>>from around the globe. It's the >>cube with >>Digital coverage of IBM think 2021 brought to you by IBM. Welcome back to the cube coverage of IBM Think 2021. I'm john for the host of the cube here for virtual event Cameron Siva who's here with corporate vice president with a M. D. Uh CVP and business development. Great to see you. Thanks for coming on the cube. >>Nice to be. It's an honor to be here. >>You know, love A. M. D. Love the growth, loved the processors. Epic 7000 and three series was just launched its out in the field. Give us a quick overview of the of the of the processor, how it's doing and how it's going to help us in the data center on the edge >>for sure. No this is uh this is an exciting time for A. M. D. This is probably one of the most exciting times uh to be honest and in my 2020 plus years of uh working in sex industry, I think I've never been this excited about a new product as I am about the the third generation Epic processor that we just announced. Um So the Epic 7003, what we're calling it a serious processor. It's just a fantastic product. We not only have the fastest server processor in the world with the AMG Epic 7763 but we also have the fastest CPU core so that the process of being the complete package, the complete socket and then we also the fastest poor in the world with the the Epic um 72 F three for frequency. So that one runs run super fast on each core. And then we also have 64 cores in the CPU. So it's it's addressing both kind of what we call scale up and scale out. So it's overall overall just just an enormous, enormous product line that that I think um you know, we'll be we'll be amazing within within IBM IBM cloud. Um The processor itself includes 256 megabytes of L three cache. Um you know, cash is super important for a variety of workloads in the large cat size. We have shown our we've seen scale in particular cloud applications, but across the board, um you know, database, uh java whole sorts of things. This processor is also based on the Zen three core, which is basically 19% more instructions per cycle relative to ours, N two. So that was the prior generation, the second generation Epic Force, which is called Rome. So this this new CPU is actually quite a bit more capable. It runs also at a higher frequency with both the 64 4 and the frequency optimized device. Um and finally, we have um we call all in features so rather than kind of segment our product line and charge you for every little, you know, little thing you turn on or off. We actually have all in features includes, you know, really importantly security, which is becoming a big, big team and something that we're partnering with IBM very closely on um and then also things like 628 lanes of pc I E gen four, um are your faces that grew up to four terabytes so you can do these big large uh large um in memory databases, the Pc I interfaces gives you lots and lots of storage capability. So all in all super products um and we're super excited to be working with IBM honest. >>Well, let's get into some of the details on this impact because obviously it's not just one place where these processes are gonna live. You're seeing a distributed surface area core to edge um cloud and hybrid is now in play. It's pretty much standard now. Multi cloud on the horizon. Company's gonna start realizing, okay, I gotta put this to work and I want to get more insights out of the data and civilian applications that are evolving on this. But you guys have seen some growth in the cloud with the Epic processors, what can customers expect and why our cloud providers choosing Epic processors, >>you know, a big part of this is actually the fact that I that am d um delivers upon our roadmap. So we we kind of do what we say and say what we do and we delivered on time. Um so we actually announced I think was back in august of 2019, their second generation. That big part and then now in March, we are now in the third generation, very much on schedule, very much um intent, expectations and meeting the performance that we had told the industry and told our customers that we're going to meet back then. So it's a really super important pieces that our customers are now learning to expect performance, jenin, jenin and on time from A. M. D, which is, which is uh, I think really a big part of our success. The second thing is, I think, you know, we are, we are a leader in terms of the core density that we provide and cloud in particular really values high density. So the 64 cores is absolutely unique today in the industry and that it has the ability to be offered both in uh, bare metal, um, as we have been deployed in uh, in IBM Club and also in virtualized type environment. So it has that ability to spend a lot of different use cases. Um And you can, you know, you can run each core really fast, But then also have the scale out and then be able to take advantage of all 64 cores. Each core has two threads up to 128 threads per socket. It's a super powerful