>> Narrator: From around the globe, it's the Cube with digital coverage of VMworld 2020, brought to you by VMware and its ecosystem partners. >> Hi everybody welcome back, this is the Cube's coverage of VMworld 2020, of course,it's remote coverage virtual VMworld 2020, Dennis Kennelly is here. He's the newly minted, General Manager of IBM storage, Dennis, thanks so much for spending some time with us, and congratulations. >> Thank you, Dave. Great to be here and great to talk. >> Yeah, so you're 30 days in, you know, so you're an expert by now, but of course, long time IBMer and you've touched a lot of different basis at IBM. So that's very exciting, but your background is in engineering and products, which I think is significant. And I want to talk about that a little bit, but you've got expertise in Cloud, hybrid Cloud. You ran the security division for quite a bit of time. Actually spent some time in data management as well. So, why do you feel as though this is a great opportunity for you and of course for IBM, given your background? >> Yeah Dave, I think as you say, I'm a technologist, I'm a product guy for many, many years, almost 30 years in the business. I came to IBM as a lot of people through an acquisition, of a small company in the networking space. But since then I've had, you know, two or three careers in IBM where I worked in security, I worked in hybrid Cloud, and actually way, way back, I worked for EMC, in the storage business. >> Yeah Right now, you know, as you look at hybrid Cloud, we're in this hybrid multicloud world, I think, and again, that ties into what VMware is also talking about, I think we're the two vendors in the Mac that really pushing and focused on that strategy. And, you know, the reality is of Clouds, if you look at Cloud today where the world is, I mean, even though we have 10, 15 years, you know, 15 years into the Cloud business is 15 years since the first hyperscaler was launched. Reality is about 20, 25% of what I would call enterprise workload have actually moved on to the Cloud. And there are many reasons for that, be it security, compliance, data, privacy, et cetera, transformation, a lot of other people challenges, et cetera. But now we are actually right in the cusp of adapting that enterprise workload. The storage has a critical role to play in that, especially in the hybrid multicloud world, and we're making sure that storage is a key enabler on that genre. And that's why I think it's a critical time right now to be in storage and to help in that journey. >> And I want to come back to talk a little bit about it, but one of the things that I am excited about, in terms of your background, you've got a strong product background, and for years I had indicated that IBM sort of for a while, lost its formula in storage, you'd do all this R&D and it never hit the market, and then under your predecessor, I think IBM has done a much, much better job and you see it in your now, the last couple of quarters have been really strong for you guys. Of course, you've got the mainframe attached, which is the gift that keeps on giving, but how are you looking at your business? Again, I know you're only 30 days in, but there's been some tailwinds lately, you guys have seemed to do pretty well relative to the market. >> Yeah, my predecessor has done a fantastic job, I mean, if you look at our core storage business, as you said Dave, like our mainframe that's all was our flagship. I mean, you know, we continue to innovate there, particularly around the mainframe, things like, you know, copy services, et cetera, where we're driving a lot of innovation, and continue to lead there. But I think the more interesting, and the really exciting part is what we've done in an our what I would call our open system storage, our flash line up, where the team had got to a single core base, and a single hardware platform where we can scan right up and down the stack. And really innovating and driving very quickly there, is a critical part of what I'm driving right now and accelerate the work that has been done today. Then I think, you know, beyond, you know, the core storage platforms, if you start to look at some of the other areas like cyber resiliency, data protection, and really driving innovation there, but also leveraging other parts of IBM. I mean, we have a very strong base in security. I'm working very closely with our security teams, because I know from my days in security, you know, data protection, data recovery, real challenges for the CISO, I'm bringing those technologies and packaging those technologists so that they can help in those challenges critical for me. And last but not least, I mean, you know, as you look at things like getting an AI and I'm bringing AI to the enterprise. One of the big challenges is being able to identify where all the data is and to get an access to the data. And again, storage is a critical role to play there in terms of discovery services, et cetera, which again is a key innovation. So I think it comes down to those three things. making sure... Obviously you need a very strong product line-up, which I think we are very well equipped right now, and we have, based on the work the team have done over the last number of year. But then applying that to some of the critical problems around cyber resiliency, data protection, and also leveraging and enabling AI in the enterprise. >> So let's stay on cyber for a minute, It's an area obviously, you know, a lot about and we used to think, okay, what's the relationship between storage and cyber, and it was maybe it was encryption, you know, data at motion or data at rest, and now the lines between data protection, and cyber are really getting blurred. I mean, it's become a... Especially with COVID, it's become, >> Mhm >> A fundamental part of business resiliency, So how are you thinking about storage, and the intersection of cyber? >> Yeah, I mean, I think, you know, when, I had the, my security hat on, I mean, reality and security is, you know, the World is, you know, how you deal with a breach because at the end of the day, pretty much there is to be a security event. It's not a case of if, it's a case of when it happens, and you know, really how you respond to that, and that was where a lot of our focus was in terms of how you respond to those events, how you recover quickly, et cetera. Now, when you come across into storage, I mean, lately in the world we live in today, where at the end of the day, when there's a cyber attack, I mean, what is it that the nefarious actor is after, they are pretty much after your data assets. And, you know, things like ransomware now there's various different techniques. But how quickly your crew can respond or recover from those is really important. And that's where storage has a critical role to play. And a lot of what we are doing in the innovations, of course, things like base encryption and encryption everywhere, they are table stakes as far as IBM is concerned, we've had that for many years, within our mainframe and in our open systems, but now really thinking about how you actually recover very quickly when an event happens, and that's really where we see a lot of innovation, and where we want to talk to both sides of the house, both the storage I've been, but also on the CISO who have frankly, a big influence in terms of where investment dollars are put today and making sure that they have the capability in place to actually recover quickly when there's an attack. >> Well, as you well know for years it was, you know, security was the problem of the, you know, the Sec-Ops team, you know, >> Yes >> Not my Swim lane, but that has really changed. I mean, security has become a board level issue. Everybody's got to be involved. We're seeing more CISOs reporting into the CIO. We're also seeing CISOs have a seat at the table, they're reporting, you know, at quarterly board meetings, and so, we see every part of the IT stack, really focused on security, and even the lines of business as well. What do you say? >> Yeah, exactly, I mean, I think the CISO role has evolved over the last number of years, I mean, I think if I think back, you know, maybe five, 10 years ago, the CISO role was very much what I would call a compliance type role. So in other words, making sure we all the checks and balances in place that, you know, at the right time. putting the fast pace, changing world with Cloud and transformation, digital transformation, the CISO has to be an active part of that. We used to use the expression that the CISO was the doctor know, in other words, how to stop, you know, innovation, or how to stop things changing, That's, you know, yesterday's news, today, the CISO has to be much more pro-active, helping technology, helping transformation, and that's why you're seeing that, they have a seat at the top table right now, because they are critical, to all decisions that are made. The fact is that, you know, massive transformation is happening in every enterprise, but you're got to do that in a secure and safe manner, and the CISO is absolutely critical to that, and is influencing a lot of fine decisions around that as well. And by that we see that as a critical part of our strategy that we make sure that we have offerings and capabilities that addresses that need. >> Love to come back to the, the Cloud discussion, the hybrid Cloud, and multi-Cloud, you mentioned that early on, you guys obviously have a big play there with Red Hat and an open shift we've seen in our data that is becoming real multicloud and there used to be, you know, a lot of vendor talk, but now it's becoming a fundamental strategy. So you were saying it, you know, as a smaller portion of workloads, you know, are in the Cloud, it's all the, all the hard stuff has stayed on Prem. What's the motivation for your customers to move to a Cloud, or a hybrid Cloud strategy? What are they trying to achieve as an outcome? >> Well, I think when everything, you know, you got to stop at a business level, right? I mean, fundamentally what enterprises are doing is, especially in this cold world, everything is becoming increasingly digital going online, et cetera. So that transformation is accelerating that digital transformation, the rate and pace of that is accelerated. Now you actually stop to think about that and say, what does that mean in terms of your existing enterprise? In many cases, you know, especially for incumbents, right? They have existing systems that have existing data repositories, et cetera. So how do they leverage that and transform those to meet these new needs? And, and then of course back to the cyber concerns, right, you have security data privacy concerns, et cetera. So you have all these multiple variables going on, in our world, you know, and if you look at what has happened over the last, as I said, 15 plus years, you know, everybody said, you know, everything is moving to the public, game over, we're done. That hasn't actually happened. We really are in a multicloud world. When we talk about multicloud, that means, you know, you have the what we refer to as a traditional hyperscalers, but also the SAS properties, et cetera, that we see in every enterprise. And also you have to have a on-premise capability, but it's different than what it was traditionally, it has to have Cloud like economics. And what has been very good about the Cloud, a tremendous innovation is the elastic scaling, et cetera, on the economics that has come with the Cloud. But you have to bring that back on-premise. You can't just have one operating model in the Cloud and have something else on-premise, your infrastructure has to be flexible at scale and across border environments. And that is the true definition of what we call a hybrid multicloud. And one with critical technologies, will give you that consistency across that, and one of the reasons why, you know, we named the strategic pattern Red Hat, is containers, because from a number of years back, we could see that was the part of the technology that enabled a lot of these hybrid multicloud capabilities. IBM talked about hybrid Cloud long before, it was a popular thing to talk about a number of years back. But we could see that, you know, to enable that to happen, the critical technology was containers, and that because of both, combination of containers and Linux and hence the acquisition of Red Hat, and now we are actually leveraging that to actually drive footprint across the Hybrid Cloud environment, and everything we're doing is integrated into