Day2IQ helps with. >> Right, right. It's pretty funny, too, right? 'Cause the apps used to have to be built for the infrastructure on which you were going to deploy them. >> That's right. >> That's now flipped upside down, right? Now the app, the infrastructure needs to support the app. The app comes first, the infrastructure second. >> That's right. >> So having an architecture, you got to have the new architecture. As you said, you just can't simply flip the functionality of an old architecture into a new paradigm. >> And then expect you're going to get the same outcomes. >> Right, right. >> Yeah, very true. >> All right, so before I let you go, I want to get your perspective specifically on security, 'cause again, you were in the security space for a long time. Security's a hot space. Everyone says security has to be baked in everywhere. It can't be the castle and moat anymore. So with your security hat on as you kind of see these migrations and you see these new deployments and you see this move to cloud native, what do you think about from security? Are people baking it in enough? Are they thinking about it in the right way? Is it just such a fundamental shift that they need to think about security and really baking it in from the bottom to the top? >> They absolutely do. And I'll tell you what the scariest thing is, if I go through my CISO networks and talk to folks who are on that side of the fence, they're not even educated to this cloud native space yet. They don't really understand how it's happening and how it's evolving and what that means. So there's a huge education that needs to happen in security, but these things need to be bolted on from the beginning. I'll give you an example. Some of the value that comes from operating cloud native is that your ability to push code and push changes is very agile and quick. So it's encouraged in a cloud native type of architecture that a company can make 100 to 200, 300 code changes a day. >> Right. >> Right? When I grew up, you'd make those monthly, quarterly, right? 'Cause you had a whole bunch of testing. And how they push code multiple times a day. If you don't have your security team in lockstep with those developers and operations staff, how quickly can you get out of compliance? How quickly can you erode your security posture? These are all questions that have to be answered, and we're just at the very earliest stages of getting that. >> Right, and we didn't even talk about IoT and edge devices. >> Absolutely. >> Which opens up a whole different kind of threat surface. >> Absolutely. >> Yeah. >> Absolutely. >> All right, Brian, well, thanks for taking a few minutes. Good luck on the journey and hope things go super for you here. >> Thanks for having me. >> All right, he's Brian, I'm Jeff. You're watching theCUBE. We're at Day2 headquarters, Day2IQ headquarters in downtown San Francisco. Thanks for watching. We'll see you next time. (techno music)

>>from San Francisco. It's the queue every day to thank you. Brought to you by day to like you. Hey, >>welcome back already, Jeffrey. Here with the Cube were a day to IQ's headquarters in downtown San Francisco. They used to be metal sphere, which is what you might know them as. And they've rebranded earlier this year. And they're really talking about helping Enterprises in their journey to cloud native. And we're really excited to have really one of the product guys he's been here and seeing this journey and how through with the customers and helping the company transforming his Chandler hosing tonight. He's the s VP of engineering and product. Chandler, great to see you. Thanks. So, first off, give everyone kind of a background on on the day to like you. I think a lot of people knew mesosphere. You guys around making noise? What kind of changed in the marketplace to to do a rebranding? >>Sure. Yeah, we've been obviously, Mason's here in the past and may so so I think a lot of people watching the cube knows No, no one knows about Mace ose as as we were going along our journey as a company. We noticed that a lot of people are also asking for carbonates. Eso We've actually been working with kubernetes since I don't know 16 4017 something that for a while now and as Maur Maur as communities ecosystem starting involving mature more. We also want to jump in and take advantage of that. And we started building some products that were specific to kubernetes and eso. We thought, Look, you know, it's a little bit confusing for people May, SOS and Kubernetes and at times those two technologies were seen almost as competitive, even though we didn't always see it that way. The market saw it that way, so we said, Look, this is going too confusing for customers being called Mesa Sphere. Let's let's rebrand around Maur what we really do. And we felt like what we do is not just focus around one specific technology. We felt like we helped customers with more than that more than just may so support more than just community support, Andi said. Look, let's let's get us a name that shows what we actually do for customers, and that's really helping them take their workloads and put them on on Not just, you know, um, a source platform, but actually take their workloads, bring them into production and enterprise way. That's really ready for day two. And that's that's why we called it data. >>And let's unpack the day to, cause I think some people are really familiar with the concept of day two. And for some people, they probably never heard it. But it's a pretty interesting concept, and I think it packs a lot of meaning in it. A number of letters. I think you >>can kind of just think about it if you were writing software, right? I mean, Day zero is okay. We're gonna design it. We're gonna start playing with some ideas. We're gonna pull into different technologies. We're gonna do a POC. We're gonna build our skateboards. So to say, that's kind of your day. Zero. What do we want? Okay, we're gonna build a Data Analytics pipeline. We want spark. We're going to store data. Cassandra, we're gonna use cough. Go to pass it around. We're gonna run our containers on top of communities. That's just kind of your day. Zero idea. You get it working, you slap it on a cluster. Things are good right? Day one might be okay. Let's actually do a beta put in production in some kind of way. You start getting customers using it. But now, in Day two, after all that's done, you're like, Wait a second. Things were going wrong. Where's our monitoring? We didn't set that up. Where's our logging? Oh, I don't know. Like, >>who do we >>call this? Our container Run time, we think has above. Who do we call like? Oh, I don't know What support contract that we cut, Right? So that's the things that we want to help customers with. We want to help them in the whole journey, getting to Day two. But once they're there, we want them to be ready for day two, right? And that's what we do. >>I love it because one of my favorite quotes I've used it 1000 times. I'll do 2001 right? Is that open source is free like a puppy. Exactly for you. When you leave you guys, you're not writing a check necessarily to the to the shelter, But there's a whole lot of other check. You got a right and take care of. And I think that's such a key piece. Thio Enterprise, right. They need somebody to call when that thing breaks. >>Yeah. I mean, I haven't come from enterprise company. I was actually a customer basis Fear before I joined. Yeah, that's exactly why we're customers that we wanted. Not only that, insurance policy, but someone that partner with us as we start figuring this out, you know? I mean, just picking. You know what container run time do I want to use with communities? That one decision could take months if you're not familiar with it. And you you put a couple of your best architects on it. Go research container. You go research, cryo go research doctor. Tell me what's what's the best one we should use with kubernetes. Whereas if you're going, if you have a partnership with a company like day two, you can say, Look, I trust these. You know this company, they they're they're experts of this and they see a lot of this. Let's go with their recommendation. It's >>okay. So you got you got your white board. You've got a whole bunch of open source things going on, right? And you've got a whole bunch of initiatives and the pressure's coming down from from on high to get going, you've got containers, Asian and Cloud native and hybrid Cloud all the stuff. And then you've got some port CEO on his team trying to figure it out. You guys have a whole plethora of service is around some of these products. So as you try it and then you got the journey right and you don't start from from a standing start. You gotta go. You gotta go. So how do you map out the combination of how people progress through their journey? What are the different types of systems that they want to put in place and into, prioritize and have some type of a logical successful implementation and roll out of these things from day zero day 132? No, it's >>a great question. I think that's actually how we formed our product. Strategy is we've been doing this for a while now and we've we've gone. We've gone on this journey with really big advanced customers like ride sharing companies and large telcos customers like that. We've also gone on this journey with smaller, less sophisticated customers like, you know, industrial customers from the Midwest. Right? And those are two very, very different customers. But what's similar is they're both going on the same journey we feel like, but they're just at different places. So we wanted to build products, find the customer where they're at in their journey, and the way we see it really is just at the very beginning. It's just training, right? So we have, ah, bunch of support. We're sorry. Service is around training. Help you understand? Not just kubernetes, but the whole cloud native ecosystem. So what is all this stuff? How does it work? How does it fit together? How do I just deploy simple app to right? That's the beginning of it. We also have some products in that area as well, to help people scale their training across the whole whole organization. So that's really exciting for us once once, once that customer has their training down there like Okay, look, get I need a cluster now, like I need a destroyer of sorts and criminals itself is great, but it needs a lot of pieces to actually get it ready for prime time. And that's where we build a product called Convoy Say Okay, here is your enterprise great. Ready to go kubernetes destro right out of the box. And that product is really it's what you could use to just fiddle around with communities. It's also what you put into production right on the game. That's that's been scale tested, security tests and mixed workload tested. It's everything. So that's that's kind of our communities. Destro. So you've gotten your training. You have your destro and now you're like, OK, I actually wanna want to run some applesauce. >>Let me hold there. Is it Is it open corps? Or, you know, there's a lot of conversation in the way the boys actually >>the way we built convoy. It's a great question. The way we build convoys said, Okay, we don't We want to pick the best of breed from each of these. Have you seen the cloud native ecosystem kind of like >>by charter, high charter, whatever it is, where they have all the logos and all the different spiral thing. So it's crazy. Got thousands of logos, right? And >>we said, Look, we're gonna navigate this for you. What's the best container run time to pick. And it's It's almost as if we were gonna build this for ourselves using all open source technology. So convoys completely opens. Okay, um, there's some special sauce that we put in on how to bring these things together. Install it. But all the actual components itself is open source. Okay, so that's so if you're a customer, you're like, OK, I want open source. I don't want to be tied to any specific vendor. I want to run on Lee open. So >>yeah, I was just thinking in terms of you know, how Duke is a reference right. And you had, you know, the Horton worst cloud there and map our strategies, which were radically different in the way they actually packaged told a dupe under the covers. Yeah, >>you can think of it similar. How Cloudera per ship, Possibly where they had cdh. And they brought in a lot of open source. But they also had a lot of proprietary components to see th and what we've tried to get away from it is tying someone in tow. Us. I know that sounds counterintuitive from a business perspective, but we don't want customers to feel like if I go with D to like you. I always have to go with me to like you. I have to drink the Kool Aid, and I'm never gonna be able to get off. >>Kind of not. Doesn't really go with the open source. Exactly this stuff. It's not >>right for our customers, right? A lot of our customers want that optionality, and they don't want to feel locked in. And so when we built convoy, he said, Look, you know, if we were to start our own company, not not an infrastructure coming that we are right now, but just a software company build any kind of ab How would we approach it? And that was one of the problems we saw for We don't wanna feel like we're tied into any. >>Right. Okay, so you got to get the training, you got the products. What's >>next? What's next is if you think about the journey, you're like, OK, a lot. What we've found and this may or may not be totally true is one of the first things people like to run on committees is actually they're builds. So see, I see. And we said, How can we help with this. We looked around the market and there's a lot of great see, I see products out there right now. There's get lab, which is great partner of ours. It's a great product. There's there's your older products. Like Jenkins. There's a bunch of sass products, Travis. See all these things. But what we we wanted to do if we were customers of our own products is something that was native to Kubernetes. And so we started looking at projects like tectonic and proud. Some of these projects, right? And we said, How can we do the same thing we did with convoy where we bring these projects together and make it easy for someone to adopt these kubernetes native. See, I see tools. And we did some stuff there that we think is pretty innovative as well. And that's what that's the product we call dispatch. >>Okay. What do you got? More than just products. You've got profession service. That's right. So now >>you need help setting all this up. How do you actually bring your legacy applications to this new platform? How do you get your legacy builds onto these new build systems That that's where our service is coming the plate and kind of steer you through this whole journey. Lastly, what we next in the journey, though? Those service's compliment Really? Well, with with the kind of the rest of the product suite, right? And we didn't just stop with C i c. He said, what is the next type of work that we want to run here? Okay, so there we looked at things like red hat operators. Right? And we said, Look, red hats doing really cool thing here with this operator framework, how can we simplify it? We learn we've done a lot of this before with D. C. O s, where we built what we called the DCS sdk to help people bring advanced complex workloads onto that platform. And we saw a lot of similarities with operators to our d c West sdk. We said, How can we bring some of our understanding and knowledge to that world? And we built this open source product called kudo. Okay, people are free to go check that out. And that's how we bring more advanced workload. So if you think about the journey back to the journey again, you got some training you have your have your cluster, you put your builds on it. Now you want to run some advance work logs? That's where Kudo comes. >>Okay? And then finally, at the end of the trail is 1 800 I need help. Well, almost into the trail. We're not there yet. There was one thing they're still moving with one more step right on >>the very last one. Actually, we said, Okay, what's next in this journey? And that's running multiple clusters of the same. Okay, so that's kind of the scale. That's the end of the journey from for us, for our proxy as it stands right now. And that's where you build a product called Commander. And that's really helping us launch and manage multiple >>companies clusters at the same time. >>So it's so great that you have the perspective of a customer and you bring that directly in two. You know what you want because you just have gone through this this journey. But I'm just curious, you know, if you put your old hat on, you know, kind of c i o your customer. You know, you just talked about the cake chart with Lord knows how many logos? How do you help people even just begin to think about about the choices and about the crazy rapid change in what? That I mean? Kubernetes wasn't a thing four years ago to help them stay on top of it to help them, you know, both kind of have a night to the vision, you know, make sure you're delivering today on not just get completely distracted by every bright, shiny object that happens to come along. Yeah, no, >>I think it's really challenging for the buyers. You know, I think there's a, especially as the industry continues to make sure there's a new concept that gets thrown at all times. Service Manager. You know, some new, cool way to do monitoring or logging right? And you almost feel like a dinosaur. If you're not right on top of these things to go to a conference in, are you using? You know, you know B P f. Yet what is that? You didn't feel right? Exactly. I think I think most importantly, what customers want is the ability what, the ability to move their technology and their platforms as their business has the need. If the need isn't there for the business, and the technology is running well. There shouldn't be a reason to move to a new platform. Our new set of technologies, in fact, with dese us with Mason charities. To us, we have a lot of happy customers that are gonna be moving crib. Amazing if they wanted to anytime soon. Do you see What's that? Something's that criminal is currently doesn't do. It may never do because the community is just not focused on it that DCS is solving. And those customers just want to see that will continue to support them in the journey that they're on with their their business. And I think that's what's most important is just really understanding our customer's understanding their business, understand where they wanna go. What are their goals, So to say, for their technology platforms and and making sure you were always one step ahead >>of them, that's a >>good place to be one step ahead of demand. All right, well, thanks for for taking a few minutes and sharing the story. Appreciate it. Okay. Thank you. All right. Thanks. Chandler. I'm Jeff. You're watching >>the Cube. Where? Day two. I >>Q in downtown San Francisco. Thanks for watching. We'll see you next time