uh CPU and it has a lot of value for um for the with a cloud cloud provider, they're actually about over 400 total instances by the way of A. M. D. Processors out there. And that's all the flavors, of course, not just that they're generation, but still it's it's starting to really proliferate. We're trying to see uh M d I think all across the cloud, >>more cores, more threads all goodness. I gotta ask you, you know, I interviewed Arvin the Ceo of IBM before he was Ceo at a conference and you know, he's always been I know him, he's always loved cloud, right? So, um but he sees a little bit differently than just being like copying the clouds. He sees it as we see it unfolding here. I think Hybrid. Um and so I can almost see the playbook evolving. You know, Red has an operating system. Cloud and Edge is a distributed system. It's got that vibe of a system architecture, you got processors everywhere. Could you give us a sense of the over an overview of the work you're doing with IBM Cloud and what a M. D s role is there? And I'm curious could you share for the folks watching too? >>For sure. For sure. By the way, IBM cloud is a fantastic partner to work with. So, so, first off you talked about about the hybrid, hybrid cloud is a really important thing for us and that's um that's an area that we are definitely focused in on, uh but in terms of our specific joint partnerships and we did an announcement last year, so it's it's it's somewhat public, but we are working together on ai where IBM is a is an undisputed leader with Watson and some of the technologies that you guys bring there. So we're bringing together, you know, it's kind of this real hard work goodness with IBM s progress and know how on the AI side. In addition, IBM is also known for um you know, really enterprise grade, yeah, security and working with some of the key sectors that need and value, reliability, security, availability um in those areas. Uh and so I think that partnership, we have quite a bit of uh quite a strong relationship and partnership around working together on security and doing confidential computer. >>Tell us more about the confidential computing. This is a joint development agreement, is a joint venture joint development agreement. Give us more detail on this. Tell us more about this announcement with IBM cloud, an AMG confidential computing. >>So that's right. So so what uh, you know, there's some key pillars to this. One of us is being able to to work together, define open standards, open architecture. Um so jointly with an IBM and also pulling in some of the assets in terms of red hat to be able to work together and pull together a confidential computer that can so some some key ideas here, we can work with, work within a hybrid cloud. We can work within the IBM cloud and to be able to provide you with, provide, provide our joint customers are and customers with with with unprecedented security and reliability uh in the cloud, >>what's the future of processors? I mean, what should people think when they expect to see innovation? Um Certainly data centers are evolving with core core features to work with hybrid operating model in the cloud. People are getting that edge relationship basically the data centers a large edge, but now you've got the other edges, we got industrial edges, you got consumers, people wearables. You're gonna have more and more devices big and small. Um What's the what's the road map look like? How do you describe the future of a. M. D. In in the IBM world? >>I think I think R I B M M. D partnership is bright, future is bright for sure, and I think there's there's a lot of key pieces there. Uh you know, I think IBM brings a lot of value in terms of being able to take on those up earlier, upper uh layers of software and that and the full stack um so IBM strength has really been, you know, as a systems company and as a software company. Right? So combining that with the Andes silicon, uh divide and see few devices really really is is it's a great combination. I see, you know, I see um growth in uh you know, obviously in in deploying kind of this, this scale out model where we have these very large uh large core count cpus, I see that trend continuing for sure. Uh you know, I think that that is gonna that is sort of the way of the future that you want cloud data applications that can scale across multi multiple cores within the socket and then across clusters of Cpus with within the data center. Um and IBM is in a really good position to take advantage of that to go to to to drive that within the cloud. That income combination with IBM s presence on prem. Uh and so that's that's where the hybrid hybrid cloud value proposition comes in. Um and so we actually see ourselves uh you know, playing in both sides. So we do have a very strong presence now and increasingly so on premises as well. And we we partner we were very interested in working with IBM on the on on premises uh with some of some of the key customers and