that container technology including storage. >> Yeah, well, of course we're here at VMworld again, virtually, but the big trends we're hearing from, from VMware and the ecosystem this week, they're, pounding on networking hybrid multicloud, as we've just talked about, you mentioned containers and Kubernetes, we're hearing a lot about security, which we just addressed the AI, ML, thinking about the points of commonality, you guys are big partners with VMworld. VMware have been for, for many, many years, a lot of open shift runs on VMware,We know that. a lot of your business critical, and mission critical workloads. So what are those points of commonality, and maybe what are some of the points of divergence in what you guys are doing? as part of >> Yeah, I mean, >> VMware tremendous partner of ours, I mean, a lot of VMware workload, as customers move to the cloud, moves to the IBM Cloud. We're probably their premier choice right now in terms of VMware workload. Also, I think in terms of, you know, I think if you look at VMware today, I think they also see a hybrid multicloud strategy, and I think there's the VMware, I would say a strategy has evolved over time. Clearly they have a huge installed base of virtual machines, which a lot of our container technology at Red Hat runs on top off. But VMware has also evolved into a container approach as well, with a lot of the announcements they've made. So I think we're on a very similar strategy when it comes to my own area on storage, in terms of how we integrate storage into that container world, there's a lot of commonality in how we approach that. I mean, developing CSI drivers, et cetera, into the container world, I think we're both doing that and doing that together. In areas, obviously we will compete and very much compete. I talked about that product lineup and obviously BMR, and obviously that relationship with Dell and others, is got to be areas where we will compete in the storage. But in terms of where we really will collaborate, I think is a lot around the hybrid multicloud strategy, and building an open ecosystem that everybody can play on. And they'll, you know, where we sit on them or they sit on us. I think you're going to see an open ecosystem across us in this hybrid multicloud World. >> Well, it seems as though from a storage standpoint, that you've got no choice, but to be open, you have to give clients as much optionality as possible. You can't say, okay, we're going to be all IBM Red Hat, you've been, you've got so many other opportunities for, term expansion. I wonder if you could talk about that, and maybe express your philosophy, just in terms of openness, and it's important in terms of competing in storage. >> I think that's been fundamental to storage since the very beginning of the storage industry. And of course, we absolutely, we have to be very open in terms of who we integrate with. And we go everywhere from like optical containers, to virtual machines to any system, all the ways for something as traditional as tape. I mean, tape, many have said, tape is dead. Tape is far from dead, even in the, hyperscaler world, where we're seeing a lot of the hyperscalers right now, are actually using tape technology and integrating tape into their environment. So there's an example, where you might not have thought about us, you know, it's something that we do, we do that in a very open fashion and continue to do that. Likewise, when it comes to security, when it comes to things like data and AI, you know, our philosophy is don't take another copy of the data, be able to access the data so that you can build your AI models, et cetera on top of that. we may have a lot to happen with some of our capabilities around spectrum scale, and we will integrate with backend arise from EMC, Hitachi, and others actually enable that to happen. So we're very open ecosystem, want to bring unique value, and if I'm making sure we can integrate both up and down the stack. >> Yeah. Well, I mean, you guys, of course, for those who have been around the storage industry, as much as I have the San volume controller, a hub was kind of the early days of storage virtualization, I think IBM was clearly one of the leaders there, and you've kind of taken that concept to data. We've seen that with Cloud packs, and so, you know, one IBM executive, you know, said to me one time, you know, we, learned our lesson many, many years ago about the importance of openness, and then you got the religion there. So I think it's pretty, >> pretty fundamental. >> I mean, >> Isn't it? >> It's pretty fundamental, I guess, we learned hard lesson many years ago, and I think, you know, when you talk about openness and something like Red Hat, I think we're definitely, putting our money where our mouth is in terms of being an open company, I'm really enabling something like Red Hat, and continue that ecosystem as you know, Red Hat is independent, was run independent of IBM, so that we want to drive that open ecosystem around Red Hat, and that is pretty fundamental to a lot of IBM, a lot of our platforms and our capability, I mean, you know, back for many years, we talked about the sound volume controller, but even if you go back far enough in history, which I can do in the storage World, and the storage API world, IBM was one of the leaders I'm building an open API around storage and storage access as well back then. So it's fundamental to the company has always been, and continues to be, I mean, we were one of the major contributors to things like Linux, That's not well known, but that, that is the truth, and, you know, things like that, what we have done over many, many years. >> Yeah, undoubtedly, I mean, I go back to Steve mills, epic decision to invest a billion dollars in Linux back in the day, and we've seen those billion dollar bets pay off in terms of flash and other areas. Dennis, what's your style going to be? I mean, again, I'm excited that you've got an engineering background, you're a product person at the end of the day, it's all about innovation, and getting that R&D out to market. What should we expect from your leadership style? >> I think you kind of said it there, I mean, I'm an engineer at heart, I really want to deliver value to our clients. You know, we have the big R&D spend in our storage unit, and I want to show value for that spend on IBM has given me a responsibility to deliver on. So to begin to deliver massive innovation and productivity from our engineering team. I mean, that's fundamentally what I do. So starting from day one, understanding our portfolio top to bottom, what are our strengths in the market, Where are our weaknesses, where we need to address some of the gaps, but also listening to our clients, which is very important to me and making sure that they see the innovation, the quality of the deliverables and that, you know, as a client or as a customer of IBM, you can be guaranteed that IBM would deliver and continues to deliver on innovation on a road map on storage. And that's really fundamental to my philosophy. I'm making sure that we can establish leadership, and continues to establish leadership, in the storage industry. So that we are a trusted partner, and a valued partner in your transformation journey. So that when you make investments with us, as a technology provider that we deliver on a roadmap and a vision that actually needs your needs going forward. I mean, that's fundamental to what, you know, my management style is about and making sure I have the right people that I can put in front of our clients and make sure they can deliver that value. >> I mean, I think that's critical, Dennis, and again, I keep hitting on your engineering background, because yes, while you have a big R&D budget, IBM probably spend $6 billion a year in R&D, you're fighting for that budget, with a lot of other divisions at IBM, so staying close to the customer is critical because you've got to place those bets. And I have firmly believed that with a strong technical background and product background, and staying close to the customer, you're going to have, you know, some big wins and more wins than losses, and you're going to be able to more efficiently deploy that capital in the form of R&D, and then quickly get it out into products. I see that as crucial today in terms of the innovation equation. >> Yeah. I mean, my philosophy, you know, fundamentally, you know, a lot of times, and I've been in engineering a long time, it's not about the size of the budget, be the dollar, be a $10, be it $100. It's how efficient we are with that dollar, and how innovative we are with that dollar. And sometimes, you know, you look at IBM and people look at a big company, maybe it doesn't move as quickly. I can guarantee you that, you know, that's fundamental that, you know, I run a startup within a small company, within a large company. I like to think of it that way and how we can innovate and move very quickly. And that's, you know, fundamental to my philosophy in terms of how I think, it's not about, okay, how can I get more budget to do exits? How can I be more efficient that I can drive more value? And then, you know, maybe then I get more budget, but you know, you got to think about detail more rather than saying, I don't want to have more inefficiency, I wanted to have more innovation, more creativity, entering new markets, looking at new capabilities, and being able to just create great new opportunities for IBM storage. >> Well, Dennis, again, congratulations on the new appointment, we look forward to at some point in the future of being able to meet face to face, but thanks so much for coming on the Cube and our coverage of VMworld. >> Thank you, Dave, and thanks for your time today, I appreciated the conversation. Thank you. >> All right, You're very welcome, and thank you for watching everybody, This is Dave Vellante for the Cube, again, wall to wall coverage of VMworld 2020, We'll be right back right after this short break. (soft music)

>> From Yorktown Heights, New York, it's theCUBE, covering IBM Cloud Innovation Day, brought to you by IBM. >> I'm Peter Burris of Wikibon. Welcome back to IBM Innovation Day, covered by theCUBE, from beautiful Yorktown Heights, New York, Thomas J. Watson Research Center. A lot of great conversations about the journey to the cloud and what it means, and we're going to have another one here with Denis Kennelly, who is the General Manager of Cloud Integration in IBM. Denis, welcome to theCUBE. >> Thank you, Peter, and welcome to Yorktown also. >> I love it here. So, very quickly, what does the GM of Cloud Integration do? >> Yeah, so, I suppose we start from the beginning, right? So I am responsible for a lot of what we call the traditional IBM middleware. So these are brands that are known to the industry and to our customers, things like WebSphere, Message Queue, or MQ, as we know it, which is kind of the core foundation stones for a lot of IT today that's out there in the industry. And it's not just about, you know, sometimes people talk about this legacy, but this is what all the systems run on today. And also, I'm involved in the whole journey of moving that middleware to the cloud and enabling customers to get on that journey to cloud. And it's not just to a cloud, because your typical enterprise today has probably on average about five different clouds, and clouds, as we know them as the IS players of the past, but also when we talk about cloud, we also think about things like SaaS properties and applications of that regard. So it's helping customers go from that traditional IT infrastructure and on their journey to the cloud. That's what I do. >> So utilizing these enterprise-ready technologies that have driven the enterprise, bringing them to the cloud as services, but also making sure that the stuff that's currently installed can engage and integrate the cloud from a management service standpoint as well. >> Absolutely, because customers have made a huge investment in this middleware, and a lot of the transactions, and a lot of the security, and a lot of the risks set in these systems, and they have served us very well for many decades. Now, as we start to move to the cloud, it isn't a binary switch. It's going to be a transition over time, and today, I think we're about 20% into that journey. I would say we've done some of the easier parts. Now we're getting into some of the more complex and some of the more difficult problems. And kind of one of the underlying pieces of technology we're using to enable