(informative tune) >> From San Francisco, it's The Cube. Covering D2 iQ. Brought to you by D2 iQ. (informative tune) >> Hey, welcome back everybody! Jeff Frick here with theCUBE. We're in downtown San Francisco at D2 iQ Headquarters, a beautiful office space here, right downtown. And we're talking about customers' journey to cloud data. We talk about it all the time, you hear about cloud native, everyone's rushing in, Kubernetes is the hottest thing since sliced bread, but the at the end of the day, you actually have to do it and we're really excited to talk to the founder who's been on his own company journey as he's watching his customers' company journeys and really kind of get into it a little bit. So, excited to have Tobi Knaup, he's a co-founder and CTO of D2 iQ. Tobi, great to see you! >> Thanks for having me. >> So, before we jump into the company and where you are now, I want to go back a little bit. I mean, looking through your resume, and your LinkedIn, etc. You're doing it kind of the classic dream-way for a founder. Did the Y Combinator thing, you've been at this for six years, you've changed the company a little bit. So, I wonder if you can just share form a founder's perspective, I think you've gone through four, five rounds of funding, raised a lot of money, 200 plus million dollars. As you sit back now, if you even get a chance, and kind of reflect, what goes through your head? As you've gone through this thing, pretty cool. A lot of people would like this, they think they'd like to be sitting in your seat. (chuckles) What can you share? >> Yeah, it's definitely been, you know, an exciting journey. And it's one that changes all the time. You know, we learned so many things over the years. And when you start out, you create a company, right? A tech company, you have you idea for the product, you have the technology. You know how to do that, right? You know how to iterate that and build it out. But there's many things you don't know as a technical founder with an engineering background, like myself. And so, I always joke with the team internally, this is that, you know, I basically try to fire myself every six months. And what I mean by that, is your role really changes, right? In the very beginning I wrote code and then is tarted managing engineers, when, you know, once you built up the team, then managed engineering managers and then did product and, you know. Nowadays, I spend a lot of time with customers to talk about our vision, you know, where I see the industry going, where things are going, how we fit into the greater picture. So, it's, you know, I think that's a big part of it, it's evolving with the company and, you know, learning the skills and evolving yourself. >> Right. It's just funny cause you think about tech founders and there's some big ones, right? Some big companies out there, to pick on Zuckerberg's, just to pick on him. But you know, when you start and kind of what your vision and your dream is and what you're coding in that early passion, isn't necessarily where you end up. And as you said, your role in more of a leadership position now, more of a guidance and setting strategy in communicating with the market, communicating with customers has changed. Has that been enjoyable for you, do you, you know, kind of enjoy more the, I don't want to say the elder states when you're a young guy, but more kind of that leadership role? Or just, you know, getting into the weeds and writing some code? >> Yeah. Yeah, what always excites me, is helping customers or helping people solve problems, right? And we do that with technology, in our case, but really it's about solving the problems. And the problems are not always technical problems, right? You know, the software that is at the core of our products, that's been running in production for many years and, you know, in some sense, what we did before we founded the company, when I worked at Airbnb and my co-founders worked at, you know, Airbnb and Twitter, we're still helping companies do those same things today. And so, where we need to help the most sometimes, it's actually on education, right? So, solving those problems. How do you train up, you know, a thousand or 10 thousand internal developers at a large organization, on what are containers, what is container management, cluster management, how does cloud native work? That's often the biggest challenge for folks and, you know, how did they transform their processes internally, how did they become really a cloud native organization. And so, you know, what motivates me is helping people solve problems in, whatever, you know, shape or form. >> Right >> It's funny because it's analogous to what you guys do, in that you got an open-source core, but people, I think, are often underestimate the degree of difficulty around all the activities beyond just the core software. >> Mm-hmm. >> Whether, as you said, it's training, it's implementation it's integration, it's best practices, it's support, it's connecting all these things together and staying on top of it. So, I think, you know, you're in a great position because it's not the software. That's not the hard part, that's arguably, the easy part. So, as you've watched people, you know, deal with this crazy acceleration of change in our industry and this rapid move to cloud native, you know, spawned by the success of the public clouds, you know, how do you kind of stay grounded and not jump too fast at the next shiny object, but still stay current, but still, you know, kind of keep to your kneading in terms of your foundation of the company and delivering real value for the customers? >> Yeah. Yeah, I know, it's exactly right. A lot of times, the challenges with adopting open-sourcing enterprise are, for example, around the skills, right? How do you hire a team that can manage that deployment and manage it for many years? Cause once software's introduced in an enterprise, it typically stays for a couple of years, right? And this gets especially challenging when you're using very popular open-source project, right? Because you're competing for those skills with, literally, everybody, right? A lot of folks want to deploy these things. And then, what people forget sometimes too is, so, a lot of the leading open-source projects, in the cloud native space, came out of, you know, big software companies, right? Kubernetes came from Google, Kafka came from LinkedIn, Cassandra from Facebook. And when those companies deploy these systems internally, they have a lot of other supporting infrastructure around it, right? And a lot of that is centered around day-two operations. Right? How do you monitor these things, how do you do lock management, how do you do do change management, how do you upgrade these things, keep current? So, all of that supporting infrastructure is what an enterprise also needs to develop in order to adopt open-source software and that's a big part of what we do. >> Right. So, I'd love to get your perspective. So, you said, you were at Airbnb, your founders were at Twitter. You know, often people, I think enterprises, fall into the trap of, you know, we want to be like the hyper-scale guys, you know. We want to be like Google or we want to be like Twitter. But they're not. But I'm sure there's a lot of lessons that you learned in watching the hyper-growth of Airbnb and Twitter. What are some of those ones that you can bring and hep enterprises with? What are some of the things that they should be aware of as, not necessarily maybe their sales don't ramp like those other companies, but their operations in some of these new cloud native things do? >> Right, right. Yeah, so, it's actually, you know, when we started the company, the key or one of the drivers was that, you know, we looked at the problems that we solved at Airbnb and Twitter and we realized that those problems are not specific to those two companies or, you know, Silicon Valley tech companies. We realized that most enterprises in the future will have, will be facing those problems. And a core one is really about agility and innovation. Right? Marc Andreessen, one of our early investors, said, "Software is eating the world." he wrote that up many years ago. And so, really what that means is that most enterprises, most companies on the planet, will transform into a software company. With all of that entails, right? With he agility that software brings. And, you know, if they don't do that, their competitors will transform into a software company and disrupt them. So, they need to become software companies. And so, a lot of the existing processes that these existing companies have around IT, don't work in that kind of environment, right? You just can't have a situation where, you know, a developer wants to deploy a new application that, you know, is very, you know, brings a lot of differentiation for the business, but the first thing they need to do in order to deploy that is file a ticket with IT and then someone will get to it in three months, right? That is a lot of waste of time and that's when people surpass you. So, that was one of the key-things we saw at Airbnb and Twitter, right? They were also in that old-school IT approach, where it took many months to deploy something. And deploying some of the software we work with, got that time down to even minutes, right? So it's empowering developers, right? And giving them the tools to make them agile so they can be innovative and bring the business forward. >> Right. The other big issue that enterprises have that you probably didn't have in some of those, you know, kind of native startups, is the complexity and the legacy. >> That's right. >> Right? So you've got all this old stuff that may or may not make any sense to redeploy, you've got stuff (laughing) stuff running in data centers, stuff running on public clouds, everybody wants to get the hyper-cloud to have a single point of view. So, it's a very different challenge when you're in the enterprises. What are you seeing, how are you helping them kind of navigate through that? >> Yeah, yeah. So, one of the first thongs we did actually, so, you know, most of our products are sort of open-core products. They have a lot of open-source at the center, but then, you know, we add enterprise components around that. Typically the first thing that shows up is around security, right? Putting the right access controls in place, making sure the traffic is encrypted. So, that's one of the first things. And then often, the companies we work with, are in a regulated environment, right? Banks, healthcare companies. So, we help them meet those requirements as well and often times that means, you know, adding features around the open-source products to get them to that. >> Right. So, like you said, the world has changed even in the six or seven years you've been at this. The, you know, containers, depending who you talk to, were around, not quite so hot. Docker's hot, Kubernetes is hot. But one of the big changes that's coming now, looking forward, is IOT and EDGE. So, you know, you just mentioned security, from the security point of view, you know, now you're tax services increased dramatically, we've done some work with Forescout and their secret sauce and they just put a sniffer on your network and find the hundreds and hundreds of devices (laughs)-- >> Yeah. >> That you don't even know are on your network. So do you look forward to kind of the opportunity and the challenges of IOT supported by 5G? What's that do for your business, where do you see opportunities, how are you going to address that? >> Yeah, so, I think IOT is really one of those big mega-trends that's going to transform a lot of things and create all kinds of new business models. And, really, what IOT is for me at the core, it's all around data, right? You have all these devices producing data, whether those are, you know, sensors in a factory in a production line, or those have, you know, cars on the road that send telemetry data in real time. IOT has been, you know, a big opportunity for us. We work with multiple customers that are in the space. And, you know, one fundamental problem with it is that, with IOT, a lot of the data that organizations need to process, are now, all of a sudden generated at the EDGE of the network, right? This wasn't the case many years for enterprises, right? Most of the data was generated, you know, at HQ or in some internal system, not at the EDGE of the network. And what always happens is when, with large-volume data is, compute generally moves where the data is and not the other way around. So, for many of these deployments, it's not efficient to move all that data from those IT devices to a central-cloud location or data-center location. So, those companies need to find ways to process data at the EDGE. That's a big part of what we're helping them with, it's automating real-time data services and machine-learning services, at the EDGE, where the EDGE can be, you know, factories all around the world, it could be cruise ships, it could be other types of locations where working with customers. And so, essentially what we're doing is we're bringing the automation that people are used to from the public cloud to the EDGE. So, you know, with the click of a button or a single command you can install a database or a machine-learning system or a message queue at all those EDGE locations. And then, it's not just that stuff is being deployed at the EDGE, I think the, you know, the standard type of infrastructure-mix, for most enterprises, is a hybrid one. I think most organizations will run a mix of EDGE, their data centers and typically multiple public cloud providers. And so, they really need a platform where they can manage applications across all of those environments and well, that's big value that our products bring. >> Yeah. I was at a talk the other day with a senior exec, formerly from Intel, and they thought that it's going to level out at probably 50-50, you know, kind of cloud-based versus on-prem. And that's just going to be the way it is cause it's just some workloads you just can't move. So, exciting stuff, so, what as you... I can't believe we're coming to the end of 2019, which is amazing to me. As you look forward to 2020 and beyond, what are some of your top priorities? >> Yeah, so, one of my top priorities is really, around machine-learning. I think machine-learning is one of these things that, you know, it's really a general-purpose tool. It's like a hammer, you can solve a lot of problems with it. And, you know, besides doing infrastructure and large-scale infrastructure, machine-learning has, you know, always been sort of my second baby. Did a lot of work during grad-school and at Airbnb. And so, we're seeing more and more customers adopt machine-learning to do all kinds of interesting, you know, problems like predictive maintenance in a factory where, you know, every minute of downtime costs a lot of money. But, machine-learning is such a new space, that a lot of the best practices that we know from software engineering and from running software into production, those same things don't always exist in machine-learning. And so, what I am looking at is, you know, what can we take from what we learned running production software, what can we take and move over to machine-learning to help people run these models in production and you know, where can we deploy machine-learning in our products too, internally, to make them smarter and automate them even more. >> That's interesting because the machine-learning and AI, you know, there's kind of the tools and stuff, and then there's the application of the tools. And we're seeing a lot of activity around, you know, people using ML in a specific application to drive better performances. As you just said,-- >> Mm-hmm. >> You could do it internally. >> Do you see an open-source play in machine-learning, in AI? Do you see, you know, kind of open-source algorithms? Do you see, you know, a lot of kind of open-source ecosystem develop around some of this stuff? So, just like I don't have time to learn data science, I won't necessarily have to have my own algorithms. How do you see that,-- >> Yeah. >> You know, kind of open-source meets AI and ML, of all things? >> Yeah. It's a space I think about a lot and what's really great, I think is that we're seeing a lot of the open-source, you know, best-practice that we know from software, actually, move over to machine-learning. I think it's interesting, right? Deep-learning is all the rage right now, everybody wants to do deep-learning, deep-learning networks. The theory behind deep-networks is actually, you know, pretty old. It's from the '70s and 80's. But for a long time, we dint have that much, enough compute-power to really use deep-learning in a meaningful way. We do have that now, but it's still expensive. So, you know, to get cutting edge results on image recognition or other types of ML problems, you need to spend a lot of money on infrastructure. It's tens of thousands or hundreds of thousands of dollars to train a model. So, it's not accessible to everyone. But, the great news is that, much like in software engineering, we can use these open-source libraries and combine them together and build upon them. There is, you know, we have that same kind of composability in machine-learning, using techniques like transfer-learning. And so, you can actually already see some, you know, open-community hubs spinning up, where people publish models that you can just take, they're pre-trained. You can take them and you know, just adjust them to your particular use case. >> Right. >> So, I think a lot of that is translating over. >> And even though it's expensive today, it's not going to be expensive tomorrow, right? >> Mm-hhm. >> I mean, if you look through the world in a lens, with, you know, the price of compute-store networking asymptotically approaching zero in the not-to-distant future and think about how you attack problems that way, that's a very different approach. And sure enough, I mean, some might argue that Moore's Law's done, but kind of the relentless march of Moore's Law types of performance increase it's not done, it's not necessarily just doubling up of transistors anymore >> Right >> So, I think there's huge opportunity to apply these things a lot of different places. >> Yeah, yeah. Absolutely. >> Can be an exciting future. >> Absolutely! (laughs) >> Tobi, congrats on all your successes! A really fun success story, we continue to like watching the ride and thanks for spending the few minutes with us. >> Thank you very much! >> All right. He's Tobi, I'm Jeff, you're watching The Cube, we're at D2 iQ Headquarters downtown in San Francisco. Thanks for watching, we'll catch you next time! (electric chime)

>>from the heart of Silicon Valley. It's the Q covering Comcast Innovation Date to you by Comcast. >>Hey, welcome back it ready? Geoffrey here with the Cube were in the Comcast Silicon Valley Innovation Center here in Sunnyvale, just off the runways here. Moffett feels really cool place, a lot of fun toys and gadgets that I have not got to play with yet, but I got to do before I leave. But the conversation today is really about customer experience. We had a small panel this morning of experts talking about customer experience. What does that mean? How do we do a better job at it? And we're excited. Have an expert brought in just for this conversation. She's a net Franz, the founder and CEO of C X Journey, and it's great to see you. >>Thank you. Thanks for having me. Glad to be here. Absolutely been a fun morning. >>What did you think? >>What were some of your impressions of the conversation this morning? You know >>what? It's always great to sit in a room with so many people who have been living and breathing this customer experience journey. And so it was great to hear what Comcast is doing. It was great to hear from some of the other folks in the room. What are some of the latest trends in terms of data and technology and where customer experiences headed? Yeah, it was awesome. >>So customer experiences, it's >>a little bit over. It's almost kind of digital transformation a little bit. Everyone's like experience, experience, experience. And that's a big, complicated topic. How do you help customers really kind of break it down, make it into something manageable, make it into something they can actually approach and have some success with >>us? So I spent a lot of my time working with clients who are brand new to this field, right? I had a former boss who said that they can't even spell C X. Right. So yes, so So yes. So I go in there and I really listen and understand what their pain points are and what they need help with and then get them started on that journey. Basically, soup did not see X strategy work. We typically start out making sure that the right foundation is in place in terms of the executives that they're all a line that they're all committed to this work. The culture. We've got the right culture in place. We've got, you know, some feedback from employees and from customers of what's going well and what's not. And then from there we dive right into a phase that I call understanding. And that's listening to customers listening to employees developing personas so that we can really understand who customers are and who are employees really are, and then also journey mapping to really walk in their shoes to understand the experience that they're having today and then design. Use that to design a better experience for tomorrow. So there's a lot of work that happens up front to make you know the things that we talked about in there this morning. >>Right? Happen. What's the biggest gap? Because everyone >>always talks about being customer centric. And I'm sure if you talk to any sea, of course, were customer centric and you know, we see it would like would like Amazon Andy Jassy and that team is just crazy hyper customer centric and they executed with specific behavior. So what's the part that's usually missing that they think their customer centric, but they're really not? >>Yeah, I think you just hit the nail on the head with the word execute, right? So there's a stat out there that's been out there for forever, and we know it. Every single company, every single business interviews or surveys us to death, right? So they have all this great feedback, but they do nothing with it. They just don't execute. They just don't act on it. And they've got such rich feedback and and and customers want to tell them, Hey, you're doing this well behaved. This is not going so well. So please fix it because we want to continue doing business with you. And so, yeah, it's about execution. I think that's one problem. The other problem is that they focus on the metrics and not on actually doing something with the feedback >>temporary experience. Do they just ignore it? Do they not have the systems to capture it? Are they are they kind of analysis? Paralysis? He just said they have all this great data, and I'm not doing anything about it. Why >>there it is that, too Analysis, paralysis. Let's just beat the numbers to death and and what's the What's the quote about beating the number until the beating the data until the talks >>kind of