>> Live from San Francisco, it's theCUBE, covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. >> Hello everyone, welcome back. It's theCUBE here in San Francisco, live coverage of Google Cloud Next 2018. I'm John Furrier, Dave Vellante, our next guest is Ben Evans, who is the director of strategic alliances at Cisco, and Connie Tang, director of product management at Cisco here to talk about the alliance with Google Cloud and the relevance of the partnership around the collab. Welcome to theCUBE, thanks for joining us. >> My pleasure to be here. >> So, we've been covering Cisco for a long time, most recently with theCUBE in Orlando, and DevNet creates huge surge of developer action going on across the Cisco ecosystem, not just network engineering stuff, the normal Cisco greatness, but up the stack with the collaboration side just cloud natives attracting and really giving a lot of energy to the developers and customers at Cisco. So, the partnership with Google is interesting. So, can you guys just share the big news, the Cisco news and how that relates to the Google Cloud. >> Yeah, absolutely, so firstly, Connie and myself have been working on this partnership for quite a while. And, as you'd said, there's multi, kind of, facets to this. There's the developer piece, so the SDKs are announcing around Android and the way that developers can now imbed calling and meeting and messaging inside their specific applications, their vertical applications. And, then there's also native integrations we're getting into around scheduling meetings from calenderings. I can go in and schedule a Webex meeting very easily. It was talked about on stage, 74 percent of, sort of, document collaboration involves some sort of co-collaboration, so it's a very kind of peanut butter and chocolate as you think about Cisco's portfolio of real time communications and meetings and how this is evolving into the team collaboration experience. Together with Google's portfolio in terms of AI and how that fits in to ultimate these work flows and make life easier for users. And, also just how this comes together in a very seamless way to enable this kind of real time collaboration and creation of documents. >> So, take us inside the partnership. How did it start? I mean, it seems like a match made in heaven. You guys aren't trying to create your own infrastructures of service. Google needs an enterprise presence, so obviously Cisco has a huge enterprise presence. But, how did it start and where did it start? >> We actually started engaging with Cisco over a year ago, and different groups start engaging because there's actually customer demand from our corporate enterprise customers wanting better integration of a collab portfolio into various aspects of G Suite. So, we worked with the calendering team because they're coming up with a brand new architecture, and so we're actually one of four front partners who work directly with them providing them feedback in what enterprises what, and then integrating our scheduling capabilities of Webex meetings directly into Google Calender. So that's one piece, and then we also work with the Chromebook group because more and more customers are starting to use and deploy Chromebook, and so they want to have an ability to start Webex meetings and be able to share content and actually join Webex meetings directly on Chromebook. So, there's another effort that went on separately. And then there's a third effort that goes on with the Chrome group where we're leveraging the WebRTC within Chrome, so that people can join Webex meeting directly without having to do download any client. So, they just open the web browser. They can have audio. They can have HD video. They can see the share. They can share content just on Chrome. >> When? >> This is what we've been waiting for with cloud. This is really, I want to expand on this notion of services. >> Yes. >> And service centric view because it has to be clean whether it's an EPI, a message que, or an event. The user experience's got to be integrated very cleanly. >> Yes. >> This is really kind of, the ah-ha moment of when people taste the Cloud, and that's the benefit. Can, because this is really interesting. You've got Webex, you've got G Suite. Two different applications. >> Very different, yes. >> This is the benefit of the services. Can you just explain the importance and why IT and why enterprises want this. >> Enterprises want ease of use. Ease of use, ease of access, and ease of deployment. So, Chrome solves that problem. There's no deployment required, right? It's already there, it's available on every desktop. And, the one simple click to join and schedule a meeting makes it easy to use, so with that combination, end use is adopted really, really quickly. So, we're seeing some of the fastest adoption of web clients based on those kind of ease of use and ease of joining. >> How has the product uptake been? Because if you have a seamless user experience, you're probably getting more customers coming in, integrating in... >> Yes. >> From G Suite and vice versa. They're getting lift. How is that partnership working? Can you share some color around that? >> Yes, as Connie said, we've really seen it's accelerating. One stat I'll share is during March, we were adding around 11 hundred new G Cal integrations every day, so we were seeing customers that were using Webex meeting, they were using G cal, and they wanted those things to work better together. So, integrating those calendars to make it easier to schedule and join meetings. So, yeah, that's 11 hundred a day. It's pretty good uptake considering we weren't really promoting it. It was just there and available to that existing customer base, so. >> What can you guys share to enterprise IT, application developers, or managers who have traditionally lived in a stone pipe world of like, let's build an app, and we'll distribute the app, and you log in, you do all the things, monolithic app. To a world that's services lead are service centric where you still do an app, but you got to think differently around some of the design criteria around integrating in with other apps. What's some of the best practices that you guys have found? Because you've seen the network all the way up to the application stack issues. You've got Kubernetes and all these new things. What are some of the best practices that companies should be developing around? >> So, what I've seen companies most concerned about is applications affecting other applications on the desktop, and hence, breaking some of their services. The web services kind of completely remove that. Because there's a web browser, they don't have to worry about it impacting any of their installed applications. And so, what we find out as IT looks into this mode of deployment, it's not really a deployment, it's an enablement. They actually really advertise it to their end users. They actually rather end users use the web client than to have to install, and they have to test and slow the roll out. >> What do you guys see as, I mean, I'm old enough to remember when Lotus Notes was the state of the art collaboration. (laughs) >> That's real old. Man, that's old. >> I was digging myself. So, now you're talking a lot about integration, simplifying the experience, obviously video has come into play. >> Yes. What do you guys see as the mega trends and maybe give us a little glimpse of the road map as to what we can expect going forward whether it's AI or other data? Where does that all fit in? >> Yeah, I think you nailed it. So, there's this kind of better together, easy join, it's just table stakes right now. The ability for me to easily join a meeting, but where that's really rapidly going is the AI space. So, how can I augment that meeting? Before I join, how do I know about you as individuals, what you care about, what's happening with your company? So, a company acquisition we did recently, you know, fits into that in terms of how do we start surfacing information about the people. If I'm in the meeting, if I want to be able to click on someone and get more context about them. What happened in my previous engagements, what have we previously talked about? How do we surface that up in a timely fashion? And, when again you think about Google Calender and the information it knows about you as an individual, Cisco with the kind of matrix of who you're calling and what meetings have taken place, there's kind of a tantalizing thing there about how you blend that together. So, you surface the information, you automate this kind of, the repetitive, more mundane tasks, and free the people up to focus more on innovation and collaboration relationships. >> And the analytics opportunity is pretty big. >> Yeah, absolutely. >> I mean Diane Green said in her keynote, security is the number one worry, AI is the number one opportunity. By freeing up the mundane tasks, automating that away, the value will shift to up the stack. We were using a metaphor with Jennifer Lynd from Google. You know, when the horse and buggy was, you know, killed by the car, those jobs went away. There was no need for stuff, you know, the horse, the hay, and all that stuff. IT, same thing. Things are shifting, operations are changing. >> Yeah. >> This is fundamental. >> Context is a great example of that. You know, if you look at what's happening in that market, you know, the predictions that they're call flows are going to decrease isn't really happening. What's happening is you're going to multi-channel, and people are doing the more basic stuff online, just fixing issues, but when it becomes complex, when it becomes relationship, it becomes high enough value, then you want the personal interaction, so I think the way personally I look at AI is it will free up computers. They're doing this kind of more repetitive finding patterns, but when it comes to talking to the doctor about, you know, your condition or you're trying to build relationships, there's things that people just naturally do very well. And, plowing through lots of data to find patterns, we don't do great, so. >> It's actually quite amazing when you look at the trends over the last decade or so in terms of collaboration. I mean, it used to be, I was joking about Lotus Notes, but it used to be you'd request people to show up 15 minutes early so you could sort out all the problems. And now today, if you're like a minute late, people are like texting you, "Where are you? Let's go." So, we become so much more productive, and the protocol has changed. So, when you think about how machine intelligence is going to affect productivity going forward, it's potentially massive. >> Yeah, we see massive opportunities. As you know, to really get the benefit from AI, you need some pretty big data sets, so again, just thinking about Webex for a second, six billion minutes a month in meetings. I'm not saying we're going to push all that straight into Google, but when you think about what's tied up in those six billion minutes. >> A lot of video. >> What's been discussed, how easily can I unlock that? How do I get insights from it? How do I train models? It's like, again, the combination of huge data sets. >> AI would be just amazing. You just go, "Hey, I missed that Webex. Give me the highlight reel." >> Yes. >> Exactly. >> That would be great. >> Not only that, but how do you customize that for the individuals? >> Or if I missed the first ten minutes, can I go scroll back? Can I actually review, get the transcription? And, if I need some additional information, can I just pull it up and it shows up, you know, for me within the meeting, right? So, there's just massive opportunities that we're looking at. >> And, the user expectations, the new experience, that's what people are really designing around, what they're expectations should be. >> Yes. >> And they're making that user... Okay, Connie and Ben, I want to get one last question in before we break. Two parts, for each of you. What's the most important story from your perspective here at the show this week that you're talking about and sharing, and what's next for you guys? Ben, we'll start with you. >> So, yeah, my two answers are firstly, the initial kind of integrations we're putting together. People should go check that out because, you know, there's some very compelling use cases that we're fixing there. But, the big item is Cisco and Google working together to really tackle this kind of future of work, and the combination of those two portfolios is going to unlock some really interesting opportunities, and that's what the teams are kind of getting together, working on, defining, and stay tuned to kind of see those phase two, phase three deliverables. >> Future words. Great, Connie, from a product perspective, what's the hottest things that you've been talking about here, most important, and then what's next. >> Yeah, for us, it's really the Google and Cisco coming together in a collaboration space, working together to make it much easier and simpler for customers to deploy and use the products. And, also to explore new opportunities in transcription and AI, leveraging Google Assist right to, and just make it even better in the future. >> Scale up the experience. >> Yes. >> Probably expect some great developer opportunities going on. >> Yes. >> Exploring and reinventing the enterprise. That was Diane Green's theme. She'll be here on theCUBE breaking it down. I'm John Furrier with Dave Vellante. Live coverage, here we have Cisco collaboration inside theCUBE, big relationship, expansion with Google. New product integrations, the value of the services within the cloud. The new model for development and user experience. theCUBE bringing you all the content here on the floor. Stay with us for more live coverage after the short break. (upbeat music)

(inspirational music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are in our Palo Alto studio. The conference season hasn't really kicked off yet into full swing so we can do a lot more kind of intimate stuff here in the studio, for a CUBE Conversation. And we're really excited to have a many time CUBE alum on, and a new guest, both from Western Digital. So Dave Tang, Senior Vice President at Western Digital. Great to see you again, Dave. >> Great to be here, Jeff. >> Absolutely and Martin Fink, he is the Chief Technology Officer at Western Digital, a longtime HP alum. I'm sure people recognized you from that and our great machine keynotes we were talking about it. So great to finally meet you, Martin. >> Thank you, nice to be here. >> Absolutely, so you guys are here talking about and we've got an ongoing program actually with Western Digital about Data Makes Possible, right. With all the things that are going on in tech at the end of the day, right, there's data, it's got to be stored somewhere and then of course there's processes and things going on. We've been exploring media and entertainment, sports, healthcare, autonomous vehicles, you know. All the places that this continues to reach out and it's such a fun project because you guys are a rising tide, lifts all boats, kind of company and really enjoy watching this whole ecosystem grow. So I really want to thank you for that. But now there's some new things that we want to talk about that you guys are doing to continue really in that same theme, and that's the support of this RISC-V. So first off, for people who have no idea, what is RISC-V? Let's jump into that and then kind of what is the announcement and why it's important. >> Sure, so RISC-V is an, you know, the tagline is, it's an open source instruction set architecture. So what does that mean, just so people can kind of understand. So today the world is dominated by two instruction set architectures. For the most part the, we'll call the desktop enterprise world is dominated by the Intel instruction set architecture and that's what's in most PCs, what people talk about as x86. And then the embedded and mobile space tends to be dominated by Arm, or by Arm Holdings. And so both of those are great architectures but they're also proprietary, they're owned by their respective companies. So RISC-V is essentially a third entrant, we'll say, into this world, but the distinction is that it's completely open source. So everything about the instruction set is available to all and anybody can implement it. We can all share the implementations. We can share the code that makes up that instruction set architecture, and very importantly for us and part of our motivation is the freedom to innovate. So we now have the ability to modify the instruction set or change the implementation of the instruction set, to optimize it for our devices and our storage and our drives, etc. >> So is this the first kind of open source play in microprocessor architecture? >> No, there's been other attempts at this. OpenSpark kind of comes to mind, and things like that, but the ability to get a community of individuals to kind of rally around this in a meaningful way has really been a challenge. And so I'd say that right now, RISC-V presents probably the best sort of clean slate, let's take some thing new to the market out there. >> So open source, obviously we've seen you know, take over the software world, first in the operating system which everybody is familiar with Linux but then we see it time and time again in different applications, Hadoop. I mean, there's just a proliferation of open source projects. The benefits are tremendous. Pretty easy to ascertain at a typical software case, how is that going to be applied do you think within the microprocessor world? >> So it's a little bit different. When we're talking about open source hardware or open source chips and microprocessors, you're dealing with a physical device. So even though you can open source all of the designs and the code associated with that device, you still have to fabricate it. You still have to create a physical design and you still have to call up a fab and say, will you make this for me at these particular volumes? And so that's the difference. So there are some differences between open source software where it's, you know, you create the bits and then you distribute those bits through the Internet and all is good. Whereas here, you still have a physical need to fabricate something. >> Now, how much more flexibility can you do then for the output when you can actually impact the architecture as opposed to just creating a custom chip design, on top of somebody else's architecture? >> Well, let me give you probably a really simple, concrete example that kind of people can internalize of some of our motivation behind this, because that might sort of help get people through this. If you think of a very typical surveillance application, you have a camera pointed into a room or a hallway. The reality is we're basically grabbing a ton of video frames but very few of them change, right? So the typical surveillance application is it never changes and you really want, only know when stuff changes. Well, today, in very simple terms, all of those frames get routed up to some big server somewhere and that server spends a lot of time trying to figure out, okay have I got a frame that changed? Have I got a frame that changed, and so on. And then eventually it'll find maybe two or three or five frames that have got something interesting. So in the world what we're trying to do is to say, okay well why don't we take that, find no changes, and push that right down to the device? So we basically store all those frames, why don't we go figure out all the frames that mean nothing, and only ship up to that big bad server the frames that have something interesting and something