customers to do that is container technology. So we've made the decision to use containers right across our middleware, our software. So what I mean by that is we've taken all our software and it's running on containers today, and that's a key enabler to make this happen, because containers give you that flexibility and that openness to run on different targeted environments and be able to run on different clouds at the end of the day. >> The model by which developers thought about integration would be through a transaction. Generally pretty stateful. So, I'll put something in a queue, I'll wait for a response, guaranteed delivery. Now we're moving to a world, containers, a lot more reliance on stateless interactions. It means we're being driven mainly by events. I'm thinking in terms of events. Talk about how that is changing the way we think about the role of middleware or the role of integration amongst all these different possible services. >> Yeah, it's a great point. I mean, so if you think about containers, we think about stateless, and we think about microservices, and we talk about event-based applications, so a lot of those front ends are on that today and building on those technologies. So you've got to enable the new developers to build in that way. Now, how do you integrate that with that backend, right? Because at the end of the day, these transactions are running in the backend, and you really want to enable, as part of the transformation, you want to open up those backends to those new developers and to those new customer insights, because what is digital transformation? It's about putting the customer at the middle and enable insights on those customers, and enable rapid development of those applications. So at the core of that is integration, and integration is not just message-based integration. It's being able to take those backend transactions and surface them up through APIs, not just the standard APIs as we think of maybe as web services, but event-based probability models, and event-based APIs also, and doing that in a consistent and a secure manner, because if you have all these complex transactional systems, who has access to that data? Who has access to make those transactions? Who can, at certain levels, et cetera, and we really have to do that in a secure and a consistent manner across these environments is critical to what we do. >> So, can you give us some examples of some customers that are successfully transitioning their backend systems to these new technologies in a way that protects the backend system, makes it economical to do so, in other words, doesn't force change, but can utilize some of these new integration technologies to make both the new investments more valuable but also the backends more valuable too. >> Yeah, I mean, if you think of, I'll give you an example of a customer, American Airlines, in the airline industry, right? So, if you think about travel and airline travel in times past, you know, you made a reservation maybe through an agent and you booked the flight from A to B. Today, you have your cellphone, you get regular updates on your flights. If you're delayed, you're possibly offered re-routing options, et cetera, right, so there's a classic example of how digital has transformed the airline industry and the airline booking industry. If your flight, you know, if there's weather patterns, et cetera, how you can get real time updates on your flights. So, okay, that's all happening on the front end, on your cellphone, or your tablet, or whatever, but the backend booking system is still a transactional-based system that says, Peter is on this flight going from A to B at this time, et cetera. So, that's an example of how we have modernized an application and we have worked with American Airlines to make that happen, to give you that kind of 360 view as a customer, where you bring in together flight information, weather information, rating information, because we'll offer you different alternatives in terms of if you need to rebook in the event of something going on, and at the backend, there's still a transaction that says, book Peter on this flight from A to B, and that's a real life example of a transformation, how we've integrated those two worlds there. >> So if we go back five or six, or more than that, say 10, 15 years, in the days of MQ, for example, the people who were developing, and setting up those systems, and administering and managing those systems were a relatively specialized group. Today, the whole concept of DevOps in many respects is borrowing from much of the stuff that those folks did many, many years ago as infrastructure builders, or developers, as I call them. How does that group move into this new world of integration in the cloud? >> Yeah, so, I think first of all, the rate and pace has multiplied, right, so the rate and pace of which we make changes to the system has multiplied. I mean, maybe traditionally, we run in changes maybe once a month. We have things like change control windows. Things were very well controlled, et cetera, right? But at the end of the day, it doesn't meet the needs of today and what we need to do in a digital world. So today, we're running in changes on the hour. So now, you're faced with a challenge, right? So when you make changes, how do you know that the system is still performing, is still operating at the level you need it to operate on? You start to think about security and you start to think about, okay, I've made a change, have I introduced vulnerabilities into the system? You've got to, you know, in the past, these were all separate groups and almost islands within the operation center, where you have the developer, who kind of over to all the code, and then operations looked at it and see how it's performed, and security checked for compliance, et cetera, and they were kind of three different islands of personas or groups within the organization. Today, that's really collapsing into one organization. The developer is responsible for making sure the change gets in, for making sure the change performs, and is also security compliant. And we call this the role of the SRE, or the systems reliability engineer, and really bringing those two worlds together into one persona, and it's not just one persona but having the systems on the inside to make that happen. And that's critical in how management is changing and the management of these systems is changing, and how the skill level is needed in this new world. >> So Denis, one more question. In a few months, IBM Think is going to take over San Francisco, February 2019, >> Looking forward to it. >> 3,000 people. Talk to us a little bit about what gets you excited about Think, and what kind of conversations you hope to be having while you're there. >> Yeah, well, you know, this is the one time of the year where all of IBM comes together, and it's new this year that we're going to San Francisco, and in particular, in our cloud business, which I'll talk about, which really encompasses everything we're talking about here, which is our middleware business and also how we move customers to the cloud, and really engaging with customers in those conversations. And this is the one time of the year where all of IBM comes together, and where you can see the full breadth of our capabilities all the ways from our systems, and the hardware, down at that level, at the chip level, right through to the middleware and the software to our cloud, and actually engaging with customers, and really understanding what the customer needs are, and making sure that what we are working on is meeting those customer needs, and of course, if we need to adapt or change, and take that feedback back into the organization, so we do that in real time. It's a very exciting time for us. It's a week in the year that I really look forward to, because that's where all of IBM comes together, including our services, et cetera, and where we actually have conversations with key customers and partners and really understanding what's going on in the industry and how we can help people on this journey to the cloud that I talked about. >> Denis Kennelly, IBM General Manager of Cloud Integration, thanks very much for being on theCUBE. >> Thank you, Peter. And once again, this is Peter Burris. We're signing off from the IBM Innovation Day, here at the Thomas J. Watson Research Center in Yorktown Heights. Thank you very much for watching. Let's carry on these conversations about cloud and the future of computing.

>> Hello everyone. Welcome to theCUBEs coverage of Compute Engineered for your Hybrid World sponsored by HPE and Intel. Today we're going to dive into advanced performance of VDI with the fourth gen Intel Zion scalable processors. Hello I'm John Furrier, the host of theCUBE. My guests today are Alan Chu, Director of Data Center Performance and Competition for Intel as well as Denis Kondakov who's the VDI product manager at HPE, and also joining us is Cynthia Sustiva, CAD/CAM product manager at HPE. Thanks for coming on, really appreciate you guys taking the time. >> Thank you. >> So accelerating VDI to the Edge. That's the topic of this topic here today. Let's get into it, Dennis, tell us about the new HPE ProLiant DL321 Gen 11 server. >> Okay, absolutely. Hello everybody. So HP ProLiant DL320 Gen 11 server is the new age center CCO and density optimized compact server, compact form factor server. It enables to modernize and power at the next generation of workloads in the diverse rec environment at the Edge in an industry standard designed with flexible scale for advanced graphics and compute. So it is one unit, one processor rec optimized server that can be deployed in the enterprise data center as well as at the remote office at end age. >> Cynthia HPE has announced another server, the ProLiant ML350. What can you tell us about that? >> Yeah, so the HPE ProLiant ML350 Gen 11 server is a powerful tower solution for a wide range of workloads. It is ideal for remote office compute with NextGen performance and expandability with two processors in tower form factor. This enables the server to be used not only in the data center environment, but also in the open office space as a powerful workstation use case. >> Dennis mentioned both servers are empowered by the fourth gen Intel Zion scale of process. Can you talk about the relationship between Intel HPE to get this done? How do you guys come together, what's behind the scenes? Share as much as you can. >> Yeah, thanks a lot John. So without a doubt it takes a lot to put all this together and I think the partnership that HPE and Intel bring together is a little bit of a critical point for us to be able to deliver to our customers. And I'm really thrilled to say that these leading Edge solutions that Dennis and Cynthia just talked about, they're built on the foundation of our fourth Gen Z on scalable platform that's trying to meet a wide variety of deployments for today and into the future. So I think the key point of it is we're together trying to drive leading performance with built-in acceleration and in order to deliver a lot of the business values to our customers, both HP and Intels, look to scale, drive down costs and deliver new services. >> You got the fourth Gen Z on, you got the Gen 11 and multiple ProLiants, a lot of action going on. Again, I love when these next gens come out. Can each of you guys comment and share what are the use cases for each of the systems? Because I think what we're looking at here is the next level innovation. What are some of the use cases on the systems? >> Yeah, so for the ML350, in the modern world where more and more data are generated at the Edge, we need to deploy computer infrastructure where the data is generated. So smaller form factor service will satisfy the requirements of S&B customers or remote and branch offices to deliver required performance redundancy where we're needed. This type of locations can be lacking dedicated facilities with strict humidity, temperature and noise isolation control. The server, the ML350 Gen 11 can be used as a powerful workstation sitting under a desk in the office or open space as well as the server for visualized workloads. It is a productivity workhorse with the ability to scale and adapt to any environment. One of the use cases can be for hosting digital workplace for manufacturing CAD/CAM engineering or oil and gas customers industry. So this server can be used as a high end bare metal workstation for local end users or it can be