thing. You know, I don't know that something. I know I'm just mess that, >>but But yeah, they don't have the system in place to actually. Then take what they learn and go do something with it. And I think a big part of it. We talked about this in the room this morning, too. Was around having that commitment from the top, having the CEO say, Listen, we're doing this and we're going to when we listen to, our customers were going toe act on what we hear, So But they don't They don't have the infrastructure in place to actually go and then do it >>right. It's pretty interesting. You have, Ah, a deck that you shared in advance Eight Principles of customer centric city. Yes. And of the aid three are people people before products people before profits people before metrics. That sounds great, but it sounds contrary to everything we hear these days about measure, measure, measure, measure, measure. Right? It's human resource is it almost feels like we're kind of back to these kind of time. Motion studies in tryingto optimize people as if they're a machine as opposed to being a person. >>Yeah, well, it's It's not, because we have to. The way that we could think about is we have to put the human into this. That's what customer experience is all about, right? It's about putting the human in the experience. And it's interesting that you bring up that back because when I opened that talk, I'm show a comm your commercial from Acura, and it's if you've never seen it. It's called the test. If you can google it and find the video and it's really about. If we don't view them as dummies, something amazing happens. That's the tagline, right? And so it's really about people. The experience is all about people. Our business is all about people. That's why we're in business, right? It's all about the customer. It's for the customer. And who's gonna deliver that? Our employees? And so we've got to put the people first, and then the numbers will come >>right. Another one that you had in there, I just have to touch on was forget the golden rule, which which I always thought the golden rules of us. You know, he has the gold makes a >>rule. You're talking about a different golden, which is really treat. Treat others >>not the way you think they want, that you want to be treated but treat people the way that they want to be treated in such a small It's the pylons, but it's so important. >>It's so important. And I love this example that I share. Thio just recently read a book by Hal Rosenbluth called The Customer Comes Second, right, and to most people, that seems counterintuitive, but he's really referring to The employee comes more first, which I love, and I'm the example that he gives us. He's left handed and he goes into a restaurant. He frequents this restaurant all the time, and until I read this story, I never even thought about this. And now that I go to restaurants, I think about this all the time. The silverware is always on the right hand side, but he's left handed, so this restaurant that he frequents the waitress. He always seemed to have the same waitress she caught on, and so when when he would come into the restaurant, she would set the silvery down on the left hand side. for him that's treating people the way that they want to be treated. And that's what customer experience is all about, >>right? One of the topics that he talked about in the session this morning was, um, the reputation that service experiences really defined by the sum of all your interactions. And it's really important to kind of keep a ah view of that that it's not just an interaction with many, many interactions over a period of time that sounds so hard to manage. And then there's also this kind of the last experience, which is probably overweighted based on the whole. >>How do people >>keep that in mind? How did they How did they, you know, make sure that they're thinking that kind of holistically about the customer engagement across a number of fronts within the company. >>Well, you've got to think >>about it as think about it as a journey, not just touch points, not just a bunch of little touch points, because if you think about just the last experience or just a touch point, then you're thinking about transactions. You're not thinking about a relationship, and what we're trying to get at is customer relationships and not just transactional, you know, it's it's they're in, they're out, they're gone, right? So what? We want relationships. We want them to be customers for life. And and that's the only way that we're gonna do it is if we focus on the journey, >>right? What about the challenge of that which was special suddenly becomes the norm. And we talked a lot about, you know, kind of consumers ations of i t. Because as soon as I get great results on a Google search or, you know, I find exactly what I need on Amazon in two clicks and then to take that into whatever my be to be your B to C application as when Now those expectations are not being driven by what I promised to deliver. But they're being driven by all these third party app said. I have a no control up and they're probably developing at a faster pace of innovation that I can keep up housing people, you know, kind of absorb that deal with it and try to take some lessons from that in the delivery of their own application >>essay. You you brought up two things there which I want to address the 1st 1 to which was about the delighting customers. But to answer your question is really about focusing on your customers and your customers needs on. And that's why I talk a lot about customer understanding, right? It's it's about listening to your customers. It's about developing personas and really understanding who they are, what their pain points are, what their problems are, what needs. Are they trying to solve our problems? Are they trying to solve on and then walking in their shoes through journey, mapping? And that understanding allows us to design an experience for our customers, right for our customers. If we don't solve a problem up for our customers, they will go elsewhere and they'll get their problems solved elsewhere, right? So I think that's really important. The first part of your question was, our point was around delighting our customers, and you're absolutely right. We don't have to delight customers at every touch point. I know that's counter to what a lot of people might say or think, but to your point, once we delighted every touch point, now it becomes the new norm. It's an expectation that has now been set and now delight, Where does it stop? You know, Delight is here, and then it's here. And then it's here. And so So it's It's a whole different. So my thinking on that is that most businesses cannot delight at every touch point, and they certainly don't. Um, I think we need to meet expectations and the and the only way that we can do that is to listen and understand and and and then act on what we hear. And, um, most businesses are still very primitive, even when it comes to that, >>right? Okay. Give you the last word. What's what's the kind of the most consistent, easy to fix stumble that most customers are doing when you when you get engaged and you walk in, what's that one thing that you know with 90% confidence factor that when you walk in, this is gonna be, you know, one of these three or four little things that they should stop doing or that they should do just just just get off the baseline? >>Yeah, I think it's You know what I think it >>za combination of sort of speed and responsiveness. I'll give an example. I won't mean the company, but But I thought, man, in this day and age, this shouldn't be happening, right? It was a company that I contacted. I was supposed to set up an account and they said I couldn't for it just wasn't working. I tried different browsers, just wasn't working. So I sent them and eat. First. I tried to call, but I got stuck in Ivy are hell. And then I sent an email and my the email that I got back was an auto responder. That's I will reply within five business days. >>Five business days, Thio like, really, where? Why don't you just ask me to send a fax, right? You know, So So that's the kind >>of stuff that seriously I I want to solve that e mails like really in 2019. We're still responding in five business days. That's just that's just ludicrous. I think that's one of the and it's such it doesn't cost anything to respond in a timely manner and to respond at all right now. Here it is. It's been I haven't heard from them yet, so it's been like seven days now, so >>there's that just tweet tweet at the CEO going to, hopefully the >>CEO tweets and maybe doesn't tweet. >>I know, right? Yeah, well, in >>that you know nothing about opportunity for you because this is not an easy it's not an easy thing to do is it's hard to stay up with people's expectations and to drive new and innovative products when they don't necessarily even know how to engage with those things. >>Yeah, absolutely. Yeah, The field is wide open because, like I said, there's still so many companies that are still just trying to get the basics right. So >>Well, thanks for taking a few minutes of your time. Thanks for participating. Absolutely, She's in that. I'm Jeff. You're watching the Cube worth the Comcast Silicon Valley Innovation Center. Thanks for watching. We'll see next time.

[Narrator] From Sunnyvale, California, in the heart of Silicon Valley, it's theCUBE. Covering Accelerate Your Journey to AI, brought to you by NetApp. >> Hi, I'm Peter Burris and welcome to a great conversation here from NetApp's Data Visionary Center. Specifically, we've got Monty Barlow from Cambridge Consultants. Monty is the head of Artificial Intelligence at a relatively well-known, august, consultant group. Monty, welcome to the Cube. >> Thank you, Peter. Pleased to be here. >> Monty, what we're going to do is we're going to spend a number of minutes talking about some of the trends and transformations that are being made by and wrought by AI. But let's start, what is Cambridge Consultants? >> So, Cambridge Consultants does technology, product, and service development for customers all across the world. Probably about 400 new projects starting each year. And what's a common thread in them is that they're technically difficult and innovative, so there's something really challenging about them they're typically strategic for our customers, and they're looking to do something disruptive and do it fast. >> So, you have a pretty broad range of customers that you're utilizing or you're working with. Let's ask the question then, give us some examples of some of the customer cases that you've been working on, specifically as it relates to AI. >> Sure, so we're working with everything from blue chips to startups, and what they're looking for is slightly different from AI. I can't talk about some of the confidential details but some really interesting applications, for example, one for me, is precision agriculture. We've heard a lot about improving crop yield, but we're reaching the point now where you can drive over a crop and recognize it from a weed and put water on just the crop and pesticide on just the weed, so you get a much better yield, you cut down on water, you cut down on pesticide, and it's a really nice application where it's a win-win for everything. >> So, as we think about some of these big issues associated with inventing technology and inventing AI-related stuff to do many of the things we're talking about, we also have to recognize that there's a social side of introducing AI. There's the invention and then there's the innovative side, which is in many respects the social things. How do you get people to adopt this stuff? What are the challenges that you're seeing customers face as they conceive how best to adopt AI and AI-related capabilities within markets? >> Sure. I think in most of the markets we work in, the benefits are becoming so clear that there's not a massive reluctance to adopt or difficulty. There's obviously in the public, those normal fears about loss of jobs, or safety or security, having machines do jobs for you that you might wish a person to do for you. And those are there in some markets like healthcare, in particular, but many markets see no such problems and the benefits of being able to do innovative things scaleably, flexibly, out performing humans in many cases, it just makes economic sense. >> So, is it just the numbers? Is that what big companies are doing to ensure more rapid time to value for AI related things, or are there other things big companies are doing to try to facilitate the introduction of some of these advanced technologies? >> It depends from company to company, there's all sorts of ways they're approaching this. Maybe trialing early services that introduce people gently to AI, get them accustomed to it, of course, that's what's been the case for social media. None of us believed we were using AI in the early days and then suddenly we realized that we're interacting with it on an almost daily basis. Through to targeted trials, all sorts of different approaches being taken. >> AI's been associated with a lot of different algorithmic forms. It's been a lot of different basic models for thinking about how you do it. Machine learning, deep learning, predictive analysis, recommendation analysis. What's the difference particularly between AI, machine learning, ML and deep learning, DL? >> OK, if I could take a step back for a moment, we've been working with AI for decades, and as you say, there's some really quite old school techniques out there. Decision support, expert systems, where the idea was that you embody the coder's, the programmer's knowledge in a system, and really, all it could do is replay that. So, at best it could act as well as the person who programmed it. >> Very rules-driven. >> Very, very rules-driven. We then, in the early 2000s, saw machine learning beginning to surface more, that's where a system learns, perhaps a few parameters from some data it does learn by itself, but it's doing something quite simple, you know it's from the vibrations in the road, counting the axles of the vehicles going past. Or in an industrial process monitoring temperature, pressure, and saying, "This process is going well." >> But not rules-driven, still data-driven? >> Data-driven. Deep learning just takes, that to a whole new scale. It is still machines learning from data but now a few parameters has become millions or billions. You can now point a camera at a road and recognize all of the different vehicle types, instead of just how many axles they've got, for example. >> And so, the notion of that is that it's a focus on patterns that it discovers out of the data, as opposed to rules or patterns that are put into the data by a developer or by a data person. >> Absolutely, you don't always know what insight you're going to derive from a data set. >> So, I understand that Cambridge Consultants uses a variety of technologies, but specifically, you're utilizing this NetApp and NVIDIA gear in your labs. Talk a little about that experience, how's that been? >> Sure. So, time is everything for us as a business, and for our customers. People want to be first to a particular market window and this AI is still at some level, experimental. We don't know what it's going to do in three or five years time. So, key to our business is a fast turnaround on proof of concepts, how would this work? What would happen? Perhaps our customer's got some data and they need to know if they need a trial to collect more. So, getting through jobs quickly is what matters most to us, and that's what the NVIDIA and NetApp equipment is all about. For GPUs, its the case of big parallel processing large models, crunching the numbers and adjusting the parameters quickly, but equally important is the ability to get data from storage into those GPUs, quickly. >> And so, there is a relationship between the characteristics of the hardware and the success of the AI efforts? >> Absolutely. And it's a really demanding application for file serving. It's the most demanding we've ever seen because it's potentially millions or billions of tiny files that have to be called up in different patterns, quite randomly, it's not just like for example, streaming video, it's too much to cache locally. You need really high performance equipment to manage the data quickly enough that you can learn something in days, and not in months. >> One of the crucial features of any AI development effort is this notion of a data pipeline. How you stage change to the data, where it is, knowing how to move it, when to move it, do it with speed, do it at scale. Talk a little bit about the differences between AI-driven data pipelines and some of the other data pipelines that have been out there. >> Sure. The difference we tend to see on AI is it's touching the real world more directly. So, you may have data coming in live from the edge, from sensors, and that's not as carefully clean, sanitized, formatted as you might expect in a normal, say, enterprise database or data application. So, knowing what to do with those difficult cases, how to format it, what to reject, what to feed in, and then at the other end, how to present that decision, because AI is often making some form of decision, how to present that efficiently back to humans or how to make a quick sensible decision based on that, how to steer the vehicle in the correct direction, how to highlight a cancer, whatever it is we're doing. That pipeline from data first coming in through intelligence and back again to the real world is longer and more complicated and sophisticated than any other data pipeline we've seen before. >> Now, it's that sophistication, that length, the duration of the transactions, for example, that increases the complexity, that ultimately, big companies working with Cambridge Consultants and others, have to address so that they can be successful, get that time to value. But as you think about ultimately the challenges that you're trying to address with customers, what is that you're seeing in their AI projects that are more consistently associated with success, or more, unfortunately, perhaps, consistently associated with having to do it again? >> Sure, I'll limit my answer to those I feel who are doing genuine AI because there is an element of people labeling anything AI. But assuming they are doing something that's only been possible in the last few years that is innovative, difficult and complicated, it's really reaching the right distance. It's stretching themselves the correct amount. So, going into a new market, with new data, a new algorithmic approach is dangerous. There'll be a lot of iteration, a lot of learning needed before that'll come good. If you can take an approach that's beginning to work in one vertical to another, or you can start with data you understand and know perhaps from a previous big data application and start to do more intelligent things with it, then you can achieve these kind of breakthrough innovations and really impressive systems that AI can today. >> So, novel data, practiced algorithms and hardware that works. >> And don't mix up too many new factors together, absolutely. >> Monty Barlow, head of Artificial Intelligence at Cambridge Consultants, thanks very much for being on theCUBE. >> Thank you, Peter. (upbeat electronic music)

