welcome back everyone to thecube's live coverage in san francisco for vmware explorer 2022 formerly vmworld i'm john furrier david live dave 12 years we've been covering this event formerly vmware first time in west now it's explore we've been in north we've been in south we've been in vegas multi-cloud is now the exploration vmware community is coming in john siegel svp at dell cube alumni dave mccraw vp at vmware guys thanks for coming back both cube alumni it's great to see you very senior organizations senior roles in the organizations of vmware and dell one year since the split great partnership continuing i mean some of the conversations we've been having over the past few years is that control plane the management layer making everything work together it's essentially been the multi-cloud hybrid cloud story what's the update what's how's the partnership look yeah i you know i just to start off i mean i would say i don't think our partnership's been any has ever been any better um if you look at you mention our vision very much a shared vision in terms of the multi-cloud world and i don't think we've ever had more joint innovation projects at one time i think we have over 40 now dave that are going on across multi-cloud ai cyber security uh modern applications and and uh you know here just at you just vmworld vmware explorer we have over 30 uh vmware sessions that are featuring dell um and this is i think more than we've ever had so look i think um there's a lot of momentum there and we're really looking forward to what's to come so you guys obviously spent a lot of time together when vmware was part of dell and then you've been it's been a year since the spin and then you codified i think it was a five-year agreement you know so you had some time to figure that out and then put it into paper so you just kind of quantified some of the stuff that's going on but now we're entering a yet another phase so that that that that agreement's probably more important than ever now i mean list in terms of getting it documented and an understanding right yeah that agreement really defines a framework for solution development and for go to market so we've been doing it and refining it for the last five years so now you know putting and codifying it into a written signed agreement it basically is instantiating what we've been doing that we know works uh where we can drive uh solution development we can drive deep architectural co-innovation together as well and as john said across multiple you know project and solution areas so we we've been talking to years to you know a lot of these strat guys guys like matt baker about things like you know you see aws do nitro and then of course project monterey and and i know that you guys have had a you know a big sort of input into that and so now to see it come to fruition is is huge because you know from our view it's the future of computing architectures how do you handle you know data rich applications ai applications that's what are your thoughts on here i couldn't agree more uh project monterey is a great example of how we're innovating together we just talked about i mean first of all it's all so we have vxrail which let's let's start there right we have over 19 000 joint customers right now we continue to innovate more and more on the vxrail architecture great example of that as our partnership with project monterey and taking essentially vsphere 8 and running it for the first time on an hci system directly on the dp used itself right on the dpus ability now to offload nsxt from from the cpus to the dpus uh hope you know in the short term first of all great benefits for customers in terms of better performance but as you just mentioned it's game changing in terms of laying the foundation for the future architectures that we plan on together helping out customers there's one other dynamic for you on is um and it's not unique to dell but dell's the biggest you know supply supplier partner etc but you're able to take vmware software and drive it through your business and and that enables you to get more subscription revenue and makes it stickier and that's a really important change from you know 10 years ago yeah and it's it's a combination as you know of dell software and vmware software together absolutely and i think what's with this is a game-changing innovation that you can run on top of our joint system vxrail if you will um and now what our customers can expect is life cycle automation of now you know the dpus as well as tanzu as well as everything else we layer on top of that core foundation that we have over 19 000 customers running today so i mean like that 19 000 number i want to get back up to the vx rail and you mentioned vsphere that's big news here this year vsphere 8 big release a lot of going on what's the hci angle you mentioned that what's in it for the customer what does that mean for the folks here because let's face it the vsphere aids got everyone in that they've all the v-sections are going going crazy right another vsphere release getting training they have the labs here what's it mean for the customers what's the value there with that hci solution with the gpus well first of all vsphere 8 as we know it has a lot of goodies in it but you know what what i think to me what's been most powerful about this is the ability to run vsphere 8 uh and and specifically on the dpus now you can run it it is open up all new possibilities now and so that nsxt that i mentioned you know running that on gpus opens up a whole new uh architecture now for our customers going forward and now really sets us up for modern distributed architecture for the future so like edge okay yeah and vsphere 8 brings in you know cloud connectivity as well so you know customers can run in a cloud disconnected mode they can run in a cloud connected mode so you know that's going to bring in the ability to do specialized things on security cycle management there's a whole series of services that can now be added as well as you know leveraging you know vcenter management capabilities so what's happening at the edge we had i think it was lows on hotel tech world right okay good not the other one um but so so that's got to be exploding now with that with that because it just changes the game for for these stores there's i mean retail uh manufacturing maybe you can give us an update on there's so much happening on the edge side as you know i mean that's where most of the a lot of the innovations happening right now is at the edge and a lot of the companies we talked to 8x right 8x expectation of increase in uh edge workloads over the next and the data challenge too and the data challenge is huge so you heard about the innovations with vsphere 8. in addition to that we just introduced today as well the smallest vx rail for the edge ever this thing is it's like think picture a couple eight and a half by 11 notebooks not much not much you know maybe a little wider than that but not much more um you know these these are stacked on top of each other these are you can rack and stack and mount these things anywhere and it also is the first aci system that has you know a built-in hardware witness so this helps set it up for environments that are you know network bandwidth constrained or have high high latency no longer an issue next gen app is going to want to have a local data server at the edge right and with compute there right high performance right right so now you're getting it across the wire yes you get racket stack a couple of these small things i mean they can they can fit into like a you know clark kent's briefcase right these things are so small um you want to do the analytics on site and return responses back you don't want to be moving massive data payloads off the egg so you got to have the right level of compute to run machine learning algorithms and and do the analytics type work that you want to do to make local decisions yeah i mean we just had david lithimon who was one of the keynote speakers here at the event and we've been talking about super cloud and multi-cloud meta cloud all the different versions of what we see as this next-gen and this brings up a point of like his advice to young people learn how multi-cloud learn about system architecture because if you can figure out how to put it together you're going to have to make more money anyway that this whole edge piece opens up huge challenges and opportunities around how do you configure these next-gen apps what does the ai look like what's the data architecture this is not like get some training curriculum online and you get you know 101 and you're getting a job no this is more complicated but with the hardware you guys make it easier so where's the complexity shift between having a powerful edge device like the vxrail with the vsphere what's the ec button on that like how do you guys what's the vision because this is going to be a major battleground this whole edge piece yeah it's going to be huge well i think when you look at the innovation that dell is bringing to market with technologies like outlander and then designing that into vxrail and then you combine that with our tonzu capabilities to manage development and deployment of applications this is about heterogeneous deployment and management at scale of applications with technologies like tons of mission control then deploying service mesh right for security being able to use sassy to be able to secure you know with cloud security over the wire so it's bringing together multiple technologies to deliver simplicity to the customer the ability to go one to many you know in terms of being able to deploy and manage and update whether that's a security patch or an application update and do that very rapidly at a low cost so the benefit with this solution now just putting this together is i can ship a box small and or stack them and essentially it's done remotely it's that's provision the provisioning issues not a truck roll as they say or professional services enabled you can just drop that out there and this is where the customers need to be yeah that absolutely is that the vision don't get that right exactly you don't you don't need the you don't need the skills yeah you don't need the specialized skills you don't need a lot of space you don't need you know high network bandwidth all these things right all these innovations that we're talking about here um really combined into really enabling a whole new whole new future here for edge is are you doing apex now is that i think thickest part sure part of yours okay so um is apex fitting into the to the edge how does it fit yeah i mean well first of all you know a lot of what we talked with apex is really about a consumption a way to ensure there's a common cloud experience wherever the data is and where the applications are and so absolutely edge fits into this as well and so we have we have common ways to consume our infrastructure today our joint infrastructure whether it's in the data center at the edge um or you know uh in the cloud usain ragu when he was on i said it was great keynote loved it one of the things that i didn't think there was enough of was security and he's like yeah we only had so much time but vmware is a very strong security story we heard a really strong security story at dell tech world i mean half the innovations and the new you know storage products were security and the new os's and it was impressive what what's how are you guys working together on security is that one of those let me give you a few key things you know our teams are working together at the engineer to engineer level you know reference architectures for zero trust as an example being able to look you know hardware root of trust up into the application layer right so we're looking at really defense in depth here you know i mentioned what we're doing with sassy right with cloud security capabilities so you really have to look at this from the edge to the core with the you know from a networking perspective getting the network the insights on things that maybe anomalies that may be happening on the network so using our network insight technology you know uh nsx and then being able to ultimately uh have a secure development pipeline as well i mean you we all know about the supply chain attacks that happen right and so being able to have a you know secure pipeline for development is critical for both of our companies working together i think the tan zoo and you mentioned the developer self-service that experience combined with kind of the power of the dell you know let's face it the boxes are awesome hardware matters and software matters so bringing that expertise together michael daley always used to say on thecube better together in respect to vmware and dell a lot of fruit has been born from that labor right specifically around and now when you add the tan zoo and you get vsphere you got the operational excellence you got the you got the performance and scale with the dell boxes and hardware and software and now you've got the tan zoo what's missing or is it all there now i mean where how would you how would you guys peg the progress bar is it like it's all rocking right now or or i'd say you're never done first of all but i you know i look at some of the innovations that we've brought to market recently where we've are combining and stacking these technologies into a more defense in-depth like solution you know bringing nsx onto vxrail so that you can flip a switch easily and light up the firewall the new plug-in yeah that's a great example simple simple um carbon black workload another example where we're taking carbon black technology that was typically on endpoints you know on pcs bringing that into the data center right and leveraging all the analytics and insights around you know being able to identify anomalies and then remediate those anomalies so we're seeing very good traction with those and the cloud native developers containers they're all native container working with compute and container storage object store in the cloud kubernetes we've embraced it yeah i mean yeah containers running containers and vms on the same infrastructure common way to manage it all i mean that that's been a big part of it as well obviously a lot of the focus that dell's bringing here as well is is the inability to run that stack easily right you heard the announcement on uh tanzu for kubernetes operators right earlier today tko we call it uh you know that running on vxrail now is really targeted at the i.t operator in allowing them to easily stand up a self-service developer devops environment on vxrail going forward and then a piece that might be invisible to them is back to monterey isolation right encryption and data moving you know absolutely storage the security the compute right the management right that's that's a complete and it's about reducing attack services as well right the security perspective as well when you when you're moving nsxt onto a dpu you're doing that as well so there's it takes the little things right at the end of the day security is a mindset up across both companies in terms of how we approach our architectures um and it's the you know a lot of times it's the little things as well that we make sure right so shared vision working at the engineering levels together for many many years know that you guys are validating more of that coming what's next take us through okay we're here 2022 we got super cloud multi-cloud hybrid full throttle right now it's hybrid's a steady state that's cloud operations infrastructure as code has happened it's happening what's next for you guys in the relationship can you share a little bit that you can if you can what we can expect what you see uh with monterrey is the start of a re-architecting of i.t infrastructure not just in the data center but also at the edge right these technologies will move out and be pervasive you know across i think edge to colo to core data center to cloud right and so that's a starting point now we're looking at memory tiering