you want to go analyze and do some work on? So that's a very typical application that's quite meaningful because we can do all of that work at the device. We can eliminate shipping a whole bunch of data to where it's just going to get discarded anyways, and we can allow the end customer to really focus on the data that matters, and get some intelligence. >> And that's critical as we get more and more immersed in a data-centric world, where we have realtime applications like Martin described as well as large data-centric applications like of course, big data analytics, but also training for AI systems or machine learning. These workloads are going to become more and more diverse and they're going to need more specialized architectures and more specialized processing. So big data is getting bigger and faster and these realtime fast data applications are getting faster and bigger. So we need ways to contend with that, that really go beyond what's available with general purpose architectures. >> So that's a great point because if we take this example of video frames, now if I can build a processor that is customized to only do that, that's the only thing it does. It can be very low power, very efficient, and do that one thing very very well, and the cost adder, if you want to call it that, to the device where we put it, is a tiny fraction, but the cost savings of the overall solution is significant. So this ability to customize the instruction set to only do what you need it to do for that very special purpose, that's gold. >> So I just wanted to, Dave, we've talked about a lot of interesting innovations that you guys have come up with over the years, with the helium launch. Which I don't know, a couple, two, three years ago, you were just at the MAMR event, really energy assisted recording. So this is really kind of foundational within the storage and the media itself and how you guys do better and take advantage of evolving land space. This is a kind of a different play for Western Digital, this isn't a direct kind of improvement in the way that storage media and architecture works but this is really more of, I'm going to ask you. What is the Western Digital play here? Why is this an important space for you guys in your core storage business? >> Well we're really broadening our focus to really develop and innovate around technologies that really help the world extract more value from data as a whole, right. So it's way beyond storage these days, right. We're looking for better ways to capture, preserve, access, and transform the data. And unless you transform it, you can't really extract the value out of it so as we see all these new applications for data and the vast possibilities for data, we really want to pave the path and help the industry innovate to bring all those applications to reality. >> It's interesting too because one of the great topics always in computing is you know, you got compute and store, which has to go to which, right. And nobody wants to move a lot of data, that's hard and may or may not be easy to get compute. Especially these IoT applications, remote devices, tough conditions and power, which we mentioned a little bit before we went on air. So the the landscape for the for the need for compute and store in networking is radically changing than either the desktop or what we're seeing a consolidation in clouds. So what's interesting here, where does the scale come, right? At the end of the day, scale always wins. And that's where we've seen historically where the general-purpose microprocessor architectures is dominated but used to be a slew of specialty purpose architectures but now there's an opportunity to bring scale to this. So how does that scale game continue to evolve? >> So it's a great point that scale does matter and we've seen that repeatedly and so it's a significant part of the reason why we decided to go early with a significant commitment was to tell the world that we were bringing scale to the equation. And so what we communicated to the marketplace is we ship on the order of a billion processor cores a year, most people don't realize that all of our devices from USB sticks to hard drives, all have processors on them. And so we said, hey we're going to basically go all-in and go big and that translates into a billion cores that we ship every year and we're going to go on a program to essentially migrate all of those cores to RISC-V. It'll take a few years to get there but we'll migrate all of those cores and so we basically were signaling to the market, hey scale is now here. Scale is here, you can make the investments, you can go forward, you can make that commitment to RISC-V because essentially we've got your back. >> So just to make sure we get that clear. So you guys have announced that you're going to slowly migrate over time your micro processors that power your devices to the tune of approximately a billion with a B, cores per year to this new architecture. >> That is correct. >> And has that started? >> So the design has started. So we have started to design and develop our first two cores but the actual manifestation into devices probably in the early stage of 2020. >> Okay, okay. But that's a pretty significant commitment and again, the ideas you explicitly said it's a signal to the ecosystem, this is worth your investment because there is some scale here. >> Martin: That's right. >> Yeah, pretty exciting. And how do you think it's going to open up the ability for you to do new things with your devices that you before either couldn't do or we're too expensive with dollars or power. >> Martin: So we're going to step and iterate through this and one key point here is a lot of people tend to want to start in this processor world at the very high end, right. I'm going to go take on a Xeon processor or something like that. It's not what we're doing. We're basically saying, we're going to go at the small end, the tiny end where power matters. Power matters a lot in our devices and where can we achieve the optimum combination of power and performance. So even in our small devices like a USB stick or a client SSD or something like that, if we can reduce power consumption and even just maintain performance that's a huge win for our customers, you know. If you think about your laptop and if I reduce the power consumption of that SSD in there so that you have longer battery life and you can get you know through the day better, that's a huge win, right. And I don't impact performance in the process, that's a huge win. So what we do, what we're doing right now is we're developing the cores based on the RISC-V architecture and then what we're going to do is once we've got that sort of design, sort of complete is we want to take all of the typical client workloads and profile them on that. Then we want to find out, okay where are the hot spots? What are the two or three things that are really consuming all the power and how do we go optimize, by either creating two or three instructions or by optimizing the micro architecture for an existing instruction. And then iterate through that a few times so that we really get a big win, even at the very low end of the spectrum and then we just iterate through that with time. >> We're in a unique position I think in that the technologies that we develop span everything from the actual media where the bits are stored, whether it's solid-state flash or rotating magnetic disk and the recording heads. We take those technologies and build them all the way up into devices and platforms and full-fledged data center systems. And if we can optimize and tune all the way from that core media level all the way up through into the system level, we can deliver significantly higher value, we believe, to the marketplace. So this is the start of that, that enables us to customize command sets and optimize the flow of data so that we can we can allow users to access it when and where they need it. >> So I think there's another actually really cool point, which goes back to the open source nature of this and we try to be very clear about this. We're not going to develop our cores for all applications. We want the world to develop all sorts of different cores. And so for many applications somebody else might come in and say, hey we've got a really cool core. So one of the companies we've partnered with and invested in for example, is Esperanto. They've actually decided to go at the high end and do a machine learning accelerator. Hey, maybe we'll use that for some machine learning applications in our system level performance. So we don't have to do it all but we've got a common architecture across the portfolio and that speaks to that sort of open source nature of the RISC-V architecture is we want the world to get going. We want our competitors to get on board, we want partners, we want software providers, we want everybody on board. >> It's such a different ecosystem with open-source and the way the contributions are made and the way contributions are valued and the way that people can go find niches that are underserved. It's this really interesting kind of bifurcation of the market really, you don't really want to be in the big general-purpose middle anymore. That's not a great place to be, there's all kinds of specialty places where you can build the competence and with software and you know with, thank goodness for Moore's law decreasing prices of the power of the compute and now the cloud, which is basically always available. Really a exciting time to develop a myriad of different applications. >> Right and you talked before about scale in terms of points of implementation that will drive adoption and drive this to critical mass but there's another aspect of scale relative to the architecture within a single system that's also important that I think RISC-V helps to break down some barriers. Because with general purpose computer architectures, they assume a certain ratio of memory and storage and processing and bandwidth for interconnect and if you exceed those ratios, you have to add a whole new processor. Even though you don't need to need the processing capability, you need it for scale. So that's another great benefit of these new architectures is that the diversity of data needs where some are going to be large data sets, some are going to be small data sets that need need high bandwidth. You can customize and blend that recipe as you need to, you're not at the mercy of these fixed ratios. >> Yeah and I think you know it's so much of kind of what is cloud computing. And the atomic nature of it, that you can apply the ratios, the amount that you need as you need, you can change it on the fly, you can tone it up, tone it down. And I think the other interesting thing that you touched on is some of these new, which are now relatively special-purpose but are going to be general-purpose very soon in terms of machine learning and AI and applying those to different places and applying them closer to the problem. It's a very very interesting evolution of the landscape but what I want to do is kind of close on you Martin, especially because again kind of back to the machine. Not the machine specifically but you have been in the business of looking way down the road for a long time. So you came out, I'd looked at your LinkedIn, you retired for three months, congratulations. (laughs) Hope you got some my golf in but you came back to Western Digital so why did you come back? And as you look down the road a ways, what do you see that it excites you, that got you off that three-month little tour around the golf course and I'm sorry I had to tease about that. But what do you see? What are you excited about that you came back and got involved in an open source microprocessor project? >> So the the short answer was that, I saw the opportunity at Western Digital to be where data lives. So I had spent my entire career, will call it at the compute or the server side of things and the interesting thing is I had a very close relationship with SanDisk, which was acquired by Western Digital. And so I had, we'll call it an insider view, of what was possible there and so what triggered was essentially what we're talking about here was given that about half the world's data lands on Western Digital devices, taking that from a real position of strength in the marketplace and say, what could we go do to make data more intelligent and rather than start kind of at that server end and so that I saw that potential there and it was just incredible, so that's that's what made me want to join. >> Exciting times. Dave good get. (laughs) >> We're delighted to have Martin with us. >> All right, well we look forward to watch it evolve. We've got another another whole set of events we're going to do again together with Western Digital that we're excited about. Again, covering Data Makes Possible but you know kind of uplifting into the application space as a lot of the cool things that people are doing in innovation. So Martin, great to finally meet you and thanks for stopping by. >> Thanks for the time. >> David as always and I think we'll see in a month or so. >> Right, always a pleasure Jeff, thanks. >> All right Martin Fink, Dave Tang. I'm Jeff Frick, you're watching theCUBE. Thanks for watching, we'll catch you next time. (inspirational music)

(upbeat techno music) >> Announcer: Live from San Jose, California it's theCUBE, covering Innovating to Fuel the Next Decade of Big Data, brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick here at theCUBE. We're at the Western Digital Headquarters off Almaden down in San Jose, a really important place. Western Digital's been here for a while, their headquarters. A lot of innovation's been going on here forever. So we're excited to be here really for the next generation. The event's called Innovating to Fuel the Next Generation of big data, and we're joined by many time Cuber, Dave Tang. He is the SVP in corporate marketing from Western Digital. Dave, always great to see you. >> Yeah. Always great to be here, Jeff. Thanks. >> Absolutely. So you got to MC the announcement today. >> Yes. >> So for the people that weren't there, let's give them a quick overview on what the announcement was and then we can dive in a little deeper. >> Great, so what we were announcing was a major breakthrough in technology that's going to allow us to drive the increase in capacity in density to support big data for the next decade and beyond, right? So capacities and densities had been starting to level off in terms of hard drive technology capability. So what we announced was microwave-assisted magnetic recording technology that will allow us to keep growing that areal density up and reducing the cost per terabyte. >> You know, it's fascinating cause everyone loves to talk about Moore's Law and have these silly architectural debates, whether Moore's Law is alive or dead, but, as anyone who's lived here knows, Moore's Law is really an attitude much more it is than the specific physics of microprocessor density growth. And it's interesting to see. As we know the growth of data is growing in giant and the types of data, and not only regular big data, but now streaming data are bigger and bigger and bigger. I think you talked about stuff coming off of people and machines compared to business data is way bigger. >> Right. >> But you guys continue to push limits and break through, and even though we expect everything to be cheaper, faster, and better, you guys actually have to execute it-- >> Dave: Right. >> Back at the factory. >> Right, well it's interesting. There's this healthy tension, right, a push and pull in the environment. So you're right, it's not just Moore's Law that's enabling a technology push, but we have this virtuous cycle, right? We've realized what the value is of data and how to extract the possibilities and value of data, so that means that we want to store more of that data and access more of that data, which drives the need for innovation to be able to support all of that in a cost effective way. But then that triggers another wave of new applications, new ways to tap into the possibilities of data. So it just feeds on itself, and fortunately we have great technologists, great means of innovation, and a great attitude and spirit of innovation to help drive that. >> Yeah, so for people that want more, they can go to the press releases and get the data. We won't dive deep into the weeds here on the technology, but I thought you had Janet George speak, and she's chief data scientist. Phenomenal, phenomenal big brain. >> Dave: Yes. >> A smart lady. But she talked about, from her perspective we're still just barely even getting onto this data opportunity in terms of automation, and we see over and over at theCUBE events, innovation's really not that complicated. Give more people access to the data, give them more access to the tools, and let them try things easier and faster and feel quick, there's actually a ton of innovation that companies can unlock within their own four walls. But the data is such an important piece of it, and there's more and more and more of this. >> Dave: Right, right. >> What used to be digital exhaust now is, I think maybe you said, or maybe it was Dave, that there's a whole economy now built on data like we used to do with petroleum. I thought that was really insightful. >> Yeah, right. It's like a gold mine. So not only are the sources of data increasing, which is driving increased volume, but, as Janet was alluding to, we're starting to come up with the tools and the sophistication with machine learning and artificial intelligence to be able to put that data to new use as well as to find the pieces of data to interconnect, to drive these new capabilities and new insights. >> Yeah, but unlike petroleum it doesn't get used up. I mean that's the beauty of data. (laughing) >> Yeah, that's right. >> It's a digital process that can be used over and over and over again. >> And a self-renewing, renewing resource. And you're right, in that sense that it's being used over and over again that the longevity of that data, the use for life is growing exponentially along with the volume. >> Right, and Western Digital's in a unique position cause you have systems and you have big systems that could be used in data centers, but you also have the media that powers a whole bunch of other people's systems. So I thought one of the real important announcements today was, yes it's an interesting new breakthrough technology that uses energy assist to get more density on the drives, but it's done in such a way that the stuff's all backward compatible. It's plug and play. You've got production scheduled in a couple years I think with test out the customers-- >> Dave: That's right. >> Next year. So, you know, that is such an important piece beyond the technology. What's the commercial acceptance? What are the commercial barriers? And this sounds like a pretty interesting way to skin that cow. >> Right, often times the best answers aren't the most complex answers. They're the more elegant and simplistic answers. So it goes from the standpoint of a user being able to plug and play with older systems, older technologies. That's beautiful, and for us, to be able to, the ability to manufacture it in high volume reliably and cost effectively is equally as important. >> And you also talked, which I think was interesting, is kind of the relationship between hard drives and flash, because, obviously, flash is a, I want to say the sexy new toy, but it's not a sexy new toy anymore. >> Right. >> It's been around for a while, but, with that pressure on flash performance, you're still