>> From Sunnyvale California, in the heart of Silicon Valley. It's theCUBE, covering, Accelerate Your Journey to AI, Brought to you by NetApp. >> Hi I'm Peter Burris, welcome to another conversation here from the Data Visionary Center at NetApp's headquarters in beautiful Sunnyvale California. I'm being joined today by Santosh Rao. Santosh is the Senior Technical Director at NetApp, Specifically Santosh we're going to talk about some of the challenges and opportunities associated with AI and how NetApp is making that possible. Welcome to theCUBE. >> Thank you Peter, I'm excited to be here. Thank you for that. >> So, Santosh what is your role at Netapp? Why don't we start there. >> Wonderful, glad to be here, my name is Santosh Rao, I'm a Senior Technical Director at NetApp, part of the Product Operations group, and I've been here 10 years. My role is to drive up new lines of opportunity for NetApp, build up new product businesses. The most recent one has been AI. So I've been focused on bootstrapping and incubating the AI effort at NetApp for the last nine months now. Been excited to be part of this effort now. >> So nine months of talking, both internally, but spending time with customers too. What are customers telling you that are NetApp's opportunities, and what NetApp has to do to respond to those opportunities? >> That's a great question. We are seeing a lot of focus around expanding the digital transformation to really get value out of the data, and start looking at AI, and Deep Learning in particular, as a way to prove the ROI on the opportunities that they've had. AI and deep learning requires a tremendous amount of data. We're actually fascinated to see the amount of data sets that customers are starting to look at. A petabyte of data is sort of the minimum size of data set. So when you think about petabyte-scale data lakes. The first think you want to think about is how do you optimize the TCO for the solution. NetApp is seen as a leader in that, just because of our rich heritage of storage efficiency. A lot of these are video image and audio files, and so you're seeing a lot of unstructured data in general, and we're a leader in NFS as well. So a lot of that starts to come together from a NetApp perspective. And that's where customers see us as the leader in NFS, the leader in files, and the leader in storage efficiency, all coming together. >> And you want to join that together with some leadership, especially in GPU's, so that leads to NVIDIA. So you've announced an interesting partnership between NetApp and NVIDIA. How did that factor into your products, and where do you think that goes? >> It's kind of interesting how that came about, because when you look at the industry it's a small place. Some of the folks driving the NVIDIA leadership have been working with us in the past, when we've bootstrapped converged infrastructures with other vendors. We're known to have been a 10 year metro vendor in the converged infrastructure space. The way this came about was NVIDIA is clearly a leader in the GPU and AI acceleration from a computer perspective. But they're also seen as a long history of GPU virtualization and GPU graphics acceleration. When they look at NetApp, what NetApp brings to NVIDIA is just the converged infrastructure, the maturity of that solution, the depth that we have in the enterprise and the rich partner ecosystem. All of that starts to come together, and some of the players in this particular case, have had aligned in the past working on virtualization based conversion infrastructures in the past. It's an exciting time, we're really looking forward to working closely with NVIDIA. >> So NVIDIA brings these lighting fast machines, optimized for some of the new data types, data forms, data structures associated with AI. But they got to be fed, got to get the data to them. What is NetApp doing from a standpoint of the underlying hardware to improve the overall performance, and insure that these solutions really scream for customers? >> Yeah, it's kind of interesting, because when you look at how customers are designing this. They're thinking about digital transformation as, "What is the flow of that data? "What am I doing to create new sensors "and endpoints that create data? "How do I flow the data in? "How do I forecast how much data I'm going to "create quarter over quarter, year over year? "How many endpoints? what is the resolution of the data?" And then as that starts to come into the data center, they got to think about, where are the bottlenecks. So you start looking at a wide range of bottlenecks. You look at the edge data aggregation, then you start looking at network bandwidth to push data into the core data centers. You got to think smart about some of these things. For example, no matter how much network bandwidth you throw at it, you want to reduce the amount of data you're moving. Smart data movement technologies like SnapMirror, which NetApp brings to the table, are some things that we uniquely enable compared to others. The fact of the matter is when you take a common operating system, like ONTAP, and you can lear it across the Edge, Core and Cloud, that gives us some unnatural advantages. We can do things that you can't do in a silo. You've got a commodities server trying to push data, and having to do raw full copies of data into the data center. So we think smart data movement is a huge opportunity. When you look at the core, obviously it's a workhorse, and you've got the random sampling of data into this hardware. And we think the A800 is a workhorse built for AI. It is a best of a system in terms of performance, it does about 25 gigabytes per second just on a dual controller pair. You'll recall that we spent several number of years building out the foundation of Clustered ONTAP to allow us to scale to gigantic sizes. So 24 node or 12 controller pad A800 gets us to over 300 gigabytes per second, and over 11 million IOPS if you think about that. That's over about four to six times greater than anybody else in the industry. So when you think about NVIDIA investment in DGX and they're performance investment they've made there. We think only NetApp can keep up with that, in terms of performance. >> So 11 million IOPS, phenomenal performance for today. But the future is going to demand ever more. Where do you think these trends go? >> Well nobody really knows for sure. The most exciting part of this journey, is nobody knows where this is going. This is where you need to future proof customers, and you need to enable the technology to have sufficient legs, and the architecture to have sufficient legs. That no matter how it evolves and where customers go, the vendors working with customers can go there with them. And actually when customers look at NetApp and say, "You guys are working with the Cloud partners, "you're now working with NVIDIA. "And in the past you worked with a "variety of data source vendors. "So we think we can work with NetApp because, "you're not affiliated to any one of them, "and yet you're giving us that full range of solutions." So we think that performance is going to be key. Acceleration of compute workloads is going to demand orders of magnitude performance improvement. We think data set efficiencies and storage efficiencies is absolutely key. And we think you got to really look at PCO, because customers want to build these great solutions for the business, but they can't afford it unless vendors give them viable options. So it's really up to partners like NVIDIA and NetApp to work together to give customers the best of breed solutions that reduce the TCO, accelerate compute, accelerate the data pipeline, and yet, bring the cost of the overall solution down, and make it simple to deploy and pre integrated. These are the things customers are looking for and we think we have the best bet at getting there. >> So that leads to... Great summary, but that leads to some interesting observations on what customers should be basing their decisions on. What would you say are the two or three most crucial things that customers need to think about right now as a conceptualized, where to go with their AI application, or AI workloads, their AI projects and initiatives? >> So when customers are designing and building these solutions, they're thinking the entire data lifecycle. "How am I getting this new type of "data for digital transformation? "What is the ingestion architecture? "What are my data aggregation endpoints for ingestion? "How am I going to build out my AI data sources? "What are the types of data? "Am I collecting sensor data? Is it a variety of images? "Am I going to add in audio transcription? "Is there video feeds that come in over time?" So customers are having to think about the entire digital experience, the types of data, because that leads to the selection of data sources. For example, if you're going to be learning sensor data, you want to be looking at maybe graph databases. If you want to be learning log data, you're going to be looking at log analytics over time, as well as AI. You're going to look at video image and audio accordingly. Architecting these solutions requires an understanding of, what is your digital experience? How does that evolve over time? What is the right and optimal data source to learn that data, so that you get the best experience from a search, from an indexing, from a tiering, from analytics and AI? And then, what is the flow of that data? And how do you architect it for a global experience? How do you build out these data centers where you're not having to copy all data maybe, into your global headquarters. If you're a global company with presence across multiple Geo's, how do you architect for regional data centers to be self contained? Because we're looking at exabyte scale opportunities in some of these. I think that's pretty much the two or three things that I'd say, across the entire gamut of space here. >> Excellent, turning that then into some simple observations about the fact that data still is physical. There's latency issues, there's the cost of bandwidth issues. There's other types of issues. This notion of Edge, Core, Cloud. How do you see the ONTAP operating system, the ONTAP product set, facilitating being able to put data where it needs to be, while at the same time creating the options that a customer needs to use data as they need to use it? >> The fact of the matter is, these things cannot be achieved overnight. It takes a certain amount of foundational work, that, frankly, takes several years. The fact that ONTAP can run on small, form factor hardware at the edge is a journey that we started several years ago. The fact that ONTAP can run on commodity white box hardware, has been a journey that we have run over the last three, four years. Same thing in the Cloud, we have virtualized ONTAP to the point that it can run on all hyperscalers and now we are in the process of consuming ONTAP as a service, where you don't even know that it is an infrastructure product, or has been. So the process of building an Edge, Core, and Cloud data pipeline leverages the investments that we've made over time. When you think about the scale of compute, data and performance needed, that's a five to six year journey in Clustered ONTAP, if you look at NetApp's past. These are all elements that are coming together from a product and solution perspective. But the reality is that leveraging years and years of investment that NetApp engineering has made. In a a way that the industry really did not invest in the same areas. So when we compare and contrast what NetApp has done versus the rest of the industry. At a time when people were building monolithic engineered systems, we were building software defined architectures. At a time when they were building tightly cobbled system for traditional enterprise, we were building flexible, scale out systems, that assumed that you would want to scale in modular increments. Now as the world has shifted from enterprise into third platform and Webscale. We're finding all those investments NetApp made over the years is really starting to pay off for us. >> Including some of the investments in how AI can be used to handle how ONTAP operates at each of those different levels of scale. >> Absolutely, yes. >> Sontash Rao, Technical Director at NetApp, talking about AI, some of the new changes in the relationships between AI and storage. Thanks very much for being on theCUBE. >> Thank you, appreciate it.

>> From Sunnyvale, California in the heart of Silicon Valley, it's theCUBE! Covering Accelerate Your Journey to AI. Brought to you by NetApp. >> Hi, I'm Peter Burris with Wikibon and theCUBE and we're broadcasting from theCUBE here from NetApp's data visionary center today. We've got a number of great conversations with some NetApp executives, specifically about the role that NetApp and AI are going to play together as the market evolves. And we're joined first by Jean English, who's a Senior Vice President and CMO of NetApp. Jean, welcome to theCUBE. >> Great, thanks Peter, nice to see you again. >> Lot of great stuff to talk about, Jean, so let's start with this interesting relationship, NetApp, AI, the centerpiece of it is data. >> Yes. >> What does that mean? >> Absolutely. Well I think just to start, technology is changing everyday lives. Digital transformation really tops the strategic agenda of most CEOs today. When you think about what does that mean in terms of data, data is the lifeblood of an organization. And it has to really be able to seamlessly flow through that organization to really add value. So we think of data as the heart, and when we think about data and AI, we want to accelerate the journey to AI. But to do that, you have to be able to holistically manage your data. >> So as we think about the need to manage data, that says that there's a number of challenges that customers face. You have to be able to bring great technology, but in ways that allow customers to do things specifically with it. So talk a little bit about the relationship that NetApp is developing with its customers to try to ensure that that journey to AI can be accelerated. >> Absolutely. Well the first is really around when we think about digital transformation, and especially as AI is the heart of it, how are they going to get more connected to their customers? A better customer experience. An experience that allows them to feel connected not only to the experience they have with the company, but to their peers, and to their customers, as well as suppliers. We also know that we want to be able to think about what do customers need to do to create new value? And new business opportunities, new services, new companies? Companies are also looking at how do they optimize their operations? How do they even take out the cost of back office, and take out that capital to be able to fuel innovation, especially when it comes to customer engagement. AI is becoming more and more a part of how companies are doing each of those. They're using those analytics and insight to be able to power through that digital transformation, which is now really being seen as a data-driven digital transformation. >> Well, we certainly agree at Wikibon, SiliconANGLE, and theCUBE. We believe that the relationship between business and digital business is, in fact, that digital business uses data as an asset. >> Mhmm. >> There's a special class of customer, though, as we move forward, and that is some of the hyperscalers, the big cloud suppliers, who are participating in this process of driving so much innovation in the industry. Serving them is a particular challenge. Talk to us a little bit about how that works. >> Sure. Well, we are definitely seeing that there is a certain class of customers that are starting to think about what do they do to thrive on data? And not only can they thrive, but they have to be able to put data as an asset. It has to be at the top of the strategic agenda of the company. It has to be able to seamlessly flow through that company. We're seeing that they're called data thrivers. Those thrivers are set apart. They're driving bottom line revenue, they're driving increased customer acquisition, they're seeing definitely higher profit margins. But in all those results we notice that there's a few things they're really doing right, and they're getting it right. One of those is that they're using the cloud. They're using public clouds. They're using private clouds. They're acquiring more dev-op skills. They're hiring data scientists. They're really being able to think about how do they hire digital officers, data officers, and a lot of that is around how they want to leverage AI. We know that over 50% of companies will start to adopt AI next year. We know that they're going to start to leverage AI to gain better insight. These new roles of people are definitely the ones who are being able to think about how did they manipulate that data, how did they use that data to have a competitive advantage, and how did they leverage the cloud to get more services like AI from these big cloud providers. >> Well, it's pretty clear as you try to service that broad a range of potential customers that you have to have a couple of touchstones to keep coming back to. Increasingly, design has to be one of them. Thinking in terms of design great products, but also designing engagement, designing how you work with customers, designing how you work with partners. Now, you have, along with your team, has bought some of that data first, data-driven notions into how you've designed this data visionary center. Talk to us a little bit about how you have been using design to ensure that great experience across the board from NetApp. >> Absolutely. Well first, as NetApp thinks about how to holistically manage data, that was really the inception of data fabric. And data fabric was a vision, it was a strategy years ago. And we've been really working to see how do we bring that strategy alive? And being that data visionary for our customers thinks about from the edge, to the core, to the cloud. And what do we do to help bring that data fabric across so you can seamlessly manage data, integration points across all those environments, you can migrate data to the cloud, you can make sure you're consuming data services, like analytics and AI, and you're really being able to bring that value back. AI is at the center of that. We wanted to design an experience that brought data fabric to life for our customers. One, how did they modernize their current architectures? Especially with cloud-connected flash and what we're doing with AI. We wanted to make sure that we were thinking about "How did they build these private clouds? What do they need to do to really bring out applications at speed?" Third is we wanted to inspire innovation with the cloud. And the work we're doing with the cloud providers. We've had new services like cloud volumes that have been launching with AWS, with Azure as well as Google. And with all of the biggest clouds, we've been thinking about how do we bring that customer experience to life? That design comes forward through the data visionary center, where we are today. This center is where we want to actually have customers come in and be inspired by what they can do in their own digital transformation journey. We want to build trust with those customers and partners. We want them to know that we understand their industry, we understand their needs, we understand what's happening in the market. IOT, AI, what's happening with securing data. How did they think about leveraging the cloud to really maximize business impact? We want to be able to have frank conversations. Inform each other of our strategies. How did they then able to interpret those and internalize that information? The whole data visionary center's been based on "How do we help them to be able to grow? How do they partner with us so they can leverage our services to help them to maximize the value of data?" So we provide those opportunities. Hands-on kiosks, demos, learning, even being able to do what we've done with NVIDIA with an advanced solution around even our events at solution where they can start to play with AI in real time. Then we want to be able to enjoy the conversation here at the data visionary center, and talk about next steps in the journey ahead. So we're excited about the data visionary center. We just opened it a few months ago, and we're thrilled to be able to invite customers and partners to be here. >> So let's extrapolate, extend beyond the data visionary center and make the observation that marketing broadly has become altered, changed, transformed as a consequence of using data. Marketing at NetApp in particular is interesting because you're fundamentally marketing data-driven as a concept. So how is marketing's evolved experience with data informing how NetApp broadly engages customers and builds products? >> CMOs are becoming one of the top functions to really drive digital transformations. When you think about how do you connect and engage with customers more? How do you engage with them on a personalized level? How do you ensure that you're having that constant communication, online, offline? And continuously being able to build that relationship. Marketing's at the center of that. We're excited that we're a data-driven organization, and as a data-driven organization, we're having real business impact to the business and real customer engagement. We're excited that we're at the heart of what we're doing to transform the business. Not only from our branding and how we think about reinventing NetApp, we are data-driven company. We are data-driven as we think about that aspiration for our customers. Our data visionary concept is about how do we inspire people to want to bring all that data together and really simplify and integrate, and then unleash that potential for their own companies. Marketing is at the center of how we're engaging people, especially in the cloud. And as we have a no-touch experience and customers are engaging with us to be able to download trials, being able to see demos, being able to watch other customers and where they are on their own journey. But being able to surface that, again, digitally as well as offline and personal engagement. Analytics, big part of what we're doing to understand customer needs better. Understanding those needs from the solutions they expect from us, understand their needs from what they're enduring in the market, and then being able to help the company think about that in terms of road maps, think about that in terms of key messages, think about that in terms of real solutions and real engagement. >> Jean English, Senior Vice President and CMO of NetApp, thanks again for being on theCUBE >> Thanks, Peter. >> And talking about data-driven and marketing. >> Thank you. Nice to see you. >> Good to see you again.