right i think we talked last time about capitola and memory tiering and you know being able to bring that forward uh being able to do more with confidential computing as an example right secure enclaves and confidential computing so you know a lot of this is focused around simplicity and security going forward and ease of management around take the heavy lifting away from the customer abstract that in offer the power and performance that's right and it's going to come down to delivering time to value for our customers you know can we cut that time to value by 25 50 percent so they can be in production faster yeah i think project monterey is something we'll be building on for a long time right i mean this is the start of a major new future architecture of these companies so if you had to pick one we have 40 initiatives that are joined together real literally project monterey is one of my favorites for sure in terms of what it's going to do not just for that common cloud experience but for the edge and and we talked a lot about the edge today and where that's headed you think it's going to explode up new apps i really do think so well it's going to put you in a new it's going to put in curve yeah absolutely right and operationally uh security wise um from a modern apps perspective i mean all it checks all the boxes and it's going to allow us to to help and take our existing customers on that journey as well what's great about this conversation we've been following both you guys for a long time and your companies and and technology upgrades and and the business impact and open source and all doing all this for customers but the wave that's coming we're seeing the expo hall here i mean it's people are really excited they're enthused they're committed highly confident that this this wave is coming they kind of see it people kind of seeing the fog lift they're seeing money making value creation people kind of feeling more comfortable but still a little nervous around you know what's coming next because it's still uncertainty but pretty good ecosystem i'd have to say that's pretty pretty interesting yeah a lot of them are excited about you know what they can do at the edge and how they can differentiate their businesses i mean that's right well congratulations guys thanks for coming on thecube and sharing the update thank you it more innovation it's not stopping here at vmware explorer dell and vm we're continuing to have that kind of relationship joint engineering it's all coming together and you can mix and match this and the stack but it's ultimately going to be cloud operations edge is the action of course hybrid cloud as well it's thecube thanks for watching [Music] you

>>The >>Cube presents HPE discover 2022 brought to you by >>HPE. Hi everybody. Welcome back to day three, the Cube's continuous coverage wall to wall coverage of HPE. Discover 2022. My name is Dave Lanta. I'm here with John furrier. Dave McGraw is here. He's the vice president in the office of the CTO at VMware. And he's joined by Scott. We, the vice president and CTO of global sales for Hewlett Packard enterprise. And we're gonna talk tech, we're gonna talk integration. Co-creation gens. Welcome to the cube. >>Thank you so much, >>Scott, let me, let me ask you a question on the Scott side on the HP, we had the sales executives on the leaders on the sales side. You're on the CTO side with customers. You're in the front lines with customers green. Lake's got traction. I got this 1600 plus customers, 70 services we heard. And just the beginning, when you're out front of customers, you've got the old HPE now the new HPE kind of developing, what are they talking to you guys about? Cause now you have this cloud layer. I call it cloud operations, architecture shift. Yeah. What is the main conversation that you're involved in? >>I think it's driven by fundamentally that customers want to consume differently, right there workloads are ever evolving. You guys have evolved to meet those and since their consumption methods have changed on how they want and right. A lot of it's agility and, and speed of business right. Has, has dramatically shifted. So I think you'll see HPE GreenLake, you know, obviously as the cloud that comes to you, try to meet the problem where the cloud experience is needed. And I think that's the fundamental shift we've seen. I spent a lot of time with customers here at this conference. And as we've moved from cloud first to cloud smart to cloud everywhere, we're sitting in the intersection of cloud ever and delivering the experience together. And I think that's the heart of most of the conversations that are going on. >>Well, VMware, you guys are on, on a cloud. You guys shifted up with the cloud play. That's accelerated the VMware proposition. Now we have yesterday, we were talking to the city, the storage folks, they're provisioning single pane of glass or storage to customers. And whether they wanna pipe it to S3 or develop at the edge, doesn't matter. It's one console. Yeah. That's brand new. That's shipping. >>Yeah. And you know, a lot of it's driven too. I think the days of trap silos of resources that support one line of business are over. So we're talking about cloud agility everywhere, right. And to be able to embrace the cloud in all the locations. Right. And you kind of see folks move beyond just like there's the cloud, it's everywhere. It's the cloud. And so things like storage and fundamental compute and fundamental network operations that we're working on together, I think are where the customers expect us to be. We no longer can just show up. We have to show up and solve and solve before their needs. And I think that's a unique shift in the experience that's going >>On. So when you go back to, you know, Antonio four years ago now said, okay, we're all in. Yeah. On as a service. And so when you do that, you say, okay, we're gonna, we have services. They're gonna help do that. We have financial models that we can take to market immediately. So let's start there. And I would imagine take, so take us back. That's the point at which, you know, you're, you got email, phone ring, whatever let's integrate from an engineering standpoint go yeah. You know, as fast as you can. So what did that mean in terms of an engineer from an engineering perspective between HPE and, and VMware take us through that progression. >>Yeah. No, thanks for the question in your spot on it started with flexible financing models around metered usage. That was sort of the need at the time to now the expectation of engineered integrated solutions where customers don't wanna be in the system integration business anymore. And that requires engineering right. Requires deep innovation partnership to evolve to where the customer's headed, like before they've thought about it. And you'll see, you know, what we've done with vCloud foundation together and the integration within the HP GreenLake ecosystem, what we're doing with unified hybrid cloud views of what's going on, I think requires deep innovation things we're doing with other projects that we're gonna talk about today. Like Monterey capital thunder, our deep integrative innovation projects, where we've got together to try to solve a big problem cross industry that our customers are expecting us to do. And I think that speaks to the spirit of our long partnership together too. It's a business partnership. Of course it's a customer partnership to solve, but it's an innovation partnership. >>I gotta, I gotta ask about the, um, hybrid, obviously hybrids, the steady state. We're all seeing that now multi-cloud is being kicked around, but it's not, multi-cloud in the sense of workload portability so much. It's more of hybrid stitched together. Um, but it's coming fast with a data plane and yeah. The fabric and control planes. Uh, VMware, you guys are talking heavy about cross cloud or multicloud. Absolutely. So this is now brings up the old school interoperability question, right? So GreenLake sits here on premise. You guys have the edge, you get public cloud together. Where's the cross cloud come in. Where are customers doing when they think about cross cloud or, or multicloud? What is that conversation? Is it, Hey, I got Azure cause I got office and teams and I got Amazon over here and I got my on premise edge. Are they moving towards just being agnostic on cloud or is what's the environment? What, what are you crossing in the cloud? What does that mean across the cloud? Can >>You, I mean, from, from our perspective at VMware on premises, it's VMware cloud foundation, having that available, it's a VMware cloud instance, full STD STDC stack, uh, that is interoperable with our VMware cloud instances at the hyperscalers. And so for us, it's really about putting the management and control planes around that so that customers can easily determine where they wanna place workloads and when they need to burst, they need to scale up scale down. They have the flexibility and we wanna make sure all of these capabilities are available with HPE >>Going forward. What's interesting is that, you know, with, with GreenLake, what I like about what I'm seeing is is that, um, the leveling up of the cloud operation model, it's always been DevOps. We've always saw dev stack ops, clearly being operationally with cloud now on premise and edge with public cloud, it's full end to end operational cloud. If you wanna call it that, what is a key technical issue the customers need to do to get that in place? Is it to be DevOps, is that have cloud native applications, um, what kind of managed services, what's the makeup of that operating model for cloud look like? >>Yeah. I think if you talk to any enterprise commercial account, a top account, they'll they'll, if you, they think about how they run their functions, right. And you got, and you spoke to one of them, you have it ops at the bottom, it's a layer cake, right? You have it ops, everybody's deeply looking for AI ops that can remediate and orchestrate and you guys are on that journey as we are, as you move up to devs and dev SecOps, cuz security's critical, you got financial ops cuz we know economic value matters all the way clear up to cloud ops and Mo ops. What we're talking about is building hybrid operating model cause hybrid, it is simplified it where you're out of the stack, we're doing that together as partners and hybrid cloud is multiple consumption methods, but an operating model is encompass encompassing, cyber resiliency, compliance, economic, operational control. >>That's what we're built and edges in there as well. Right? Folks is, and it's not OT and it touching that's happening too, as we build edge tax, but folks need a simplified way. And as you saw in a lot of announcements here, our job was to bridge the cloud locations, right? So the customer didn't have to back to the portability statement you made, we announced a lot here that will allow you to float back and forth. So you have choice, choice and control control is the me is what every customer wants and they want the right workload at the right place at the right time at the right economic with the right capability. So I think that's in our mission together. Right? So, and >>A big part of engineering obviously is, is futures and roadmap. Yeah. Thought you mentioned Monterey cap thunder, you know, Monterey's kind of the smart Nick. One of the mega trends in the industry is Silicon diversity that handle all these new workloads to help with the edge. You know, capital is like the VSAN of memory as I, I would describe it. It obviously fits in there as well. So talk a little bit about the engineering roadmap, whatever you can share with us and how you guys are working together on that. Yeah. >>Yeah. I mean, those are three key projects for us. So there's constant interaction and integration with the HPE engineering team and the VMware team to make sure we bring those solutions to market with full capability. And for us, ultimately it's taking that technology and having it available in a VMware cloud context so that customers can have a, a consistent experience on premises running VMware cloud running with HPE GreenLake and then two are various VMware cloud suppliers around the world. And it's not just the hyperscalers, right? There's thousands of VMware cloud, uh, you know, partners that we work with manage service providers across the board. So it's, it's a very significant network of cloud. And you know, being consistent allows for mobility of workloads allows for consistency and skill sets for it operators as well. Mm-hmm >><affirmative> yeah. I wanna get into that, um, manage service trend around skill sets, but yeah, I have a, the number one thing that we've got in our, my notes here on multi-cloud challenges and I wanna get your reaction to it real quick, inconsistent infrastructure, API database network, and security constructs are different by cloud. How do you guys view that? And when you go to customers and they say, well, I got APIs that are different. I got different security constructs. What do I do? What does that, how do you answer that, that, that, that objection. >>Well, it's, it's a great call out cuz it is still the ongoing challenge, right? To gets to some of the portability, some of unified model and how they treat resources and consumption. Right? And so we're, we've all gotten together as an industry. You'll see purposely that the hyperscalers are all here at, at the conference, right? We're working on deep integration with all of our partners to make sure the customer doesn't have to. And I think it does extend to the different security models are troubling for customers. We're all working hard on unified security models as well. It's not just a developer saying, I like this set of APIs anymore, right? Or this framework customers need to run tier zero tier one, tier three applications when it really comes down to it and we need to create that unified model together. So, and I think that's really what the, the spirit or the embodiment of hybrid really is. >>When you talk to any customer, who's running a big operation, they're running in that model, right? They're not just doing cool. They want operationally simplicity. And I think you'll see these, these things we're engineering together are going after some of the hard problems, applications are hungry or all the time customers need more and more resources. And I think we would all agree. We've spent a lot of time in industry together when we're all working on sort of systems of record. What I call the shift ride effect is happening. Now we're in systems of interaction and systems of engagement out at the edge. That's the creation point of data. We need to be able to have that unified model all the way through the data path for the customer so they can monetize business value. >>And the data model is coming together. That's right. Where all three of those types of work that's right. There's two iconic names. And the other thing is that their trusted names and you're right, you're solving some of those hard problems making it simpler, but also you people trust that if something goes wrong, you're gonna be able to recover. So guys. >>Yeah. And I, and I'll tell you on the security front, you know, we've worked closely together here. If you look at, you know, VMware strategy of intrinsic security, it's really around going back to the development of our products, making sure there's a secure bill of materials, working with these guys on route of trust. Right? Making sure there's a full stack, uh, solution for our customers. Ultimately >>That's a whole nother cube segment that's bombs and shifting left and supply chain. Absolutely >>Shifting game. Absolutely. Right. Shifting >>Lift we're >>Shifting. Right guys. Awesome story. Congrats on the collaboration. Really appreciate your time in the cube. Thank you so >>Much. Thank you so >>Much. All right. You're very welcome. Okay, John and I will be back right after this short break. You're watching the Cube's coverage of HPE discover 2022 from Las Vegas, right back.