virtualized desktop solution environments for local and remote users. And talk about the DL320 Gen 11, I will pass it on to Dennis. >> Okay. >> Sure. So when we are talking about age of location we are talking about very specific requirements. So we need to provide solution building blocks that will empower and performance efficient, secure available for scaling up and down in a smaller increments than compared to the enterprise data center and of course redundant. So DL 320 Gen 11 server is the perfect server to satisfy all of those requirements. So for example, S&B customers can build a video solution, for example starting with just two HP ProLiant TL320 Gen 11 servers that will provide sufficient performance for high density video solution and at the same time be redundant and enable it for scaling up as required. So for VGI use cases it can be used for high density general VDI without GP acceleration or for a high performance VDI with virtual VGPU. So thanks to the modern modular architecture that is used on the server, it can be tailored for GPU or high density storage deployment with software defined compute and storage environment and to provide greater details on your Intel view I'm going to pass to Alan. >> Thanks a lot Dennis and I loved how you're both seeing the importance of how we scale and the applicability of the use cases of both the ML350 and DL320 solutions. So scalability is certainly a key tenant towards how we're delivering Intel's Zion scalable platform. It is called Zion scalable after all. And we know that deployments are happening in all different sorts of environments. And I think Cynthia you talked a little bit about kind of a environmental factors that go into how we're designing and I think a lot of people think of a traditional data center with all the bells and whistles and cooling technology where it sometimes might just be a dusty closet in the Edge. So we're defining fortunes you see on scalable to kind of tackle all those different environments and keep that in mind. Our SKUs range from low to high power, general purpose to segment optimize. We're supporting long life use cases so that all goes into account in delivering value to our customers. A lot of the latency sensitive nature of these Edge deployments also benefit greatly from monolithic architectures. And with our latest CPUs we do maintain quite a bit of that with many of our SKUs and delivering higher frequencies along with those SKUs optimized for those specific workloads in networking. So in the end we're looking to drive scalability. We're looking to drive value in a lot of our end users most important KPIs, whether it's latency throughput or efficiency and 4th Gen Z on scalable is looking to deliver that with 60 cores up to 60 cores, the most builtin accelerators of any CPUs in the market. And really the true technology transitions of the platform with DDR5, PCIE, Gen five and CXL. >> Love the scalability story, love the performance. We're going to take a break. Thanks Cynthia, Dennis. Now we're going to come back on our next segment after a quick break to discuss the performance and the benefits of the fourth Gen Intel Zion Scalable. You're watching theCUBE, the leader in high tech coverage, be right back. Welcome back around. We're continuing theCUBE's coverage of compute engineer for your hybrid world. I'm John Furrier, I'm joined by Alan Chu from Intel and Denis Konikoff and Cynthia Sistia from HPE. Welcome back. Cynthia, let's start with you. Can you tell us the benefits of the fourth Gen Intel Zion scale process for the HP Gen 11 server? >> Yeah, so HP ProLiant Gen 11 servers support DDR five memory which delivers increased bandwidth and lower power consumption. There are 32 DDR five dim slots with up to eight terabyte total on ML350 and 16 DDR five dim slots with up to two terabytes total on DL320. So we deliver more memory at a greater bandwidth. Also PCIE 5.0 delivers an increased bandwidth and greater number of lanes. So when we say increased number of lanes we need to remember that each lane delivers more bandwidth than lanes of the previous generation plus. Also a flexible storage configuration on HPDO 320 Gen 11 makes it an ideal server for establishing software defined compute and storage solution at the Edge. When we consider a server for VDI workloads, we need to keep the right balance between the number of cords and CPU frequency in order to deliver the desire environment density and noncompromised user experience. So the new server generation supports a greater number of single wide and global wide GPU use to deliver more graphic accelerated virtual desktops per server unit than ever before. HPE ProLiant ML 350 Gen 11 server supports up to four double wide GPUs or up to eight single wide GPUs. When the signing GPU accelerated solutions the number of GPUs available in the system and consistently the number of BGPUs that can be provisioned for VMs in the binding factor rather than CPU course or memory. So HPE ProLiant Gen 11 servers with Intel fourth generation science scalable processors enable us to deliver more virtual desktops per server than ever before. And with that I will pass it on to Alan to provide more details on the new Gen CPU performance. >> Thanks Cynthia. So you brought up I think a really great point earlier about the importance of achieving the right balance. So between the both of us, Intel and HPE, I'm sure we've heard countless feedback about how we should be optimizing efficiency for our customers and with four Gen Z and scalable in HP ProLiant Gen 11 servers I think we achieved just that with our built-in accelerator. So built-in acceleration delivers not only the revolutionary performance, but enables significant offload from valuable core execution. That offload unlocks a lot of previously unrealized execution efficiency. So for example, with quick assist technology built in, running engine X, TLS encryption to drive 65,000 connections per second we can offload up to 47% of the course that do other work. Accelerating AI inferences with AMX, that's 10X higher performance and we're now unlocking realtime inferencing. It's becoming an element in every workload from the data center to the Edge. And lastly, so with faster and more efficient database performance with RocksDB, we're executing with Intel in-memory analytics accelerator we're able to deliver 2X the performance per watt than prior gen. So I'll say it's that kind of offload that is really going to enable more and more virtualized desktops or users for any given deployment. >> Thanks everyone. We still got a lot more to discuss with Cynthia, Dennis and Allen, but we're going to take a break. Quick break before wrapping things up. You're watching theCUBE, the leader in tech coverage. We'll be right back. Okay, welcome back everyone to theCUBEs coverage of Compute Engineered for your Hybrid World. I'm John Furrier. We'll be wrapping up our discussion on advanced performance of VDI with the fourth gen Intel Zion scalable processers. Welcome back everyone. Dennis, we'll start with you. Let's continue our conversation and turn our attention to security. Obviously security is baked in from day zero as they say. What are some of the new security features or the key security features for the HP ProLiant Gen 11 server? >> Sure, I would like to start with the balance, right? We were talking about performance, we were talking about density, but Alan mentioned about the balance. So what about the security? The security is really important aspect especially if we're talking about solutions deployed at the H. When the security is not active but other aspects of the environment become non-important. And HP is uniquely positioned to deliver the best in class security solution on the market starting with the trusted supply chain and factories and silicon route of trust implemented from the factory. So the new ISO6 supports added protection leveraging SPDM for component authorization and not only enabled for the embedded server management, but also it is integrated with HP GreenLake compute ops manager that enables environment for secure and optimized configuration deployment and even lifecycle management starting from the single server deployed on the Edge and all the way up to the full scale distributed data center. So it brings uncompromised and trusted solution to customers fully protected at all tiers, hardware, firmware, hypervisor, operational system application and data. And the new intel CPUs play an important role in the securing of the platform. So Alan- >> Yeah, thanks. So Intel, I think our zero trust strategy toward security is a really great and a really strong parallel to all the focus that HPE is also bringing to that segment and market. We have even invested in a lot of hardware enabled security technologies like SGX designed to enhance data protection at rest in motion and in use. SGX'S application isolation is the most deployed, researched and battle tested confidential computing technology for the data center market and with the smallest trust boundary of any solution in market. So as we've talked about a little bit about virtualized use cases a lot of virtualized applications rely also on encryption whether bulk or specific ciphers. And this is again an area where we've seen the opportunity for offload to Intel's quick assist technology to encrypt within a single data flow. I think Intel and HP together, we are really providing security at all facets of execution today. >> I love that Software Guard Extension, SGX, also silicon root of trust. We've heard a lot about great stuff. Congratulations, security's very critical as we see more and more. Got to be embedded, got to be completely zero trust. Final question for you guys. Can you share any messages you'd like to share with the audience each of you, what should they walk away from this? What's in it for them? What does all this mean? >> Yeah, so I'll start. Yes, so to wrap it up, HPR Proliant Gen 11 servers are built on four generation science scalable processors to enable high density and extreme performance with high performance CDR five memory and PCI 5.0 plus HP engine engineered and validated workload solutions provide better ROI in any consumption model and prefer by a customer from Edge to Cloud. >> Dennis? >> And yeah, so you are talking about all of the great features that the new generation servers are bringing to our customers, but at the same time, customer IT organization should be ready to enable, configure, support, and fine tune all of these great features for the new server generation. And this is not an obvious task. It requires investments, skills, knowledge and experience. And HP is ready to step up and help customers at any desired skill with the HP Greenlake H2 cloud platform that enables customers for cloud like experience and convenience and the flexibility with the security of the infrastructure deployed in the private data center or in the Edge. So while consuming all of the HP solutions, customer have flexibility to choose the right level of the service delivered from HP GreenLake, starting from hardwares as a service and scale up or down is required to consume the full stack of the hardwares and software as a service with an option to paper use. >> Awesome. Alan, final word. >> Yeah. What should we walk away with? >> Yeah, thanks. So I'd say that we've talked a lot about the systems here in question with HP ProLiant Gen 11 and they're delivering on a lot of the business outcomes that our customers require in order to optimize for operational efficiency or to optimize for just to, well maybe just to enable what they want to do in, with their customers enabling new features, enabling new capabilities. Underpinning all of that is our fourth Gen Zion scalable platform. Whether it's the technology transitions that we're driving with DDR5 PCIA Gen 5 or the raw performance efficiency and scalability of the platform in CPU, I think we're here for our customers in delivering to it. >> That's great stuff. Alan, Dennis, Cynthia, thank you so much for taking the time to do a deep dive in the advanced performance of VDI with the fourth Gen Intel Zion scalable process. And congratulations on Gen 11 ProLiant. You get some great servers there and again next Gen's here. Thanks for taking the time. >> Thank you so much for having us here. >> Okay, this is theCUBEs keeps coverage of Compute Engineered for your Hybrid World sponsored by HP and Intel. I'm John Furrier for theCUBE. Accelerate VDI at the Edge. Thanks for watching.