seeing the massive amounts of big data, which is growing faster than that. And there is a rule for the high density hard drives in that environment, and, based on the forecast you shared, which I'm presuming came from IDC or people that do numbers for a living, still a significant portion of a whole lot of data is not going to be on flash. >> Yeah, that's right. I think we have a tendency, especially in technology, to think either or, right? Something is going to take over from something else, but in this case it's definitely an and, right. And a lot of that is driven by this notion that there's fast data and big data, and, while our attention seems to shift over to maybe some fast data applications like autonomous vehicles and realtime applications, surveillance applications, there's still a need for big data because the algorithms that drive those realtime applications have to come from analysis of vast amounts of data. So big data is here to stay. It's not going away or shifting over. >> I think it's a really interesting kind of cross over, which Janet talked about too, where you need the algorithms to continue sharing the system that are feeding, continuing, and reacting to the real data, but then that just adds more vocabulary to their learning set so they can continue to evolve overtime. >> Yeah, what really helps us out in the market place is that because we have technologies and products across that full spectrum of flash and rotating magnetic recording, and we sell to customers who buy devices as well as platforms and systems, we see a lot of applications, a lot of uses of data, and we're able to then anticipate what those needs are going to be in the near future and in the distant future. >> Right, so we're getting towards the end of 2017, which I find hard to say, but as you look forward kind of to 2018 and this insatiable desire for more storage, cause this insatiable creation of more data, what are some of your priorities for 2018 and what are you kind of looking at as, like I said, I can't believe we're going to actually flip the calendar here-- >> Dave: Right, right. >> In just a few short months. (laughing) >> Well, I think for us, it's the realization that all these applications that are coming at us are more and more diverse, and their needs are very specialized. So it's not just the storage, although we're thought of as a storage company, it's not just about the storage of that data, but you have contrive complete environments to capture and preserve and access and transform that data, which means we have to go well beyond storage and think about how that data is accessed, technical interfaces to our memory products as well as storage products, and then where compute sits. Does it still sit in a centralized place or do you move compute to out closer to where the data sits. So, all this innovation and changing the way that we think about how we can mine that data is top of the mind for us for the next year and beyond. >> It's only job security for you, Dave. (laughing) >> Dave: Funny to think about. >> Alright. He's Dave Tang. Thanks for inviting us and again congratulations on the presentation. >> Always a pleasure. >> Alright, Dave Tang, I'm Jeff Frick. You're watching theCUBE from Western Digital headquarters in San Jose, California. Thanks for watching. (upbeat techno music)

>> Presenter: From the Fairmont Hotel, in the heart of Silicon Valley, it's theCUBE. Covering When IoT Met AI The Intelligence of Things. Brought to you by Western Digital. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Jose at the Fairmont Hotel, at an event called When IoT Met AI The Intelligence of Things. You've heard about the internet of things, and on the intelligence of things, it's IoT, it's AI, it's AR, all this stuff is really coming to play, it's very interesting space, still a lot of start-up activity, still a lot of big companies making plays in this space. So we're excited to be here, and really joined by our host, big thanks to Western Digital for hosting this event with WDLabs' Dave Tang. Got newly promoted since last we spoke. The SVP of corporate marketing and communications, for Western Digital, Dave great to see you as usual. >> Well, great to be here, thanks. >> So I don't think the need for more storage is going down anytime soon, that's kind of my takeaway. >> No, no, yeah. If this wall of data just keeps growing. >> Yeah, I think the term we had yesterday at the Ag event that we were at, also sponsored by you, is really the flood of data using an agricultural term. But it's pretty fascinating, as more, and more, and more data is not only coming off the sensors, but coming off the people, and used in so many more ways. >> That's right, yeah we see it as a virtual cycle, you create more data, you find more uses for that data to harness the power and unleash the promise of that data, and then you create even more data. So, when that virtual cycle of creating more, and finding more uses of it, and yeah one of the things that we find interesting, that's related to this event with IoT and AI, is this notion that data is falling into two general categories. There's big data, and there's fast data. So, big data I think everyone is quite familiar with by this time, these large aggregated likes of data that you can extract information out of. Look for insights and connections between data, predict the future, and create more prescriptive recommendations, right? >> Right. >> And through all of that you can gain algorithms that help to make predictions, or can help machines run based on that data. So we've gone through this phase where we focused a lot on how we harness big data, but now we're taking these algorithms that we've gleaned from that, and we're able to put them in real time applications, and that's sort of been the birth of fast data, it's been really-- >> Right, the streaming data. We cover Spark Summit, we cover Flink, and New, a new kind of open source project that came out of Berlin. That some people would say the next generation of Spark, and the other thing, you know, good for you guys, is that it used to be, not only was it old data, but it was a sampling of old data. Now on this new data, and the data stream that's all of the data. And I would actually challenge, I wonder if that separation as you describe, will stay, because I got to tell you, the last little drive I bought, just last week, was an SSD drive, you know, one terabyte. I needed some storage, and I had a choice between spinning disc and not, and I went with the flat. I mean, 'cause what's fascinating to me, is the second order benefits that we keep hearing time, and time, and time again, once people become a data-driven enterprise, are way more than just that kind of top-level thing that they thought. >> Exactly, and that's sort of that virtual cycle, you got to taste, and you learn how to use it, and then you want more. >> Jeff: Right, right. >> And that's the great thing about the breadth of technologies and products that Western Digital has, is from the solid state products, the higher performance flash products that we have, to the higher capacity helium-filled drive technologies, as well as devices going on up into systems, we cover this whole spectrum of fast data and big data. >> Right, right. >> I'll give an example. So credit card fraud detection is an interesting area. Billions of dollars potentially being lost there. Well to learn how to predict when transactions are fraudulent, you have to study massive amounts of data. Billions of transactions, so that's the big data side of it, and then as soon as you do that, you can take those algorithms and run them in real time. So as transactions come in for authorization, those algorithms can determine, before they're approved, that one's fraudulent, and that one's not. Save a lot of time and processing for fraud claims. So that's a great example of once you learn something from big data, you apply it to the real-time realm, and it's quite dire right? And then that spawned you to collect even more data, because you want to find new applications and new uses. >> Right, and too kind of this wave of computing back and forth from the shared services computer, then the desktop computer, now it's back to the cloud, and then now it's-- >> Dave: Out with the edge. >> IoT, it's all about the edge. >> Yeah, right. >> And at the end of the day, it's going to be application-specific. What needs to be processed locally, what needs to be processed back at the computer, and then all the different platforms. We were again at a navigation for autonomous vehicles show, who knew there was such a thing that small? And even the attributes of the storage required in the ecosystem of a car, right? And the environmental conditions-- >> That's right. >> Is the word I'm looking for. Completely different, new opportunity, kind of new class of hardware required to operate in that environment, and again that still combines cloud and Edge, sensors and maps. So just the, I don't think that the man's going down David. >> Yeah, absolutely >> I think you're in a good spot. (Jeff laughing) >> You're absolutely right, and even though we try to simplify into fast data, and big data, and Core and Edge, what we're finding is that applications are increasingly specialized, and have specialized needs in terms of the type of data. Is it large amounts of data, is it streaming? You know, what are the performance characteristics, and how is it being transformed, what's the compute aspect of it? And what we're finding, is that the days of general-purpose compute and storage, and memory platforms, are fading, and we're getting into environments with increasingly specialized architectures, across all those elements. Compute, memory and storage. So that's what's really exciting to be in our spot in the industry, is that we're looking at creating the future by developing new technologies that continue to fuel that growth even further, and fuel the uses of data even further. >> And fascinating just the ongoing case of Moore's law, which I know is not, you know you're not making microprocessors, but I think it's so powerful. Moore's law really is a philosophy, as opposed to an architectural spec. Just this relentless pace of innovation, and you guys just continue to push the envelope. So what are your kind of priorities? I can't believe we're halfway through 2017 already, but for kind of the balance of the year kind of, what are some of your top-of-mind things? I know it's exciting times, you're going through the merger, you know, the company is in a great space. What are your kind of top priorities for the next several months? >> Well, so, I think as a company that has gone through serial acquisitions and integrations, of course we're continuing to drive the transformation of the overall business. >> But the fun stuff right? It's not to increase your staff (Jeff laughing). >> Right, yeah, that is the hardware. >> Stitching together the European systems. >> But yeah, the fun stuff includes pushing the limits even further with solid state technologies, with our 3D NAND technologies. You know, we're leading the industry in 64 layer 3D NAND, and just yesterday we announced a 96 layer 3D NAND. So pushing those limits even further, so that we can provide higher capacities in smaller footprints, lower power, in mobile devices and out on the Edge, to drive all these exciting opportunities in IoT an AI. >> It's crazy, it's crazy. >> Yeah it is, yeah. >> You know, terabyte SD cards, terabyte Micro SD cards, I mean the amount of power that you guys pack into these smaller and smaller packages, it's magical. I mean it's absolutely magic. >> Yeah, and the same goes on the other end of the spectrum, with high-capacity devices. Our helium-filled drives are getting higher and higher capacity, 10, 12, 14 terabyte high-capacity devices for that big data core, that all the data has to end up with at some point. So we're trying to keep a balance of pushing the limits on both ends. >> Alright, well Dave, thanks for taking a few minutes out of your busy day, and congratulations on all your success. >> Great, good to be here. >> Alright, he's Dave Tang from Western Digital, he's changing your world, my world, and everyone else's. We're here in San Jose, you're watching theCUBE, thanks for watching.

>>Hello everybody. I am Toshihiko Nishimura from Stanford. University is there to TTT out here, super aging, global OMIM global transportation group about infections, uh, or major point of concerns. In addition, this year, we have the COVID-19 pandemic. As you can see here, while the why the new COVID-19 patients are still increasing, meanwhile, case count per day in the United state, uh, beginning to decrease this pandemic has changed our daily life to digital transformation. Even today, the micro segmentation is being conducted online and doctor and the nurse care, uh, now increase to telemedicine. Likewise, the drug development process is in need of major change paradigm shift, especially in vaccine in drug development for COVID-19 is, should be safe, effective, and faster >>In the >>Anastasia department, which is the biggest department in school of medicine. We have Stanford, a love for drug device development, regulatory science. So cold. Say the DDT RDS chairman is Ron Paul and this love leaderships are long mysel and stable shaper. In the drug development. We have three major pains, one exceedingly long duration that just 20 years huge budget, very low success rate general overview in the drug development. There are Discoverly but clinical clinical stage, as you see here, Tang. Yes. In clinical stage where we sit, say, what are the programs in D D D R S in each stages or mix program? Single cell programs, big data machine learning, deep learning, AI mathematics, statistics programs, humanized animal, the program SNS program engineering program. And we have annual symposium. Today's the, my talk, I do like to explain limitation of my science significance of humanized. My science out of separate out a program. I focused on humanized program. I believe this program is potent game changer for drug development mouse. When we think of animal experiment, many people think of immediately mouse. We have more than 30 kinds of inbred while the type such as chief 57, black KK yarrow, barber C white and so on using QA QC defined. Why did the type mice 18 of them gave him only one intervention using mouse, genomics analyzed, computational genetics. And then we succeeded to pick up fish one single gene in a week. >>We have another category of gene manipulated, mice transgenic, no clout, no Kamal's group. So far registered 40,000 kind as over today. Pretty critical requirement. Wrong FDA PMDA negative three sites are based on arteries. Two kinds of animal models, showing safety efficacy, combination of two animals and motel our mouse and the swine mouse and non-human primate. And so on mouse. Oh, Barry popular. Why? Because mouse are small enough, easy to handle big database we had and cost effective. However, it calls that low success rate. Why >>It, this issue speculation, low success rate came from a gap between preclinical the POC and the POC couldn't stay. Father divided into phase one. Phase two has the city FDA unsolved to our question. Speculation in nature biology using 7,372 new submissions, they found a 68 significant cradle out crazy too, to study approved by the process. And in total 90 per cent Radia in the clinical stages. What we can surmise from this study, FDA confirmed is that the big discrepancy between POC and clinical POC in another ward, any amount of data well, Ms. Representative for human, this nature bio report impacted our work significantly. >>What is a solution for this discrepancy? FDA standards require the people data from two species. One species is usually mice, but if the reported 90% in a preclinical data, then huge discrepancy between pretty critical POC in clinical POC. Our interpretation is data from mice, sometime representative, actually mice, and the humor of different especially immune system and the diva mice liver enzyme are missing, which human Liba has. This is one huge issue to be taught to overcome this problem. We started humanized mice program. What kind of human animals? We created one humanized, immune mice. The other is human eyes, DBA, mice. What is the definition of a humanized mice? They should have human gene or human cells or human tissues or human organs. Well, let me share one preclinical stages. Example of a humanized mouse that is polio receptor mice. This problem led by who was my mentor? Polio virus. Well, polio virus vaccine usually required no human primate to test in 13 years, collaboration with the FDA w H O polio eradication program. Finally FDA well as w H O R Purdue due to the place no human primate test to transgenic PVL. This is three. Our principle led by loss around the botch >>To move before this humanized mouse program, we need two other bonds donut outside your science, as well as the CPN mouse science >>human hormone, like GM CSF, Whoah, GCSF producing or human cytokine. those producing emoji mice are required in the long run. Two maintain human cells in their body under generation here, South the generation here, Dr. already created more than 100 kinds based on Z. The 100 kinds of Noe mice, we succeeded to create the human immune mice led the blood. The cell quite about the cell platelets are beautifully constituted in an mice, human and rebar MAs also succeeded to create using deparent human base. We have AGN diva, humanized mouse, American African human nine-thirty by mice co-case kitchen, humanized mice. These are Hennessy humanized, the immune and rebar model. On the other hand, we created disease rebar human either must to one example, congenital Liba disease, our guidance Schindel on patient model. >>The other model, we have infectious DDS and Waddell council Modell and GVH Modell. And so on creature stage or phase can a human itemize apply. Our objective is any stage. Any phase would be to, to propose. We propose experiment, pose a compound, which showed a huge discrepancy between. If Y you show the huge discrepancy, if Y is lucrative analog and the potent anti hepatitis B candidate in that predict clinical stage, it didn't show any toxicity in mice got dark and no human primate. On the other hand, weighing into clinical stage and crazy to October 15, salvage, five of people died and other 10 the show to very severe condition. >>Is that the reason why Nicole traditional the mice model is that throughout this, another mice Modell did not predict this severe side outcome. Why Zack humanized mouse, the Debar Modell demonstrate itself? Yes. Within few days that chemistry data and the puzzle physiology data phase two and phase the city requires huge number of a human subject. For example, COVID-19 vaccine development by Pfizer, AstraZeneca Moderna today, they are sample size are Southeast thousand vaccine development for COVID-19. She Novak UConn in China books for the us Erica Jones on the Johnson in unite United Kingdom. Well, there are now no box us Osaka Osaka, university hundred Japan. They are already in phase two industry discovery and predict clinical and regulatory stage foster in-app. However, clinical stage is a studious role because that phases required hugely number or the human subject 9,000 to 30,000. Even my conclusion, a humanized mouse model shortens the duration of drug development humanize, and most Isabel, uh, can be increase the success rate of drug development. Thank you for Ron Paul and to Steven YALI pelt at Stanford and and his team and or other colleagues. Thank you for listening.