(bright music) >> Hello, and welcome to this CUBE conversation. I'm John Furrier, your host of theCUBE, here in Palo Alto, California. It's a hybrid world, we're still doing remote in news. Of course, events are coming back in person, but more importantly conversations continue. We've got two great guests here, John Siegal, SVP ISG Marketing at Dell Technologies, and Dave McGraw, office of the CTO at VMware. Gentlemen, great to see you moving forward. Dell Technologies and VMware great partnership. Thanks for coming on. >> Great to be back. >> Yeah, hi, John, thanks for having us. >> You know, the world's coming back to kind of real life, Omnicon virus is out there, but people say it's not going to be as bad as we think, but it looks like events are happening. But more importantly, the cloud native, cloud operations is definitely forcing lots of great new things happening, new innovations on-premises and at the Edge. A lot of new things happening in Dell and VMware, both have been working together for a long time now. VMware a separate company, we'll get to that in a second, but let's get to the partnership. What's new, what's changed with the relationship? >> Yeah, so I mean, just to kick that off and certainly Dave can chime in, but I think in a word, you know, John, nothing changes in terms of my customer's perspective. I mean, in many ways our joint relationship has never been stronger. We've put a ton of investment in both joint engineering innovation, Joint Go To Market over the last several years. And we're really been making what was our vision a couple of years ago a reality, and we only expect that to continue. And I think much of the reason we expect that to continue is because we have a shared vision of this distributed multi-cloud, you know, cloud native, modern app environment that customers want to drive. >> Yeah, and John, I would add that we've been building platforms together for the last five years, a great example is VxRail. You know, it's a market-leading technology that we've co-engineered together. And now it's a platform that we're actually building out use cases on top of whether it's multi-cloud solutions, whether it's private and hybrid cloud or including Tansu for developer environments. You know, we're using the investments we made and then we're layering in and building more value into those investments together. And we put agreements in place by the way that, you know, multi-year agreements around commercial arrangements and partnering together as well as our technology collaboration together. So we feel really confident about the future and that's what we're communicating to our customer base. >> Yeah, indeed just go ahead sorry, John. >> No, good. >> I was going to say just to build on that, as he said, I really, when I say not much changes, I mean, VMware has always been an open ecosystem partner, right? With its OEM vendors out there. And I think the difference here is Dell has made a strategic choice and a decision to make a significant investment in joint innovation, joint engineering, joint testing for VMware environments. And so I think a lot of this comes down to the commitment and focus that we've already made. You mentioned VxRail, which is a fantastic example where we at Dell, we've invested our own IP. You know, HCI systems software, that's sort of the secret ingredient that the secret sauce that delivers that single click, you know, automated lifecycle management experience. And we're investing lots of dollars in test labs just to ensure that customers always have that, you know, that seamless experience. >> You know, one of the benefits of doing theCUBE for 11 years now, it's just been that long, both EMC World and Dell World back in the day was our first events we went to. We've watched you guys together over the years. One of the things that strikes to be consistently the same is this focus of end to end, but also modularity, but also interoperability and kind of componentizing kind of the solution, not to oversimplify it, but this is kind of the big discussion right now as cloud scale, horizontal scale is with cloud resources are being put into the development stream where modern applications now are clear using only cloud native operations. That doesn't mean it's just cloud. I mean, it's cloud everywhere, but it's distributed computing. So this is kind of the original vision if you go back even five years or more. You guys have been working on this. This is kind of an important inflection point because now it's well known that the modern application is going to have to be programmable under the hood. Meaning everything's going to be scaling and rise of superclouds or new Edge technologies, which is coming fast. This is the new normal. This is not something that we were talking about mainstream five years ago, but you guys have been working on this kind of simplicity solutions-based approach. What's your reaction? >> That's right, John, I'll tell you, you might remember at VMworld a couple of years ago we announced Project Monterey. And now this was really a redefining architecture for not only data center, core data centers, but also for cloud and Edge environments. And so it's leveraging technology, you know, data processing units also known as smart NICs. You know, we're essentially redefining what that infrastructure looks like, making it more efficient, more performance, depending on the use case. So we've been partnering very closely with Dell to develop that technology and it's going to really transform what you see at the Edge and what you also see in core data centers going forward. >> Yeah, and there's so many of those. I mean, I think it seems Monterey is a great example of one that we continue to invest in. I think there's also NBME over TCP is another, if you will key ingredient to how customer is going to essentially get the performance they need out of the infrastructure going forward. And so we were proud to be a partner there, at most recent VMware where we announced, you know, the ability to essentially automate the integration of MBME over TCP with Dell EMC system integrated with vSphere. And that's a great example as well, right? I think there's countless. >> John: Yeah. >> And I'll tell you, we are so excited to see what Dell has done in the storage business with PowerStore X, where they've integrated vSphere ESXi into a storage array. And, you know, that creates all kinds of opportunities going forward for better integration and really for plug and play of, you know, the storage technology into cloud infrastructure. >> What's interesting about what you guys talking about is remember the old DevOps moving infrastructure as code. Okay, that became DevSecOps. That's big part of Tansu and security. Now it's all about devs, right? So now devs have all that built in and now the operations are the big conversation because one of the things we pointed out in the theCUBE recently is that, you know, VMware has owned the IT operations world, in our opinion for a long, long time. Dell has owned the enterprise for a very long time in terms of infrastructure in front solutions. The operational efficiency of cloud hybrid is really kind of what's the gateway to multi-cloud. This has been a big part of IT transformation. Can you guys share how you guys were working together to make that flexibility to transform from the old IT to the new IT? And what are some of the things that you're seeing with your customers that can give them a map of how to do this? >> Yeah, so I would say, you know, one area in particular that we're really coming together is around APEX, right? From an as a service perspective. I think what APEX is really doing is really unifying much of what you just described. It's taking as a service, it's taking multi-cloud, it's taking cloud native development if you will, and modern app development. And we together partner to ensure that's a consistent experience for customers. And we have a number of new APEX cloud services that keep that in mind and that are built on joint innovations, like frankly, VxRail at the bottom of that as they've said earlier. So for customers are looking to get, you know, item managing infrastructure altogether, which we, you know, we're seeing more and more now, we recently announced the APEX Cloud Services With VMware Cloud you know, which is again, a joint solution that'll be available soon. And it's one that is managed by Dell, but, you know, it gives customers that simplicity and scale of the public cloud, but certainly that control and security and performance, if you will, that they prefer to have in the private club. >> Yeah, and I think because, you know, the APEX Cloud Service is designed with the VMware Cloud, you have a capability that drives consistency and portability of workloads for customers. So they don't have to re-skill and retrain to be able to manage the environment. They also are not locked in to any particular solution. They have this ability to move workloads depending on what their needs are; economically, performance, you know, logistics requirements, and they can react accordingly as they digitize their business going forward. >> It's interesting, you guys are talking about this demand in a way, addressing this demand for as a service, which is, you know, it can be one cloud or multiple clouds, but it's really more of an abstraction layer of what you deploy to essentially create that connective tissue between what's existing, what's new and how to make it all work together to again, satisfy the developer 'cause the new apps are coming, right? They want more data is coming into them. So this has been, is this the as a service focus, is that what's happening? >> Yes, absolutely, yeah. The, as a service focus is, you know, at the end of the day is how are we going to really simplify this. We've been on this journey now for at least a year and much more to go. And VMware has been a key partner here, you know, on that journey. So a number of cloud services. We've had APEX Hybrid Cloud, APEX Private Cloud, you know, out there for some time. In fact, that's where we're getting a lot of the traction right now, and this new offering that's going to come out soon that we just mentioned with VMware cloud is just going to build on that. >> And VMware is a super cloud, isn't it Dave? Because you guys would be considered by our new definition of Supercloud because you can sit on Amazon. You also have other clouds too, so your customers can operate on any cloud. >> Our view is that, you know, from a multi-cloud future for customers to be able to be on-premises with a, you know, APEX service, to be able to be operating in a Colo, to be able to operate in one of many different hyperscalers, you know, providing that consistency and flexibility is going to be key. And I think also you mentioned Tansu earlier, John. You know, being able to have the customer have choice around whether they're operating with VMs and containers is really key as well. So, you know, what Dell has done with APEX is they set up again, another platform that we can just provide our SASE offerings to very simply and easily and deliver that value to customers in a consistent fashion going forward here. >> You know, I just love the term Supercloud. Actually, I called it subclass, but Dave Vellante called them Superclouds. But the idea is that you can have all the super power in the cloud capabilities, but it's also distributed clouds, right? Where you have Edge, you've got the Core and the notion of a cloud isn't like one place in which there's distributed computing. This is what the world now realizes. Again, we've talked about in theCUBE many times. So let's discuss this whole Core to Edge dynamic because if everything's cloudified, if you will, or cloud operations, you've got devs and ops kind of working together with security, all that good stuff. Now you have almost a seamless environment where code can run anywhere, data should traverse anywhere, but the idea of an Edge changes dramatically and certainly with 5G. So can you guys tie that Edge computing story together how Dell and VMware are addressing this massive growth at the Edge? >> Yeah, I would say, you know, first and foremost, we are seeing a major shift. As you mentioned today, the data being generated at the Edge it's, I think Michael Dell has actually gone on record talking about the next frontier, right? So it's especially happening because we're seeing all these smart monitoring capabilities, IOT, right? At almost any end point now from retail, traffic lights, manufacturing floors, you name it. I think anywhere where data is being acted upon to generate critical insights, right? That's considered an Edge now and we're expecting to see, as ITC has already gone out there on record as saying 50% of the new infrastructure out there will be deployed at the Edge in the next couple of years, so. And it's a different world, right? I mean, I think in terms of what's needed and what the challenges are, there's certainly a lack of specialized technical resources, typically at the Edge, there's typically a scaling issue. How do you manage all those distributed endpoints and do so successfully? And how do you ensure you lay any concerns around security as well? So, you know, once again, we've had a very collaborative approach when it comes to working on challenges like Edge, and, you know, we, again, common theme here, but the VxRail, which is a leading, you know, joint ACI off in the market is the foundation of many of our Edge offerings out there in the market today. The new satellite nodes that we just announced just a few months ago, extends VxRail's, you know, value proposition to the Edge, using a single node deployment. And it's really perfect for customers that don't have that local technical resource expertise or specialized resources. And it still has cyber resilience built right in. >> And John, just to follow up on that real quick, before Dave chimes in. On the Edge, compute has been a huge issue. And I've talked with you guys about this too. You guys have the compute, you have the integrated systems now, any update there on what VxRail is doing different or other Edge power (John laughs) PowerEdge sounds familiar? We need some more power at the Edge. So what's new there? >> Well, you know, first of all, we had new PowerEdge platforms of course, come out in this past year, and, you know, there's, we're building on that. I mean, the latest VxRail is of course, leveraged that power of PowerEdge. Yeah, lots of a good naming arrogance, right? PowerEdge. >> John: I love that. And, but, you know, it's, you know, it's at the heart of much of what we're doing. We're taking a lot of our capabilities that have been IP, like streaming data platform, which enables streaming, video and real-time analytics and running that on a VxRail or PowerEdge platform. You know, we're doing the same thing, you know, with, in the manufacturing side. We're working with partners that have IOT Edge platforms, you know, and running those on VxRail and PowerEdge. So we are taking very much the idea here that, yes, you're right with our rich resources of infrastructure, both with PowerEdge and VxRail, you know, building on that. But working with partners like VMware and others to collapse an integrated solution for the Edge. And so we're seeing really good uptake so far. >> Dave, what's your take on the Dell Edge with VMware, because automation is big theme, not moving data across an internet that's obviously huge. And you got to have that operational stability there. >> Absolutely, and, you know, to your point, being able to do the processing at the Edge and move results around versus moving massive amounts of data around is really key to the future going forward. And, you know, we've taken an approach with Dell where we're working with customers, we're having detailed conversations, really using a "Tiger Team Approach" around the use cases; manufacturing and retail being two of the real key focuses, healthcare another one where we're understanding customer requirements, it's both today and where they want to go. And, you know, so it's about distributed computing, certainly at the Edge. Dell is coming out with some great new platforms that we're integrating our software with. At the same time, we have technology in STWIN and SASE that become part of that solution as well, with VeloCloud. And we're developing a global network of points of presence that really will help support distributed application environments and Edge-native Application environments working with Dell going forward. >> That's great stuff. The next ending question is what's next. I want to just tee that up by bringing up what you kind of made me think of there, Dave, and this is key supply chain on both hardware and software talking about security. So when you say those things you're talking about in terms of functionality, the question is security, right? Both hardware and software supply chain with open source, with automation. I mean, this is a big discussion. What do you guys react to that about what's next.. >> Yeah, I can tell you from a central engineering perspective, you know, we're looking at security compliance and privacy every day, we're working closely with Dell. In fact, we're in the middle of meetings today in this area. And, you know, I look at a few key areas of investment that we're making collectively together. One is in the area of end to end encryption of