(upbeat music) >> From Cambridge, Massachusetts, it's the CUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. (upbeat music) >> Welcome back to MIT in Cambridge Massachusetts everybody you're watching the CUBE the leader in live tech coverage. We go out to the events and extract the signal from the noise we hear at the MIT CDOIQ. It's the MIT Chief Data Officer event the 13th annual event. The CUBE started covering this show in 2013. I'm Dave Vellante with Paul Gillin, my co-host, and Michael Conlin is here as the chief data officer of the Department of Defense, Michael welcome, thank you for coming on. >> Thank you, it's a pleasure to be here. >> So the DoD is, I think it's the largest organization in the world, what does the chief data officer of the DoD do on a day to day basis? >> A range of things because we have a range of challenges at the Department of Defense. We are the single largest organization on the planet. We have the greatest scope and scale and complexity. We have the most dangerous competitors of anybody on the planet, it's not a trivial issue for us. So, I've a range of challenges. Challenges around, how do I lift the overall performance of the department using data effectively? How do I help executives make better decisions faster, using more recent, more common data? More common enterprise data is the expression we use. How do I help them become more sophisticated consumers of data and especially data analytics? And, how do we get to the point where, I can compare performance over here with performance over there, on a common basis? And compared to commercial benchmark? Which is now an expectation for us, and ask are we doing this as well as we should, right across the patch? Knowing, that all that data comes from multiple different places to start with. So we have to overcome all those differences and provide that department wide view. That's the essence of the role. And now with the recent passage of the Foundations for Evidenced-Based Policymaking Act, there are a number of additional expectations that go on top of that, but this is ultimately about improving affordability and performance of the department. >> So overall performance of the organization... >> Overall performance. >> ...as well, and maybe that comes from supporting various initiatives, and making sure you're driving performance on that basis as well. >> It does, but our litmus test is are we enabling the National Defense Strategy to succeed? Only reason to touch data is to enable the National Defense Strategy to be more successful than without it. And so we're always measuring ourselves against that. But it is, can we objectively say we're performing better? Can we objectively say that we are more affordable? In terms of the way we support the National Defense Strategy. >> I'm curious about your motivations for taking on this assignment because your background, as I see, is primarily in the private sector. A year ago you joined the US Department of Defense. A huge set of issues that you're tackling now, why'd you do it? >> So I am a capitalist, like most Americans, and I'm a serial entrepreneur. This was my first opportunity to serve government. And when I looked at it, knowing that I could directly support national defense, knowing that I could make a direct meaningful contribution, let me exercise that spirit of patriotism that many of us have, but we just not found ourselves an opportunity. When this opportunity came along I just couldn't say no to it. There's so much to be done and so much appetite for improvement that I just couldn't walk away for this. Now I've to tell you, when you start you take an oath of office to protect and defend the constitution. I don't know, it's maybe a paragraph or maybe it's two paragraphs. It felt like it took an hour to choke it out, because I was suddenly struck with all of this emotion. >> The gravity of what you were doing. >> Yeah, the gravity of what I'm doing. And that was just a reinforcement of the choice I'd already made, obviously right. But the chance to be the first chief data officer of the entire Department of Defense, just an enormous privilege. The chance to bring commercial sector best practices in and really lift the game of the department, again enormous privilege. There's so many people who could do this, probably better than me. The fact that I got the opportunity I just couldn't say no. Just too important, to many places I could see that we could make things better. I think anybody with a patriotic bone in their body would of jumped at the opportunity. >> That's awesome, I love that congratulations on getting that role and seemingly thrive in it. A big part of preserving that capitalist belief, defending the constitution and the American way, it sounds corny, but... >> It's real. >> I'm a patriot as well, is security. And security and data are intertwined. And just the whole future of warfare is dramatically changing. Can you talk about in a format like this, security, you're thinking on that, the department's thinking on that from a CDO's perspective? >> So as you know we have a number of swimlanes within the department and security is very clear swimlane, it's aligned under our chief information officer, but security is everybody's responsibility, of course. Now the longstanding criticism of security people is that they think they best way to secure anything is to permit nobody to touch it. The clear expectation for me as chief data officer is to make sure that information is shared to the right people as rapidly as possible. And, that's a different philosophy. Now I'm really lucky. Lieutenant General Denis Crall our principal cyber advisor, Dana Deasy our CIO, these people understand how important it is to get information in the right place at the right time, make it rapidly available and secure it every step along the way. We embrace the zero trust mantra. And because we embrace the zero trust mantra we're directly concerned with defending the data itself. And as long as we defend the data and the same mechanisms are the mechanisms we use to let people share it, suddenly the tension goes away. Suddenly we all have the same goal. Because the goal is not to prevent use of data, it's to enable use of data in a secure way. So the traditional tension that might be in that place doesn't exist in the department. Very productive, very professional level of collaboration with those folks in this space. Very sophisticated people. >> When we were talking before we went live you mentioned that the DoD has 10,000 plus operational systems... >> That's correct. >> A portfolio of that magnitude just overwhelming, I mean how did you know what to do first when you moved into this job, or did you have a clear mandate when you were hired? >> So I did have a clear mandate when I was hired and luckily that was spelled out. We knew what to do first because we sat down with actual leaders of the department and asked them what their goals were for improving the performance of the department. And everything starts from that conversation. You find those executives that what to improve performance, you understand what those goals are, and what data they need to manage that improvement. And you capture all the critical business questions they need answers to. From that point on they're bought in to everything that happens, right. Because they want those answers to those critical business questions. They have performance targets of their own, this is now aligned with. And so you have the support you need to go down the rest of the path of finding the data, standardizing it, et cetera. In order to deliver the answers to those questions. But it all starts which either the business mission leaders or the warfighting mission leaders who define the steps they're taking to implement the National Defense Strategy. Everything gets lined up against that, you get instant support and you know you're going after the right thing. This is not, an if you build it they will come. This is not, a driftnet the organization try to gather up all the data. This is spear fishing for specific answers to materially important questions, and everything we do is done on that basis. >> We hear Mark Ramsey this morning talk about the... He showed a picture of stove pipes and then he complicated that picture by showing multiple copies within each of those stove pipes, and says this is organizations that we've all lived in. >> That's my organization too. >> So talk about some of those data challenges at the DoD and how you're addressing those, specifically how you're enabling soldiers in the field to get the right data to the field when they need it. >> So what we'll be delicate when we talk about what we do for soldiers in the field. >> Understood, yeah. >> That tends to be sensitive. >> Understand why, sure. >> But all of those dynamics that Mark described in that presentation are present in every large cooperation I've ever served. And that includes the Department of Defense. That heterogeneity and sprawl of IT that what I would refer to, he showed us a hair ball of IT. Every large organization has a hair ball of IT. And data scattered all over the place. We took many of the same steps that he described in terms of organizing and presenting meaningful answers to questions, in almost exactly the same sequence. The challenge as you heard me use the statistics that our CIO's published digital monetization strategies, which calls out that we have roughly 10,000 operational systems. Well, every one of them is different. Every one's put in place by a different group of people at a different time, with a different set of requirements, and a different budget, and a different focus. You know organizational scope. We're just like he showed. We're trying to blend all that in to a common view. So we have to find what's the real authoritative piece of data, cause it's not all of those systems. It's only a subset of those systems. And you have to do all of the mapping and translations, to make the result add up. Otherwise you double count or you miss something. This is work in progress. This will always be a work in progress to any large organization. So I don't want to give you impression it's all sorted. Definitely not all sorted. But, the reality is we're trying to get to the point where people can see the data that's available and that's a requirement by the way under the Foundations Act that we have a data catalog, an authoritative data catalog so people can see it and they have the ability to then request access to that through automation. This is what's critical, you need to be able to request access and have it arbitraged on the basis of whether you should directly have access based on your role, your workflow, et cetera, but it should happen in real time. You don't want to wait weeks, or months, or however long for some paperwork to move around. So this all has to become highly automated. So, what's the data, who can access it under what policy, for what purpose? Our roles and responsibilities? Identity management? All this is a combined set of solutions that we have to put in place. I'm mostly worried about a subset of that. My colleagues in these other swimlanes are working to do the rest. Most people in the department have access to data they need in their space. That hasn't been a problem. The problem is you go from space to space, you have to learn a new set of systems and a new set of techniques for a new set of data formats which means you have to be retrained. That really limits our freedom of maneuver of human beings. In the ideal world you'd be able to move from any job in any part of the department to the same job in another part of the department with no retraining whatsoever. You'd be instantly able to make a contribution. That's what we're trying to get to. So that's a different kind of a challenge, right. How do we get that level of consistency in the user experience, a modern user experience. So that if I'm a real estate manager, or I'm a medical business manager, or I'm a clinical professional, or I'm whatever, I can go from this location in this part of the department to that location in that part and my experience is the same. It's completely modern, and it's completely consistent. No retraining. >> How much of that challenge pie is people, process and technology? How would you split that opportunity? >> Well everything starts for a process perspective. Because if you automate a bad process, you just make more mistakes in less time at greater costs. Obviously that's not the ideal. But the biggest single challenge is people. It's talent, it's culture. Both on the demand side and on the supply side. If fact a lot of what I talked about in my remarks, was the additional changes we need to put in place to bring people into a more modern approach to data, more modern consumption. And look, we have pockets of excellence. And they can hold their own against any team, any place on the planet. But they are pockets of excellence. And what we're trying to do is raise the entire organization's performance. So it's people, people, and people and then the other stuff. But the products, don't care about (laughs). >> We often here about... >> They're going to change in 12 to 18 months. I'm a technologist, I'm hands on. The products are going to change rapidly, I make no emotional commitment to products. But the people that's a different story. >> Well we know that in the commercial world we often hear that cultural resistance is what sabotages modernization efforts. The DoD is sort of the ultimate top-down organization. It is any easier to get buy-in because the culture is sort of command and control oriented? >> It's hard in the DoD, it's not easier in the DoD. Ultimately people respond to their performance incentives. That's the dirty secrets performance incentives, they work every time. So unless you restructure performance measures and incentives for people their behavior's never going to change. They need to see their personal future in the future you're prescribing. And if they don't see it, you're going to get resistance every time. They're going to do what they believe they're incented to do. Making those changes, cascading those performance measures down, has been difficult because much of the decision-making processes in the department have been based on slow-moving systems and slow-moving data. I mean think about it, our budget