>> Announcer: From around the globe, it's theCUBE with digital coverage of MIT Chief Data Officer and Information Quality symposium brought to you by SiliconANGLE Media. >> Hi everybody. This is Dave Vellante and welcome back to theCUBE's coverage of the MIT CDOIQ 2020 event. Of course, it's gone virtual. We wish we were all together in Cambridge. They were going to move into a new building this year for years they've done this event at the Tang Center, moving into a new facility, but unfortunately going to have to wait at least a year, we'll see, But we've got a great guest. Nonetheless, Doug Laney is here. He's a Business Value Strategist, the bestselling author, an analyst, consultant then a long time CUBE friend. Doug, great to see you again. Thanks so much for coming on. >> Dave, great to be with you again as well. So can I ask you? You have been an advocate for obviously measuring the value of data, the CDO role. I don't take this the wrong way, but I feel like the last 150 days have done more to accelerate people's attention on the importance of data and the value of data than all the great work that you've done. What do you think? (laughing) >> It's always great when organizations, actually take advantage of some of these concepts of data value. You may be speaking specifically about the situation with United Airlines and American Airlines, where they have basically collateralized their customer loyalty data, their customer loyalty programs to the tunes of several billion dollars each. And one of the things that's very interesting about that is that the third party valuations of their customer loyalty data, resulted in numbers that were larger than the companies themselves. So basically the value of their data, which is as we've discussed previously off balance sheet is more valuable than the market cap of those companies themselves, which is just incredibly fascinating. >> Well, and of course, all you have to do is look to the Trillionaire's Club. And now of course, Apple pushing two trillion to really see the value that the market places on data. But the other thing is of course, COVID, everybody talks about the COVID acceleration. How have you seen it impact the awareness of the importance of data, whether it applies to business resiliency or even new monetization models? If you're not digital, you can't do business. And digital is all about data. >> I think the major challenge that most organizations are seeing from a data and analytics perspective due to COVID is that their traditional trend based forecast models are broken. If you're a company that's only forecasting based on your own historical data and not taking into consideration, or even identifying what are the leading indicators of your business, then COVID and the economic shutdown have entirely broken those models. So it's raised the awareness of companies to say, "Hey, how can we predict our business now? We can't do it based on our own historical data. We need to look externally at what are those external, maybe global indicators or other kinds of markets that proceed our own forecasts or our own activity." And so the conversion from trend based forecast models to what we call driver based forecast models, isn't easy for a lot of organizations to do. And one of the more difficult parts is identifying what are those external data factors from suppliers, from customers, from partners, from competitors, from complimentary products and services that are leading indicators of your business. And then recasting those models and executing on them. >> And that's a great point. If you think about COVID and how it's changed things, everything's changed, right? The ideal customer profile has changed, your value proposition to those customers has completely changed. You got to rethink that. And of course, it's very hard to predict even when this thing eventually comes back, some kind of hybrid mode, you used to be selling to people in an office environment. That's obviously changed. There's a lot that's permanent there. And data is potentially at least the forward indicator, the canary in the coal mine. >> Right. It also is the product and service. So not only can it help you and improve your forecasting models, but it can become a product or service that you're offering. Look at us right now, we would generally be face to face and person to person, but we're using video technology to transfer this content. And then one of the things that I... It took me awhile to realize, but a couple of months after the COVID shutdown, it occurred to me that even as a consulting organization, Caserta focuses on North America. But the reality is that every consultancy is now a global consultancy because we're all doing business remotely. There are no particular or real strong localization issues for doing consulting today. >> So we talked a lot over the years about the role of the CDO, how it's evolved, how it's changed the course of the early... The pre-title days it was coming out of a data quality world. And it's still vital. Of course, as we heard today from the Keynote, it's much more public, much more exposed, different public data sources, but the role has certainly evolved initially into regulated industries like financial, healthcare and government, but now, many, many more organizations have a CDO. My understanding is that you're giving a talk in the business case for the CDO. Help us understand that. >> Yeah. So one of the things that we've been doing here for the last couple of years is a running an ongoing study of how organizations are impacted by the role of the CDO. And really it's more of a correlation and looking at what are some of the qualities of organizations that have a CDO or don't have a CDO. So some of the things we found is that organizations with a CDO nearly twice as often, mention the importance of data and analytics in their annual report organizations with a C level CDO, meaning a true executive are four times more often likely to be using data, to transform the business. And when we're talking about using data and advanced analytics, we found that organizations with a CIO, not a CDO responsible for their data assets are only half as likely to be doing advanced analytics in any way. So there are a number of interesting things that we found about companies that have a CDO and how they operate a bit differently. >> I want to ask you about that. You mentioned the CIO and we're increasingly seeing lines of reporting and peer reporting alter shift. The sands are shifting a little bit. In the early days the CDO and still predominantly I think is an independent organization. We've seen a few cases and increasingly number where they're reporting into the CIO, we've seen the same thing by the way with the chief Information Security Officer, which used to be considered the fox watching the hen house. So we're seeing those shifts. We've also seen the CDO become more aligned with a technical role and sometimes even emerging out of that technical role. >> Yeah. I think the... I don't know, what I've seen more is that the CDOs are emerging from the business, companies are realizing that data is a business asset. It's not an IT asset. There was a time when data was tightly coupled with applications of technologies, but today data is very easily decoupled from those applications and usable in a wider variety of contexts. And for that reason, as data gets recognized as a business, not an IT asset, you want somebody from the business responsible for overseeing that asset. Yes, a lot of CDOs still report to the CIO, but increasingly more CDOs you're seeing and I think you'll see some other surveys from other organizations this week where the CDOs are more frequently reporting up to the CEO level, meaning they're true executives. Along I advocated for the bifurcation of the IT organization into separate I and T organizations. Again, there's no reason other than for historical purposes to keep the data and technology sides of the organizations so intertwined. >> Well, it makes sense that the Chief Data Officer would have an affinity with the lines of business. And you're seeing a lot of organizations, really trying to streamline their data pipeline, their data life cycles, bringing that together, infuse intelligence into that, but also take a systems view and really have the business be intimately involved, if not even owned into the data. You see a lot of emphasis on self-serve, what are you seeing in terms of that data pipeline or the data life cycle, if you will, that used to be wonky, hard core techies, but now it really involving a lot more constituent. >> Yeah. Well, the data life cycle used to be somewhat short. The data life cycles, they're longer and they're more a data networks than a life cycle and or a supply chain. And the reason is that companies are finding alternative uses for their data, not just using it for a single operational purpose or perhaps reporting purpose, but finding that there are new value streams that can be generated from data. There are value streams that can be generated internally. There are a variety of value streams that can be generated externally. So we work with companies to identify what are those variety of value streams? And then test their feasibility, are they ethically feasible? Are they legally feasible? Are they economically feasible? Can they scale? Do you have the technology capabilities? And so we'll run through a process of assessing the ideas that are generated. But the bottom line is that companies are realizing that data is an asset. It needs to be not just measured as one and managed as one, but also monetized as an asset. And as we've talked about previously, data has these unique qualities that it can be used over and over again, and it generate more data when you use it. And it can be used simultaneously for multiple purposes. So companies like, you mentioned, Apple and others have built business models, based on these unique qualities of data. But I think it's really incumbent upon any organization today to do so as well. >> But when you observed those companies that we talk about all the time, data is at the center of their organization. They maybe put people around that data. That's got to be one of the challenge for many of the incumbents is if we talked about the data silos, the different standards, different data quality, that's got to be fairly major blocker for people becoming a "Data-driven organization." >> It is because some organizations were developed as people driven product, driven brand driven, or other things to try to convert. To becoming data-driven, takes a high degree of data literacy or fluency. And I think there'll be a lot of talk about that this week. I'll certainly mention it as well. And so getting the organization to become data fluent and appreciate data as an asset and understand its possibilities and the art of the possible with data, it's a long road. So the culture change that goes along with it is really difficult. And so we're working with 150 year old consumer brand right now that wants to become more data-driven and they're very product driven. And we hear the CIO say, "We want people to understand that we're a data company that just happens to produce this product. We're not a product company that generates data." And once we realized that and started behaving in that fashion, then we'll be able to really win and thrive in our marketplace. >> So one of the key roles of a Chief Data Officers to understand how data affects the monetization of an organization. Obviously there are four profit companies of your healthcare organization saving lives, obviously being profitable as well, or at least staying within the budget, depending upon the structure of the organization. But a lot of people I think oftentimes misunderstand that it's like, "Okay, do I have to become a data broker? Am I selling data directly?" But I think, you pointed out many times and you just did that unlike oil, that's why we don't like that data as a new oil analogy, because it's so much more valuable and can be use, it doesn't fall because of its scarcity. But what are you finding just in terms of people's application of that notion of monetization? Cutting costs, increasing revenue, what are you seeing in the field? What's that spectrum look like? >> So one of the things I've done over the years is compile a library of hundreds and hundreds of examples of how organizations are using data and analytics in innovative ways. And I have a book in process that hopefully will be out this fall. I'm sharing a number of those inspirational examples. So that's the thing that organizations need to understand is that there are a variety of great examples out there, and they shouldn't just necessarily look to their own industry. There are inspirational examples from other industries as well, many clients come to me and they ask, "What are others in my industry doing?" And my flippant response to that is, "Why do you want to be in second place or third place? Why not take an idea from another industry, perhaps a digital product company and apply that to your own business." But like you mentioned, there are a variety of ways to monetize data. It doesn't involve necessarily selling it. You can deliver analytics, you can report on it, you can use it internally to generate improved business process performance. And as long as you're measuring how data's being applied and what its impact is, then you're in a position to claim that you're monetizing it. But if you're not measuring the impact of data on business processes or on customer relationships or partner supplier relationships or anything else, then it's difficult to claim that you're monetizing it. But one of the more interesting ways that we've been working with organizations to monetize their data, certainly in light of GDPR and the California consumer privacy act where I can't sell you my data anymore, but we've identified ways to monetize your customer data in a couple of ways. One is to synthesize the data, create synthetic data sets that retain the original statistical anomalies in the data or features of the data, but don't share actually any PII. But another interesting way that we've been working with organizations to monetize their data is what I call, Inverted data monetization, where again, I can't share my customer data with you, but I can share information about your products and services with my customers. And take a referral fee or a commission, based on that. So let's say I'm a hospital and I can't sell you my patient data, of course, due to variety of regulations, but I know who my diabetes patients are, and I can introduce them to your healthy meal plans, to your gym memberships, to your at home glucose monitoring kits. And again, take a referral fee or a cut of that action. So we're working with customers and the financial services firm industry and in the healthcare industry on just those kinds of examples. So we've identified hundreds of millions of dollars of incremental value for organizations that from their data that we're just sitting on. >> Interesting. Doug because you're a business value strategist at the top, where in the S curve do you see you're able to have the biggest impact. I doubt that you enter organizations where you say, "Oh, they've got it all figured out. They can't use my advice." But as well, sometimes in the early stages, you may not be able to have as big of an impact because there's not top down support or whatever, there's too much technical data, et cetera, where are you finding you can have the biggest impact, Doug? >> Generally we don't come in and run those kinds of data monetization or information innovation exercises, unless there's some degree of executive support. I've never done that at a lower level, but certainly there are lower level more immediate and vocational opportunities for data to deliver value through, to simply analytics. One of the simple examples I give is, I sold a home recently and when you put your house on the market, everybody comes out of the woodwork, the fly by night, mortgage companies, the moving companies, the box companies, the painters, the landscapers, all know you're moving because your data is in the U.S. and the MLS directory. And it was interesting. The only company that didn't reach out to me was my own bank, and so they lost the opportunity to introduce me to a Mortgage they'd retain me as a client, introduce me to my new branch, print me new checks, move the stuff in my safe deposit box, all of that. They missed a simple opportunity. And I'm thinking, this doesn't require rocket science to figure out which of your customers are moving, the MLS database or you can harvest it from Zillow or other sites is basically public domain data. And I was just thinking, how stupid simple would it have been for them to hire a high school programmer, give him a can of red bull and say, "Listen match our customer database to the MLS database to let us know who's moving on a daily or weekly basis." Some of these solutions are pretty simple. >> So is that part of what you do, come in with just hardcore tactical ideas like that? Are you also doing strategy? Tell me more about how you're spending your time. >> I trying to think more of a broader approach where we look at the data itself and again, people have said, "If you tortured enough, what would you tell us? We're just take that angle." We look at examples of how other organizations have monetized data and think about how to apply those and adapt those ideas to the company's own business. We look at key business drivers, internally and externally. We look at edge cases for their customers' businesses. We run through hypothesis generating activities. There are a variety of different kinds of activities that we do to generate ideas. And most of the time when we run these workshops, which last a week or two, we'll end up generating anywhere from 35 to 50 pretty solid ideas for generating new value streams from data. So when we talk about monetizing data, that's what we mean, generating new value streams. But like I said, then the next step is to go through that feasibility assessment and determining which of these ideas you actually want to pursue. >> So you're of course the longtime industry watcher as well, as a former Gartner Analyst, you have to be. My question is, if I think back... I've been around a while. If I think back at the peak of Microsoft's prominence in the PC era, it was like windows 95 