data. For virtualized environments or containerized environments, being able to have end-to-end encryption and manage a very efficient way, the keys and maintain the data compression and deduplication capabilities for customers, you know, efficiency and cost purposes while being very secure. The second area we're working closely on is in Zero Trust. You know, being able to develop Zero Trust infrastructure across Edge, to Core, to Colo, to Cloud and making sure that, you know, we have reference designs available to customers with procedures, policies, best practices, to be able to drive Zero Trust environments. >> John what you're (indistinct) is huge and you guys have, literally could be the keys to the kingdom pun intended. You guys are doing a lot of great security at the Edge too, whether the traffic stays with the Edge or goes across the network. >> That's all right, I'm as curious, like you said, it's been a joint focus and initiative across much of our portfolio for quite a while now. And I think, you know, you asked what's next and I think, you know, sky's the limit right now. I mean, we've got the shared vision, right? I think at the end of the day, you know, we've shared a number of joint initiatives that are ongoing right now with Project Monterrey. Obviously our integration with Tansu and a number of solutions we have there, work around APEX, et cetera. I think we have complimentary capabilities. You mentioned, you know, areas like supply chain, areas like security, you know, and I think these are all things that we both do well together. And the thing I will say that I think is probably the most key to us sustaining this great execution together is our collaborative cultures. I think, you know, there's something to be said for what we built, you know, all these last several years, you know, around these collaborative cultures, working together on joint roadmaps and focusing on really end of the day solving our customer's biggest challenges, whatever those may be, you know? And so at the end of the day behind us, we have the greatest supply chains, you know, services, support, and innovation engines. But I think, you know, I think that the passion, our groups working together I think is going to be key to us going forward. >> Well, great stuff moving forward together with Dell Technologies and VMware. David, thanks for coming on. John, great to see you. Thanks for sharing insight. Great CUBE conversation talking encryption, we've spoken about Edge and supply chain as well. Great stuff, great conversation. Thanks for coming on. >> Thank you >> Thank you so much, John. >> Okay, this is theCUBE conversation. I'm John Furrier, with theCUBE. You're watching CUBE coverage. Thank you so much for watching. (bright music)

>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, its theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer event in San Francisco, Spring, 2018. About 100 people, predominantly practitioners, which is a pretty unique event. Not a lot of vendors, a couple of them around, but really a lot of people that are out in the wild doing this work. We're really excited to have a return guest. We last saw him at Spark Summit East 2017. Can you believe I keep all these shows straight? I do not. Alfred Essa, he is the VP, Analytics and R&D at McGraw-Hill Education. Alfred, great to see you again. >> Great being here, thank you. >> Absolutely, so last time we were talking it was Spark Summit, it was all about data in motion and data on the fly, and real-time analytics. You talked a lot about trying to apply these types of new-edge technologies and cutting-edge things to actually education. What a concept, to use artificial intelligence, a machine learning for people learning. Give us a quick update on that journey, how's it been progressing? >> Yeah, the journey progresses. We recently have a new CEO come on board, started two weeks ago. Nana Banerjee, very interesting background. PhD in mathematics and his area of expertise is Data Analytics. It just confirms the direction of McGraw-Hill Education that our future is deeply embedded in data and analytics. >> Right. It's funny, there's a often quoted kind of fact that if somebody came from a time machine from, let's just pick 1849, here in San Francisco, everything would look different except for Market Street and the schools. The way we get around is different. >> Right. >> The things we do to earn a living are different. The way we get around is different, but the schools are just slow to change. Education, ironically, has been slow to adopt new technology. You guys are trying to really change that paradigm and bring the best and latest in cutting edge to help people learn better. Why do you think it's taken education so long and must just see nothing but opportunity ahead for you. >> Yeah, I think the... It was sort of a paradox in the 70s and 80s when it came to IT. I think we have something similar going on. Economists noticed that we were investing lots and lots of money, billions of dollars, in information technology, but there were no productivity gains. So this was somewhat of a paradox. When, and why are we not seeing productivity gains based on those investments? It turned out that the productivity gains did appear and trail, and it was because just investment in technology in itself is not sufficient. You have to also have business process transformation. >> Jeff Frick: Right. >> So I think what we're seeing is, we are at that cusp where people recognize that technology can make a difference, but it's not technology alone. Faculty have to teach differently, students have to understand what they need to do. It's a similar business transformation in education that I think we're starting to see now occur. >> Yeah it's great, 'cause I think the old way is clearly not the way for the way forward. That's, I think, pretty clear. Let's dig into some of these topics, 'cause you're a super smart guy. One thing's talk about is this algorithmic transparency. A lot of stuff in the news going on, of course we have all the stuff with self-driving cars where there's these black box machine learning algorithms, and artificial intelligence, or augmented intelligence, bunch of stuff goes in and out pops either a chihuahua or a blueberry muffin. Sometimes it's hard to tell the difference. Really, it's important to open up the black box. To open up so you can at least explain to some level of, what was the method that took these inputs and derived this outpout. People don't necessarily want to open up the black box, so kind of what is the state that you're seeing? >> Yeah, so I think this is an area where not only is it necessary that we have algorithmic transparency, but I think those companies and organizations that are transparent, I think that will become a competitive advantage. That's how we view algorithms. Specifically, I think in the world of machine learning and artificial intelligence, there's skepticism, and that skepticism is justified. What are these machines? They're making decisions, making judgments. Just because it's a machine, doesn't mean it can't be biased. We know it can be. >> Right, right. >> I think there are techniques. For example, in the case of machine learning, what the machines learns, it learns the algorithm, and those rules are embedded in parameters. I sort of think of it as gears in the black box, or in the box. >> Jeff Frick: Right. >> What we should be able to do is allow our customers, academic researchers, users, to understand at whatever level they need to understand and want to understand >> Right. >> What the gears do and how they work. >> Jeff Frick: Right. >> Fundamental, I think for us, is we believe that the smarter our customers are and the smarter our users are, and one of the ways in which they can become smarter is understanding how these algorithms work. >> Jeff Frick: Right. >> We think that that will allow us to gain a greater market share. So what we see is that our customers are becoming smarter. They're asking more questions and I think this is just the beginning. >> Jeff Frick: Right. >> We definitely see this as an area that we want to distinguish ourselves. >> So how do you draw lines, right? Because there's a lot of big science underneath those algorithms. To different degrees, some of it might be relatively easy to explain as a simple formula, other stuff maybe is going into some crazy, statistical process that most layman, or business, or stakeholders may or may not understand. Is there a way you slice it? Is there kind of wars of magnitude in how much you expose, and the way you expose within that box? >> Yeah, I think there is a tension. The tension traditionally, I think organizations think of algorithms like they think of everything else, as intellectual property. We want to lock down our intellectual property, we don't want to expose that to our competitors. I think... I think that's... We do need to have intellectual property, however, I think many organizations get locked into a mental model, which I don't think is just the right one. I think we can, and we want our customers to understand how our algorithm works. We also collaborate quite a bit with academic researchers. We want validation from the academic research community that yeah, the stuff that you're building is in fact based on learning science. That it has warrant. That when you make claims that it works, yes, we can validate that. Now, where I think... Based on the research that we do, things that we publish, our collaboration with researchers, we are exposing and letting the world know how we do things. At the same time, it's very, very difficult to build an engineer, an architect, scalable solutions that implement those algorithms for millions of users. That's not trivial. >> Right, right, right. >> Even if we give away quite a bit of our secret sauce, it's not easy to implement that. >> Jeff Frick: Right. >> At the same time, I believe and we believe, that it's good to be chased by our competition. We're just going to go faster. Being more open also creates excitement and an ecosystem around our products and solutions, and it just makes us go faster. >> Right, which gives to another transition point, which would you talk about kind of the old mental model of closed IP systems, and we're seeing that just get crushed with open source. Not only open source movements around specific applications, and like, we saw you at Spark Summit, which is an open source project. Even within what you would think for sure has got to be core IP, like Facebook opening up their hardware spec for their data centers, again. I think what's interesting, 'cause you said the mental model. I love that because the ethos of open source, by rule, is that all the smartest people are not inside your four walls. >> Exactly. >> There's more of them outside the four walls regardless of how big your four walls are, so it's more of a significant mental shift to embrace, adopt, and engage that community from a much bigger accumulative brain power than trying to just trying to hire the smartest, and keep it all inside. How is that impacting your world, how's that impacting education, how can you bring that power to bear within your products? >> Yeah, I think... You were in effect quoting, I think it was Bill Joy saying, one of the founders of Sun Microsystems, they're always, you have smart people in your organization, there are always more smarter people outside your organization, right? How can we entice, lure, and collaborate with the best and the brightest? One of the ways we're doing that is around analytics, and data, and learning science. We've put together a advisory board of learning science researchers. These are the best and brightest learning science researcher, data scientists, learning scientists, they're on our advisory board and they help and set, give us guidance on our research portfolio. That research portfolio is, it's not blue sky research, we're on Google and Facebook, but it's very much applied research. We try to take the no-knowns in learning science and we go through a very quick iterative, innovative pipeline where we do research, move a subset of those to product validation, and then another subset of that to product development. This is under the guidance, and advice, and collaboration with the academic research community. >> Right, right. You guys are at an interesting spot, because people learn one way, and you've mentioned a couple times this interview, using good learning science is the way that people learn. Machines learn a completely different way because of the way they're built and what they do well, and what they don't do so well. Again, I joked before about the chihuahua and the blueberry muffin, which is still one of my favorite pictures, if you haven't seen it, go find it on the internet. You'll laugh and smile I promise. You guys are really trying to bring together the latter to really help the former. Where do those things intersect, where do they clash, how do you meld those two methodologies together? >> Yeah, it's a very interesting question. I think where they do overlap quite a bit is... in many ways machines learn the way we learn. What do I mean by that? Machine learning and deep learning, the way machines learn is... By making errors. There's something, a technical concept in machine learning called a loss function, or a cost function. It's basically the difference between your predicted output and ground truth, and then there's some sort of optimizer that says "Okay, you didn't quite get it right. "Try again." Make this adjustment. >> Get a little closer. >> That's how machines learn, they're making lots and lots of errors, and there's something behind the scenes called the optimizer, which is giving the machine feedback. That's how humans learn. It's by making errors and getting lots and lots of feedback. That's one of the things that's been absent in traditional schooling. You have a lecture mode, and then a test. >> Jeff Frick: Right. >> So what we're trying to do is incorporate what's called formative assessment, this is just feedback. Make errors, practice. You're not going to learn something, especially something that's complicated, the first time. You need to practice, practice, practice. Need lots and lots of feedback. That's very much how we learn and how machines learn. Now, the differences are, technologically and state of knowledge, machines can now do many things really well but there's still some things and many things, that humans are really good at. What we're trying to do is not have machines replace humans, but have augmented intelligence. Unify things that machines can do really well, bring that to bear in the case of learning, also insights that we provide. Instructors, advisors. I think this is the great promise now of combining the best of machine intelligence and human intelligence. >> Right, which is great. We had Gary Kasparov on and it comes up time and time again. The machine is not better than a person, but a machine and a person together are better than a person or a machine to really add that context. >> Yeah, and that dynamics of, how do you set up the context so that both are working in tandem in the combination. >> Right, right. Alright Alfred, I think we'll leave it there 'cause I think there's not a better lesson that we could extract from our time together. I thank you for taking a few minutes out of your day, and great to catch up again. >> Thank you very much. >> Alright, he's Alfred, I'm Jeff. You're watching theCUBE from the Corinium Chief Analytics Officer event in downtown San Francisco. Thanks for watching. (energetic music)