planning process was created by Robert McNamara, as the Secretary of Defense. It requires you to plan everything for five years. And it takes more than a year to plan a single year's worth of activities, it's slow-moving. And we have regulation, we have legislation, we're a law-abiding organization, we do what we have to do. All of those things slow things down. And there's a culture of expecting macro-level consensus building. Which means everybody feels they can say no. If everybody can say no, then change becomes peanut butter spread across an organization. When you peanut butter spread across something our size and scale, the layer's pretty thin. So we have the same problem that other organizations have. There is clearly a perception of top-down change and if the Secretary or the Deputy Secretary issue an instruction people will obey it. It just takes some time to work it's way down into all the detailed combinations and permutations. Cause you have to make sophisticated decisions now. How am I going to change for my performance measures for that group to that group? And that takes time and energy and thought. There's a natural sort of pipeline effect in this. So there's real tension I think in between this perception of top-down and people will obey the orders their given. But when you're trying to integrate those changes into a board set of policy and process and people, that takes time and energy. >> And as a result the leaders have to be circumspect about the orders they give because they want to see success. They want to make sure that what they say is actually implemented or it reflects poorly on the organization. >> I think that out leaders are absolutely concerned about accomplishing the outcomes that they set out. And I think that they are rightfully determined to get the change as rapidly as possible. I would not expect them to be circumspect. I would anticipate that they would be firm and clear in the direction that they set and they would set aggressive targets because you need aggressive targets to get aggressively changed outcomes. Now. >> But they would have to choose wisely, they can't just fire off orders and expect everything to be done. I would think that they got to really think about what they want to get done, and put all the wood behind the arrow as you... >> I think that they constantly balance all those considerations. I must say, I did not appreciate before I joined the department the extraordinary caliber of leadership we enjoy. We have people with real insight and experience, and high intellectual horsepower making the decisions in the department. We've been blessed with the continuing stream of them at all of the senior ranks. These people could go anywhere, or do anything that they wanted in the economy and they've chosen to be in the department. And they bring enormous intellectual firepower to bear on challenges. >> Well you mentioned the motivation at the top of the segment, that's largely pretty powerful. >> Yeah, oh absolutely. >> I want to ask you, we have to break, but the organizational structure, you talked about the CIO, actually the responsibility for security within the CIO. >> Sure. >> To whom do you report. What's the organization look like? >> So I report to the Chief Management Officer of the Department of Defense. So if you think about the order of precedents, there's the Secretary of Defense, the Deputy Secretary of Defense and third in order is the Chief Management Officer. I report to the Chief Management Officer. >> As does the CIO, is that right? >> As does the CIO, as does the CIO. And actually this is quite typical in large organizations, that you don't have the CDO and the CIO in the same space because the concerns are very different. They have to collaborate but very different concerns. We used to see CDOs reporting to CIOs that's fallen dramatically in terms of the frequency you see that. Cause we now recognize that's just a failure mode. So you don't want to go down that path. The number one most common reporting relationship is actually to a CEO, the chief executive officer, of an organization. It's all about, what executive is driving performance for the organization? That's the person the CDO should report to. And I'm blessed in that I do find myself reporting to the executive driving organizational improvement. For me, that's a critical thing. That would make the difference between whether I could succeed or whether I'm doomed to fail. >> COO would be common too in a commercial organization. >> Yeah, in certain commercial organizations, it's a COO. It just depends on the nature of the business and their maturity with data. But if you're in the... If data's the business, CDO will report to the CEO. There are other organizations where it'll be the COO or CFO, it just depends on the nature of that business. And in our case I'm quite fortunate. >> Well Michael, thank you for, not only the coming to the CUBE but the service you're providing to the country, we really appreciate your insights and... >> It's a pleasure meeting you. >> It's a pleasure meeting you. All right, keep it right there everybody we'll be right back with our next guest. You're watching the CUBE live from MIT CDOIQ, be right back. (upbeat music)

>> Narrator: Live from Las Vegas It's theCUBE covering IBM Think 2018. Brought to you by IBM. >> We're back at Mandalay Bay in Las Vegas. This is IBM Think 2018. This is day three of theCUBE's wall-to-wall coverage. My name is Dave Vellante, I'm here with Peter Burris. You're watching theCUBE, the leader in live tech coverage. Daniel Hernandez is here. He's the Vice President of IBM Analytics, a CUBE alum. It's great to see you again, Daniel >> Thanks >> Dave: Thanks for coming back on >> Happy to be here. >> Big tech show, consolidating a bunch of shows, you guys, you kind of used to have your own sort of analytics show but now you've got all the clients here. How do you like it? Compare and contrast. >> IBM Analytics loves to share so having all our clients in one place, I actually like it. We're going to work out some of the kinks a little bit but I think one show where you can have a conversation around Artificial Intelligence, data, analytics, power systems, is beneficial to all of us, actually. >> Well in many respects, the whole industry is munging together. Folks focus more on workloads as opposed to technology or even roles. So having an event like this where folks can talk about what they're trying to do, the workloads they're trying to create, the role that analytics, AI, et cetera is going to play in informing those workloads. Not a bad place to get that crosspollination. What do you think? >> Daniel: Totally. You talk to a client, there are so many problems. Problems are a combination of stuff that we have to offer and analytics stuff that our friends in Hybrid Integration have to offer. So for me, logistically, I could say oh, Mike Gilfix, business process automation. Go talk to him. And he's here. That's happened probably at least a dozen times so far in not even two days. >> Alright so I got to ask, your tagline. Making data ready for AI. What does that mean? >> We get excited about amazing tech. Artificial intelligence is amazing technology. I remember when Watson beat Jeopardy. Just being inspired by all the things that I thought it could do to solve problems that matter to me. And if you look over the last many years, virtual assistants, image recognition systems that solve pretty big problems like catching bad guys are inspirational pieces of work that were inspired a lot by what we did then. And in business, it's triggered a wave of artificial intelligence can help me solve business critical issues. And I will tell you that many clients simply aren't ready to get started. And because they're not ready, they're going to fail. And so our attitude about things are, through IBM Analytics, we're going to deliver the critical capabilities you need to be ready for AI. And if you don't have that, 100% of your projects will fail. >> But how do you get the business ready to think about data differently? You can do a lot to say, the technology you need to do this looks differently but you also need to get the organization to acculturate, appreciate that their business is going to run differently as a consequence of data and what you do with it. How do you get the business to start making adjustments? >> I think you just said the magic word, the business. Which is to say, at least all the conversations I have with my customers, they can't even tell that I'm from the analytics because I'm asking them about the problems. What do you try to do? How would you measure success? What are the critical issues that you're trying to solve? Are you trying to make money, save money, those kinds of things. And by focusing on it, we can advise them then based on that how we can help. So the data culture that you're describing I think it's a fact, like you become data aware and understand the power of it by doing. You do by starting with the problems, developing successes and then iterating. >> An approach to solving problems. >> Yeah >> So that's kind of a step zero to getting data ready for AI >> Right. But in no conversation that leads to success does it ever start with we're going to do AI or machine learning, what problem are we going to solve? It's always the other way around. And when we do that, our technology then is easily explainable. It's like okay, you want to build a system for better customer interactions in your call center. Well, what does that mean? You need data about how they have interacted with you, products they have interacted with, you might want predictions that anticipate what their needs are before they tell you. And so we can systematically address them through the capabilities we've got. >> Dave, if I could amplify one thing. It makes the technology easier when you put it in these constants I think that's a really crucial important point. >> It's super simple. All of us have had to have it, if we're in technology. Going the other way around, my stuff is cool. Here's why it's cool. What problems can you solve? Not helpful for most of our clients. >> I wonder if you could comment on this Daniel. I feel like we're, the last ten years about cloud mobile, social, big data. We seem to be entering an era now of sense, speak, act, optimize, see, learn. This sort of pervasive AI, if you will. How- is that a reasonable notion, that we're entering that era, and what do you see clients doing to take advantage of that? What's their mindset like when you talk to them? >> I think the evidence is there. You just got to look around the show and see what's possible, technically. The Watson team has been doing quite a bit of stuff around speech, around image. It's fascinating tech, stuff that feels magical to me. And I know how this stuff works and it still feels kind of fascinating. Now the question is how do you apply that to solve problems. I think it's only a matter of time where most companies are implementing artificial intelligence systems in business critical and core parts of their processes and they're going to get there by starting, by doing what they're already doing now with us, and that is what problem am I solving? What data do I need to get that done? How do I control and organize that information so I can exploit it? How can I exploit machine learning and deep learning and all these other technologies to then solve that problem. How do I measure success? How do I track that? And just systematically running these experiments. I think that crescendos to a critical mass. >> Let me ask you a question. Because you're a technologist and you said it's amazing, it's like magic even to you. Imagine non technologists, what `it's like to me. There's a black box component of AI, and maybe that's okay. I'm just wondering if that's, is that a headwind, are clients comfortable with that? If you have to describe how you really know it's a cat. I mean, I know a cat when I see it. And the machine can tell me it's a cat, or not a hot dog Silicon Valley reference. (Peter laughs) But to tell me actually how it works, to figure that out there's a black box component. Does that scare people? Or are they okay with that? >> You've probably given me too much credit. So I really can't explain how all that just works but what I can tell you is how certainly, I mean, lets take regulated industries like banks and insurance companies that are building machine learning models throughout their enterprise. They've got to explain to a regulator that they are offering considerations around anti discriminatory, basically they're not buying systems that cause them to do things that are against the law, effectively. So what are they doing? Well, they're using tools like ones from IBM to build these models to track the process of creating these models which includes what data they used, how that training was done, prove that the inputs and outputs are not anti-discriminatory and actually go through their own internal general counsel and regulators to get it done. So whether you can explain the model in this particular case doesn't matter. What they're trying to prove is that the effect is not violating the law, which the tool sets and the process around those tool sets allow you to get that done today. >> Well, let me build on that because one of the ways that it does work is that, as Ginni said yesterday, Ginni Rometty said yesterday that it's always going to be a machine human component to it. And so the way it typically works is a machine says I think this is a cat and a human validates it or not. The machine still doesn't really know if it's a cat but coming back to this point, one of the key things that we see anyway, and one of the advantages that IBM likely has, is today the folks running Operational Systems, the core of the business, trust their data sources. >> Do they? >> They trust their DB2 database, they trust their Oracle database, they trust the data that's in the applications. >> Dave: So it's the data that's in their Data Lake? >> I'm not saying they do but that's the key question. At what point in time, and I think the real important part of your question is, at what point in time do the hardcore people allow AI to provide a critical input that's going to significantly or potentially dramatically change the behavior of the core operational systems. That seems a really crucial