>> From Sunnyvale, California, in the heart of Silicon Valley, it's theCUBE, covering Accelerate Your Journey to AI. Brought to you by NetApp. >> Hi, I'm Peter Burris, with theCUBE and Wikibon, and we're here at the NetApp Data Visionary Center today to talk about NetApp, NVIDIA, AI, and data. We're being joined by two great guests. Jim McHugh is the Vice President and General Manager of Deep Learning Systems at NVIDIA, and Octavian Tanase is the Senior Vice President of ONTAP at NetApp. Gentlemen, welcome to theCUBE. >> Thanks for having me. >> So Jim, I want to start with you. NVIDIA's been all over the place regarding AI right now. You've had a lot of conversations with customers. What is the state of those conversations today? >> Well, I mean, it really depends on the industry that the customer's in. So, AI at at its core, is really a horizontal technology, right? It's when when we engage with a customer and their data and their vertical domain knowledge that it becomes very specialized from there. So you're seeing a lot of acceleration where there's been a lot of data, right? So it's not any secret that you're seeing a lot around autonomous driving vehicles and the activity going there. Health care, right? Because when you can marry the technology of AI with the years, and years, and years of medical research that's going on out there, incredible things come out, right? We've seen some things around looking at cancer cells, we're looking at your retina being sort of the gateway to so many health indications. We can tell you whether you have everything from Dengue fever, to malaria, to whether you're susceptible to have hypertension. All of these kind of things that we're finding, that data is actually letting us to be superhuman in our knowledge about what we're trying to accomplish. Now the exciting thing is, if you grew up like we did, in the IT industry, is you're seeing it go into mainstream companies, so you're seeing it in financial services, where they for years were, quants were very specialized, and they were writing their own apps, and now they figured out, hey, look, I could broaden this out. You're seeing it in cybersecurity, right? For years, if you wanted to check malware, what did we do? We looked up the definition in a database and said, okay, yeah, that's malware, stop it, right? But now, they're learning the characteristics of malware. They're studying the patterns of it, and that's kind of what it is. Go industry by industry, and tell me if there's enough data to show a pattern, and AI will come in and change it. >> Enough data to show a pattern? Well, that kind of introduces NetApp to the equation. A company that's been, especially more recently, very focused on the relationship between data and business value. Octavian, what has NetApp seen from customers? >> Well, we know a little bit about data. We've been the stewards of that data in the enterprise for more than 25 years, and AI comes up in every single customer conversation. They're looking to leverage AI in their digital transformation, so we see this desire to extract more value out of the data, and make better decisions, faster decisions in every sector of the industry. So, it's ubiquitous, and we are uniquely positioned to enable customers to do their data management wherever data is being created. Whether the data is created at the edge, in the traditional data center, what we call the core, or in the cloud, we enable this seamless data management via the data fabric architecture and vision. >> So, data fabric, data management, the ability to extract that, turn it into patterns. Sounds like a good partnership, Jim? >> Yeah, no, we say, data's the new source code. Really, what AI is, we're changing the way software's written. Where, instead of having humans going in, do the feature engineering and feature sets that would be required, you're letting data dictate and guide you on what the features are going to be of software. >> So right now, we've got the GPU, Graphic Data Processing revolution, you guys driving that. We've got some real advances in how data fabric works. You have come together and created a partnership. Talk a little bit about that partnership. >> Well, when we started down this journey, and it began, really, in 2012 in AI, right? So when Alex Krizhevsky discovered how to create AlexNet, NVIDIA's been focused on how do we meet the needs of the data scientists every step of the way. So beginning started around making sure they had enough compute power to solve things that they couldn't solve before. Then we started focusing on what is the software that was required, right? So how do we get them the frameworks they need? How do we integrate that? How do we get more tuned, so they could get more and more performance? Our goal has always been, if we can make the data scientists more productive, we can actually help democratize AI. As it's starting to take hold, and get more deployments, obviously we need the data. We need it to help them with the data ingest, and then deployments are starting to scale out to the point where we need to make this easy, right? We need to take the headaches of trying to figure out what are all the configurations between our product lines, but also the networking product lines, as well. We have to bring that whole, holistic picture, and do it from there. So our goal, and what we're seeing, is not only we've made the data scientists more productive, but if we can help the guys that have to do the equipment for him more productive as well, the data scientists, she and he, can get back to doing what their real core work is. They can add value, and really change a lot of the things that are going on in our lives. >> So fast, flexibility, simpler to use. Does that, kind of, capture some of the, summarize some of the strategies that NetApp has for Artificial Intelligence workloads? >> Absolutely, I think simplicity, it's one of the key attributes, because the audience for some of the infrastructure that we're deploying together, it's a data scientist, and he wants to adopt that solution with confidence, and it has to be simple to deploy. He doesn't have to think about the infrastructure. It's also important to have an integrated approach, because, again, a lot of the data will be created in the future at the core, or at edge more than in the core, and more in the cloud than in traditional data center. So that seamless data management across the edge, to the core, to the cloud, it's also important. And scalability, it's also important, because customers who look to start, perhaps, simple, with a small deployment, and have that ability to seamlessly scale. Currently, the performance of the solution that we just announced, basically beats the competition by a 4x, in terms of the performance and capability. >> So as we think about where we're going, this is a crucial partnership for both companies, and it's part of a broader ecosystem that NVIDIA's building out. How does the NetApp partnership fit into that broader ecosystem? >> Well, starting with our relationship, when the announcement we made, it should be no secret that we engaged our channel partners, right? 'Cause they are that last mile. They are those trusted advisors, a lot of times, of our customers, and going in, and we want them to add this to their portfolio, take it out to 'em, and I think we've had resounding feedback, so far, that this is something that they can definitely take, and drive out. On top of that, NVIDIA is focused on, again, this new way of writing software, right? The software that leverages the data to do the things, and so we have an ecosystem that's built around our inception program, which are thousan%ds of startups. If you add to that the thousands of startups that are coming through Sand Hill, and the investment community, that are based around NVIDIA compute, as well, all of these guys are standardizing saying, hey we need to leverage this new model. We need to go as quickly as possible, and what we've pulled together, together, is the ability for them to do that. So whether they want to do the data center, or whether they want to go with one of our joint cloud providers and do it through their service, as well. >> So a great partnership that's capable of creating a great horizontal platform. It's that last mile that does the specialization. Have I got that right? >> You had the last mile helping reach the customers who are the specialization. The customers, and their data, and their vertical domain expertise, and what the data scientists that they have bring to it. Look, they're creating the magic. We're giving them the tools to make sure they can create that magic as easy as possible. >> That's great, so one of the things, Octavian, that Jim mentioned, was industries that are able to generate significant value out of data are moving first. One of the more important industries is IT Operations, because we have a lot of devices, we're generating a lot of data. How is NetApp going to use AI in your product set to drive further levels of productivity, from a simplicity standpoint, so customers can, in fact, spend more time on creating value? >> So interestingly enough, we've been users, or practitioners, of AI for quite a while. I don't know if a lot of people in the audience know, we have a predictive analytics system called Active IQ, which is an implementation of AI in the enterprise. We take data from more than 300 thousand assets that we have deployed in the field, more than 70 billion data points every day, and we correlate that together. We put them in a data lake. We train a cluster, and we enable our customers to drive value in best practices from the data that we collect from the broader set of deployments that we have in the field, so this is something that we are sharing with our customers, in terms of blueprint, and we're looking to drive the ubiquity in the type of solutions that we enable customers to build on top of our joint infrastructure. >> Excellent, Jim McHugh, NVIDIA, Octavian Tanase, NetApp. Great partnership represented right here on theCUBE. Thanks very much for being on theCUBE tonight. >> All right. >> Thank you. >> Thank you for having us. (electronic music)

>> Narrator: From the SiliconANGLE Media office in Boston, Massachusetts, it's the Cube. Now here's your host, Dave Vellante. >> Hi everybody, welcome to this special Cube conversation with a practitioner, a real-world enterprise journey to the Cloud. I'm here with Jake Burns, who's the Vice President of Cloud Services at Live Nation Entertainment, in from L.A. Jake, thanks for coming in to our Marlborough Studio, appreciate you having in. >> I'm glad to be here. >> So tell me about your role. >> So, I'm head of cloud services for Live Nation, and what that means is, me and my team are in charge of infrastructure for IT, including cloud infrastructure, as well as the move to the cloud, which we completed early 2017, enterprise messaging, which includes e-corporate email, DNS, database services, and storage management. >> So, recent journey. How did it start? Was it a top-down push, did you go to management and say, "Hey, we got to do this," describe that dynamic. >> Yeah, so it started off as kind of a bottom-up push. >> Dave: Really? >> For a number of years I've been really wanting to get us involved in public cloud, at least in some level. But, it really didn't hit critical mass until our CEO, late 2015, had a mandate that we're going to move 100% to cloud, and modernize all of IT. And that's when we really hit the ground running. >> Why did, from a bottom-up standpoint, why did you guys want to do that? Was it because cloud's cool, that's where all the action is, the developers want to be there, or was it something else? >> We spent a lot of time managing infrastructure and data centers, and it's just not part of our core business. We wanted to focus more on satisfying the business, and providing value to the business. And, our time could be better spent really helping solve their problems, rather than deal with hardware and systems. Another thing is just business agility in general. If we want to stand up a new system, the typical lifecycle could be three to six months, just to get an application up and running. With cloud, we can do that in days, weeks, worst case. So, being able to respond quickly to business needs is something that's really important to us, and we saw with public cloud that we could do that a lot more efficiently. >> And when you think about the early cloud days, the rhetoric was all about agility, and it actually, that really was the main business benefit. You guys of course saved a lot of money too, and I want to get into that, but how did you get started? It must have been, kind of a little nervous, like the first time you jumped off a high cliff or something. Right, because you have an existing business to run, and yet you're going to migrate everything. Migrate's like this evil word, so how did you get started? >> For us, we realized very early on that this was a big technology change for us, and it was going to require new skills that we didn't have, so the first thing we did, was we really just got training across the board. We brought trainers from AWS to our offices, and we did every training program that they offered. Got the certifications. And made sure that we really understood what we were dealing with before we got started. So that was really step number one. >> And how did that go? Were they really supportive? Everybody says AWS, really not hands-on, they just send me an email. How did that go? >> In the beginning, there's resistance. Just like all projects like this, people are concerned they're going to lose their jobs. >> Dave: Resistance from your guys. >> Oh, yeah, yeah- >> Not the AWS people, they were- >> Oh, no, of course, right. No no, our guys, before they really understand the situation, it looks like we're being outsourced. We're moving all of our infrastructure. This is our job. We're managing hardware, we're managing servers, we're managing data centers, and all that stuff's going to go away, so what are we going to do, right? So, really, even before the training, the priority for me was to get people to understand that this is not something that's a danger for your career. Quite the contrary. This is going to make you more valuable. You're going to get trained on this technology. You're going to get real world experience, moving a Fortune 500 company to the cloud, and at the end of this, someone is going to need to maintain it. So not only will you have job security, but you're probably not going to care about job security at the end of this, because you're going to be so valuable in the marketplace. >> So, we're all in sales, aren't we? So you had to sell them a little bit on the concept, but then they responded positively, it sounds like. >> Yeah, and part of that is because it's the truth. I was telling them the truth, so it was an easy sell. But it's a very important component of any cloud migration project like this. If you don't have support from your people, it's not going to succeed. >> Okay, so you get through the training. Your guys are onboard, you have alignment there, and then take us through sort of the journey. How long did it take, what were some of the challenges that you faced? >> The target was 12 months to move everything, and we're talking about 668 servers, 118 applications, including Oracle, SAP, some really things that are not trivial to move to the cloud. We were able to move 90% of everything in 12 months, and then the long tail took an additional five months, so that's 17 months in total to move everything. >> And that long tail, was that the Oracle apps? >> Yeah, so our strategy was to move the easy stuff first, as we learned, because we learned along the way. We really didn't know what we were doing when we started. By the end of the project, we knew exactly how to do the project. >> Easy stuff like messaging? >> Like single server applications that are running supported software, where we have a business stakeholder that's cooperative. >> Dave: Web stuff? >> Yeah, like internal stuff, like our monitoring systems, things that we completely control. >> Dave: Things that were under the control of IT, didn't involve a lot of politics, and ... >> Jake: Exactly. >> Learn there, okay. >> Right, so the idea was, get real world experience moving live production systems on the easy stuff, and it kind of builds up our skillset, but at the same time it builds forward momentum, which is critical for a project like this. There's a lot of people that are just waiting for the first failure to kind of put a stop to the whole thing, right? There's a lot of skepticism as to whether this can even be accomplished or not. So, getting, I truly believe a key component for a project like this is to get momentum on your side early on, and the way you do that is by attacking the easy problems first, and then get progressively more difficult as you go along. And so at the end, you end up with the most difficult applications to move, but at that point, you have full buy in from everyone because you've been successful so far, and you and your team are practiced and accomplished, and have the skillsets necessary through moving all the more easy stuff before that. >> Okay, and just a quick aside, I have to ask. So, Oracle is kind of using licensing as a weapon, especially, there's this, I call it urinary Olympics, sorry, with Oracle and AWS. You may not have visibility on it, if you don't we can move on, but was that a concern? >> Absolutely, yeah. So this was a major problem that we've had to deal with, and Oracle doesn't make it easy. They don't necessarily want their customers moving to AWS. So, that was part of the challenge. Part of the challenge was, how do we move this without having to pay more in licensing? And what it really comes down to, is you have to make your Oracle databases run more efficiently in AWS, in order to lower the core count, which is what the licensing is based on, in order to keep your costs neutral, because Oracle will charge you double for your database, per processor, in the cloud in AWS than they will on prem. So, really the only way around that, besides negotiating with Oracle if you're able to do that, if you're not able to do that, then your only option is to make it run twice as efficiently from a processor standpoint. >> Thank you for sharing that with our audience. We've written a lot about ways to reduce your core count. Ways to make IO optimized, and if you can do that, you can actually save a lot of money. Maybe we'll have you back on at Reinvent, and we can talk more about that. But so, back to your story here. You got a huge budget to do this, right? Big bag of money to say, go move to AWS? >> Unfortunately, we didn't have that luxury. So, we run very lean. So we had essentially a flat budget, 2016, when we did the majority of these moves. So we just had to find a way to do it without spending money. And so, it was a bit of a juggling act. We were decommissioning systems in the data center, and canceling support contracts, so we were able to kind of use some of that money and repurpose some of that money for moving to AWS, but we really didn't have a budget for hiring consultants, or to buy expensive software, or anything like that. So, what we had to do was, basically become the consultants, to do the cloud migration. And so, that's where that training comes into play. So by training the team, and getting them up to speed, and essentially creating cloud engineers, we were able to be internal consultants to the business, perform the move internally at a very low cost. >> All within that sort of 12/17 month timeframe, you were able to affect that skills transition. >> Right, so we were simultaneously maintaining the old infrastructure, moving the infrastructure to AWS, and maintaining the infrastructure in AWS. So there were a lot of long hours. >> I'll bet. That's weekends. >> But, we were enthusiastic about doing it. Everyone was very excited once we got going, and so people were willing to do it. You talk about the people challenges. I think we've addressed that a little bit anyway. What were some of the other challenges? You got a reasonably sized application portfolio, you got data, you got your backup systems. What were some of the challenges that you faced, and how did you address them? >> Yeah, so that's a great question. One thing that people don't realize is that AWS isn't necessarily designed for enterprise applications. It's getting a lot better. But, there are some things where it just doesn't fit automatically. So, one area where that's especially true is with storage. AWS has a fantastic storage offering, especially with S3, their object storage. But unfortunately, most enterprise applications, they can't utilize. Legacy enterprise applications won't utilize object stores, they want block storage. >> They don't want get put, they want block storage, okay. >> Yeah exactly. And then the block storage in AWS is different than the block storage than what you're used to in the data center, typically. So, kind of allowing these applications, like Oracle, to work on AWS's block storage can be a challenge. It can be expensive, and there can be some risk there, just because of the way that it works. So, this is where using a third party makes sense. This is one of the rare circumstances where I think using a third party makes sense. We found a company called Actifio that does virtual storage in AWS, and one of the great things about this product is it essentially mimics the way that the old storage worked in our old environment, in our data center. So the application continued to function. So we're able to take snapshots, we're able to clone environments, we're able to do all of these things that we are not able to do in AWS natively, with the Actifio product. And it saved us a lot of money, and allowed us to avoid a lot of having to change our workflows to get around some of the delays with doing snapshots and stuff natively. >> And is your strategy to have this sort of hybrid approach between on prem and public cloud, or multiple public clouds? Is that part of the strategy, and how does this capability