>> Announcer: Live from Boston, Massachusetts this is the CUBE covering Spark Summit East 2017 brought to you by Databricks. Now, here are your hosts Dave Vellante and George Gilbert. >> Welcome back to Boston everybody this is the CUBE. We're live here at Spark Summit East in the Hynes Convention Center. This is the CUBE, check out SiliconANGLE.com for all the news of the day. Check out Wikibon.com for all the research. I'm really excited about this session here. Al Essa is here, he's the vice president of analytics and R&D at McGraw-Hill Education. And I'm so excited because we always talk about digital transformations and transformations. We have an example of 150 year old company that has been, I'm sure, through many transformations. We're going to talk about a recent one. Al Essa, welcome to the CUBE, thanks for coming on. >> Thank you, pleasure to be here. >> So you heard my little narrative up front. You, obviously, have not been with the company for 150 years (laughs), you can't talk about all the transformations, but there's certainly one that's recent in the last couple of years, anyway which is digital. We know McGraw Hill is a print publisher, describe your business. >> Yeah, so McGraw Hill Education has been traditionally a print publisher, but beginning with our new CEO, David Levin, he joined the company about two years ago and now we call ourselves a learning science company. So it's no longer print publishing, it's smart digital and by smart digital we mean we're trying to transform education by applying principles of learning science. Basically what that means is we try to understand, how do people learn? And how they can learn better. So there are a number of domains, cognitive science, brain sciences, data science and we begin to try to understand what are the known knowns in these areas and then apply it to education. >> I think Marc Benioff said it first, at least the first I heard he said there were going to be way more Saas companies that come out of non-tech companies than tech companies. We're talking off camera, you're a software company. Describe that in some detail. >> Yeah, so being a software company is new for us, but we've moved pretty quickly. Our core competency has been really expert knowledge about education. We work with educators, subject matter experts, so for over a hundred years, we've created vetted content, assessments, and so on. So we have a great deal of domain expertise in education and now we're taking, sort of the new area of frontiers of knowledge, and cognitive science, brain sciences. How can learners learn better and applying that to software and models and algorithms. >> Okay, and there's a data component to this as well, right? >> So yeah, the way I think about it is we're a smart digital company, but smart digital is fueled by smart data. Data underlies everything that we do. Why? Because in order to strengthen learners, provide them with the optimal pathway, as well as instructors. We believe instructors are at the center of this new transformation. We need to provide immediate, real-time data to students and instructors on, how am I doing? How can I do better? This is the predictive component and then you're telling me, maybe I'm not on the best path. So what's my, "How can I do better?" the optimal path. So all of that is based on data. >> Okay, so that's, I mean, the major reason. Do you do any print anymore? Yes, we still do print, because there's still a huge need for print. So print's not going to go away. >> Right. Okay, I just wanted to clarify that. But what you described is largely a business model change, not largely, it is a business model change. But also the value proposition is changing. You're providing a new service, related, but new incremental value, right? >> Yeah, yeah. So the value proposition has changed, and here again, data is critical. Inquiring minds want to know. Our customers want to know, "All right, we're going to use your technology "and your products and solutions, "show us "rigorously, empirically, that it works." That's the bottom line question. Is it effective? Are the tools, products, solutions, not just ours, but are our products and solutions have a context. Is the instruction effective? Is it effective for everyone? So all that is reliant on data. >> So how much of a course, how much of the content in a course would you prepare? Is it now the entire courseware and you instrument the students interaction with it? And then, essentially you're selling the outcomes, the improved outcomes. >> Yeah, I think that's one way to think about it. Here's another model change, so this is not so much digital versus non-digital, but we've been a closed environment. You buy a textbook from us, all the material, the assessments is McGraw Hill Education. But now a fundamental part of our thinking as a software company is that we have to be an open company. Doesn't mean open as in free, but it's an open ecosystem, so one of the things that we believe in very much is standards. So there's a standard body in education called IMS Global. My boss, Stephen Laster, is on the board of IMS Global. So think of that as, this encompasses everything from different tools working together, interoperability tools, or interoperability standards, data standards for data exchange. So, we will always produce great content, great assessments, we have amazing platform and analytics capability, however, we don't believe all of our customers are going to want to use everything from McGraw Hill. So interoperability standards, data standards is vital to what we're doing. >> Can you explain in some detail this learning science company. Explain how we learn. We were talking off camera about sort of the three-- >> Yeah, so this is just one example. It's well known that memory decays exponentially, meaning when you see some item of knowledge for the first time, unless something happens, it goes into short-term memory and then it evaporates. One of the challenges in education is how can I acquire knowledge and retain knowledge? Now most of the techniques that we all use are not optimal. We cram right before an exam. We highlight things and that creates the illusion that we'll be able to recall it. But it's an illusion. Now, cognitive science and research in cognitive science tells us that there are optimal strategies for acquiring knowledge and recalling it. So three examples of that are effort for recall. If you have to actively recall some item of knowledge, that helps with the stickiness. Another is space practice. Practicing out your recall over multiple sessions. Another one is interleaving. So what we do is, we just recently came out with a product last week called, StudyWise. What we've done is taken those principles, written some algorithms, applies those algorithms into a mobile product. That's going to allow learners to optimize their acquisition and recall of knowledge. >> And you're using Spark to-- >> Yeah, we're using Spark and we're using Databricks. So I think what's important there is not just Spark as a technology, but it's an ecosystem, it's a set of technologies. And it has to be woven together into a workflow. Everything from building the model and algorithm, and those are always first approximations. We do the best we can, in terms of how we think the algorithm should work and then deploy that. So our data science team and learning science team builds the models, designs the models, but our IT team wants to make sure that it's part of a workflow. They don't want to have to deal with a new set of technologies, so essentially pressing the button goes into production and then it doesn't stop there, because as Studywise has gone on the market last week, now we're collecting data real-time as learners are interacting with our products. The results of their interactions is coming in to our research environment and we're analyzing that data, as a way of updating our models and tuning the models. >> So would it be fair to say that it was interesting when you talked about these new ways of learning. If I were to create an analogy to Legacy Enterprise apps, they standardize business transactions and the workflows that went with them. It's like you're picking out the best practices in learning, codifying them into an application. And you've opened it up so other platforms can take some or all and then you're taking live feedback from the models, but not just tuning the existing model, but actually adding learning to the model over time as you get a better sense for how effort of recall works or interleaving works. >> Yeah, I think that's exactly right. I do want to emphasize something, an aspect of what you just said is we believe, and it's not just we believe, the research in learning science shows that we can get the best, most significant learning gains when we place the instructor, the master teacher, at the center of learning. So, doing that, not just in isolation, but what we want to do is create a community of practitioners, master teachers. So think of the healthcare analogy. We have expert physicians, so when we have a new technique or even an old technique, What's working? What's not working? Let's look at the data. What we're also doing is instrumenting our tools so that we can surface these insights to the master practitioners or master teachers. George is trying this technique, that's working or not working, what adjustments do we need to make? So it's not just something has to happen with the learner. Maybe we need to adjust our curriculum. I have to change my teaching practices, my assessments. >> And the incentive for the master practitioners to collaborate is because that's just their nature? >> I think it is. So let's kind of stand back, I think the current paradigm of instruction is lecture mode. I want to impart knowledge, so I'm going to give a lecture. And then assessment is timed tests. In the educational, the jargon for that is summit of assessments, so lecture and tests. That's the dominant paradigm in education. All the research evidence says that doesn't work. (laughs) It doesn't work, but we still do it. >> For how many hundreds of years? >> Yeah. Well, it was okay if we needed to train and educate a handful of people. But now, everyone needs to be educated and it's lifelong learning rate, so that paradigm doesn't work. And the research evidence is overwhelming that it doesn't work. We have to change our paradigm where the new paradigm, and this is again based on research, is differentiated instruction. Different learners are at different stages in their learning and depending on what you need to know, I'm at a different stage. So, we need assessments. Assessments are not punitive, they're not tests. They help us determine what kind of knowledge, what kind of information each learner needs to know. And the instructor helps with the differentiated instruction. >> It's an alignment. >> It's an alignment, yeah. Really to take it to the next stage, the master practitioners, if they are armed with the right data, they can begin to compare. All right, practices this way of teaching for these types of students works well, these are the adjustments that we need to make. >> So, bringing it down to earth with Spark, these models of how to teach, or perhaps how to differentiate the instruction, how to do differentiated assessments, these are the Spark models. >> Yeah, these are the Spark models. So let's kind of stand back and see what's different about traditional analytics or business intelligence and the new analytics enabled by Spark, and so on. First, traditional analytics, the questions that you need to be able to answer are defined beforehand. And then they're implemented in schemas in a data warehouse. In the new order of things, I have questions that I need to ask and they just arise right now. I'm not going to anticipate all the questions that I might want to be able to ask. So, we have to be enable the ability to ask new questions and be able to receive answers immediately. Second, the feedback loop, traditional analytics is a batch mode. Overnight, data warehouse gets updated. Imagine you're flying an airplane, you're the pilot, a new weather system emerges. You can't wait a week or six months to get a report. I have to have corrective course. I have to re-navigate and find a new course. So, the same way, a student encounters difficulty, tell me what I need to do, what course correction do I need to apply? The data has to come in real-time. The models have to run real-time. And if it's at scale, then we have to have parallel processing and then the updates, the round trip, data back to the instructor or the student has to be essentially real-time or near real-time. Spark is one of the technologies that's enabling that. >> The way you got here is kind of interesting. You used to be CIO, got that big Yale brain (laughs) working for you. You're not a developer, I presume, is that right? >> No. >> How did you end up in this role? >> I think it's really a passion for education and I think this is at McGraw Hill. So I'm a first generation college student, I went to public school in Los Angeles. I had a lot of great breaks, I had great teachers who inspired me. So I think first, it's education, but I think we have a major, major problem that we need to solve. So if we look at... So I spent five years with the Minnesota state colleges and university system, most of the colleges, community colleges are open access institutions. So let me just give you a quick statistic. 70% of students who enter community colleges are not prepared in math and english. So seven out of 10 students need remediation. Of the seven out of 10 students who need remediation, only 15% not 5-0, one-five succeed to the next level. This is a national tragedy. >> And that's at the community college level? >> That's at the community college level. We're talking about millions of students who are not making it past the first gate. And they go away thinking they've failed, they incurred debt, their life is now stuck. So this is playing itself out, not to tens of thousands of students, but hundreds of thousands of students annually. So, we've got to solve this problem. I think it's not technology, but reshaping the paradigm of how we think about education. >> It is a national disaster, because often times that's the only affordable route for folks and they are taking on debt, thinking okay, this is a gateway. Al, we have to leave it there. Awesome segment, thanks very much for coming to the CUBE, really appreciate it. >> Thank you very much. >> All right, you're welcome. Keep it right there, my buddy, George and I will be back with our next guest. This is the CUBE, we're live from Boston. Be right back. (techno music) >> Narrator: Since the dawn of the cloud

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCube! (funky electronic music) Now, here's your host, Dave Vellante! >> Welcome, everybody, to this Cube conversation here in our Marlborough offices. I am very excited today, I spent a number of years at IDC, which, of course, is owned by IDG. And there's a new book out, relatively new, called Future Forward: Leadership Lessons from Patrick McGovern, the Visionary Who Circled the Globe and Built a Technology Media Empire. And it's a great book, lotta stories that I didn't know, many that I did know, and the author of that book, Glenn Rifkin, is here to talk about not only Pat McGovern but also some of the lessons that he put forth to help us as entrepreneurs and leaders apply to create better businesses and change the world. Glenn, thanks so much for comin' on theCube. >> Thank you, Dave, great to see ya. >> So let me start with, why did you write this book? >> Well, a couple reasons. The main reason was Patrick McGovern III, Pat's son, came to me at the end of 2016 and said, "My father had died in 2014 and I feel like his legacy deserves a book, and many people told me you were the guy to do it." So the background on that I, myself, worked at IDG back in the 1980s, I was an editor at Computerworld, got to know Pat during that time, did some work for him after I left Computerworld, on a one-on-one basis. Then I would see him over the years, interview him for the New York Times or other magazines, and every time I'd see Pat, I'd end our conversation by saying, "Pat, when are we gonna do your book?" And he would laugh, and he would say, "I'm not ready to do that yet, there's just still too much to do." And so it became sort of an inside joke for us, but I always really did wanna write this book about him because I felt he deserved a book. He was just one of these game-changing pioneers in the tech industry. >> He really was, of course, the book was even more meaningful for me, we, you and I started right in the same time, 1983-- >> Yeah. >> And by that time, IDG was almost 20 years old and it was quite a powerhouse then, but boy, we saw, really the ascendancy of IDG as a brand and, you know, the book reviews on, you know, the back covers are tech elite: Benioff wrote the forward, Mark Benioff, you had Bill Gates in there, Walter Isaacson was in there, Guy Kawasaki, Bob Metcalfe, George Colony-- >> Right. >> Who actually worked for a little stint at IDC for a while. John Markoff of The New York Times, so, you know, the elite of tech really sort of blessed this book and it was really a lot to do with Pat McGovern, right? >> Oh, absolutely, I think that the people on the inside understood how important he was to the history of the tech industry. He was not, you know, a household name, first of all, you didn't think of Steve Jobs, Bill Gates, and then Pat McGovern, however, those who are in the know realize that he was as important in his own way as they were. Because somebody had to chronicle this story, somebody had to share the story of the evolution of this amazing information technology and how it changed the world. And Pat was never a front-of-the-TV-camera guy-- >> Right. >> He was a guy who put his people forward, he put his products forward, for sure, which is why IDG, as a corporate name, you know, most people don't know what that means, but people did know Macworld, people did know PCWorld, they knew IDC, they knew Computerworld for sure. So that was Pat's view of the world, he didn't care whether he had the spotlight on him or not. >> When you listen to leaders like Reed Hoffman or Eric Schmidt talk about, you know, great companies and how to build great companies, they always come back to culture. >> Yup. >> The book opens with a scene of, and we all, that I usually remember this, well, we're just hangin' around, waitin' for Pat to come in and hand out what was then called the Christmas bonus-- >> Right. >> Back when that wasn't politically incorrect to say. Now, of course, it's the holiday bonus. But it was, it was the Christmas bonus time and Pat was coming around and he was gonna personally hand a bonus, which was a substantial bonus, to every single employee at the company. I mean, and he did that, really, literally, forever. >> Forever, yeah. >> Throughout his career. >> Yeah, it was unheard of, CEOs just didn't do that and still don't do that, you were lucky, you got a message on the, you know, in the lunchroom from the CEO, "Good work, troops! Keep up the good work!" Pat just had