and you felt like, "Wow, Microsoft is just so strong." And then of course the Linux comes along and a lot of open source changes and low and behold, a whole new set of leaders emerges. And you see the same thing today with the Trillionaire's Club and you feel like, "Wow, even COVID has been a tailwind for them." But you think about, "Okay, where could the disruption come to these large players that own huge clouds, they have all the data." Is data potentially a disruptor for what appear to be insurmountable odds against the newbies" >> There's always people coming up with new ways to leverage data or new sources of data to capture. So yeah, there's certainly not going to be around for forever, but it's been really fascinating to see the transformation of some companies I think nobody really exemplifies it more than IBM where they emerged from originally selling meat slicers. The Dayton Meat Slicer was their original product. And then they evolved into Manual Business Machines and then Electronic Business Machines. And then they dominated that. Then they dominated the mainframe software industry. Then they dominated the PC industry. Then they dominated the services industry to some degree. And so they're starting to get into data. And I think following that trajectory is something that really any organization should be looking at. When do you actually become a data company? Not just a product company or a service company or top. >> We have Inderpal Bhandari is one of our huge guests here. He's a Chief-- >> Sure. >> Data Officer of IBM, you know him well. And he talks about the journey that he's undertaken to transform the company into a data company. I think a lot of people don't really realize what's actually going on behind the scenes, whether it's financially oriented or revenue opportunities. But one of the things he stressed to me in our interview was that they're on average, they're reducing the end to end cycle time from raw data to insights by 70%, that's on average. And that's just an enormous, for a company that size, it's just enormous cost savings or revenue generating opportunity. >> There's no doubt that the technology behind data pipelines is improving and the process from moving data from those pipelines directly into predictive or diagnostic or prescriptive output is a lot more accelerated than the early days of data warehousing. >> Is the skills barrier is acute? It seems like it's lessened somewhat, the early Hadoop days you needed... Even data scientist... Is it still just a massive skill shortage, or we're starting to attack that. >> Well, I think companies are figuring out a way around the skill shortage by doing things like self service analytics and focusing on more easy to use mainstream type AI or advanced analytics technologies. But there's still very much a need for data scientists and organizations and the difficulty in finding people that are true data scientists. There's no real certification. And so really anybody can call themselves a data scientist but I think companies are getting good at interviewing and determining whether somebody's got the goods or not. But there are other types of skills that we don't really focus on, like the data engineering skills, there's still a huge need for data engineering. Data doesn't self-organize. There are some augmented analytics technologies that will automatically generate analytic output, but there really aren't technologies that automatically self-organize data. And so there's a huge need for data engineers. And then as we talked about, there's a large interest in external data and harvesting that and then ingesting it and even identifying what external data is out there. So one of the emerging roles that we're seeing, if not the sexiest role of the 21st century is the role of the Data Curator, somebody who acts as a librarian, identifying external data assets that are potentially valuable, testing them, evaluating them, negotiating and then figuring out how to ingest that data. So I think that's a really important role for an organization to have. Most companies have an entire department that procures office supplies, but they don't have anybody who's procuring data supplies. And when you think about which is more valuable to an organization? How do you not have somebody who's dedicated to identifying the world of external data assets that are out there? There are 10 million data sets published by government, organizations and NGOs. There are thousands and thousands of data brokers aggregating and sharing data. There's a web content that can be harvested, there's data from your partners and suppliers, there's data from social media. So to not have somebody who's on top of all that it demonstrates gross negligence by the organization. >> That is such an enlightening point, Doug. My last question is, I wonder how... If you can share with us how the pandemic has effected your business personally. As a consultant, you're on the road a lot, obviously not on the road so much, you're doing a lot of chalk talks, et cetera. How have you managed through this and how have you been able to maintain your efficacy with your clients? >> Most of our clients, given that they're in the digital world a bit already, made the switch pretty quick. Some of them took a month or two, some things went on hold but we're still seeing the same level of enthusiasm for data and doing things with data. In fact some companies have taken our (mumbles) that data to be their best defense in a crisis like this. It's affected our business and it's enabled us to do much more international work more easily than we used to. And I probably spend a lot less time on planes. So it gives me more time for writing and speaking and actually doing consulting. So that's been nice as well. >> Yeah, there's that bonus. Obviously theCUBE yes, we're not doing physical events anymore, but hey, we've got two studios operating. And Doug Laney, really appreciate you coming on. (Dough mumbles) Always a great guest and sharing your insights and have a great MIT CDOIQ. >> Thanks, you too, Dave, take care. (mumbles) >> Thanks Doug. All right. And thank you everybody for watching. This is Dave Vellante for theCUBE, our continuous coverage of the MIT Chief Data Officer conference, MIT CDOIQ, will be right back, right after this short break. (bright music)

>>Live from Copenhagen, Denmark. It's the cube covering Nutanix dot. Next 2019. Brought to you by Nutanix. >>Welcome back everyone to the cubes live coverage of dot. Next Nutanix. We are here in Copenhagen. I'm your host, Rebecca Knight. Along with my cohost Stu Miniman. We're joined by Karen Openshaw. She is the head of engineering at Zen intranet and Justin fielder, the CTO at Zen internet. Thank you both so much for your first timers on the cube. So welcome. We're gonna. We're really excited to have you. Why don't you start by telling our viewers a little bit about Zen internet, who, who you are, what you're all about. >>Yeah, sure. So, um, Zen is um, a UK based where up in near Manchester, um, managed service provider. Um, we turned over this year about 76 million pounds, um, which is, um, a great achievement for us that spout. Um, that's double digit growth we've had for the last few years. So we're really starting to motor as a business. Um, we employ about 550 people. Um, we have about 150,000 customers split across retail, um, indirect. So we have a very big channel business. We have a wholesale business where we sell our infrastructure, um, that then other people productize and put into, um, solutions for their customers. And then we have a corporate business, which is where Nutanix really comes in. Um, so we offer managed services both in networking, um, hosting the value added services that are required to make all of that safe and secure and, um, a solution for a corporate. Great. >>So managed service provider, uh, your company has been around for quite awhile. Predates when everyone was talking about cloud. Maybe give us a kind of the update today as to where you really see yourself fitting. What differentiates your, uh, your, your company in the marketplace? >>So I suppose, um, I mean Karen can add sort of what her team does, but I suppose the, the big difference is Zen is a very people first company. So Richard Tang, our founder, he founded the company nearly 25 years ago. Um, he stated publicly, he's never going to sell it. It's, it's, it's a, it's a very, very people orientated company, which of course has great, um, affinity to Newtanics his own, um, people first values. And fundamentally we believe that we always want to do the right thing for the customer even if that is difficult. Um, and so I still do whatever you want to say about, you know, how you pick up some of the, the, the hardness about keeping up with customers. >>Yeah. So we have customers that come to us asking for things that we don't necessarily sell at the time. And uh, we, we put quite a lot of effort into adapting our products at the time to deliver them what they need. Um, some of those challenging conversations can be about making sure the customer is getting the right product for what they want. So understanding what they need, making sure that we can support them not only in taking that product, but coming onto the product in the first place. And that's what we use a lot of our Nutanix infrastructure for. >>Good. Can you maybe, can you dig us in a little bit? Do you know, what does Nutanix enable for your business that ultimately then has an impact on your ultimate end user? >>It's done two things for us. So the first is our it operations. So we've been on a journey, I guess over the last three, four years, consolidating all our legacy and um, physical 10 onto virtual, uh, services. We've used Nutanix to do that. So with, with collated all of our services, we've got about 90 odd percent of all our legacy services on that it infrastructure now. So operationally it saves us a lot of time, effort, uh, costs, et cetera, much more reliable as well. But conversely to that, we also use it for our, our products offerings as well. So we used to be, um, managed hosting where a customer would come, give us a spec and we'd, we'd go and build a physical server hosted in our data center, host their applications on there, support them with that. We don't really do that anymore. We now use Nutanix as our hosting environment. So we've reduced our environmental footprint, we've reduced the amount of space that we need in a data center. And the power that we put through there again, operating that is, is it's easier for us because we can consolidate where the skills are from in terms of both it ops and in terms of the infrastructure for the managed services as well. >>One of the things that you said Justin, is that you're very people first company and that really fits in well with the culture at Nutanix. Can you, can you riff on that a little bit and just describe what it is to be working so closely with a company like Nutanix and how important it is that your cultures mesh? >>Yeah, sure. Um, I mean Nutanix has been part of Zen for, for many, many years. Um, and you know, we work in Israel, watched this industry for 25 years. Nothing stands still, literally nothing stands still. And therefore whatever you fought was a good idea last year, probably is now the worst possible idea because there's some great new idea. And I think it's that pace of change. And so what we've really found with Nutanix is as, as they've got to know us and we've got to know them and they can see that we're starting to really be able to take some solutions to the market that really resonate the, what they've done is they've literally embedded their people in our company. So we have, um, our systems engineers or account managers, they come up to our offices, they sit down, they understand our people, they understand where we're trying to go, they understand our propositions. >>And this is a journey for Nutanix. I mean Nutanix in the MSP land is not where it really, where they started. They started like Karen just said like we use them. That's actually where we started was Oh my God, I've got a thousand servers or this is just too much. Yeah, it's too much hassle to try and segment it yourself. Um, and it, it, it's that, it's that sort of hypervisor of hypervisors of hypervisors type approach. It just makes it easier. But conversely, it's therefore really important that you work out how take that value proposition to a customer. Because if you can't explain it, cause it's so easy, how do they know where, whether this is going to solve their problems. So that's been a fantastic part. Nutanix, it's really the Nutanix team felt like the Zen team and they're saying that they also feel the same. >>So you know, things like nothing ever goes 100% right. But it's always, you know who to call. They're all work because you've got that personal relationship and that's really important to us. >> It's more than that. So what we found with the Nutanix guys is that they'll help us fix problems that aren't necessarily Nutanix problems as well. So that's something we don't get from any of the, uh, of our suppliers. It's normally, no, that's nothing to do with me. You need to phone someone else, get support on that. It's done. It's guys will, they'll bring in their own experts on that particular combo and they'll support us through that. So that's good. >> At six speaks very much to the partnership that you're saying. They're not just a supplier of a product to you. Um, no, no. When I talked to the customer base, one of the biggest challenges and you know, any company has these days is a really understanding their application portfolio. >>What needs to change, what needs to stay the same, you know, Microsoft pushing everybody to office three 65, you know, changed a lot of companies out there. You know, what do I Salsify, what do I put in managed service provider? What do I just, you know, build natively in the public cloud. Can you bring us through kind of, you know, what you're seeing at your customer base and you know, where, where that does interact with the journey that Nutanix is bringing people on? Yeah, I mean maybe I can say that like the, all of our customers are on a journey, um, and they need help. They seriously need help for the, exactly. That reason that you've said. Um, I mean, this is, this is my, this is my job to understand this stuff. That's, that's what a CTO of an MSP is required to do. Um, the problem is is if you're a CIO of, we were really good in construction, you can revolutionize the construction in C by the application of it, particularly during the sales cycle. You know, the ability to VR walk through, you know, argument or, all of that sort of really cool stuff. >>And then you've got a thousand sub-contractors that you're trying to manage from an it perspective. And that juxtaposition of the problem is really problematic I think for a lot of people. And so what we've done is we said the first step you can do is just take what you've got and get rid of the management overhead. That's the easiest, simplest, straightforward. And some of the Nutanix, the sort of lift and shift capability that has got that, they will go and inspect a work load somewhere else. They will work out what resources are required for it. They will pick it up and then we'll move it. And we've had some fantastic success of our customers. They're, they're, they're our greatest advocates. They just say, Oh my God, it just happened one day it was over that and next day it was over there. Um, and then you can start to analyze what that is, what's happening. >>And that's where we can really add value because this is not as simple as just an application because it's about your security posture. It's about your Dar requirements. It's about what, what your appetite for risk versus reward versus cost. And that's really hard to do when you don't have the simple thing which is there, which is, Oh, that serve, that piece of tin costs me $10,000 and therefore you can work that out yourself. So I think the key to all of this is giving tools to the end users so that the CIO in that company and their it team so that they can make those choices in collaboration with an MSP like us. Um, and that goes back to what you were saying. It's about, you know, when we hit problems, we might not even know there's a problem before we've hit it. And therefore having Nutanix deeply embedded within us is really important to them. Being able to go back to the customer and sometimes to the customer, you actually have to go, what are you doing that isn't going to work in the longterm? >>And, and, and as you said, you also have to provide the value so that the customer understands what they're actually getting to in terms of a customer's future needs are we are living in this multicloud world. How are we, how would you describe the customer mindset and how are you coming in with solutions that work for the customer and then having to break that, break the news to them on occasion that what on earth are you trying to do here? This is not gonna work. >>Yeah, we have a few, um, interesting. I sort of like, okay, are you going or am I going to tell them? You know, and I actually can tell, I always send Karen, I'll be going. He doesn't. Um, I, I think it, it's, and, and this is where I think we weren't really, well, you know, it is about what is going on. Karen. Work with your engineering teams. Try and understand deeply actually what is going, why is it not a good idea to do that? And that's the, that's the thing. Once you're going to explain why most of it, Oh God, thank God for that. Finally someone's telling me why what I'm trying to achieve isn't the best way to do it. Because I think a lot of, a lot of people's just sort of, you know, it's a bit buzzwordy and they just think that they need to do this. And you know, it's, I mean, talk about, you know, the journey we've been through. Just sort of how do we move stuff onto there? What's that for years. I mean, you know, it's a huge amount of work. Carry any, any lessons learned maybe that you could do it for one 50 years. >>Are there any that I could repeat here as practices? Okay. It is, I think one of the biggest challenges is the, the reskilling of your teams. So I'm guessing everybody, first of all, to understand this, this bright new future that you're moving into. And then getting them trained upon it and training is >>not just going and sitting in a classroom. It's going and working