fit into that? >> Yeah, it's a great question. Our initial strategy was 100% going all in with AWS, and officially that's still our strategy. I am a proponent of multi-cloud in certain circumstances. For example, disaster recovery and backups, I think it makes sense, if your 100% in the cloud, to have a second cloud provider to hold your backup data, just so you don't have everything in one place. I think, for the same reason, hybrid cloud makes a lot of sense. And I think also hybrid cloud makes a lot of sense, just because not all applications are a good fit for a public cloud, and Oracle, SAP, would be two of those examples. Now we were forced to move everything to AWS, and it was a fun challenge, and we were able to accomplish that. But doing it over again, if we had the option of doing hybrid cloud, there may be a couple applications that I would say keep it on prem, because it just works better that way. >> And, can you double click on the storage virtualization capability that you talked about. Kind of how does that work, and how do you have to ... Were there any kind of things that you had to do to prepare for that? Any sort of out of scope expectations that customers should be aware of? >> With Actifio it's a pretty turnkey solution. So, there's a little bit of a learning curve, but there's a learning curve with using the AWS native tools as well. So I would say probably less of a learning curve if you use a product like Actifio, because it's more familiar to the people that are already working on these systems. So if you have existing staff, and they're used to doing things in the data center, and they're used to doing things with traditional enterprise storage, the Actifio tools is going to look a bit more familiar than the AWS tools. So, there's a learning curve either way, but I would say look at a product like Actifio if you're an enterprise trying to do this. >> So what was the business impact of using Actifio? Then I want to ask you about the whole move to AWS. Did it speed the time to deployment for AWS? Did it help you cut cost? What was the business impact? >> Unfortunately, we didn't become aware of this product until after we had moved. So, we're in the process now of replacing some of our storage devices with virtual storage with Actifio. But I wish we had found this product sooner. I advise anyone who's new at this, anyone who's doing a migration, to leverage something like this to actually move their data, because it's a much more efficient way to do it. So, if I could go back in time, I would do that. >> What would have been the business impact? Is this time and money? >> Yeah, time and money, for sure. So, the moving of the data is one of the biggest challenges that you're going to have moving to cloud. We had a petabyte of data that we had to move, and that's no small task to get that moved in 12 months. So, any tool that you can use that can make that more efficient, is going to shorten the amount of time you're going to be doing the migration. And, consequently, shorten the amount of money that you spend doing the migration. Also it would have saved us a lot of time, because now we're going back and having to change things, and put things under Actifio. If we would have done it like that to begin with, we wouldn't have to spend that effort after the fact. >> Why does Actifio make it more efficient? Is it data reduction? Is it automation? >> So essentially the biggest benefit is that it allows you to not have duplicates of your data. So, if you have a dozen or so copies of your database, for different types of environments, test, UAT, dev, etc., and you're duplicating those, and storing each one of those separately, you're going to pay for each one of those separately, and have to manage each one of those separately. If you're able to use virtual storage, then you really have one copy of the data, or however many copies of data you really need to be protected, and the rest of those can be virtual copies. And those don't cost you anything from a storage point of view. The other benefit is, if you want to clone an environment, or copy an environment, or take a snapshot of an environment, it can happen instantaneously, rather than wait for the hours or days that it would take to copy a large dataset. >> So it becomes the single point of control, with a catalog, and give you visibility over all your data, and your copies, and allows you to manage that, is that correct? >> Yeah, and the management becomes a lot easier, because you have software that's keeping track of your snapshots, and keeping track of all your copies of data, rather than try to track that all manually. >> Okay. Let's bring it back to the big AWS picture. So you move to the cloud. What was the business impact of that? You mentioned agility. Did you save money? How much? Maybe give us some visibility on that. >> Because we're so cost conscious, saving money was a priority. I don't think it's necessarily something to expect, especially initially, if you're an enterprise moving to the cloud. Cost shouldn't be the driver. Agility should be the driver. But, in our case, we were able to achieve 18% reduction in TCO, on year one. And, that's just because we were just very focused on cost. We're very cost sensitive, and it's very important for us to be efficient, and to not spend money unnecessarily. I know that's a priority for everyone, but it's a top priority for us. And so, my point is it can be done. You can move to the cloud. You can move 100% to public cloud if you're an enterprise, and you could make it cost neutral, or even favorable. It is possible. >> So you hear a lot of stuff in the press about how the cloud is very expensive. You could actually do it cheaper on prem. Based on your experience, you don't buy that. >> Well, I wouldn't say that's false. You can, in a lot of circumstances, do it cheaper on prem. It really depends on the workload. So I mentioned earlier that I think hybrid is probably the right approach for most people. So just because we're saving money by going 100% cloud, doesn't mean we wouldn't save more money if we went hybrid cloud, and put the more expensive things that run in cloud, on prem. So, because it's pay for what you use, the things that you very heavily utilize, those are good candidates to keep on prem. The things that are more bursty, those are the things that are better candidates to put in the cloud. The easiest things, candidates to put in the cloud, are disaster recovery and backups, those are no-brainers. DR because that's only something you need to scale up when you use it. So anything that you need to scale up when you use it, or anything that scales up and down, those are the best candidates for cloud. >> Okay, now I understand you're kind of an expert at cutting the AWS utility bill. Maybe you could give us some advice on how to do that, and how'd you learn how to do that? >> Yeah, so that's kind of my area of focus now, is now that we're in the cloud, getting those costs reduced as much as possible. So, there's a lot of ways to do this, but I like to keep it simple, and attack the things that have the biggest impact first. So, people like fancy solutions, but it's really simple. The biggest thing you can do is delete things you're not using. You're paying for consumption, so find things that are not being used, and simply delete them. After that, then find things that are oversized, and right-size them. And then, another big thing is, in the cloud, you have such an easy access to spin things up. To take snapshots of data, to copy data, and it's the classic problem in IT, where everyone requests what they want, and they never tell you when they're done with it. So, it needs to be a full-time effort, to be actively looking for resources that are unused. Snapshots that are no longer needed, volumes that are no longer needed, instances that are no longer needed, and be cleaning those things up on a continuous basis. I find that that's a large percentage of what my team does now, and that's one of the things that keeps our costs in line. >> That's interesting. We always talk here about GRS, getting rid of stuff. Not only did you get rid of a bunch of stuff when you moved in the cloud, you said 600 servers, you got rid of unused capacity, you got rid of a bunch of data, which must have made your general counsel happy, but you're now actively continuing to get rid of stuff. Like you said, it's volumes, it's snaps, and so the things, now you're in the cloud, that GRS mentality is sort of ingrained. >> It has to be. I think that anyone who's in the cloud for some time is going to realize this. You're going to have inflation of costs, simply by doing nothing. So, just to keep your cost neutral, you're going to have to be deleting things on a continuous basis. Now if you want your costs to go down, that's even more difficult. You have to be more aggressive with it. But, just as it's easier to spin things up in the cloud, the good news is it's easier to keep track of what you have, and find things that can be deleted in the cloud, because you don't have to go in the data center and track things down. Everything is virtual. It all can be automated. It's all done, it can be scripted. So, everything's easier. Spinning things up's easier. Cleaning things up is easier, you just have to make it a priority, and make sure it gets done. >> So, some of the financial people in our audience might be listening and saying, "Eh, you know, okay, year one. Roughly 20% savings. It's not that exciting." But we haven't quantified the sort of other business impacts in terms of agility, and that's a harder thing to quantify, but it's early days for you still. Do you expect to get on that S curve, and really start to see a major business impact, beyond that 17, 18%? >> That's a great question. That 18% reduction in TCO, that's just infrastructure costs, so that's not taking into account things like how long does it take for us to spin up an application, and what does that cost the business, that delay? We're not taking that into account. How about the opportunity cost of, we want to try something, but it's too expensive because we've got to buy servers, and we got to hire people to build the application, and install the operating system, all that kind of stuff. Those opportunity costs, they're not captured either. Now, we can try as many things as we want, very inexpensively, and only keep the things that work. So I think there's a lot of hidden cost savings, a lot of hidden value that's very difficult to capture. But, we certainly have those benefits, even if we're not articulating it, and counting it very well, the business feels it, and it's certainly a superior level of service. >> Well it's kind of like when we first got email. Nobody really quantified it, but the productivity impact was enormous. Or the first local area network that you ever installed, and the collaboration that that brought, it's one of those things that's, it's probably telephone numbers, but it's hard to quantify, right? You said the business people see it. Do the finance people see it as well, and are they supportive of this? >> Yeah, it takes a while I think for the non-technical teams to catch up, and really get to where we're at in terms of an understanding of what we're dealing with at this point. So, they're starting to see it. But, all the financial models have to change. All the budgeting needs to change. There's a lot of things that, beyond IT, this kind of transformation affects, and those processes have to change, and those processes generally change more slowly. So procurement needs to change, finance needs to change, security needs to change. Everything really, it's a new world. And once they catch up and kind of really grasp what we're dealing with, I think the whole business is going to be transformed. >> So two last questions. You talked about maybe things you'd do differently. Maybe some advice. But let's focus clearly on advice to your colleagues that are trying to do something similar, get to the cloud, what would you tell them? >> Invest in your people. Focus on cost savings day one. Don't look at doing that after the fact. And don't get too caught up in all the fancy methodologies, and fancy tools. Everybody's going to try to sell you something. Everybody's going to try to tell you they have the best way to do it. But, in general, those things are just going to add complexity to your project. I say keep it simple, keep it lean. Leverage your own people. Because at the end of the day, somebody's going to have to support this environment as well, and if you're relying too much on outside help, then they're not going to be there when it's all said and done. So, consider the endgame. Consider the end state, and how you're going to support that, because it's one thing to be successful migrating to the cloud, but then you have a whole new set of challenges after that. And you're going to have to live with that moving forward. And, I'm not saying it's a bad thing. It's a great thing. But it's something different, and you're going to have to be prepared for that. >> Own it. >> Jake: Own it. >> Yeah, okay. And then, last question, just sort of what's next for you guys? You're just sort of getting started here. You've made a tremendous amount of progress in a year and a half. What's next? Where do you want to take this thing? >> Like I said, right now we're really focused on cost optimization. I think that, like you alluded to earlier, the cloud could be very expensive. The range of how much it can cost is, it's amazing, right? So, this is uncharted territory. We don't know how expensive it should be, how cheap it should be. We just now that we can affect that, to a large degree. So I'm interested in seeing to what degree we can affect that, and I want to see how efficient we can make this. 18% favorable TCO is one thing. Let's see if we can get 30% or 40%. So, really I'm focused on optimizing for cost, security, which is a whole new world in the cloud, and going from there. >> Jake Burns, awesome having you on. Thanks very much for your insights. >> Jake: My pleasure. >> Really appreciate your time. And thank you for watching, everybody. This is Dave Vellante. We'll see you next time. (upbeat music)

(cheery xylophone music) >> Welcome back everybody Jeff Frick here with theCUBE. We're in the Palo Alto studios today to talk really about the customer journey. We're excited to have our guest today who flew in all the way from Istanbul, Turkey which is a very long flight. It's Attila Bayrak he's the Chief Analytics Officer for Akbank, welcome. >> Hi, hello. >> So first of all I hope you get some time to catch up on your sleep before you turn around and fly all the way back. >> Yeah it's a little bit quick to speak about finance and banking, but it's good to be here. >> Well we're glad you made the trip. And so before we jump in, for people that aren't familiar. Give us a little bit about Akbank, and the history of the bank. >> Yeah sure, sure, Akbank is one of the leading private bank in Turkey. And it's almost 70 years old, and we have nearly 14,000 employees and with the 850 branches around 4,000 ATMs and probably half a million merchant point of sales. We can say that we have a good footprint in Turkey. And also we are keen on to be a leading digital bank in Turkey. And just a brief information about Turkey. The Turkish market is quite young. And 50% of the population is under the age 29. >> Jeff: 50% is under the age of 29, okay. >> It's huge and the total population is around 80 million. >> Jeff: Okay. >> So Turkish economy is quite performing very well for the last 10, nine years. So that's why being digital leader is quite a crucial issue for us. So with these numbers we're performing around probably the best or the second in many KPIs. >> Jeff: Okay. >> We can say that we nominated, we are nominated many times as the best bank in Turkey with the bank in Europe from some of the companies. >> Okay and how long have you been there? >> So I've been there in 11 years. >> 11 years, but you said before that you were at some other banks. You've been in the banking industry for a while. >> Yes, yes I've been banking industry for almost 20 years. So I used to work two other competitors of Akbank. >> Okay so I'm curious especially with that large percentage of younger people, how many of those people ever come into a branch or go to an ATM? As opposed to using their phone. >> So they should prefer doing business in phone because it's quicker, faster, and easy. And the experience is quite much more under control in the phone. And we have, we can say that we have 80, 85% of younger people preferring the digital business rather than the classical ways. >> It's just fascinating to me, especially in banking, 'cause in banking you know, it was that trusted facility on the corner right in every town that you knew was stable, and it was always there, and you went into the branch, and you knew some of the people that worked there. And now almost the entire experience between the bank and its customers is a digital interaction, especially for the young people. They've never been to a branch. They don't hardly ever go to an ATM, in fact the whole concept of cash is kind of funny to them. You know it's a very different world. So digital transformation in banking is so so important. >> Yes they're going in hand in hand. You know the millennials are living in the digital world. And after the millennials they born in the digital world. So it's always that, the business are transformed itself into the digital way, and to deliver the products and needs in the way of doing things with the digital processes. >> So as Chief Analytics Officer, with that move in the millennials, of course there's always regulation and other things that are driving you know your KPIs, but how has that migration to younger people interacting in a digital way, impacted your job and what you measure what you have to do every day. >> They directly impacted my job. (laughing) I used to lead the customer relationship management initiative for 10 years which covers the sales and marketing automation, and the analytics and the design of the processes in the sales. A year ago, one and a half year ago, we transformed the role into the analytics office, and we are keen on to the deep dive in the customer behave, and define what are the needs of the customer, and how is evolving in the digital era. And we are trying to position the bank's products and the communication skills in the digital world with the customers. So it is similar in the old days in the subjects, but it's really different in details. So the story begins to understand the customer, and then segmenting the customer, for sure for probably more than 30, 50 years. But in the digital world, the footprint of the customer and the digital footprint is quite diversifying the thoughts in the corporate side. So we have around 50 million customers, and 90% is a retail one in the new ages. So we need to optimize the banking let's say, the cost structure of the bank, and for sure the digital business gives us the enablement of the optimizing the customer service. >> Right, right. >> So the segmenting the customers, not for the value basis, the behavioral and the other perspectives, and creating a very well defined segments is the initial step. And we are redefining ourselves in serving in this era. >> So I'm just curious, you know 20 years ago, I won't go back to 30, but 20 years ago how many segments did you use to segment your customers? I mean how many kind of classes and how has that changed today? >> Well 20 years ago we have three to five segments. >> Jeff: Three to five segments, that's what I thought. >> So it's like the big ones and the small ones. And if you have the analytic capability you have the mid ones. >> Jeff: Right, right. >> For nowadays we have 80, 85 different perspectives for the customers. So we created that platform to enhance these segmentation capability to serve our specified problems of the bank. I mean problems with the missions of the marketing-- >> Right. >> Let's say so we are considering now the life stage, the life style, and some spending behaviors, and some investment behaviors, some credit risk behaviors also as well. And the potentials of the economic size. >> Jeff: Right. >> And we can say that now we have more than hundreds, but the optimal point of the segmentation is so there is no meaning to create some segments that you do not take some actions-- >> Right, right. >> The action ability of the segment is quite coming forward in this topic. So we created the platform to enhance the capability, to create dynamic segments and dynamic targets to each marketing event. >> Right, and I was gonna say and hand in hand with that, and you just mentioned a bunch of different variables, how many variables fed that segmentation before versus how many variables today feed that segmentation analysis. >> So it increases probably hundred times. So we used to I don't know analyze couple of hundreds of dimensions and variables in older days. It's more than 10,000 today. >> More than 10,000 variables to segment into hundreds of classifications of customers? >> Yeah why not. >> Wow, well there's a good opportunity for an analytics executive. (laughing) So how are you addressing that challenge? So obviously you're here as a Datameer customer. How did you do it in the past? What were the things you couldn't do? And what forced you to go with kind of a new platform and a new approach? >> So we can say that we have a quite well defined analytic architecture in the Akbank. And we are using different types of technologies in different types of solution areas. Datameer is positioned in the measuring of our marketing campaigns. And as we mentioned we have more than millions of customers and we have quite, we can say that in a given period of time we have more than hundreds of campaigns. So we need to speed up the measurement of the campaigns and the results in a business perspective. And once we come across with the Datameer and the capabilities of the technologies much more related with the Hadoop structure and integration of different data sources in one place. So we think that we can