a really different view of the culture of this company, as you know from having been there, and I know. It was very familial, there was a sense that we were all in this together, and it really was important for him to let every employee know that. The idea that he went to every desk in every office for IDG around the United States, when we were there in the '80s there were probably 5,000 employees in the US, he had to devote substantial amount-- >> Weeks and weeks! >> Weeks at a time to come to every building and do this, but year after year he insisted on doing it, his assistant at the time, Mary Dolaher told me she wanted to sign the cards, the Christmas cards, and he insisted that he ensign every one of them personally. This was the kind of view he had of how you keep employees happy, if your employees are happy, the customers are gonna be happy, and you're gonna make a lot of money. And that's what he did. >> And it wasn't just that. He had this awesome holiday party that you described, which was epic, and during the party, they would actually take pictures of every single person at the party and then they would load the carousel, you remember the 35-mm. carousel, and then, you know, toward the end of the evening, they would play that and everybody was transfixed 'cause they wanted to see their, the picture of themselves! >> Yeah, yeah. (laughs) >> I mean, it was ge-- and to actually pull that off in the 1980s was not trivial! Today, it would be a piece of cake. And then there was the IDG update, you know, the Good News memos, there was the 10-year lunch, the 20-year trips around the world, there were a lot of really rich benefits that, you know, in and of themselves maybe not a huge deal, but that was the culture that he set. >> Yeah, there was no question that if you talked to anybody who worked in this company over, say, the last 50 years, you were gonna get the same kind of stories. I've been kind of amazed, I'm going around, you know, marketing the book, talking about the book at various events, and the deep affection for this guy that still holds five years after he died, it's just remarkable. You don't really see that with the CEO class, there's a couple, you know, Steve Jobs left a great legacy of creativity, he was not a wonderful guy to his employees, but Pat McGovern, people loved this guy, and they st-- I would be signing books and somebody'd say, "Oh, I've been at IDG for 27 years and I remember all of this," and "I've been there 33 years," and there's a real longevity to this impact that he had on people. >> Now, the book was just, it was not just sort of a biography on McGovern, it was really about lessons from a leader and an entrepreneur and a media mogul who grew this great company in this culture that we can apply, you know, as business people and business leaders. Just to give you a sense of what Pat McGovern did, he really didn't take any outside capital, he did a little bit of, you know, public offering with IDG Books, but, really, you know, no outside capital, it was completely self-funded. He built a $3.8 billion empire, 300 publications, 280 million readers, and I think it was almost 100 or maybe even more, 100 countries. And so, that's an-- like you were, used the word remarkable, that is a remarkable achievement for a self-funded company. >> Yeah, Pat had a very clear vision of how, first of all, Pat had a photographic memory and if you were a manager in the company, you got a chance to sit in meetings with Pat and if you didn't know the numbers better than he did, which was a tough challenge, you were in trouble! 'Cause he knew everything, and so, he was really a numbers-focused guy and he understood that, you know, his best way to make profit was to not be looking for outside funding, not to have to share the wealth with investors, that you could do this yourself if you ran it tightly, you know, I called it in the book a 'loose-tight organization,' loose meaning he was a deep believer in decentralization, that every market needed its own leadership because they knew the market, you know, in Austria or in Russia or wherever, better than you would know it from a headquarters in Boston, but you also needed that tightness, a firm grip on the finances, you needed to know what was going on with each of the budgets or you were gonna end up in big trouble, which a lot of companies find themselves in. >> Well, and, you know, having worked there, I mean, essentially, if you made your numbers and did so ethically, and if you just kind of followed some of the corporate rules, which we'll talk about, he kind of left you alone. You know, you could, you could pretty much do whatever you wanted, you could stay in any hotel, you really couldn't fly first class, and we'll maybe talk about that-- >> Right. >> But he was a complex man, I mean, he was obviously wealthy, he was a billionaire, he was very generous, but at the same time he was frugal, you know, he drove, you know, a little, a car that was, you know, unremarkable, and we had buy him a car. He flew coach, and I remember one time, I was at a United flight, and I was, I had upgraded, you know, using my miles, and I sat down and right there was Lore McGovern, and we both looked at each other and said right at the same time, "I upgraded!" (laughs) Because Pat never flew up front, but he would always fly with a stack of newspapers in the seat next to him. >> Yeah, well, woe to, you were lucky he wasn't on the plane and spotted you as he was walking past you into coach, because he was not real forgiving when he saw people, people would hide and, you know, try to avoid him at all cost. And, I mean, he was a big man, Pat was 6'3", you know, 250 lbs. at least, built like a linebacker, so he didn't fit into coach that well, and he wasn't flying, you know, the shuttle to New York, he was flyin' to Beijing, he was flyin' to Moscow, he was going all over the world, squeezing himself into these seats. Now, you know, full disclosure, as he got older and had, like, probably 10 million air miles at his disposal, he would upgrade too, occasionally, for those long-haul flights, just 'cause he wanted to be fresh when he would get off the plane. But, yeah, these are legends about Pat that his frugality was just pure legend in the company, he owned this, you know, several versions of that dark blue suit, and that's what you would see him in. He would never deviate from that. And, but, he had his patterns, but he understood the impact those patterns had on his employees and on his customers. >> I wanna get into some of the lessons, because, really, this is what the book is all about, the heart of it. And you mentioned, you know, one, and we're gonna tell from others, but you really gotta stay close to the customer, that was one of the 10 corporate values, and you remember, he used to go to the meetings and he'd sometimes randomly ask people to recite, "What's number eight?" (laughs) And you'd be like, oh, you'd have your cheat sheet there. And so, so, just to give you a sense, this man was an entrepreneur, he started the company in 1964 with a database that he kind of pre-sold, he was kind of the sell, design, build type of mentality, he would pre-sold this thing, and then he started Computerworld in 1967, so it was really only a few years after he launched the company that he started the Computerworld, and other than Data Nation, there was nothing there, huge pent-up demand for that type of publication, and he caught lightning in a bottle, and that's really how he funded, you know, the growth. >> Yeah, oh, no question. Computerworld became, you know, the bible of the industry, it became a cash cow for IDG, you know, but at the time, it's so easy to look in hindsight and say, oh, well, obviously. But when Pat was doing this, one little-known fact is he was an editor at a publication called Computers and Automation that was based in Newton, Massachusetts and he kept that job even after he started IDC, which was the original company in 1964. It was gonna be a research company, and it was doing great, he was seeing the build-up, but it wasn't 'til '67 when he started Computerworld, that he said, "Okay, now this is gonna be a full-time gig for me," and he left the other publication for good. But, you know, he was sorta hedging his bets there for a little while. >> And that's where he really gained respect for what we'll call the 'Chinese Wallet,' the, you know, editorial versus advertising. We're gonna talk about that some more. So I mentioned, 1967, Computerworld. So he launched in 1964, by 1971, he was goin' to Japan, we're gonna talk about the China Stories as well, so, he named the company International Data Corp, where he was at a little spot in Newton, Mass.-- >> Right, right. >> So, he had a vision. You said in your book, you mention, how did this gentleman get it so right for so long? And that really leads to some of the leadership lessons, and one of them in the book was, sort of, have a mission, have a vision, and really, Pat was always talking about information, about information technology, in fact, when Wine for Dummies came out, it kind of created a little friction, that was really off the center. >> Or Wine for Dummies, or Sex for Dummies! >> Yeah, Sex for Dummies, boy, yeah! >> With, that's right, Ruth Westheimer-- >> Dr. Ruth Westheimer. >> But generally speaking, Glenn, he was on that mark, he really didn't deviate from that vision. >> Yeah, no, it was very crucial to the development of the company that he got people to, you know, buy into that mission, because the mission was everything. And he understood, you know, he had the numbers, but he also saw what was happening out there, from the 1960s, when IBM mainframes filled a room, and, you know, only the high priests of data centers could touch them. He had a vision for, you know, what was coming next and he started to understand that there would be many facets to this information about information technology, it wasn't gonna be boring, if anything, it was gonna be the story of our age and he was gonna stick to it and sell it. >> And, you know, timing is everything, but so is, you know, Pat was a workaholic and had an amazing mind, but one of the things I learned from the book, and you said this, Pat Kenealy mentioned it, all American industrial and social revolutions have had a media company linked to them, Crane and automobiles, Penton and energy, McGraw-Hill and aerospace, Annenberg, of course, and TV, and in technology, it was IDG. >> Yeah, he, like I said earlier, he really was a key figure in the development of this industry and it was, you know, one of the key things about that, a lot publications that came and went made the mistake of being platform or, you know, vertical market specific. And if that market changed, and it was inevitably gonna change in high tech, you were done. He never, you know, he never married himself to some specific technology cycle. His idea was the audience was not gonna change, the audience was gonna have to roll with this, so, the company, IDG, would produce publications that got that, you know, Computerworld was actually a little bit late to the PC game, but eventually got into it and we tracked the different cycles, you know, things in tech move in sine waves, they come and go. And Pat never was, you know, flustered by that, he could handle any kind of changes from the mainframes down to the smartphone when it came. And so, that kind of flexibility, and ability to adjust to markets, really was unprecedented in that particular part of the market. >> One of the other lessons in the book, I call it 'nation-building,' and Pat shared with you that, look, that you shared, actually, with your readers, if you wanna do it right, you've gotta be on the ground, you've gotta be there. And the China story is one that I didn't know about how Pat kind of talked his way into China, tell us, give us a little summary of that story. >> Sure, I love that story because it's so Pat. It was 1978, Pat was in Tokyo on a business trip, one of his many business trips, and he was gonna be flying to Moscow for a trade show. And he got a flight that was gonna make a stopover in Beijing, which in those days was called Peking, and was not open to Americans. There were no US and China diplomatic relations then. But Pat had it in mind that he was going to get off that plane in Beijing and see what he could see. So that meant that he had to leave the flight when it landed in Beijing and talk his way through the customs as they were in China at the time with folks in the, wherever, the Quonset hut that served for the airport, speaking no English, and him speaking no Chinese, he somehow convinced these folks to give him a day pass, 'cause he kept saying to them, "I'm only in transit, it's okay!" (laughs) Like, he wasn't coming, you know, to spy on them on them or anything. So here's this massive American businessman in his dark suit, and he somehow gets into downtown Beijing, which at the time was mostly bicycles, very few cars, there were camels walking down the street, they'd come with traders from Mongolia. The people were still wearing the drab outfits from the Mao era, and Pat just spent the whole day wandering around the city, just soaking it in. He was that kind of a world traveler. He loved different cultures, mostly eastern cultures, and he would pop his head into bookstores. And what he saw were people just clamoring to get their hands on anything, a newspaper, a magazine, and it just, it didn't take long for the light bulb to go on and said, this is a market we need to play in. >> He was fascinated with China, I, you know, as an employee and a business P&L manager, I never understood it, I said, you know, the per capita spending on IT in China was like a dollar, you know? >> Right. >> And I remember my lunch with him, my 10-year lunch, he said, "Yeah, but, you know, there's gonna be a huge opportunity there, and yeah, I don't know how we're gonna get the money out, maybe we'll buy a bunch of tea and ship it over, but I'm not worried about that." And, of course, he meets Hugo Shong, which is a huge player in the book, and the home run out of China was, of course, the venture capital, which he started before there was even a stock market, really, to exit in China. >> Right, yeah. No, he was really a visionary, I mean, that word gets tossed around maybe more than it should, but Pat was a bonafide visionary and he saw things in China that were developing that others didn't see, including, for example, his own board, who told him he was crazy because in 1980, he went back to China without telling them and within days he had a meeting with the ministry of technology and set up a joint venture, cost IDG $250,000, and six months later, the first issue of China Computerworld was being published and within a couple of years it was the biggest publication in China. He said, told me at some point that $250,0000 investment turned into $85 million and when he got home, that first trip, the board was furious, they said, "How can you do business with the commies? You're gonna ruin our brand!" And Pat said, "Just, you know, stick with me on this one, you're gonna see." And the venture capital story was just an offshoot, he saw the opportunity in the early '90s, that venture in China could in fact be a huge market, why not help build it? And that's what he did. >> What's your take on, so, IDG sold to, basically, Chinese investors. >> Yeah. >> It's kind of bittersweet, but in the same time, it's symbolic given Pat's love for China and the Chinese people. There's been a little bit of criticism about that, I know that the US government required IDC to spin out its supercomputer division because of concerns there. I'm always teasing Michael Dow that at the next IDG board meeting, those Lenovo numbers, they're gonna look kinda law. (laughs) But what are your, what's your, what are your thoughts on that, in terms of, you know, people criticize China in terms of IP protections, etc. What would Pat have said to that, do you think? >> You know, Pat made 130 trips to China in his life, that's, we calculated at some point that just the air time in planes would have been something like three and a half to four years of his life on planes going to China and back. I think Pat would, today, acknowledge, as he did then, that China has issues, there's not, you can't be that naive. He got that. But he also understood that these were people, at the end of the day, who were thirsty and hungry for information and that they were gonna be a player in the world economy at some point, and that it was crucial for IDG to be at the forefront of that, not just play later, but let's get in early, let's lead the parade. And I think that, you know, some part of him would have been okay with the sale of the company to this conglomerate there, called China Oceanwide. Clearly controversial, I mean, but once Pat died, everyone knew that the company was never gonna be the same with the leader who had been at the helm for 50 years, it was gonna be a tough transition for whoever took over. And I think, you know, it's hard to say, certainly there's criticism of things going on with China. China's gonna be the hot topic page one of the New York Times almost every single day for a long time to come. I think Pat would have said, this was appropriate given my love of China, the kind of return on investment he got from China, I think he would have been okay with it. >> Yeah, and to invoke