on this thing and seeing problems occur and understanding how to fix them. That's the, that's the biggest problem that we, that we probably went through. I guess we want our customers to not have that though. So we, we want them to give us the, their work loads in there. It will solve that for them and that that's where we wanna we want to take it, I think in the future, helping them understand what they can do with cloud. So we, we don't just do private cloud, we do public cloud as well. So we could introduce um, opportunities and concepts from a public cloud perspective as well. Um, that will, that will, AWS is a, is a really good one and we are looking at other providers as well, so we help customers solve their problems, whatever that problem is. >>One of the things that's so salient about Zen internet is that it has a really strong culture. You said it's a people, people first culture, but it's also a very diverse culture. Uh, bringing in multiple perspectives, uh, women in technology, LGBTQ, uh, other races. Can you talk a little bit about what it means to work at a diverse company and how it changes how you think about problems and go about solving, >>solving them? Yeah, I guess it's a good question. I guess working in a company we're not as diverse as we'd like to be. We were not where we're at in terms of balancing out the number of women in the tech roles in particular. Um, and, and the diversity. If we give everybody a voice, which is the main thing, then uh, we will see a more, a more wide range in set of inputs there. So, um, developing our teams, high performing teams, you need that mixture of input there, not just about women by the way. It's about, it's about, we have a private zone network for example, where we try to ensure that diverse diversity and diverse people feel included in what we do as a business and work as well and have an opportunity to have an input into that. So where does it add for us? >>I guess people just think differently when they're from different cultural backgrounds. They're from different, um, different nationalities, different, um, races I guess different sexuality, different gender. They've all got different life experiences. So solving problems is probably the main thing that you get the benefit from that. And this industry is full of people trying to solve problems, um, and bring in diverse teams, not just about women in tech. Cause w we saw three women speaking this morning or the keynote, which was fantastic to see. Um, but it is about the diversity as well. So, uh, innovation is the key there, I guess. And I think, I think it's, it's not just about your staff. Um, if you've got the ability to think differently, that applies for out >>the entire ecosystem. Um, and you, you know, you can, you can take a different view. So we work very closely with the TM forum because you know, that that's sort of our industry and it's the sort of the, the, the whole application stack about how you approach that. And the TM forum of have really done some fantastic research that that now proves that the output is different if you have a diverse input. And that I think for our customers is really different. It's really important because then it's different. We're not one of the big guys. We're not BT, we're not Deutsche Telekom, we're not, you know, we're not one of these people. We think differently. We act differently, we behave differently. We have a different approach and the people first, I mean, you know, that doesn't mean we're, you know, we're, we're just here for a good fun time. >>We're here to drive this business forward, to try to generate profitability that we can reverse back in the business to enable us to get onto bigger and greater things. And we've got a five year plan which will see us, you know, at least double revenues quite happily. And we've very confident now that we can execute that. Assuming we can get that diversity in the business. And it's a huge challenge. It's how do you reach out to those people? How do you use the right language? How do you overcome unconscious bias? Yeah, that's a massive thing and it's great. Again, it Newtanics just resonates with us. Just some of the little stickers around that they are diverse, they've got different representations of people and it shows that someone has fought about that and that will resonate. And it's always the classic thing that, you know, you do something wrong once people remember it forever. You do a hundred things right. People won't even notice it. And that's the, that's the type of approach. So, um, for us, we, you know, we think it's a really exciting bear and it's something that the entire executive at Zen are absolutely focused on is getting this right because we know it will secure off. >>It'll make all the difference. Great. Justin and Karen, thank you so much for coming on the cube. That's great. I'm Rebecca Knight for Stu Miniman. Stay tuned for more of the cubes live coverage of.next from Copenhagen.

>> From Cambridge, Massachusetts, it's theCUBE covering MIT Chief Data Officer and Information Quality Symposium 2019 brought to you by SiliconANGLE Media. (upbeat techno music) >> Hi everybody, welcome back to Cambridge, Massachusetts. You're watching theCUBE, the leader in tech coverage. We go out to the events. We extract the signal from the noise, and we're here at the MIT CDOIQ Conference, Chief Data Officer Information Quality Conference. It is the 13th year here at the Tang building. We've outgrown this building and have to move next year. It's fire marshal full. Gokula Mishra is here. He is the Senior Director of Global Data and Analytics and Supply Chain-- >> Formerly. Former, former Senior Director. >> Former! I'm sorry. It's former Senior Director of Global Data Analytics and Supply Chain at McDonald's. Oh, I didn't know that. I apologize my friend. Well, welcome back to theCUBE. We met when you were at Oracle doing data. So you've left that, you're on to your next big thing. >> Yes, thinking through it. >> Fantastic, now let's start with your career. You've had, so you just recently left McDonald's. I met you when you were at Oracle, so you cut over to the dark side for a while, and then before that, I mean, you've been a practitioner all your life, so take us through sort of your background. >> Yeah, I mean my beginning was really with a company called Tata Burroughs. Those days we did not have a lot of work getting done in India. We used to send people to U.S. so I was one of the pioneers of the whole industry, coming here and working on very interesting projects. But I was lucky to be working on mostly data analytics related work, joined a great company called CS Associates. I did my Master's at Northwestern. In fact, my thesis was intelligent databases. So, building AI into the databases and from there on I have been with Booz Allen, Oracle, HP, TransUnion, I also run my own company, and Sierra Atlantic, which is part of Hitachi, and McDonald's. >> Awesome, so let's talk about use of data. It's evolved dramatically as we know. One of the themes in this conference over the years has been sort of, I said yesterday, the Chief Data Officer role emerged from the ashes of sort of governance, kind of back office information quality compliance, and then ascended with the tailwind of the Big Data meme, and it's kind of come full circle. People are realizing actually to get value out of data, you have to have information quality. So those two worlds have collided together, and you've also seen the ascendancy of the Chief Digital Officer who has really taken a front and center role in some of the more strategic and revenue generating initiatives, and in some ways the Chief Data Officer has been a supporting role to that, providing the quality, providing the compliance, the governance, and the data modeling and analytics, and a component of it. First of all, is that a fair assessment? How do you see the way in which the use of data has evolved over the last 10 years? >> So to me, primarily, the use of data was, in my mind, mostly around financial reporting. So, anything that companies needed to run their company, any metrics they needed, any data they needed. So, if you look at all the reporting that used to happen it's primarily around metrics that are financials, whether it's around finances around operations, finances around marketing effort, finances around reporting if it's a public company reporting to the market. That's where the focus was, and so therefore a lot of the data that was not needed for financial reporting was what we call nowadays dark data. This is data we collect but don't do anything with it. Then, as the capability of the computing, and the storage, and new technologies, and new techniques evolve, and are able to handle more variety and more volume of data, then people quickly realize how much potential they have in the other data outside of the financial reporting data that they can utilize too. So, some of the pioneers leverage that and actually improved a lot in their efficiency of operations, came out with innovation. You know, GE comes to mind as one of the companies that actually leverage data early on, and number of other companies. Obviously, you look at today data has been, it's defining some of the multi-billion dollar company and all they have is data. >> Well, Facebook, Google, Amazon, Microsoft. >> Exactly. >> Apple, I mean Apple obviously makes stuff, but those other companies, they're data companies. I mean largely, and those five companies have the highest market value on the U.S. stock exchange. They've surpassed all the other big leaders, even Berkshire Hathaway. >> So now, what is happening is because the market changes, the forces that are changing the behavior of our consumers and customers, which I talked about which is everyone now is digitally engaging with each other. What that does is all the experiences now are being captured digitally, all the services are being captured digitally, all the products are creating a lot of digital exhaust of data and so now companies have to pay attention to engage with their customers and partners digitally. Therefore, they have to make sure that they're leveraging data and analytics in doing so. The other thing that has changed is the time to decision to the time to act on the data inside that you get is shrinking, and shrinking, and shrinking, so a lot more decision-making is now going real time. Therefore, you have a situation now, you have the capability, you have the technology, you have the data now, you have to make sure that you convert that in what I call programmatic kind of data decision-making. Obviously, there are people involved in more strategic decision-making. So, that's more manual, but at the operational level, it's going more programmatic decision-making. >> Okay, I want to talk, By the way, I've seen a stat, I don't know if you can confirm this, that 80% of the data that's out there today is dark data or it's data that's behind a firewall or not searchable, not open to Google's crawlers. So, there's a lot of value there-- >> So, I would say that percent is declining over time as companies have realized the value of data. So, more and more companies are removing the silos, bringing those dark data out. I think the key to that is companies being able to value their data, and as soon as they are able to value their data, they are able to leverage a lot of the data. I still believe there's a large percent still not used or accessed in companies. >> Well, and of course you talked a lot about data monetization. Doug Laney, who's an expert in that topic, we had Doug on a couple years ago when he, just after, he wrote Infonomics. He was on yesterday. He's got a very detailed prescription as to, he makes strong cases as to why data should be valued like an asset. I don't think anybody really disagrees with that, but then he gave kind of a how-to-do-it, which will, somewhat, make your eyes bleed, but it was really well thought out, as you know. But you talked a lot about data monetization, you talked about a number of ways in which data can contribute to monetization. Revenue, cost reduction, efficiency, risk, and innovation. Revenue and cost is obvious. I mean, that's where the starting point is. Efficiency is interesting. I look at efficiency as kind of a doing more with less but it's sort of a cost reduction, but explain why it's not in the cost bucket, it's different. >> So, it is first starts with doing what we do today cheaper, better, faster, and doing more comes after that because if you don't understand, and data is the way to understand how your current processes work, you will not take the first step. So, to take the first step is to understand how can I do this process faster, and then you focus on cheaper, and then you focus on better. Of course, faster is because of some of the market forces and customer behavior that's driving you to do that process faster. >> Okay, and then the other one was risk reduction. I think that makes a lot of sense here. Actually, let me go back. So, one of the key pieces of it, of efficiency is time to value. So, if you can compress the time, or accelerate the time and you get the value that means more cash in house faster, whether it's cost reduction or-- >> And the other aspect you look at is, can you automate more of the processes, and in that way it can be faster. >> And that hits the income statement as well because you're reducing headcount cost of your, maybe not reducing headcount cost, but you're getting more out of different, out ahead you're reallocating them to more strategic initiatives. Everybody says that but the reality is you hire less people because you just automated. And then, risk reduction, so the degree to which you can lower your expected loss. That's just instead thinking in insurance terms, that's tangible value so certainly to large corporations, but even midsize and small corporations. Innovation, I thought was a good one, but maybe you could use an example of, give us an example of how in your career you've seen data contribute to innovation. >> So, I'll give an example of oil and gas industry. If you look at speed of innovation in the oil and gas industry, they were all paper-based. I don't know how much you know about drilling. A lot of the assets that goes into figuring out where to drill, how to drill, and actually drilling and then taking the oil or gas out, and of course selling it to make money. All of those processes were paper based. So, if you can imagine trying to optimize a paper-based innovation, it's very hard. Not only that, it's very, very by itself because it's on paper, it's in someone's drawer or file. So, it's siloed by design and so one thing that the industry has gone through, they recognize that they have to optimize the processes to be better, to innovate, to find, for example, shale gas was a result output of digitizing the processes because otherwise you can't drill faster, cheaper, better to leverage the shale gas drilling that they did. So, the industry went through actually digitizing a lot of the paper assets. So, they went from not having data to knowingly creating the data that they can use to optimize the process and then in the process they're innovating new ways to drill the oil well cheaper, better, faster. >> In the early days of oil exploration in the U.S. go back to the Osage Indian tribe in northern Oklahoma, and they brilliantly, when they got shuttled around, they pushed him out of Kansas and they negotiated with the U.S. government that they maintain the mineral rights and so they became very, very wealthy. In fact, at one point they were the wealthiest per capita individuals in the entire world, and they used to hold auctions for various drilling rights. So, it was all gut feel, all the oil barons would train in, and they would have an auction, and it was, again, it was gut feel as to which areas were the best, and then of course they evolved, you remember it used to be you drill a little hole, no oil, drill a hole, no oil, drill a hole. >> You know how much that cost? >> Yeah, the expense is enormous right? >> It can vary from 10 to 20 million dollars. >> Just a giant expense. So, now today fast-forward to this century, and you're seeing much more sophisticated-- >> Yeah, I can give you another example in pharmaceutical. They develop new drugs, it's a long process. So, one of the initial process is to figure out what molecules this would be exploring in the next step, and you could have thousand different combination of molecules that could treat a particular condition, and now they with digitization and data analytics, they're able to do this in a virtual world, kind of creating a virtual lab where they can test out thousands of molecules. And then, once they can bring it down to a fewer, then the physical aspect of that starts. Think about innovation really shrinking their processes. >> All right, well I want to say this about clouds. You made the statement in your keynote that how many people out there think cloud is cheaper, or maybe you even said cheap, but cheaper I inferred cheaper than an on-prem, and so it was a loaded question so nobody put their hand up they're afraid, but I put my hand up because we don't have any IT. We used to have IT. It was a nightmare. So, for us it's better but in your experience, I think I'm inferring correctly that you had meant cheaper than on-prem, and certainly we talked to many practitioners who have large systems that when they lift and shift to the cloud, they don't change their operating model, they don't really change anything, they get a bill at the end of the month, and they go "What did this really do for us?" And I think that's what you mean-- >> So what I mean, let me make it clear, is that there are certain use cases that cloud is and, as you saw, that people did raise their hand saying "Yeah, I have use cases where cloud is cheaper." I think you need to look at the whole thing. Cost is one aspect. The flexibility and agility of being able to do things is another aspect. For example, if you have a situation where your stakeholder want to do something for three weeks, and they need five times the computing power, and the data that they are buying from outside to do that experiment. Now, imagine doing that in a physical war. It's going to take a long time just to procure and get the physical boxes, and then you'll be able to do it. In cloud, you can enable that, you can get GPUs depending on what problem we