optimize our ETL type of measurement data load technologies transformed into the Hadoop structure. And it seems it worked. So we reduced the time to transform the data into a single platform from diversified places. And we created easy to use measurement platform to give some feedbacks before the things are happen. >> Right right 'cause there's a lot of elements to it. Just on the data side, there's the ingest as you said, now you have many many variables so you gotta pull from multiple sources, you gotta get it into a single place, you gotta get it into kind of a single format that then you can drive the analytics on it. Then you got to enable more people to have the power. And I'm curious how that piece of your business has evolved where before probably very few people had access to the data, very few people had access to the tools and the training to use them, but to really get the power out of this effort you need to let a lot of people have access to that data, access to the tools to design these hundreds of campaigns. So how has that evolved over time? >> To be frankly speaking, there are thousands of variables are related to the predictive part of the analytics. But the other critical point is so the results are how are things are going on in the business side. So banking let's say culture of Akbank, so we are keen on to put the business value on the front and then think with that mind and design each and every process in that way. So that's an other perspective to get support to change the classical data load and upload and transform the data and analyze the data to see the results. That's the old way. And we were good to be frankly. But we transformed that into a much more dynamic structure. And the knowledge as you mentioned is a critical point in the team. So the easy to use, the usage of easy to use of the technology is quite another critical point to create that type of thing into the place. So at the end of the day, you are measuring hundreds of marketing actions just in a single month. And if there's something happening that doesn't plan, so you need some time to re-think on this issue and redesign it so we think that we are at the door of this stage. We can say that we can use the output of the predictive analytics much more in an efficient way by understanding the results in much more frequently and speedly I'd say. >> Right, right, and would you say this effort has really been offensive in terms of you trying to get ahead of the competition to be aggressive. Or has it been defensive and you know, if you're not playing this game, you're not really in the game anymore. >> So it depends on the prior subject. If it's retention action, it can be defensive. It seems like defensive. But if it's let's say op selection it can be offensive. So there's no chance to choose one of them because we have variety of products and variety of businesses in Turkey that we are operating. And at the end of the day we need to serve each and every action. >> And I think it was very insightful too that you said that you don't do it just for the sake of doing it and because you can do it. That if there's no action that can come from it, or if it's not actionable, what's the point, it's a wasted effort. >> Yeah, sure at the end of the day we are doing banking business. So we are not doing the analytics business. >> Right, right. >> That's the point. >> Yeah exactly. So as you look back kind of, what has been the high level result of this effort if you're reporting to your boss or the board of using this type of approach. And then secondly, where do you go next? We're almost at the end of 2017. What are some of your objectives and kind of priorities for 2018? >> So we are creating, we are now just nowadays, seeing the results of the new system. And we can say that in some actions we've started to increase the results 10 to 15%. >> Jeff: 10 to 15%? >> Yes it's in the result phase. And it gives us some courage to design new use cases. So the new use cases are much more related with the visualizing of the results in real time, these type of things. Basically I can say that we are trying to get everything in real time. And the modeling in real time. Measuring in real time. Visualizing in real time. So we are trying to push each and every action in the analytics to the closer. We do not want to work in the offline phase. >> Yeah it's fascinating to me to think that we used to make decisions based on a sampling of things that happened in the past. Now we want to make decisions on all the data that's happening now. It's a very different approach. >> Yeah. >> Alright, great well Attila thank you for stopping by and sharing your insights. >> It's a pleasure to share. >> Alright absolutely, alright so he's Attila Bayrak, I'm Jeff Frick, you're watching theCUBe. Thanks for watching we'll see you next time. (electronic music)

>> Hey, welcome back, everybody. Jeff Frick, here with The Cube. We're in the Palo Alto studio, talking about customer journeys. We're really excited to have our next guest on, from Vivint. We have Miki Seltzer, she's a data scientist. Welcome, Miki. >> Thank you. >> And with her, also, is Raul Olvera, a senior data engineer at Vivint. First off, welcome. >> Thank you. >> So, for people that aren't familiar with Vivint, what is Vivint? >> So, we are a home security and home automation company. >> Okay. >> We've been around for 20 years. We like to make people's homes safer and smarter, and we're trying to do that in a way that customers can just use their home as they normally would, and we learn from what they do, and make their home smarter. >> Okay, so, I won't call you Nest of security, but probably a lot of people say Nest of security, because we always think of Nest, right, as that first smart home appliance that learns about what's going on. So what does that mean when you say that we learn about what you do and how you move about your house, probably your patterns? What does that really mean, when you talk about learning about a person in their house? >> Well, we have a lot of different devices in the user's house, and we can tell when they come home, how they like their thermostat set, and so all of those things, you know, sometimes you have to do that manually. You know, sometimes people have to come home, and they set their thermostat to 72, and when they go to bed, sometimes they have to set it cooler, because they want to save money when they sleep. >> Jeff: Right. >> But with Vivint, you can set all those controls to happen automatically, and Vivint can detect patterns and know you tend to like your home cooler at night. >> Jeff: Okay. >> And you want to save money during the day, because a lot of times, people aren't home during the day, and so, they don't want to run their air conditioning and cool down a house that's not occupied. >> Right, right. >> So we like to use all those patterns, and just make your home smarter, so that it knows how to save you money, and how to make you safer. >> So, that's a lot of data ingest. So, what are the types of sensors, appliances, inputs that you leverage to feed the front end of that process? >> We have motion detectors, there's locks, there's the main panel that you use to interact with the system, the thermostat, the cameras. >> Miki: We've got smoke alarms, carbon monoxide detectors. >> Oh, a whole host of things. >> We've got a whole host of things, yeah. >> Yeah, and then when people put Vivint in, do they usually want to put it in because of that whole array of stuff, or do they usually start with the doorbell camera, or a thermostat, or a carbon monoxide detector? How does that engagement work, and does it grow over time? >> Well, I think the thing that's really important about Vivint is that we're kind of a one-stop shop solution, so a lot of these products are coming out where you can get a thermostat on its own, and you can get a doorbell camera on its own, and you can get a security system on its own, but the good thing about Vivint is that everything is integrated, and an installer will come to your house, and do everything for you. >> Okay. >> And, so, there's not configuration that has to be done. It's kind of, we come in, we set everything up. >> Okay. >> And you're good to go. >> Okay. >> And a lot of times, people will sign up just for security, and then find out that we have all these great products, and all these smarts that go behind it, and it just makes the product that much more valuable to customers. >> Right, because I would imagine the more of the pieces that you integrate, the more value you get out of the whole system. >> Absolutely. >> One and one makes three type of scenario. And then what's the business model? Do they buy the gear, kind of the classic security, you buy the gear and then you have some type of monthly subscription for the service, or how does the business model work? >> So right now, we are moving more towards a you buy everything up front, and then you just pay a monitoring fee, going forward. >> Jeff: Okay, okay. >> So, you will own all of your equipment. >> Okay, great. So, that's on the data collection side. Now you guys are pulling this back in. You both are data scientists, data engineers, so then what are some of the challenges you have, pulling all this for data? I guess the good news is it's all coming from your own systems, right? Or are you pulling data from other systems, as well? >> It's a lot of the sensor data that we have, and I think a lot of the challenge in that is understanding the data, how it behaves, and creating the metrics out of billions and billions of rows of data. >> Jeff: Right. >> For all the customers that we have, so that's one of our challenges, and we do have other sources from CRM, data sources, to NPS, and other systems that we use, that we combine with all of our data from the sensors, just to get a better view of the customer and understand them better. >> Okay, what's NPS? You said NPS. >> Miki: Net Promoter Score. >> Net Promoter Score. >> Net Promoter Score. Okay, good, and then do you use other external stuff like the weather? I would imagine there's other external factors, public dataset, set impact, whether you turn the furnace up or down. >> Yeah, absolutely. We have a whole host of data sources that we use, in order to power the smarts behind. >> Jeff: Okay. >> Behind our products, and weather is absolutely right. That's one of them. We also need information on peoples' homes in order to figure out how long it's gonna take to heat or cool their house. >> Jeff: Okay. >> Because somebody who lives in maybe a condo, it's gonna take a shorter amount of time to heat up their house than somebody that lives in a 3000 square foot house. >> Right, right. Okay, so then you guys get the data, you can analyze the data, you're both smart people. You both are data scientists. How do you package that up in a way for the consumer? Because I would imagine the consumer interface clearly doesn't have billions of rows of data, and doesn't incorporate that, so how have you guys, I don't wanna say dumbed it down, but dumbed it down to the consumer, so they've got a much easier engagement with the system? >> I think we basically work with each business or person, and from their request, we start working with them, understand what they wanna measure, and usually, as with big data happens, you kind of create a story with metrics for them, so we start with that. It's mostly on a request basis. >> Jeff: Okay. >> And we have some automations, just to keep track of some metrics that we like to keep historical measurements. >> Jeff: Right. >> But it's mostly we talk with the business people to see what they want to track, and kind of create our own story with the data that we have. >> Okay, and then I would imagine over time, the objective would be for the system to take over a lot more the control, without engagement with the consumer in their home, right? Ultimately, you wanna learn what they do and start adapting your patterns to how they act, so that their direct engagement with the system decreases over time. >> Yeah, so that's the ultimate goal, is that we can infer all of these data points without having to confirm with the customer that, yes, I'm not home, or yes, I do want my home to be cooler. >> Jeff: Right. >> So that is something that we're working towards. >> Okay. So, you've been at it for a while. 20 years, the company's been around. That's pretty amazing. How have the challenges changed over that course of time? Are you looking at things differently? Are you pulling in more data sources? Or has it changed very much in the last 20 years, or have you just added more to the portfolio, which adds more data input, which is probably a good thing? >> Well, the journey that we've been on really started in about 2014. >> Jeff: Okay. >> When we launched our own platform for security and home automation, because at that point, that's when we started getting the whole fire hose of data. >> Jeff: Okay. >> And so at that point, that was the beginning of our data journey, and when that happened, we kind of had to harness all of that data and figure out what do people want to know? Like, what does our business need to know about how people are using the system? >> Jeff: Right. >> And so at the very beginning, it was simpler questions, but now that we've kind of evolved more, we can answer the more complex questions that don't necessarily have straightforward answers. So, it's kind of evolved from 2014, when we were able to get all of that rich data. >> Jeff: Right. >> From the platform, and it's evolved to now, where we can use all of that data to inform the smarts for our products. >> And I love the way you said that there's not necessarily an answer. >> Mmhmm. >> Right, it's very nuance, right? >> Right. >> Everything's got some type of a score variable or some type of a trade-off, so have you created your own scoring and trade-off tools internally, to help make those value decisions? >> Yeah, so it's really all driven by context. >> Okay. >> So a lot of our data, without any context, it doesn't matter, it doesn't provide any use. We're in a unique situation, where we define our own success metrics. So a lot of times we'll monitor things like what percentage of the time is a camera connected to the internet? Because if it's not connected to the internet, then you can't view it from your phone or from your computer. >> Jeff: Right. >> So... >> So, a tight relationship with Comcast, hopefully? (laughter) We're all together. >> Yeah. >> Okay, so there's that, and then, again, how much of that stuff do you display back to the customer? How much control do they have? How much control do they want? You know, those are all, kind of, squishy decisions, as well. All right, so you're here on behalf of Datameer. So, you chose them. So what was it that attracted you to the Datameer solution? >> I think it's the fact that just interacting with your big data is way simpler than going to, even if it's on a scale environment like HIVE, it takes a longer process to get your data out, and it's more visual, so you're seeing the transformations that you're doing in there, and I think it allows people with a more analytical skill set to get in to the data, and go through the whole journey from knowing the data from almost raw, to getting their own metrics, which I think it adds value for the end product and metrics reports. >> So more value for the people who have the knowledge and the data science jobs. >> Yeah. >> And how many hardcore data scientists do you have in your team? >> On our team, I think we have about five or six hardcore data scientists. >> Five or six? Okay. >> We're kind of split into two different teams. One teams does real time streaming analytics, and our team does more batch analytics. >> Jeff: Okay. >> So we're all using a whole host of different machine learning and data science techniques. >> Jeff: Okay. >> But on the batch side, we use Datameer a lot to be able to transform and pull insights out of that raw data that would be really difficult otherwise. >> Right, and then what about for the people that aren't in your core team? You know, that aren't the more hardcore data scientists. What's been the impact of Datameer and this type of a tool to enable them to see the data, play with the data, create reports, ask for more specific data? What's been the impact for them to be able to actually engage with this data without being a data scientist, per se? >> They can go into Datameer and get answers quicker than, like I mentioned, just writing something that will take longer time, and we also feed data to them because we have more access to historical data, and aggregations, like probabilities, and those type of metrics, we can create for them, and they can utilize that in their more real time environments, and use probably these metrics for creating or, I forget that one... >> Miki: Predicting. >> Predicting, yes. >> Right, right. >> Predicting actions the customer are going to take. >> Right, right, and I wonder if you could speak a little bit about how the two groups work together between the batch and the real time, because a lot of talk about real time, it's the hot, sexy topic right now, but the two go hand in hand, right? They're not either or. So how do you see the relationship between the two groups working? How do you leverage each other? What's the business benefit that you deliver versus the real time people? How does that work out? >> So when you're doing real time and streaming analytics, you really need to have your analytics based in something that's already happened. So we inform our real time analytics by looking at past behaviors, and that helps us develop methodologies that'll be able to go real quick (snaps fingers) in real time. So using past insights to inform our real time analytics is really important to us. >> Which is a big part of the MLPs, right? The machine learning. You build a model based on the past, you take the data that's streaming in now, make the adjustment to continue to modify it. I'm just curious to get your take on the evolution of machine learning and artificial intelligence, and how your guys are leveraging that to get more value out of the data, out of your platform, deliver more value to your customer. Here's an interesting little example. I always joke with people, they think these big things, I'm like, well how about when Google reads your email and puts your flight information on your calendar? I think that's pretty cool. That's a pretty cool application. I mean, are there some cool little ones that you can highlight that may not seem that big to the outside world, but in fact they're really high value things? >> Well, I think one of the biggest challenges for Vivint is something simple like knowing whether there's somebody home. So occupancy has been a big challenge for us because we have all these sensors, and we can easily tell when somebody's home, because they'll have a motion detector, and we'll be able to see that there's somebody moving around the house. However, knowing that somebody is not home is the bigger challenge because the lack of motion in the house doesn't mean that somebody not home. They could be taking a nap, they could be in a room that doesn't have a motion sensor, and so using machine learning algorithms and data science to figure those problems out, it's been really interesting, and it seems like it's a relatively simple problem, but when you break it down, it gets a little more complicated. >> Check their Instagram feed probably, you get a starting point. >> Right. >> Or if the dog is running around, setting off the motion sensors, I'd imagine is another interesting challenge. >> Yeah, that's also a big challenge. >> All right, so as you look forward to 2018, I can't believe this year's already over, what are some of your priorities? What are some of the things that you're working on? If we were to sit down a year from now, what would we be talking about? >> I think create something that is more approachable, as in people can get their own value from it, rather than doing one of timed requests, is when we're moving from on our data journey. >> Right, so basically democratizing the data, democratizing the tools, letting more people engage with it to get their own solutions. >> Yeah, because like Miki said, the data that we're getting, it wasn't available to us until like 2014. So people are just realizing that we have this amount of data, and first the questions come, and they're kind of specific, and eventually you start getting similar requests to the point that, to speed development on other reports, we want to be able to provide some of the more important metrics that we have received in the past years to a more automated way, so that we can keep track of them historically and for people that need to know those metrics. >> Jeff: Miki? >> Yeah, as Raul said, we're trying to move more toward self service. In the past, since our data is constantly evolving, there are not many people who know the context and the nuance of all of our data, so it's been really important for us to work with our business stake holders, so that we know that they're getting the right data with the right context, and so moving towards having them be able to pull their own data is a really big opportunity for us. >> With that context overlay. >> Absolutely. >> So they know what they're actually looking at. It feels so under reported the importance of context to anything, right? Without the context, is it big, is it small, what are we comparing it to? >> Exactly. >> Well, Miki and Raul, thanks for taking a few minutes of your time and sharing your story. Fascinating little look into more about Vivint, and I guess you just have to get more motion sensors around the house, under the bed, keep an eye on that Instagram account, are they taking pictures? >> Let's not be creepy. (laughs) >> Well that's a great line, right? Data science done great is magic, and data science not done well is creepy. So there's a fine line. So thanks again for sharing your story, really appreciate it. >> Thanks for having us. >> And I'm Jeff Frick, and you're watching The Cube. Thanks for tuning in, and we'll catch you next time. Thanks for watching.