the Ben Franklin maxim, "Trading partners seldom wage war," and so, you know, I think Pat would have probably looked at it that way, but, huge home run, I mean, I think he was early on into Baidu and Alibaba and Tencent and amazing story. I wanna talk about decentralization because that was always something that was just on our minds as employees of IDG, it was keep the corporate staff lean, have a flat organization, if you had eight, 10, 12 direct reports, that was okay, Pat really meant it when he said, "You're the CEO of your own business!" Whether that business was, you know, IDC, big company, or a manager at IDC, where you might have, you know, done tens of millions of dollars, but you felt like a CEO, you were encouraged to try new things, you were encouraged to fail, and fail fast. Their arch nemesis of IDG was Ziff Davis, they were a command and control, sort of Bill Ziff, CMP to a certain extent was kind of the same way out of Manhasset, totally different philosophies and I think Pat never, ever even came close to wavering from that decentralization philosophy, did he? >> No, no, I mean, I think that the story that he told me that I found fascinating was, he didn't have an epiphany that decentralization would be the mechanism for success, it was more that he had started traveling, and when he'd come back to his office, the memos and requests and papers to sign were stacked up two feet high. And he realized that he was holding up the company because he wasn't there to do this and that at some point, he couldn't do it all, it was gonna be too big for that, and that's when the light came on and said this decentralization concept really makes sense for us, if we're gonna be an international company, which clearly was his mission from the beginning, we have to say the people on the ground in those markets are the people who are gonna make the decisions because we can't make 'em from Boston. And I talked to many people who, were, you know, did a trip to Europe, met the folks in London, met the folks in Munich, and they said to a person, you know, it was so ahead of its time, today it just seems obvious, but in the 1960s, early '70s, it was really not a, you know, a regular leadership tenet in most companies. The command and control that you talked about was the way that you did business. >> And, you know, they both worked, but, you know, from a cultural standpoint, clearly IDG and IDC have had staying power, and he had the three-quarter rule, you talked about it in your book, if you missed your numbers three quarters in a row, you were in trouble. >> Right. >> You know, one quarter, hey, let's talk, two quarters, we maybe make some changes, three quarters, you're gone. >> Right. >> And so, as I said, if you were makin' your numbers, you had wide latitude. One of the things you didn't have latitude on was I'll call it 'pay to play,' you know, crossing that line between editorial and advertising. And Pat would, I remember I was at a meeting one time, I'm sorry to tell these stories, but-- >> That's okay. (laughs) >> But we were at an offsite meeting at a woods meeting and, you know, they give you a exercise, go off and tell us what the customer wants. Bill Laberis, who's the editor-in-chief at Computerworld at the time, said, "Who's the customer?" And Pat said, "That's a great question! To the publisher, it's the advertiser. To you, Bill, and the editorial staff, it's the reader. And both are equally important." And Pat would never allow the editorial to be compromised by the advertiser. >> Yeah, no, he, there was a clear barrier between church and state in that company and he, you know, consistently backed editorial on that issue because, you know, keep in mind when we started then, and I was, you know, a journalist hoping to, you know, change the world, the trade press then was considered, like, a little below the mainstream business press. The trade press had a reputation for being a little too cozy with the advertisers, so, and Pat said early on, "We can't do that, because everything we have, our product is built, the brand is built on integrity. And if the reader doesn't believe that what we're reporting is actually true and factual and unbiased, we're gonna lose to the advertisers in the long run anyway." So he was clear that that had to be the case and time and again, there would be conflict that would come up, it was just, as you just described it, the publishers, the sales guys, they wanted to bring in money, and if it, you know, occasionally, hey, we could nudge the editor of this particular publication, "Take it a little bit easier on this vendor because they're gonna advertise big with us," Pat just would always back the editor and say, "That's not gonna happen." And it caused, you know, friction for sure, but he was unwavering in his support. >> Well, it's interesting because, you know, Macworld, I think, is an interesting case study because there were sort of some backroom dealings and Pat maneuvered to be able to get the Macworld, you know, brand, the license for that. >> Right. >> But it caused friction between Steve Jobs and the writers of Macworld, they would write something that Steve Jobs, who was a control freak, couldn't control! >> Yeah. (laughs) >> And he regretted giving IDG the license. >> Yeah, yeah, he once said that was the worst decision he ever made was to give the license to Pat to, you know, Macworlld was published on the day that Mac was introduced in 1984, that was the deal that they had and it was, what Jobs forgot was how important it was to the development of that product to have a whole magazine devoted to it on day one, and a really good magazine that, you know, a lot of people still lament the glory days of Macworld. But yeah, he was, he and Steve Jobs did not get along, and I think that almost says a lot more about Jobs because Pat pretty much got along with everybody. >> That church and state dynamic seems to be changing, across the industry, I mean, in tech journalism, there aren't any more tech journalists in the United States, I mean, I'm overstating that, but there are far fewer than there were when we were at IDG. You're seeing all kinds of publications and media companies struggling, you know, Kara Swisher, who's the greatest journalist, and Walt Mossberg, in the tech industry, try to make it, you know, on their own, and they couldn't. So, those lines are somewhat blurring, not that Kara Swisher is blurring those lines, she's, you know, I think, very, very solid in that regard, but it seems like the business model is changing. As an observer of the markets, what do you think's happening in the publishing world? >> Well, I, you know, as a journalist, I'm sort of aghast at what's goin' on these days, a lot of my, I've been around a long time, and seeing former colleagues who are no longer in journalism because the jobs just started drying up is, it's a scary prospect, you know, unlike being the enemy of the people, the first amendment is pretty important to the future of the democracy, so to see these, you know, cutbacks and newspapers going out of business is difficult. At the same time, the internet was inevitable and it was going to change that dynamic dramatically, so how does that play out? Well, the problem is, anybody can post anything they want on social media and call it news, and the challenge is to maintain some level of integrity in the kind of reporting that you do, and it's more important now than ever, so I think that, you know, somebody like Pat would be an important figure if he was still around, in trying to keep that going. >> Well, Facebook and Google have cut the heart out of, you know, a lot of the business models of many media companies, and you're seeing sort of a pendulum swing back to nonprofits, which, I understand, speaking of folks back in the mid to early 1900s, nonprofits were the way in which, you know, journalism got funded, you know, maybe it's billionaires buying things like the Washington Post that help fund it, but clearly the model's shifting and it's somewhat unclear, you know, what's happening there. I wanted to talk about another lesson, which, Pat was the head cheerleader. So, I remember, it was kind of just after we started, the Computerworld's 20th anniversary, and they hired the marching band and they walked Pat and Mary Dolaher walked from 5 Speen Street, you know, IDG headquarters, they walked to Computerworld, which was up Old, I guess Old Connecticut Path, or maybe it was-- >> It was actually on Route 30-- >> Route 30 at the time, yeah. And Pat was dressed up as the drum major and Mary as well, (laughs) and he would do crazy things like that, he'd jump out of a plane with IDG is number one again, he'd post a, you know, a flag in Antarctica, IDG is number one again! It was just a, it was an amazing dynamic that he had, always cheering people on. >> Yeah, he was, he was, when he called himself the CEO, the Chief Encouragement Officer, you mentioned earlier the Good News notes. Everyone who worked there, at some point received this 8x10" piece of paper with a rainbow logo on it and it said, "Good News!" And there was a personal note from Pat McGovern, out of the blue, totally unexpected, to thank you and congratulate you on some bit of work, whatever it was, if you were a reporter, some article you wrote, if you were a sales guy, a sale that you made, and people all over the world would get these from him and put them up in their cubicles because it was like a badge of honor to have them, and people, I still have 'em, (laughs) you know, in a folder somewhere. And he was just unrelenting in supporting the people who worked there, and it was, the impact of that is something you can't put a price tag on, it's just, it stays with people for all their lives, people who have left there and gone on to four or five different jobs always think fondly back to the days at IDG and having, knowing that the CEO had your back in that manner. >> The legend of, and the legacy of Patrick J. McGovern is not just in IDG and IDC, which you were interested in in your book, I mean, you weren't at IDC, I was, and I was started when I saw the sort of downturn and then now it's very, very successful company, you know, whatever, $3-400 million, throwin' off a lot of profits, just to decide, I worked for every single CEO at IDC with the exception of Pat McGovern, and now, Kirk Campbell, the current CEO, is moving on Crawford del Prete's moving into the role of president, it's just a matter of time before he gets CEO, so I will, and I hired Crawford-- >> Oh, you did? (laughs) >> So, I've worked for and/or hired every CEO of IDC except for Pat McGovern, so, but, the legacy goes beyond IDG and IDC, great brands. The McGovern Brain Institute, 350 million, is that right? >> That's right. >> He dedicated to studying, you know, the human brain, he and Lore, very much involved. >> Yup. >> Typical of Pat, he wasn't just, "Hey, here's the check," and disappear. He was goin' in, "Hey, I have some ideas"-- >> Oh yeah. >> Talk about that a little. >> Yeah, well, this was a guy who spent his whole life fascinated by the human brain and the impact technology would have on the human brain, so when he had enough money, he and Lore, in 2000, gave a $350 million gift to MIT to create the McGovern Institute for Brain Research. At the time, the largest academic gift ever given to any university. And, as you said, Pat wasn't a guy who was gonna write a check and leave and wave goodbye. Pat was involved from day one. He and Lore would come and sit in day-long seminars listening to researchers talk about about the most esoteric research going on, and he would take notes, and he wasn't a brain scientist, but he wanted to know more, and he would talk to researchers, he would send Good News notes to them, just like he did with IDG, and it had same impact. People said, "This guy is a serious supporter here, he's not just showin' up with a checkbook." Bob Desimone, who's the director of the Brain Institute, just marveled at this guy's energy level, that he would come in and for days, just sit there and listen and take it all in. And it just, it was an indicator of what kind of person he was, this insatiable curiosity to learn more and more about the world. And he wanted his legacy to be this intersection of technology and brain research, he felt that this institute could cure all sorts of brain-related diseases, Alzheimer's, Parkinson's, etc. And it would then just make a better future for mankind, and as corny as that might sound, that was really the motivator for Pat McGovern. >> Well, it's funny that you mention the word corny, 'cause a lot of people saw Pat as somewhat corny, but, as you got to know him, you're like, wow, he really means this, he loves his company, the company was his extended family. When Pat met his untimely demise, we held a crowd chat, crowdchat.net/thankspat, and there's a voting mechanism in there, and the number one vote was from Paul Gillen, who posted, "Leo Durocher said that nice guys finish last, Pat McGovern proved that wrong." >> Yeah. >> And I think that's very true and, again, awesome legacy. What number book is this for you? You've written a lot of books. >> This is number 13. >> 13, well, congratulations, lucky 13. >> Thank you. >> The book is Fast Forward-- >> Future Forward. >> I'm sorry, Future Forward! (laughs) Future Forward by Glenn Rifkin. Check out, there's a link in the YouTube down below, check that out and there's some additional information there. Glenn, congratulations on getting the book done, and thanks so much for-- >> Thank you for having me, this is great, really enjoyed it. It's always good to chat with another former IDGer who gets it. (laughs) >> Brought back a lot of memories, so, again, thanks for writing the book. All right, thanks for watching, everybody, we'll see you next time. This is Dave Vellante. You're watchin' theCube. (electronic music)

>> Live from Boston, it's theCUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back, everyone, to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Paul Gillin. We have two guests for this session, we have Rebecca Shockley, she is executive consultant and IBM Global Business Services, and Alfred Essa, vice president analytics and R&D at McGraw-Hill Education. Rebecca and Alfred, thanks so much for coming on theCUBE. >> Thanks for having us. >> So I'm going to start with you, Rebecca. You're giving a speech tomorrow about the AI ladder, I know you haven't finished writing it-- >> Shh, don't tell. >> You're giving a speech about the AI ladder, what is the AI ladder? >> So, when we think about artificial intelligence, or augmented intelligence, it's very pervasive, we're starting to see it a lot more in organizations. But the AI ladder basically says that you need to build on a foundation of data, so that data and information architecture's your first rung, and with that data, then you can do analytics, next rung, move into machine learning once you're getting more comfortable, and that opens up the whole world of AI. And part of what we're seeing is organizations trying to jump to the top of the ladder or scramble up the ladder really quickly and then realize they need to come back down and do some foundational work with their data. I've been doing data and analytics with IBM for 21 years, and data governance is never fun. It's hard. And people would just as soon go do something else than do data governance, data security, data stewardship. Especially as we're seeing more business-side use of data. When I started my career, data was very much an IT thing, right. And part of my early career was basically just getting IT and business to communicate in a way that they were saying the same things. Well now you have a lot more self-service analytics, and business leaders, business executives, making software decisions and various decisions that impact the data, without necessarily understanding the ripples that their decisions can have throughout the data infrastructure, because that's not their forte. >> So what's the outcome, what's the result of this? >> Well, you start to see organizations, it's similar to what we saw when organizations first started making data lakes, right? The whole concept of a data lake, very exciting, interesting, getting all the data in together, whether it's virtual or physical. What ended up happening is without proper governance, without proper measures in place, you ended up with a data swamp instead of a data lake. Things got very messy very quickly, and instead of creating opportunities you were essentially creating problems. And so what we're advising clients, is you really have to make sure that you're focused on taking care of that first rung, right? Your data architecture, your information architecture, and treating the data with the respect as a strategic asset that it is, and making sure that you're dealing with that data in a proper manner, right? So, basically telling them, yes we understand that's fun up there, but come back down and deal with your foundation. And for a lot of organizations, they've never really stepped into data governance, because again, data isn't what they think makes the company run, right? So banks are bankers, not data people, but at the same time, how do you run a bank without data? >> Well exactly. And I want to bring you into this conversation, Alfred, as McGraw-Hill, a company that is climbing the ladder, in a more steady fashion. What's your