are trying to solve. That's another benefit. You can get the fit for purpose computing environment to that and so there are a lot of flexibility, agility all of that. It's a new way of managing it so people need to pay attention to the cost because it will add to the cost. The other thing I will point out is that if you go to the public cloud, because they make it cheaper, because they have hundreds and thousands of this canned CPU. This much computing power, this much memory, this much disk, this much connectivity, and they build thousands of them, and that's why it's cheaper. Well, if your need is something that's very unique and they don't have it, that's when it becomes a problem. Either you need more of those and the cost will be higher. So, now we are getting to the IOT war. The volume of data is growing so much, and the type of processing that you need to do is becoming more real-time, and you can't just move all this bulk of data, and then bring it back, and move the data back and forth. You need a special type of computing, which is at the, what Amazon calls it, adds computing. And the industry is kind of trying to design it. So, that is an example of hybrid computing evolving out of a cloud or out of the necessity that you need special purpose computing environment to deal with new situations, and all of it can't be in the cloud. >> I mean, I would argue, well I guess Microsoft with Azure Stack was kind of the first, although not really. Now, they're there but I would say Oracle, your former company, was the first one to say "Okay, we're going to put the exact same infrastructure on prem as we have in the public cloud." Oracle, I would say, was the first to truly do that-- >> They were doing hybrid computing. >> You now see Amazon with outposts has done the same, Google kind of has similar approach as Azure, and so it's clear that hybrid is here to stay, at least for some period of time. I think the cloud guys probably believe that ultimately it's all going to go to the cloud. We'll see it's going to be a long, long time before that happens. Okay! I'll give you last thoughts on this conference. You've been here before? Or is this your first one? >> This is my first one. >> Okay, so your takeaways, your thoughts, things you might-- >> I am very impressed. I'm a practitioner and finding so many practitioners coming from so many different backgrounds and industries. It's very, very enlightening to listen to their journey, their story, their learnings in terms of what works and what doesn't work. It is really invaluable. >> Yeah, I tell you this, it's always a highlight of our season and Gokula, thank you very much for coming on theCUBE. It was great to see you. >> Thank you. >> You're welcome. All right, keep it right there everybody. We'll be back with our next guest, Dave Vellante. Paul Gillin is in the house. You're watching theCUBE from MIT. Be right back! (upbeat techno music)

>> From Cambridge, Massachusetts, it's The Cube, covering MIT Chief data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, everybody. We're here at Tang building at MIT for the MIT CDOIQ Conference. This is the 13th annual MIT CDOIQ. It started as a information quality conference and grew through the big data era, the Chief Data Officer emerged and now it's sort of a combination of those roles. That governance role, the Chief Data Officer role. Critical for organizations for quality and data initiatives, leading digital transformations ans the like. I'm Dave Vallante with my cohost Paul Gillin, you're watching The Cube, the leader in tech coverage. Mark Chrisco is here, the deputy, sorry, Principle Deputy Director for Enterprise Information at the Department of Defense. Good to see you again, thanks for coming on. >> Oh, thank you for having me. >> So, Principle Deputy Director Enterprise Information, what do you do? >> I do data. I do acquisition data. I'm the person in charge of lining the acquisition data for the programs for the Under Secretary and the components so a strong partnership with the army, navy, and air force to enable the department and the services to execute their programs better, more efficiently, and be efficient in the data management. >> What is acquisition data? >> So acquisition data generally can be considered best in the shorthand of cost schedule performance data. When a program is born, you have to manage, you have to be sure it's resourced, you're reporting up to congress, you need to be sure you have insight into the programs. And finally, sometimes you have to make decisions on those programs. So, cost schedule performance is a good shorthand for it. >> So kind of the key metrics and performance metrics around those initiatives. And how much of that is how you present that data? The visualization of it. Is that part of your role or is that, sort of, another part of the organization you partner with, or? >> Well, if you think about it, the visualization can take many forms beyond that. So a good part of the role is finding the authoritative trusted source of that data, making sure it's accurate so we don't spend time disagreeing on different data sets on cost schedule performance. The major programs are tremendously complex and large and involve and awful lot of data in the a buildup to a point where you can look at that. It's just not about visualizing, it's about having governed authoritative data that is, frankly, trustworthy that you can can go operate in. >> What are some of the challenges of getting good quality data? >> Well, I think part of the challenge was having a common lexicon across the department and the services. And as I said, the partnership with the services had been key in helping define and creating a semantic data model for the department that we can use. So we can have agreement on what it would mean when we were using it and collecting it. The services have thrown all in and, in their perspective, have extended that data model down through their components to their programs so they can better manage the programs because the programs are executed at a service level, not at an OSD level. >> Can you make that real? I mean, is there an example you can give us of what you mean by a common semantic model? >> So for cost schedule, let's take a very simple one, program identification. Having a key number for that, having a long name, a short name, and having just the general description of that, were in various states amongst the systems. We've had decades where, however the system was configured, configured it the way they wanted to. It was largely not governed and then trying to bring those data sets together were just impossible to do. So even with just program identification. Since the majority of the programs and numbers are executed at a service level, we worked really hard to get the common words and meanings across all the programs. >> So it's a governance exercise the? >> Yeah. It is certainly a governance exercise. I think about it as not so much as, in the IT world or the data world will call it governance, it's leadership. Let's settle on some common semantics here that we can all live with and go forward and do that. Because clearly there's needs for other pieces of data that we may or may not have but establishing a core set of common meanings across the department has proven very valuable. >> What are some of the key data challenges that the DOD faces? And how is your role helping address them? >> Well in our case, and I'm certain there's a myriad of data choices across the department. In our place it was clarity in and the governance of this. Many of the pieces of data were required by statute, law, police, or regulation. We came out of eras where data was the piece of a report and not really considered data. And we had to lead our ways to beyond the report to saying, "No, we're really "talking about key data management." So we've been at this for a few years and working with the services, that has been a challenge. I think we're at the part where we've established the common semantics for the department to go forward with that. And one of the challenges that I think is the access and dissemination of knowing what you can share and when you can share it. Because Michael Candolim said earlier that the data in mosaic, sometimes you really need to worry about it from our perspective. Is too much publicly available or should we protect on behalf of the government? >> That's a challenge. Is the are challenge in terms of, I'm sure there is but I wonder if you can describe it or maybe talk about how you might have solved it, maybe it's not a big deal, but you got to serve the mission of the organization. >> Absolutely. >> That's, like, number one. But at the same time, you've got stakeholders and they're powerful politicians and they have needs and there's transparency requirements, there are laws. They're not always aligned, those two directives, are they? >> No, thank goodness I don't have to deal with misalignments of those. We try to speak in the truth of here's the data and the decisions across the organization of our reports still go to congress, they go to congress on an annual basis through the selected acquisition report. And, you know, we are better understanding what we need to protect and how to advice congress on what should be protected and why. I would not say that's an easy proposition. The demands for those data come from the GAO, come from congress, come from the Inspector General and having to navigate that requires good access and dissemination controls and knowing why. We've sponsored some research though the RAND organization to help us look and understand why you have got to protect it and what policies, rules, and regulations are. And all those reports have been public so we could be sure that people would understand what it is. We're coming out of an era where data was not considered as it is today where reports were easily stamped with a little rubber stamp but data now moves at the velocities of milliseconds not as the velocity of reports. So we really took a comprehensive look at that. How do you manage data in a world where it is data and it is on infrastructures like data models. >> So, the future of war. Everybody talks about cyber as the future of war. There's a lot of data associated with that. How does that change what you guys do? Or does it? >> Well, I think from an acquisition perspective, you would think, you know. In that discussion that you just presented us, we're micro in that. We're equipping and acquiring through acquisitions. What we've done is we make sure that our data is shareable, you know? Open I, API structures. Having our data models. Letting the war fighters have our data so they could better understand where information is here. Letting other communities to better help that. By us doing our jobs where we sit, we can contribute to their missions and we've aways been every sharing in that. >> Is technology evolving to the point where, let's assume you could dial back 10 or 15 years and you had the nirvana of data quality. We know how fast technology is changing but is it changing as an enabler to really leverage that quality of data in ways that you might not have even envision 10 or 15 years ago? >> I think technology is. I think a lot of this is not in tools, it's now in technique and management practices. I think many of us find ourselves rethinking of how to do this now that you have data, now that you have tools that you can get them. How can you adopt better and faster? That requires a cultural change to organization. In some cases it requires more advanced skills, in other cases it requires you to think differently about the problems. I always like to consider that we, at some point, thought about it as a process-driven organization. Step one to step two to step three. Now process is ubiquitous because data becomes ubiquitous and you could refactor your processes and decisions much more efficiently and effectively. >> What are some of the information quality problems you have to wrestle with? >> Well, in our case, by setting a definite semantic meaning, we kicked the quality problems to those who provide the authoritative data. And if they had a quality problem, we said, "Here's your data. "We're going to now use it." So it spurs, it changes the model of them ensuring the quality of those who own the data. And by working with the services, they've worked down through their data issues and have used us a bit as the foil for cleaning up their data errors that they have from different inputs. And I like to think about it as flipping the model of saying, "It's not my job to drive quality, "it's my job to drive clarity, "it's their job to drive the quality into the system." >> Let's talk about this event. So, you guys are long-time contributors to the event. Mark, have you been here since the beginning? Or close to it? >> Um... About halfway through I think. >> When the focus was primarily on information quality? >> Yes. >> Was it CDOIQ at the time or was it IQ? >> It was the very beginnings of CDOIQ. It was right before it became CDOIQ. >> Early part of this decade? >> Yes. >> Okay. >> It was Information Quality Symposium originally, is that was attracted you to it? >> Well, yes, I was interested in it because I think there were two things that drew my interest. One, a colleague had told me about it and we were just starting the data journey at that point. And it was talking about information quality and it was out of a business school in the MIT slenton side of the house. And coming from a business perspective, it was not just the providence of IT, I wanted to learn form others because I sit on the business side of the equation. Not a pure IT-ist or technology. And I came here to learn. I've never stopped learning through my entire journey here. >> What have you learned this week? >> Well, there's an awful lot I learned. I think it's been... This space is evolving so rapidly with the law, policy, and regulation. Establishing the CDOs, establishing the roles, getting hear from the CDOs, getting to hear from visions, hear from Michael Conlan and hear from others in the federal agencies. Having them up here and being able to collaborate and talk to them. Also hearing from the technology people, the people that're bringing solutions to the table. And then, I always say this is a bit like group therapy here because many of us have similar problems, we have different start and end points and learning from each other has proven to be very valuable. From the hallway conversations to hearing somebody and seeing how they thought about the products, seeing how commercial industry has implemented data management. And you have a lot of similarity of focus of people dealing with trying to bring data to bring value to the organizations and understanding their transformations, it's proven invaluable. >> Well, what did the appointment of the DOD's first CDO last year, what statement did that make to the organization? >> That data's important. Data are important. And having a CDO in that and, when Micheal came on board, we shared some lessons learned and we were thinking about how to do that, you know? As I said, I function in a, arguably a silo of the institution is the acquisition data. But we were copying CDO homework so it helped in my mind that we can go across to somebody else that would understand and could understand what we're trying to do and help us. And I think it becomes, the CDO community has always been very sharing and collaborative and I hold that true with Micheal today. >> It's kind of the ethos of this event. I mean, obviously you guys have been heavily involved. We've always been thrilled to cover this. I think we started in 2013 and we've seen it grow, it's kind of fire marshal full now. We got to get to a new facility, I understand. >> Fire marshal full. >> Next year. So that's congratulations to all the success. >> Yeah, I think it's important and we've now seen, you know, you hear it, you can read it in every newspaper, every channel out there, that data are important. And what's more important than the factor of governance and the factor of bringing safety and security to the nation? >> I do feel like a lot in, certainly in commercial world, I don't know if it applies in the government, but a lot of these AI projects are moving really fast. Especially in Silicon Valley, there's this move fast and break things mentality. And I think that's part of why you're seeing some of these big tech companies struggle right now because they're moving fast and they're breaking things without the governance injected and many CDOs are not heavily involved in some of these skunk works projects and it's almost like they're bolting on governance which has never been a great formula for success in areas like governance and compliance and security. You know, the philosophy of designing it in has tangible benefits. I wonder if you could comment on that? >> Yeah, I can talk about it as we think about it in our space and it may be limited. AI is a bit high on the hype curve as you might imagine right now, and the question would be is can it solve a problem that you have? Well, you just can't buy a piece of software or a methodology and have it solve a problem if you don't know what problem you're trying to solve and you wouldn't understand the answer when it gave it to you. And I think we have to raise our data intellectualism across the organization to better work with these products because they certainly represent utility but it's not like you give it with no fences on either side or you open up your aperture to find basic solution on this. How you move forward with it is your workforce has got to be in tune with that, you have to understand some of the data, at least the basics, and particularly with products when you get the machine learning AI deep learning, the models are going to be moving so fast that you have to intellectually understand them because you'll never be able to go all the way back and stubby pencil back to an answer. And if you don't have the skills and the math and the understanding of how these things are put together, it may not bring the value that they can bring to us. >> Mark, thanks very much for coming on The Cube. >> Thank you very much. >> Great to see you again and appreciate all the work you guys both do for the community. All right. And thank you for watching. We'll be right back with our next guest right after this short break. You're watching The Cube from MIT CDOIQ.