point. What kind of feedback do you get from customers as you talk about turning AI from something that has an insight every now and then to becoming effectively, an element or essential to the operation of the business? >> One of the critical issues in getting especially machine learning models, integrated in business critical processes and workflows is getting those models running where that work is done. So if you look, I mean, when I was here last time I was talking about the, we were focused on portfolio simplification and bringing machine learning where the data was. We brought machine learning to private cloud, we brought it onto Gadook, we brought it on mainframe. I think it is a critical necessary ingredient that you need to deliver that outcome. Like, bring that technology where the data is. Otherwise it just won't work. Why? As soon as you move, you've got latency. As soon as you move, you've got data quality issues you're going to have contending. That's going to exacerbate whatever mistrust you might have. >> Or the stuff's not cheap to move. It's not cheap to ingest. >> Yeah. By the way, the Machine Learning on Z offering that we launched last year in March, April was one of our highest, most successful offerings last year. >> Let's talk about some of the offerings. I mean, at the end of the day you're in the business of selling stuff. You've talked about Machine Learning on Z X, whatever platform. Cloud Private, I know you've got perspectives on that. Db2 Event Store is something that you're obviously familiar with. SPSS is part of the portfolio. >> 50 year, the anniversary. >> Give us the update on some of these products. >> Making data ready for AI requires a design principled on simplicity. We launched in January three core offerings that help clients benefit from the capability that we deliver to capture data, to organize and control that data and analyze that data. So we delivered a Hybrid Data Management offering which gives you everything you need to collect data, it's anchored by Db2. We have the Unified Governance and Integration portfolio that gives you everything you need to organize and control that data as anchored by our information server product set. And we've got our Data Science and Businesses Analytics portfolio, which is anchored by our data science experience, SPSS and Cognos Analytics portfolio. So clients that want to mix and match those capabilities in support of artificial intelligence systems, or otherwise, can benefit from that easily. We just announced here a radical- an even radical step forward in simplification, which we thought that there already was. So if you want to move to the public cloud but can't, don't want to move to the public cloud for whatever reason and we think, by the way, public cloud for workload to like, you should try to run as much as you can there because the benefits of it. But if for whatever reason you can't, we need to deliver those benefits behind the firewall where those workloads are. So last year the Hybrid Integration team led by Denis Kennelly, introduced an IBM cloud private offering. It's basically application paths behind the firewall. It's like run on a Kubernetes environment. Your applications do buildouts, do migrations of existing workloads to it. What we did with IBM Cloud Private for data is have the data companion for that. IBM Cloud Private was a runaway success for us. You could imagine the data companion to that just being like, what application doesn't need data? It's peanut butter and jelly for us. >> Last question, oh you had another point? >> It's alright. I wanted to talk about Db2 and SPCC. >> Oh yes, let's go there, yeah. >> Db2 Event Store, I forget if anybody- It has 100x performance improvement on Ingest relative to the current state of the order. You say, why does that matter? If you do an analysis or analytics, machine learning, artificial intelligence, you're only as good as whatever data you have captured of your, whatever your reality is. Currently our databases don't allow you to capture everything you would want. So Db2 Event Store with that Ingest lets you capture more than you could ever imagine you would want. 250 billion events per year is basically what it's rated at. So we think that's a massive improvement in database technology and it happens to be based in open source, so the programming model is something that developers feel is familiar. SPSS is celebrating it's 50th year anniversary. It's the number one digital offering inside of IBM. It had 510,000 users trying it out last year. We just renovated the user experience and made it even more simple on stats. We're doing the same thing on Modeler and we're bringing SPSS and our data science experience together so that there's one tool chain for data science end to end in the Private Cloud. It's pretty phenomenal stuff. >> Okay great, appreciate you running down the portfolio for us. Last question. It's kind of a, get out of your telescope. When you talk to clients, when you think about technology from a technologist's perspective, how far can we take machine intelligence? Think 20 plus years, how far can we take it and how far should we take it? >> Can they ever really know what a cat is? (chuckles) >> I don't know what the answer to that question is, to be honest. >> Are people asking you that question, in the client base? >> No. >> Are they still figuring out, how do I apply it today? >> Surely they're not asking me, probably because I'm not the smartest guy in the room. They're probably asking some of the smarter guys-- >> Dave: Well, Elon Musk is talking about it. Stephen Hawking was talking about it. >> I think it's so hard to anticipate. I think where we are today is magical and I couldn't have anticipated it seven years ago, to be honest, so I can't imagine. >> It's really hard to predict, isn't it? >> Yeah. I've been wrong on three to four year horizons. I can't do 20 realistically. So I'm sorry to disappoint you. >> No, that's okay. Because it leads to my real last question which is what kinds of things can machines do that humans can't and you don't even have to answer this, but I just want to put it out there to the audience to think about how are they going to complement each other. How are they going to compete with each other? These are some of the big questions that I think society is asking. And IBM has some answers, but we're going to apply it here, here and here, you guys are clear about augmented intelligence, not replacing. But there are big questions that I think we want to get out there and have people ponder. I don't know if you have a comment. >> I do. I think there are non obvious things to human beings, relationships between data that's expressing some part of your reality that a machine through machine learning can see that we can't. Now, what does it mean? Do you take action on it? Is it simply an observation? Is it something that a human being can do? So I think that combination is something that companies can take advantage of today. Those non obvious relationships inside of your data, non obvious insights into your data is what machines can get done now. It's how machine learning is being used today. Is it going to be able to reason on what to do about it? Not yet, so you still need human beings in the middle too, especially when you deal with consequential decisions. >> Yeah but nonetheless, I think the impact on industry is going to be significant. Other questions we ask are retail stores going to be the exception versus the normal. Banks lose control of the payment systems. Will cyber be the future of warfare? Et cetera et cetera. These are really interesting questions that we try and cover on theCUBE and we appreciate you helping us explore those. Daniel, it's always great to see you. >> Thank you, Dave. Thank you, Peter. >> Alright keep it right there buddy, we'll be back with our next guest right after this short break. (electronic music)

>> Narrator: Live from Las Vegas, it's theCUBE. Covering Oracle Modern Customer Experience, 2017. Brought to you by, Oracle. (upbeat music) >> Welcome back to theCUBE. I'm Peter Burris, and once again theCUBE is here at Oracle Modern Marketing... Modern Customer Experience, having a great series of conversations about the evolution of marketing, the role technology is playing, and especially important, the centerpiece that data now has within a overall orientation towards customer experience. Now one of the key features of that notion of customer experience is what's going on with service. And this is a great session, because we've got a representative from Oracle, but also a customer, as well. Welcome to Steve Fioretti, who's the VP of Product Management, Oracle Service Cloud and Heather Miksch, who's the Vice President of Field and Product Operations at Carbon. >> Thank you. >> Peter: Welcome to the (mumbles) >> Thanks. >> Glad to be here. >> So, Steve why don't we start with you. >> Steve: Sure. >> Oracle is here talking about how the cloud can help transform field and service operations. >> Steve: Right. >> How is it transforming it, what're the trends? >> Well, there's a lot of interesting trends that are affecting customer service, and I would, you talked about marketing and a lot of people say customer service is the new marketing. A lot of, a lot of interactions that people have with a company is in the customer service group and that really affects their impact on the brand. And there's a lot of things going on in the industry that are affecting customer service. There's new dynamic channels emerging, for example, people want to use Facebook Messenger, or WeChat, or WhatsApp as customer service channels to interact with their brand. It's much beyond just email, phone, chat, things like that. So, new channels are emerging and companies have to think about how do I integrate that into my customer service organization. Automation has really come into the fore. So, you know, in our personal lives we use Siri, and other V, you know, interactions we have with Alexa. So, those are coming into businesses to automate those, perhaps more simple, customer service processes. The internet of things is really taking off, where connected devices are allowing organizations to deliver predictive and proactive service. And on the automation front, they're even extending to where organizations are taking robotics and making robots agents in a retail store, for example. >> Are you talking about me? >> Wow it's Pepper. Hi, Pepper, what are...(Peter laughs) I didn't know you were here, that's awesome. So, Pepper, I'll ask you a question. What makes you a great Customer Service Agent? >> I'm smart, I'm connected, and I'm cool and, most importantly, I'm effective. (Steve laughs) >> And we replaced John Furrier with Pepper. >> Steve: Excellent.(Heather laughs) >> So, going to the next question about the, as we use robotics, as we use many of these things: we have to remember that these are not magic, they're really is no intelligence, in the classical sense, in them, they are still being driven to perform functions, take action, based on the availability of data that is coming off of customers. So talk a bit about the role the data, data integration, and some of these new tools: AI, or Adaptive Intelligence as you're calling it, are playing in ensuring that we can, enhance Customer Experience with new devices, and these new channels. >> You're absolutely right. I mean, if, you know, it's all about making the experience with a device like, like Pepper personalized and effective, and data, knowing what a consumer wants, what their preferences, and perhaps anticipating their preferences before, you know, they even know that; their past buying history, and taking all that, first-party data and third-party data, combining that with artificial intelligence, to deliver those personalized smart experiences is what's really happening. You heard a lot at this conference about Oracle's Adaptive Intelligence Initiative, and in the context of service, we're going to be building applications for things like account health, predictive field service, so, you know, you can predict ahead of time that a machine may, you know, may need service or break. And, you know, our customer here, Heather from Carbon is going to talk a lot about what they're doing with-- >> Well, so-- >> You know, smarts and the experience-- >> Got it, so how does this resonate with Carbon? >> Well, so, Carbon, is a, we manufacture an industrial 3D printer, and we have a process we call Digital Light Synthesis, which allows us to make photo-polymer materials that are robust enough to use in final production. So, our goal is to take customers from their design, of their part, straight into production, using the 3D printer as a means of production. And the reason why this is so exciting to Carbon, is our printer is actually an IOT device. It operates over the internet, and it operates through a browser. As a result, all types of data, from machine data from the printer, are flowing into our databases; as well as operational data, how long is the print taking, what type of resin is the customer using, how often are they printing, are they running into problems with their print? We've also built in a feedback system for the user, directly in the user interface, that flows directly through our channels into our databases, and it actually opens tickets in our Oracle Service Cloud for agents to contact the customers. The way we use this in a very practical standpoint, to give you one example, is for machine failures. The idea that we can monitor our printers in the field, and we can see if a part is having problems, and might fail, and we can actually proactively reach out to the customer and say, "We'd like to be there "in a couple weeks, change out this part. "It's not affecting your machine yet. "It's not affecting your prints." And, the customer is now able, instead of having unplanned downtime, which can be very difficult for a production environment, they now have planned downtime. This technology is nothing new. The example I like to use is, in the nuclear power industry, you don't wait until you have a core meltdown and then call your service engineer.