approach? How do you think about bringing your teams of data scientists together to work to improve the company's bottom line, to enhance the customer experience? >> First I'd sort of like to start with laying some of the context of what we do. McGraw-Hill Education has been traditionally a textbook publisher, we've been around for over a hundred years, I started with the company over a hundred years ago. (all laughing) >> You've aged well. >> But we no longer think of ourselves as a textbook publisher. We're in the midst of a massive digital transformation. We started that journey over five years ago. So we think of ourselves as a software company. We're trying to create intelligent software based on smart data. But it's not just about software and AI and data, when it comes to education it's a tale of two cities. This is not just the U.S., but internationally. Used to be, we were born, went to school, got a job, raised a family, retired, and then we die. Well now, education is not episodic. People need to be educated, it's life-long learning. It's survival, but also flourishing. So that's created a massive problem and a challenge. It's a tale of two cities, by that I mean there's an incredible opportunity to apply technology, AI, we see a lot of potential in the new technologies. In that sense, it's the best of times. The worst of times is, we're faced with massive problems. There's a lot of inequity, we need to educate a people who have largely been neglected. That's the context. So I think in now answering your question about data science teams, first and foremost, we like to get people on the teams excited about the mission. It's like, what are we trying to achieve? What's the problem that we're trying to achieve? And I think the best employees, including data scientists, they like solving hard problems. And so, first thing that we try to do is, it's not what skills you have, but do you like solving really, really hard problems. And then taking it next step, I think the exciting thing about data science is it's an interdisciplinary field. It's not one skill, but you need to bring together a combination of skills. And then you also have to excel and have the ability to work in teams. >> You said that the AI has potential to improve the education process. Now, people have only so much capacity to learn, how can AI accelerate that process? >> Yeah, so if we stand back a little bit and look at the traditional model of education, there's nothing wrong with it but it was successful for a certain period of years, and it works for some people. But now the need for education is universal, and life long. So what our basic model, current model of education is lecture mode and testing. Now from a learning perspective, learning science perspective, all the research indicates that that doesn't work. It might work for a small group of people, but it's not universally applicable. What we're trying to do, and this is the promise of AI, it's not AI alone, but I think this is a big part of AI. What we can do is begin to customize and tailor the education to each individual's specific needs. And just to give you one quick example of that, different students come in with different levels of prior knowledge. Not everyone comes into a class, or a learning experience, knowing the same things. So what we can do with AI is determine, very, very precisely, just think of it as a brain scan, of what is it each student need to know at every given point in time, and then based on that we can determine also, this is where the models and algorithms are, what are you ready to learn next. And what you might be ready to learn next and what I might be ready to learn next is going to be very different. So our algorithms also help route delivery of information and knowledge at the right time to the right person, and so on. >> I mean, you're talking about these massive social challenges. Education as solving global inequity, and not every company has maybe such a high-minded purpose. But does it take that kind of mission, that kind of purpose, to unite employees? Both of you, I'm interested in your perspectives here. >> I don't think it takes, you know, a mission of solving global education. I do firmly agree with what Al said about people need a mission, they need to understand the outcome, and helping organizations see that outcome as being possible, gives them that rally point. So I don't disagree, I think everybody needs a mission to work towards but it doesn't have to be solving-- >> You want to extract that mission to a higher level, then. >> Exactly. >> Making the world a better place. >> Exactly, or at least your little corner of the world. Again what we're seeing, the difficulty is helping business leaders or consumers or whomever understand how data plays into that. You may have a goal of, we want better relationship with our customer, right? And at least folks of my age think that's a personal one-on-one kind of thing. Understanding who you are, I can find that much more quickly by looking at all your past transactions, and all of your past behaviors, and whether you clicked this or that. And you should expect that I remember things from one conversation to the next. And helping people understand that, you know, helping the folks who are doing the work, understand that the outcome will be that we can actually treat our customers the way that you want to be treated as a person, gives them that sense of purpose, and helps them connect the dots better. >> One of the big challenges that we hear CDOs face is getting buy-in, and what you're proposing about this new model really appending the old sage on the stage model, I mean, is there a lot of pushback? Is it difficult to get the buy-in and all stakeholders to be on the same page? >> Yeah, it is, I think it's doubly difficult. The way I think about it is, it's like a shift change in hockey, where you have one shift that's on the ice and another one that's about to come on the ice, that's a period of maximum vulnerability. That's where a lot of goals are scored, people get upset, start fighting. (all laughing) That's hockey. >> That's what you do. >> Organizations and companies are faced with the same challenge. It's not that they're resisting change. Many companies have been successful with one business model, while they're trying to bring in a new business model. Now you can't jettison the old business model because often that's paying the bills. That's the source of the revenue. So the real challenge is how are you going to balance out these two things at the same time? So that's doubly difficult, right. >> I want to ask you quickly, 'cause we have to end here, but there's a terrible shortage of cybersecurity professionals, data science professionals, the universities are simply not able to keep up with demand. Do you see the potential for AI to step in and fill that role? >> I don't think technology by itself will fill that role. I think there is a deficit of talented people. I think what's going to help fill that is getting people excited about really large problems that can be solved with this technology. I think, actually I think the talent is there, what I see is, I think we need to do a better job of bringing more women, other diverse groups, into the mix. There are a lot of barriers in diversity in bringing talented people. I think they're out there, I think we could do a much better job with that. >> Recruiting them, right. Alfred, Rebecca, thanks so much for coming on theCUBE, it was a pleasure. >> Thank you so much for having us. >> I'm Rebecca Knight, for Paul Gillin, we will have more from theCUBE's live coverage of the IBM CDO Summit here in Boston coming up in just a little bit.

>> Narrator: Live from Boston, Massachusetts, this is theCUBE, covering Spark Summit East 2017. Brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> Welcome back to day two in Boston where it is snowing sideways here. But we're all here at Spark Summit #SparkSummit, Spark Summit East, this is theCUBE. Sound like an Anglo flagship product. We go out to the event, we program for our audience, we extract the signal from the noise. I'm here with George Gilbert, day two, at Spark Summit, George. We're seeing the evolution of so-called big data. Spark was a key part of that. Designed to really both simplify and speed up big data oriented transactions and really help fulfill the dream of big data, which is to be able to affect outcomes in near real time. A lot of those outcomes, of course, are related to ad tech and selling and retail oriented use cases, but we're hearing more and more around education and deep learning and affecting consumers and human life in different ways. We're now 10 years in to the whole big data trend, what's your take, George, on what's going on here? >> Even if we started off with ad tech, which is what most of the big internet companies did, we always start off in any new paradigm with one application that kind of defines that era. And then we copy and extend that pattern. For me, on the rethinking your business the a McGraw-Hill interview we did yesterday was the most amazing thing because they took, what they had was a textbook business for their education unit and they're re-thinking the business, as in what does it mean to be an education company? And they take cognitive science about how people learn and then they take essentially digital assets and help people on a curriculum, not the centuries old sort of teacher, lecture, homework kind of thing, but individualized education where the patterns of reinforcement are consistent with how each student learns. And it's not just a break up the lecture into little bits, it's more of a how do you learn most effectively? How do you internalize information? >> I think that is a great example, George, and there are many, many examples of companies that are transforming digitally. Years and years ago people started to think about okay, how can I instrument or digitize certain assets that I have for certain physical assets? I remember a story when we did the MIT event in London with Andy MacAfee and Eric Binyolsen, they were giving the example of McCormick Spice, the spice company, who digitized by turning what they were doing into recipes and driving demand for their product and actually building new communities. That was kind of an interesting example, but sort of mundane. The McGraw-Hill education is massive. Their chief data scientist, chief data scientist? I don't know, the head of engineering, I guess, is who he was. >> VP of Analytics and Data Science. >> VP of Analytics and Data Science, yeah. He spoke today and got a big round of applause when he sort of led off about the importance of education at the keynote. He's right on, and I think that's a classic example of a company that was built around printing presses and distributing dead trees that is completely transformed and it's quite successful. Over the last only two years brought in a new CEO. So that's good, but let's bring it back to Spark specifically. When Spark first came out, George, you were very enthusiastic. You're technical, you love the deep tech. And you saw the potential for Spark to really address some of the problems that we faced with Hadoop, particularly the complexity, the batch orientation. Even some of the costs -- >> The hidden costs. >> Associated with that, those hidden costs. So you were very enthusiastic, in your mind, has Spark lived up to your initial expectations? >> That's a really good question, and I guess techies like me are often a little more enthusiastic than the current maturity of the technology. Spark doesn't replace Hadoop, but it carves out a big chunk of what Hadoop would do. Spark doesn't address storage, and it doesn't really have any sort of management bits. So you could sort of hollow out Hadoop and put Spark in. But it's still got a little ways to go in terms of becoming really, really fast to respond in near real time. Not just human real time, but like machine real time. It doesn't work sort of deeply with databases yet. It's still teething, and sort of every release, which is approximately every 12 to 18 months, it gets broader in its applicability. So there's no question sort of everyone is piling on, which means that'll help it mature faster. >> When Hadoop was first sort of introduced to the early masses, not the main stream masses, but the early masses, the profundity of Hadoop was that you could leave data in place and bring compute to the data. And people got very excited about that because they knew there was so much data and you just couldn't keep moving it around. But the early insiders of Hadoop, I remember, they would come to theCUBE and everybody was, of course, enthusiastic and lot of cheerleading going on. But in the hallway conversations with Hadoop, with the real insiders you would have conversations about, people are going to realize how much this sucks some day and how hard this is and it's going to hit a wall. Some of the cheerleaders would say, no way, Hadoop forever. Now you've started to see that in practice. And the number of real hardcore transformations as a result of Hadoop in and of itself have been quite limited. The same is true for virtually, most anyway, technology, not any technology. I'd say the smartphone was pretty transformative in and of itself, but nonetheless, we are seeing that sort of progression and we're starting to see a lot of the same use cases that you hear about like fraud detection and retargeting as coming up again. I think what we're seeing is those are improving. Like fraud detection, I talked yesterday about it used to be six months before you'd even detect fraud, if you ever did. Now it's minutes or seconds. But you still get a lot of false positives. So we're going to just keep turning that crank. Mike Gualtieri today talked about the efficacy of today's AI and he gave some examples of Google, he showed a plane crash and he said, it said plane and it accurately identified that, but also the API said it could be wind sports or something like that. So you can see it's still not there yet. At the same time, you see things like Siri and Amazon Alexa getting better and better and better. So my question to you, kind of long-winded here, is, is that what Spark is all about? Just making better the initial initiatives around big data, or is it more transformative than that? >> Interesting question, and I would come at it with a couple different answers. Spark was a reaction to you can't, you can't have multiple different engines to attack all the different data problems because you would do a part of the analysis here, push it into a disk, pull it out of a disk to another engine, all of that would take too long or be too complex a pipeline to go from end to the other. Spark was like, we'll do it all in our unified engine and you can come at it from SQL, you can come at it from streaming, so it's all in one place. That changes the sophistication of what you can do, the simplicity, and therefore how many people can access it and apply it to these problems. And the fact that it's so much faster means you can attack a qualitatively different setup of problems. >> I think as well it really underscores the importance of Open Source and the ability of the Open Source community to launch projects that both stick and can attract serious investment. Not only with IBM, but that's a good example. But entire ecosystems that collectively can really move the needle. Big day today, George, we've got a number of guests. We'll give you the last word at the open. >> Okay, what I thought, this is going to sound a little bit sort of abstract, but a couple of two takeaways from some of our most technical speakers yesterday. One was with Juan Stoyka who sort of co-headed the lab that was the genesis of Spark at Berkeley. >> AMPLabs. >> The AMPLab at Berkeley. >> And now Rise Labs. >> And then also with the IBM Chief Data Officer for the Analytics Unit. >> Seth Filbrun. >> Filbrun, yes. When we look at what's the core value add ultimately, it's not these infrastructure analytic frameworks and that sort of thing, it's the machine learning model in its flywheel feedback state where it's getting trained and re-trained on the data that comes in from the app and then as you continually improve it, that was the whole rationale for Data Links, but not with models. It was put all the data there because you're going to ask questions you couldn't anticipate. So here it's collect all the data from the app because you're going to improve the model in ways you didn't expect. And that beating heart, that living model that's always getting better, that's the core value add. And that's going to belong to end customers and to application companies. >> One of the speakers today, AI kind of invented in the 50s, a lot of excitement in the 70s, kind of died in the 80s and it's coming back. It's almost like it's being reborn. And it's still in its infant stages, but the potential is enormous. All right, George, that's a wrap for the open. Big day today, keep it right there, everybody. We got a number of guests today, and as well, don't forget, at the end of the day today George and I will be introducing part two of our WikiBon Big Data forecast. This is where we'll release a lot of our numbers and George will give a first look at that. So keep it right there everybody, this is theCUBE. We're live from Spark Summit East, #SparkSummit. We'll be right back. (techno music)