>> Live from San Francisco, it's theCUBE covering DockerCon 18, brought to you by Docker and its ecosystem partners. >> Welcome back to theCUBE's coverage of DockerCon 2018. We are in San Francisco at Moscone, US. It's a spectacular day in San Francisco. It's a day to play hooky frankly, or play hooky and watch theCUBE. I'm Lisa Martin with John Troyer, and we're excited to welcome to theCUBE Abby Fuller, Developer Relations from AWS. Abby, great to have you here. >> Happy to be here. >> So you were a speaker at DockerCon 2018. Tell us a little bit about that and your role in Developer Relations. >> So I work in Developer Relations for AWS. So I used to be a devops engineer, and now I go around talking to customers and developers and other software engineers, and teaching them how to use things with AWS, or this morning it was teaching everyone how to build effective Docker images. >> So I read in your bio on the DockerCon website of the speakers that you're a container fan. We know you're a music fan, but you're also a container fan. What is it about that technology that you just go, "Oh, this is awesome, "and I can't wait to teach people "about the benefits of this"? >> So I switched over to container as a customer before I started working at AWS, and the biggest reasons for me, the first one was portability, so that I could do everything that I needed to run my application all in one place. So I think a big problem for a lot of developers is the whole what works on my machine? So being able to package everything together so that it worked on my machine, but also on a staging environment, a QA environment, and on your machine, that was the biggest thing for me. And that it removed some of the spaghetti code that came before, and it just made everything, it was all packaged nicely, I could deploy it a little bit more easily, a little bit faster, and I eliminated a lot of the why doesn't it work now when it worked before? >> Abby, one of the paradoxes of where we are in 2018 is AWS has been around for a decade, but yet here at the show, about half the folks raised their hand to the question, this is your first DockerCon? Are you just getting started with Docker and containers? So as an evangelist, Evangelist Developer Relations, you're the front line of talking with people at the grassroots. So can you talk a little bit about some of the different personas you encounter? Are you meeting people who are just getting started with their container journey? Or are you spending a lot of time kind of finessing the details about that API, APIs and changes and things like that at AWS? >> I think my favorite part about talking to AWS customers is that you get the whole range, right? So you get people that are just starting and they wanna know how do I build a container? How do I run it? How do I start from zero? And then you get the people that have been doing it for maybe a year or maybe two years, and they're looking for like advanced black belt tips, and then you get the other group which is not everyone is building a greenfield application, so then you get a really interesting subset where they're trying to move over from the whole monolith to micro services story. So they're trying to containerize and kind of adopt agile containerize approaches as they're moving over, and I think the best part is being able to talk to the whole range 'cause then it's never boring. >> What are some of the big barriers that you see for organizations that are maybe on the very very beginning of the journey or maybe before it, when you're talking with customers or developers, what are some of the things that you're hearing them say, "Ah, but what about these? "How can you help me eliminate these challenges?" >> Two big ones for me. The first one is the organizational changes that go around the infrastructure change. So it doesn't always work to just containerize what you already had, and then call it a day. So a lot of people are decomposing, they're going with micro services at the same time as they're going with containers. And I think wrapping your head around that kind of decomposition is the first kind of big challenge. And I think that we really just had to educate better. So show people, so here are some ways that you can break your service up, here are some things to think about when you're figuring out service boundaries. And I think the other one is that they often want a little bit of help when they're getting started. So either educational resources or how can AWS manage part of their infrastructure? Will they focus on the container part? So it's really interesting and it runs a whole gamut. >> Abby, you in Developer Relations, I love the trend, the community orient and trend, they're great, of peers helping peers, you're out there, you're wearing a Bruce Springsteen shirt right now, you made a Wu Tang joke in your talk today which is something that one did not do a few years back, right? You had to kinda dress up, and you were usually a man, and you wore a tie. >> Got my blazer on today. >> You look very sharp. Don't get me wrong. But as you talk to people, one, what's your day like or week like? How many miles do you have this year? That's private. But also as people come up to you, what do they ask you? Are you a role model for folks? Do people come up and say, "How can I do this too?" >> Yeah, so miles for this year. I think like 175,000. >> Already just in June? >> Already this year. So, this is a lot of what I do. I talk to all kinds of customers. I do bigger events like this, I do meet-ups, I do user groups, I go to AWS summits, and dev days and builders days, and things like that. I meet with customers. So day-to-day changes everyday. I'm obviously big on Twitter, spend a lot of time tweeting on planes. It really depends. This is a lot of what I do and I think people, I don't think you can ever really call yourself a role model, right? I love showing people that there's pass into tech that didn't start off with a computer science degree, that there's tons of ways to participate and be part of the tech community, 'cause it's a great community. >> You're not just a talker, you're a coder too. >> Yeah, yeah, so every job before this one with the exception of my very first job which was in sales. I was a dev ops engineer right up until I took the job at AWS, and I like to think that I never left, I'm just no longer on call. But I build my own demos, I write my own blog posts, I do all my own slides and workshops, so still super active, just not on call, so it's the best of all the worlds. >> So you went to Tufts, you didn't major in computer science. >> No. >> You are, I would say, a role model. You might not consider yourself one-- >> Well you can say it, yeah. >> I can say it exactly. It's PC if I say it. But, one of the things that's exciting to have females on the show, and I geek out on this is, we don't have a lot of females in tech. I mean, I think the last stat that I saw recently was less than 25% of technical roles are held by women. What was your career path if we can kinda pivot on that for a second, 'cause I think that's quite interesting. And what are some of the things that you've said, "You know what, I don't care. "I enjoy this, I wanna do this,"? 'Cause in all circumstances you are a role model, but I'd love to understand some of the things you encountered, and maybe some of your advice to those that'll be following in your footsteps. >> Yeah, so I went to school for politics. Programming was a little bit of a side hobby before that, mostly of the how can I do this thing, do this thing that it's not supposed to be doing? So I did that, I went to school. I took a computer science class my very last semester in school. I did not know that it was a thing before then, so I'm I guess a little slow in the comp sci uptake. And I was like, oh wow cool, this is an awesome, this could be an awesome career, but I don't know how to get into it. So I was like okay, I'm gonna go to a startup, and I'm gonna do whatever. So I take a sales job. I did that for maybe nine or 10 months. And I started taking on side projects. So how to write email templates in HTML that I could use that directly showed an impact to my sales job. Then the startup, as startups do, got acquired. And as part of the acquisition I moved my little CRM engineering job to the product team. And then, I'm gonna be honest, I bothered the CTO a lot. And I learned side projects. I was like I've learned Python now, what can you have for me? So I basically bothered him a lot until he helped me do some projects, and totally old enough now to admit that he was very kind to take a chance on me. And then I worked hard. I did a lot of online classes. I read a lot of books. I read a lot of blogs. I'm a big proponent in learning by doing. So I still learn things the same way. I read about it, I decide that I wanna use it, I try it out, and then at the point where I get where I don't quite know what's happening, I go back to documentation. And that got me through a couple of devops jobs until I got to evangelism. And I think the biggest advice I have for people is it's okay to not know what you want right away which is how I have a politics degree. But you can work at it. And don't be afraid to have mentors and communities and peers that can help you 'cause it's the best way to participate, and it's actually whether you have a comp sci job or not, it's still the best way to participate, and that you can have, there are so many nontraditional paths to tech, and I think everyone is equally valuable, because I think I write better coming from a liberal arts degree than I would have otherwise. So I think every skill that you bring in is valuable. So once you figure out what you want, don't be afraid to ask for it. >> The thing I'm hearing here is persistence. And it just reminded me, a quick pivot, of I hosted theCUBE at Women Transforming Technology just a couple weeks ago at VMWare, and they just made a massive investment, 15 million into a lab, a research lab at Stanford, to look at the barriers that women in tech are facing. And one of our guests, Pratima Rao Gluckman, just wrote a book called Nevertheless, She Persisted. It reminded me of you because that's one of the things that I'm hearing from you is that persistence that I think is a really unique thing there. Sorry, I just had to take a little side. >> I saw you looked that up. And actually I saw the title and I have not read it yet, but I have a flight back to New York after this so I'll have to find that. >> You've got time. >> Yeah. >> Over and over again as I talk with folks about IT and tech careers, right? It's that thinking expansively about your job, trying things, being a continuous learner, that is the thing that actually works. Maybe pivoting back to the tech for a sec then, obviously here container central, DockerCon 2018, Kubernetes actually was a big news this morning at the keynote, a big announcement, how Docker EE is gonna connect to Amazon EKS among others, kind of being able to manage the Kubernetes clusters up there in the cloud. And EKS actually just had, it just had its general availability I believe, right? In the last week or so? >> Yeah, so, excited to see EKS in the keynote this morning. We're always happy to deepen our partnerships. Yeah, and we've been in preview since re:Invent, and then we announced the general though of EKS, so Amazon Elastic Container Service for Kubernetes, long acronym. So EKS, we announced the GA last Tuesday. >> The interesting thing about AWS is somebody just compared it, I saw a tweet today to an industrial supply store and it's a huge warehouse full of tools that you can use, and that includes containers. But for containers, the three pieces that are the largest are EKS, ECS, and Fargate. Can you kinda tease those out for us really briefly? >> Yeah so envision if you would a flow chart. So if you wanna run a managed container on AWS, first you pick your orchestration tool, so EKS or ECS. ECS is the one that we've been working on for quite a few years now, so Elastic Container Service. Once you've chosen your orchestration tool, for ECS you have another set of choices which is either to run your containers in the EC2 mode which is manager, cluster, infrastructure as well, so the underlying EC2 hosts. And Fargate mode, where you only manage everything at the container level and task definition level, so no cluster management. >> And that's all taken care of for you. >> That's all taken care of for you. So Fargate I think is not actually a service in the traditional way that we would say that ECS is a service, and more of like an underlying technology, so that's what enables you to manage everything at just the container level and not at the cluster level. But I think the best way of describing it is actually is, there's a really nice quote floating around that said, "When I ask someone for a sandwich, "they don't wanna know the whole sandwich logistics chain, "so how do I get turkey, how do I get cheese, "how do I get mayo on the bread, "they just want the sandwich." So Fargate for, I think, a lot of people, is the sandwich. So I just want the sandwich, just give me your container, don't worry about the rest. >> So we've already established Abby has a lot of miles already in half a year, so I'm thinking two things. One, we should travel with her 'cause we're probably gonna get free upgrades. And two, you speak with a lot of customers. So tell us about that customer feedback loop. >> Something that I really love about working at Amazon is that so much of our roadmap is driven by customer feedback. So actually something that was really cool is that this morning, so ECS announced a daemon-scheduler, so run tasks one per host on every host in the cluster, so for things like metrics, containers, and log containers. And something that is so cool for me is that I asked for that as a customer, and I just watched us announced it this morning. It's incredible to see every single time that the feedback loop is closed, that people ask for it and then we build it. The same thing with EKS, right? We want you to have a great experience running your infrastructure on AWS, full stop. >> Can you give us an example of a customer that's really been impactful in terms of that feedback loop? One that really sticks out to you as a great hallmark of what you guys are enabling. >> I think that all of our customers are impactful in the feedback loop, right? Anyone from a really small startup to a really large enterprise. I think one that was really exciting to me was a very small Israeli startup. They went all in on managing no EC2 instances very quickly. They're called The Tree. So they were my customer speaker at the Tel Aviv summit, and they managed zero EC2 instances. So they have Fargate, they have Lambda, they managed no infrastructure themselves. And I just think it's so cool to watch people want things, and then adopt them so quickly. And the response on Twitter after the daemon-scheduler this morning is like, my favorite tweet was, "This is customer feedback done right." And I love seeing how happy people are when they ask for something or are saying, "Now that you've added that, "I can delete three Lambda functions "because you made it easy." And I love seeing feedback like that. So I think everyone is impactful, but that one stuck out to me as someone that adopted something incredibly quickly and have been so, they're just so happy to have a need solved for them. >> Well that's the best validation that you can get is through the voice of the customer. So to hear that must feel good that not only are we listening, but we're doing things right in a way that our customers are feeling how valuable they are to us. >> Happy customers are the best customers. >> They definitely are. >> Yeah. >> We learn a lot from the ones that aren't happy, and there's a lot of learnings there, but hearing that validation is icing on the cake. >> Always. >> Last question for you. With some of the announcements that came out today, and as this conference and its figure has grown tremendously, when I was walking out of the general session this morning, I took a photo because I don't think I've seen a general session room that big in a long time, and that was just at the Sapphire last week which has 20,000 attendees. I was impressed with how captivated the audience was. So last question, what excites you about some of the things that Docker announced today? >> So I think that's interesting. Something that's excited me in general is watching the community itself flourished. So there's many, there's Kubernetes CGroups, and there's user groups, the discussion online is always incredibly rich and vibrant, and there are so many people that are just so excited for anything. It's all companies building what they're looking for. And I love seeing things like the Docker Enterprise Edition announcement this morning where the demo is EKS, but I just love seeing customers get the choice to do whatever they want. They have all the options out there, and that you can see how much more rich and vibrant everything is. From even a couple years ago, there's more people every year, there's more sessions every year, the sessions are bigger every year. And I just love that. And I love seeing when people get so excited, and then seeing people that came to your talk two years ago, come back and give their own talk I think is amazing. >> Oh, talk about feedback. That must have felt really good. >> I think it's not a reflection on me, it's a reflection on the community. And it's a very supportive community, and it's a very excited and curious audience. So if you see their reception to other people that talk a lot being like, oh we're really happy to have you, then the next year you're like, well I have a story and I wanna tell it, so I'm gonna sit in my own session, and I think that's the best. >> Well Abby, it's been such a pleasure to have you on theCUBE, thank you. >> Thank you for having me. >> Thank you for stopping by. And your energy is infectious so you'll have to come back. >> Anytime. >> We wanna thank you for watching theCUBE. I'm Lisa Martin with John Troyer, live from San Francisco at DockerCon 2018. Stick around, we'll be right back after a short break. (upbeat music)