(Steve laughs) Like, it's been around for for decades. >> Form has been around for a while. >> But what's new, is actually taking this technology and putting it in capital equipment, or putting it in devices like Peppper. I mean, she's also an IOT device; or even putting it into some of our wearables, or just other consumer products as well. And once you actually have this data coming through to the manufacturer of the device, it's really almost limitless what you can do with it. And, just in our short time of Carbon actually working on this problem, we have about 70% of our hardware failures are actually predictive. So that we're able to go out and repair the printer before the customer even realizes they have a problem. And some of the problems, we can actually fix before the customer knows anything, and we can fix them remotely from our offices in Redwood City. >> And it's interesting, theCUBE this week was also at the National Association of Broadcasters, in the NEB show, and we actually had an astronaut present over theCUBE. >> Yes, yes. >> One of the things that's interesting is there are 3D printers now on-- >> There are. >> Up on the Space Station. >> Yes, yes. >> So that you can print things a long ways away. That's one of the advantages, one of the great use cases of 3D printers >> Yes. >> Is that you can actually assemble, or you can create and assemble things, in very very, you know, unfriendly environments. >> Yes, yes. So, being able to schedule, and being able to plan that, is absolutely essential. >> Yes, yes and you can see, so for us, for 3D printers, some of the use cases that our customers are coming to us with, is they are companies, their own capital equipment manufacturers that have hundreds of thousands of spare parts, and they don't want to have to keep these inventories of massive spare parts. They want to have a design sent directly to a printer, maybe it's located in another country, closer to the point of use for that part, print out the part, and get it to the user faster. The idea is to actually move, one of the ideas, is to move manufacturing closer to the point of use. So that we're not spending all this time shipping products, you know, across the entire world, when we can actually be producing them much closer to the user. >> So that suggests, when we think about, again, the role of integration, the role of data, the idea of the Service Cloud; that there will be circumstances in which the part is printed and the capital equipment, Lessor, or the person who sold it, is on site to then put it in place, and assemble it. So now we're talking about multiple people operating very very quickly with a lot of new technology. >> Right. >> And, we now see why these types of devices and the need for that data sharing is so crucial. So, how is Oracle, in Oracle's vision of how service is going to be performed in the future, facilitating these types of interactions. >> So, I mean what we have to do is think about the technologies that are powering devices like robots, that are, providing technologies that are powering virtual assistants to automate customer interactions, to deliver technologies that help customers serve themselves. Another example is, more and more people, particularly younger generation, they don't want to phone. You've got a phone in home, they don't want to call you. They don't want to have anything to do with the phone. So, that's why things like messaging, self-service, going to a website and finding their own answer are critical. So, enabling and anticipating the data, the technologies, the way, the channels that people want to use, are all going to allow brands like Carbon and others to deliver great customer service for-- >> How are you using the Oracle Service Cloud, then, to facilitate many of these changes in your organization. >> So right now, what we have is for... We actually have a database we use for our big machine data. So, all the big machine data comes through, all the data coming off of our printers. And then we've integrated that database into Oracle Service Cloud; so then, instead of a customer having to phone up if they have a problem, we actually have, on our user interface, a little button, it just says "Request Help", that's all they need to do, and it's within the print job that they've been working on. All of that data about their print job: who the user is, what the company is, which printer they were using, how long was the print. Any specific information they want to say about the print, like why they're having trouble with it, it flows through into Oracle Service Cloud, and within the Oracle Service Cloud environment we can open up our big machine database, within that same environment, we can look at the actual print job. And then, we have an escalation tool we use for our engineering team. If we need to escalate, we can do that out of Service Cloud as well. And the idea is that there's very little manual entry of any other information. All of that is just flowing through, and everybody within the organization, whether it's the people that are first in front of the customer, or whether it's our engineers, have access to the exact same data. >> But is the system also then, through the escalation process, saying, well, we really got to get someone at the hardware level, or someone here, or someone at the design level. So you're flowing it to the right person. >> Yes, yes, absolutely. And the other fabulous thing about having these internet connected devices, is even when we do need to send somebody out on site to make a hardware fix, because of the diagnostic data we have from the device, we have, until now, 100% success rate in having the right part on-hand. Which is, if you've ever had much experience with capital equipment repairs, or even a repair of your dishwasher, sometimes the people don't have the right parts. We always have the right parts. >> That's too bad you couldn't >> So far, nothing-- >> print the part with the printer when it's down.(laughs) >> That's an interesting thing. We actually do have some parts within our printer that are printed on our printers, so its (laughs) it's pretty fun >> Can I talk about one other short example-- >> Of course. >> Of another customer that actually Heather's met here at the show, Denon & Marantz, so, they make all sorts of audio equipment, high-end audio equipment, and they've got a new brand of speakers, wireless speakers, called HEOS. And, when they first started, selling those to consumers they noticed, these are connected as well, they noticed that a number of them were having, a chip problem, remotely. People were calling in. So they went out, and they, they pinged, if you will, because they're connected, all of their consumer deployments, and they could tell that, you know, a small percentage of them are going to fail. They actually shipped speakers to those consumers before they even knew they had a problem, and they arranged to pick up the old ones, and you can imagine the value the customer, loyalty, and customer sat that that had. So that proactive predictive customer service example in the consumer world, and in a business world, really makes service that much-- >> Yeah. >> So, customer service, increasingly, is taking some degree of responsibility for ensuring that things operate within the threshold, as opposed to fixing things after they've broken. >> Yes, absolutely. >> Exactly. >> Heather: Yes, yeah. >> So how does that tie back into marketing and sales. So, at Carbon what is the, what is the way these feedback loops are being used to also inform marketing and selling. >> So, the interesting thing is that because we're also gathering operational data, we actually use the data coming off our printers for much more than just a service organization. In fact, our entire company is becoming more and more dependent on this printer data. So, for instance, our product group, when they're looking at bringing out a new feature they're actually looking at the data of the actual prints and the features that the customers are currently using, and deciding, do we need to augment this feature? Do we need to bring out another tool for our customers to use? And then looking at the printer data to make those decisions, and to prioritize what projects to work on because as you can imagine we've just got a ton of projects that we'd like to work on, and we need to make some priorities. The other thing that we're looking at is changing customer dynamics. Like we have, all of our customers are broken down into different industries, and we monitor the different printing behaviors, across industries, and we've been surprised. Like, there's certain industries that have grown faster than we would have expected, and because we've got this data that we look at every single day, we're looking at our customers' print data, we can actually make much faster corrections to either marketing campaigns, or sales strategies, or things like that, rather than waiting for a monthly roll-up or a quarterly roll-up or something like that. >> So who's the steward of data within Carbon? >> Who is the steward of data? We actually have a Director of Business Operations, his name is Chris Hutton. He actually works a lot with Oracle. He recently spoke at the Modern Finance Experience with Safra Catz, and I would say that if anyone's the steward of the data, he's probably the Grand Poobah of this data? But many of us have access to it. I mean, I can go into some of these databases and pull all the data I need. We don't really restrict it. >> But he's making sure that every, he's making sure that the data works for everybody in the organization. >> Yeah. Yeah, I'd say to some degree, yes. We also have our software engineers, making sure the printer data is actually-- >> Well, they're always... >> Heather, I think I would... >> Always behind the scenes. >> I think I would like the title Steward of Data. >> Yeah. (laughs) >> I think that's, I think I just found my new title. >> It's a little geeky.(laughs) >> Well it won't be long. Somebody's going to be called, and-- >> Exactly. One other quick example of how that feedback's happening between a customer service experience and let's say marketing, is, back to my Denon & Marantz example. They had another set of speakers, and they can tell, they often, the consumer will label the speaker, based upon, you know, this is the living room, this is the bedroom... And they had some failures on another brand of speakers, and they noticed a commonality, they were all labeled Bathroom. And, basically, they realized that their speakers... Some of these speakers couldn't handle the humidity that was happening in the bathroom; drove that back into product development, built a new series of speakers quickly for bathroom that were more waterproof, >> Yeah. >> Or, more moisture resistant, and created a new product extension that actually sells quite well. So, there's just a simple example of how that data flowed back into product development and marketing. >> So, Heather, you're not feeling like a fish out water here at a customer experience show with all of the-- >> Oh, no, of course not. No, I love this kind of stuff. >> What's exciting you about listening to, mainly marketers, but a lot of customer experience, too? >> I, you know people-- >> Talk about customer service >> That are in service, they get excited. I mean, fundamentally, there's all kinds of reasons for growing the business, and increasing revenue, and cutting costs, and all those things, but fundamentally, people are in service to help other people. Like, that's what gets us up in the morning. That's what makes us jump out of bed. So, the idea that there's all these companies doing these super-cool things, where you can, really, proactively be helping people instead of waiting till they're already in trouble. That's like, you've just burst through a barrier that's existed for millennia; the fact that we can actually start predicting problems. >> But that's also, we also talked a lot here on theCUBE this week about the role that talent's going to play. And, while I've never been in a hardcore customer service job, I know that people who have gone in, often got demoralized because they were always being yelled at because there was problem. >> Yes, yes, yes. >> And I had to believe it's attracting a new class of person because they can actually be participating, and anticipating, and solving problems >> Yes, yes, yes. Well, and I, it does take a certain type to be a customer, to be in front of customers all the time. We always say that the number one rule is you have to hire happy people to be put in that position, because (laughs) >> Peter: So, how about (Heather laughs) >> Actually, that was a very insightful question, because we were on a panel yesterday with an analyst, Denis Pombriant from the Beagle Research and he talked about, well, a couple of dynamics. One is, agents, the profile of the agents that you hire is changing. Because all the simple things are being solved online through self-service, and now that agent has to be a more gifted, even arguably, he called it a controller, a more aggressive agent that's going to be a problem-solver, able to collaborate with others. So, more empowered, and that's one thing, so I thought your question was really insightful. The nature of that agent is changing. And another thing that smart companies do, is they empower those agents. You know, not just with technology, but they give them the ability to, you know, the a brand of hotels, high-end hotels, I won't use the brand, but their agents are given a couple thousand dollars a day, and are empowered to use that to fix any issues. You know, somebody shows up and the room's booked, they don't drag them out of the hotel. (all laugh) They actually find them... Maybe they upgrade the room or they get them a meal if they have a problem so, empowering them also makes the agent feel much better about delivering customer service-- >> Alright, so Steve Fioretti, VP Product Management Oracle Service Cloud. Heather Miksch the Vice President of Field and Product Operations at Carbon, and Pepper from SoftBank. >> Yay! >> Thank you all for being a part of theCUBE here at the Oracle-- >> Thank you. >> Modern Customer Experience >> Thank you Peter. >> And talking about the role that service is now playing in driving customer experience and the role that the Cloud is playing in improving customer service. >> Steve: Great, awesome. >> We'll be back with a wrap-up in a few minutes, and in fact, John will magically reappear. Give us a few minutes and we'll be back with more from theCUBE. (upbeat music)