>> Welcome back everybody. Jeff Frick here with theCube. We're in the Palo Alto studio talking about customer journeys today. And we're really excited to have professional, who's been doing this for a long time, he's Jeff Weidener, he's an Information Management Professional at this moment in time, and still, in the past and future, Jeff Welcome. >> Well thank you for having me. >> So you've been playing in the spheres for a very long time, and we talked a little bit before we turned the cameras on, about one of the great topics that I love in this area is, the customer, the 360 view of the customer. And that the Nirvana that everyone says you know, we're there, we're pulling in all these data sets, we know exactly what's going on, the person calls into the call center and they can pull up all their records, and there's this great vision that we're all striving for. How close are we to that? >> I think we're several years away from that perfect vision that we've talked about, for the last, I would say, 10, 10 to 15 years, that I've dealt with, from folks who were doing catalogs, like Sears catalogs, all the way to today, where we're trying to mix and match all this information, but most companies are not turning that into actionable data, or actionable information, in any way that's reasonable. And it's just because of the historic kind of Silo, nature of all these different systems, I mean, you know, I keep hearing about, we're gonna do it, all these things can tie together, we can dump all the data in a single data lake and pull it out, what are some of the inhibitors and what are some of the approaches to try to break some of those down? >> Most has been around getting that data lake, in order to put the data in its spot, basically try and make sure that, do I have the environment to work in? Many times a traditional enterprise warehouse doesn't have the right processing power, for you, the individual, who wants to do the work, or, doesn't have the capacity that'll allow you to just bring all the data in, try to ratify it. That's really just trying to do the data cleansing, and trying to just make some sense of it, cause many times, there aren't those domain experts. So I usually work in marketing, and on our Customer 360 exercise, was around, direct mail, email, all the interactions from our Salesmaker, and alike. So, when we look at the data, we go, I don't understand why the Salesmaker is forgetting X, of that behavior that we want to roll together. >> Right. >> But really it's finding that environment, second is the harmonization, is I have Bob Smith and Robert Smith, and Master Data Management Systems, are perhaps few and far between, of being real services that I can call as a data scientist, or as a data worker, to be able to say, how do I line these together? How can I make sure that all these customer touchpoints are really talking about the same individual, the company, or maybe just the consumer? >> Right. >> And finally, it is in those Customer 360 projects getting those teams to want to play together, getting that crowdsourcing, either to change the data, such as, I have data, as you mentioned around Chat, and I want you to tell me more about it, or I want you to tell me how I can break it down. >> Right, right. >> And if I wanna make changes to it, you go, we'll wait, where's your money, in order to make that change. >> Right, right. >> And there's so many aspects to it, right. So there's kind of the classic, you know, ingest, you gotta get the data, you gotta run it through the processes you said did harmonize it to bring it together, and then you gotta present it to the person who's in a position at the moment of truth, to do something with it. And those are three very very different challenges. They've been the same challenges forever, but now we're adding all this new stuff to it, like, are you pulling data from other sources outside of the system of record, are you pulling social data, are you pulling other system data that's not necessarily part of the transactional system. So, we're making the job harder, at the same time, we're trying to give more power to more people and not just the data scientists. But as you said I think, the data worker, so how's that transformation taking place where we're enabling more kind of data workers if you will, that aren't necessarily data scientists, to have the power that's available with the analytics, and an aggregated data set behind them. >> Right. Well we are creating or have created the wild west, we gave them tools, and said, go forth and make, make something out of it. Oh okay. Then we started having this decentralization of all the tools, and when we finally gave them the big tools, the big, that's quote unquote, big data tools, like the process, billings of records, that still is the wild west, but at least we're got them centralized with certain tools. So we were able to do at least standardize on the tool set, standardize on the data environment, so that at least when they're working on that space, we get to go, well, what are you working on? How are you working on that? What type of data are you working with? And how do we bring that back as a process, so that we can say, you did something on Chat Data? Great! Bob over here, he likes to work with that Chat data. So that, that exposure and transparency because of these centralization data. Now, new tools are adding on top of that, data catalogs, and putting inside tools that will make it so that you actually tell, that known information, all-in-one wiki-like interface. So we're trying to add more around putting the right permissions on top of that data, cataloging them in some way, with these either worksheets, or these information management tools, so that, if you're starting to deal with privacy data, you've got a flag, from, it's ingest all the way to the end. >> Right. >> But more controls are being seen as a way that a business is improving its maturity. >> Yeah. Now, the good news bad news is, more and more of the actual interactions are electronic. You want it going to places, they're not picking up the phone as much, as they're engaging with the company either via web browser or more and more a mobile browser, a mobile app, whatever. So, now the good news is, you can track all that. The bad news is, you can track all that. So, as we add more complexity, then there's this other little thing that everybody wants to do now, which is real-time, right, so with Kafka and Flink and Spark and all these new technologies, that enable you to basically see all the data as it's flowing, versus a sampling of the data from the past, a whole new opportunity, and challenge. So how are you seeing it and how are you gonna try to take advantage of that opportunity as well as address that challenge in your world. >> Well in my data science world, I've said, hey, give me some more data, keep on going, and when I have to put on the data sheriff hat, I'm now having to ask the executives, and our stakeholders, why streaming? Why do you really need to have all of this? >> It's the newest shiny toy. >> New shiny toy! So, when you talk to a stakeholder and you say, you need a shiny toy, great. I can get you that shiny toy. But I need an outcome. I need a, a value. And that helps me in tempering the next statement I give to them, you want streaming, so, or you want real time data, it's gonna cost you, three X. Are you gonna pay for it? Great. Here's my shiny toy. But yes, with the influx of all of this data, you're having to change the architecture and many times IT traditionally hasn't been able to make that, that rapid transition, which lends itself to shadow IT, or other folks trying to cobble something together, not to make that happen. >> And then there's this other pesky little thing that gets in the way, in the form of governance, and security. >> Compliance, privacy and finally marketability, I wanna give you a, I want you to feel that you're trusting me, in handling your data, but also that when I respond back to you, I'm giving you a good customer experience so called, don't be creepy. >> Right, right. >> Lately, the new compliance rule in Europe, GDPR, a policy that comes with a, well, a shotgun, that says, if there are violations of this policy, which involves privacy, or the ability for me to be forgotten, of the information that a corporation collects, it can mean four percent of a total company's revenue. >> Right. >> And that's on every instance, that's getting a lot of motivation for information governance today. >> Right. >> That risk, but the rules are around, trying to be able to say, where did the data come from? How did the data flow through the system? Who's touched that data? And those information management tools are mostly the human interaction, hey what are you guys working on? How are you guys working on it? What type of assets are you actually driving, so that we can bring it together for that privacy, that compliance, and workflow, and then later on top of that, that deliverability. How do you want to be contacted? How do you, what are the areas that you feel, are the ways that we should engage with you? And of course, everything that gets missed in any optimization exercise, the feedback loop. I get feedback from you that say, you're interested in puppies, but your data set says you're interested in cats. How do I make that go into a Customer 360 product. So, privacy, and being, and coming at, saying, oh, here's an advertisement for, for hippos and you go, what do you know about me that I don't know? >> Wrong browser. >> So you chose Datameer, along the journey, why did you choose them, how did you implement them, and how did they address some of these issues that we've just been discussing? >> Datameer was chosen primarily to take on that self-service data preparational layer from the beginning. Dealing with large amounts of online data, we move from from taking the digital intelligence tools that are out there, knowing about browser activities, the cookies that you have to get your identity, and said, we want the entire feed. We want all of that information, because we wanna make that actionable. I don't wanna just give it to a BI report, I wanna turn it into marketing automation. So we got the entire feed of data, and we worked on that with the usual SQL tools, but after a while, it wasn't manageable, by either, all of the 450 to 950 columns of data, or the fact that there are multiple teams working on it, and I had no idea, what they were able to do. So I couldn't share in that value, I couldn't reuse, the insights that they could have. So Datameer allowed for a visual interface, that was not in a coding language, that allowed people to start putting all of their work inside one interface, that didn't have to worry about saving it up to the server, it was all being done inside one environment. So that it could take not only the digital data, but the Salesforce CRN data, marry them together and let people work with it. And it broadened on the other areas, again allowing it that crowdsourcing of other people's analytics. Why? Mostly because of the state we are in around IT, is an inability to change rapidly, at least for us, in our field. >> Right. >> That my, the biggest problem we had, was there wasn't a scheduler. We didn't have the ability to get value out of our, on our work, without having someone to press the button and run it, and if they ran it, it took eight hours, they walked away, it would fail. And you had no, you had to go back and do it all over again. >> Oh yeah. >> So Datameer allows us to have that self-service interface, that had management that IT could agree upon, to let us have our own lab environment, and execute our work. >> So what was the results, when you suddenly give people access to this tool? I mean, were they receptive, did you have to train them a lot, did some people just get it and some people just don't, they don't wanna act on data, what was kind of the real-world results of rolling this out, within the population? Real-world results allowed us to get ten million dollars in uplift, in our marketing activities across multiple channels. >> Ten million dollars in uplift? How did you measure that? >> That was measured through the operating expenses, by one not sending that work outside, some of the management, of the data, is being, was sent outside, and that team builds their own models off of them, we said, we should be able to drink our own champagne, second, it was on the uplift of a direct mail and email campaign, so having a better response rate, and generally, not sending out a bunch of app store messages, that we weren't needing too. And then turning that into a list that could be sent out to our email and direct mail vendors, to say, this is what we believe, this account or contact is engaged with on the site. Give those a little bit more context. So we add that in, that we were hopefully getting and resonating a better message. >> Right. >> In, and where did you start? What was the easiest way to provide an opportunity for people new to this type of tooling access to have success? >> Mostly it was trying to, was taking pre-doctored worksheets, or already pre-packaged output, and one of the challenges that we had were people saying well I don't wanna work in a visual language, while they're users of tools like Tableau or Clicks, and others that are happy to drag-and-drop in their data, many of the data workers, the tried-and-true, are saying, I wanna write it in SQL. >> Mm hm. >> So, we had to give at least that last mile, analytical data set to them, and say, okay. Yeah, go ahead and move it over to your SQL environment, move it over into the space that you feel comfortable and you feel confident to control, but let' come on back and we'll translate it back to, this tool, we'll show you how easy it was, to go from, working with IT, which would take months, to go and doing it on yourself, which would take weeks, and the processing and the cost of your Siloed, shadowed IT environment, will go down in days. We're able to show them that, that acceleration of time to market of their data. >> What was your biggest surprise? An individual user, an individual use case, something that really you just didn't see coming, that's kind of a pleasant, you know the law of unintended consequences on the positive side. >> That's was such a wide option, I mean honestly, beginning back from the data science background, we thought it would just be, bring your data in, throw it on out there, and we're done. We went from, maybe about 20 large datasets of AdTech and Martech, and information, advertising, technology, marketing technology, data, to CRMM formation, order activity, and many other categories, just within marketing alone, and I think perhaps, the other big ah-ha moment was, since we brought that in, of other divisions data, those own teams came in, said, hey, we can use this too. >> Right. >> The adoption really surprised me that it would, you would have people that say, oh I can work with this, I have this freedom to work with this data. >> Right right. >> Well we see it time and time again, it's a recurring theme of all the things we cover, which is, you know a really, big piece of the innovation story, is giving, you know, more people access to more data, and the tools to actually manipulate it. So that you can unlock that brain power, as opposed to keeping it with the data scientists on Mahogany Row, and the super-big brain. So, sounds like that really validates that whole hypothesis. >> I went through reviewing hands-on 11 different tools, when I chose Datameer. This was everything from, big name companies, to small start-up companies, that have wild artificial intelligence slogans in their marketing material, and we chose it mostly because it had the right fit, as an end-to-end approach. It had the scheduler, it had the visual interface, it had the, enough management and other capabilities that IT would leave us alone. Some of the other products that we were looking at gave you, Pig-El-Lee to work with data, will allow you to schedule data, but they never came all together. And for the value we get out of it, we needed to have something altogether. >> Right. Well Jeff, thanks for taking a few minutes and sharing your story, really appreciate it, and it sounds like it was a really successful project. >> Was! >> All right. He's Jeff Weidener, I'm Jeff Frick, you're watching theCube from Palo Alto. Thanks for watching.