then offering that hybrid connectivity onto, onto the the IBM cloud as >>well. I B M and M. D. Great partnership, great for clarifying and and sharing that insight come. I appreciate it. Thanks for for coming on the cube. I do want to ask you while I got you here. Um kind of a curveball question if you don't mind. You know, as you see hybrid cloud developing one of the big trends is this ecosystem play, right? So you're seeing connections between IBM and their and their partners being much more integrated. So cloud has been a big KPI kind of model. You connect people through a. P. I. S. There's a big trend that we're seeing and we're seeing this really in our reporting on silicon angle the rise of a cloud service provider within these ecosystems where hey, I could build on top of IBM cloud and build a great business. Um and as I do that, I might want to look at an architecture like an AMG, how does that fit into to your view as a doing business development over at AMG because because people are building on top of these ecosystems are building their own clouds on top of clouds, just seeing data cloud, just seeing these kinds of clouds, specialty clouds. So we could have a cute cloud on on top of IBM maybe someday. So, so I might want to build out a whole, I might be a cloud, so that's more processors needed for you. So how do you see this enablement? Because IBM is going to want to do that, it's kind of like, I'm kind of connecting the dots here in real time, but what's your, what's your take on that? What's your reaction? >>I think, I think that's I think that's right and I think m d isn't it isn't a pretty good position with IBM to be able to to enable that. Um we do have some very significant OsD partnerships, a lot of which that are leveraged into IBM um such as red hat of course, but also like VM ware and Nutanix. Um this provide these OS V partners provide kind of the base level infrastructure that we can then build upon and then have that have that A P. I. And be able to build, build um uh the the multi cloud environments that you're talking about. Um and I think that I think that's right, I think that is that is one of the uh you know, kind of future trends that that we will see uh you know, services that are offered on top of IBM cloud that take advantage of the the capabilities of the platform that come with it. Um and you know, the bare metal offerings that that IBM offer on their cloud is also quite unique um and hyper very performance. Um and so this actually gives um I think uh the the kind of uh I've been called a meta cloud, that unique ability to kind of go in and take advantage of the M. D. Hardware at a performance level and at a um uh to take advantage of that infrastructure better than they could in another crowd environments. I think that's that's that's actually very key and very uh one of the, one of the features of the IBM problems that differentiates it >>so much headroom there corns really appreciate you sharing that. I think it's a great opportunity. As I say, if you're you want to build and compete. Finally, there's no with the white space, with no competition or be better than the competition. So as they say in business, thank you for coming on sharing. Great, great future ahead for all builders out there. Thanks for coming on the cube. >>Thanks thank you very >>much. Okay. IBM think cube coverage here. I'm john for your host. Thanks for watching. Mm mm

>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Okay. Welcome back, everyone. He live in San Francisco. Here on Mosconi St for the cubes. Exclusive coverage of IBM. Think twenty nineteen. I'm Jeffrey David Long. Four days of coverage bringing on all the action talking. The top executives, entrepreneurs, ecosystem partners and everyone who can bring the signal from the noise here on the Q and excuses. Rob Thomas, general manager, IBM Data and a I with an IBM Cube Alumni. Great to see you again. >> Great. There you go. >> You read a >> book yet? This year we've written ten books on a data. Your general manager. There's >> too much work. Not enough time >> for that's. Good sign. It means you're working hard. Okay. Give us give us the data here because a I anywhere in the center of the announcements we have a story up on. Slick earnings have been reported on CNBC. John Ford was here earlier talking to Ginny. This is a course centerpiece of it. Aye, aye. On any cloud. This highlights the data conversation you've been part of. Now, I think what seven years seems like more. But this is now happening. Give us your thoughts. >> Go back to basics. I've shared this with you before. There's no AI without IA, meaning you need an information architecture to support what you want to do in AI. We started looking into that. Our thesis became so clients are buying into that idea. The problem is their data is everywhere onpremise, private cloud, multiple public clouds. So our thesis became very simple. If we can bring AI to the data, it will make Watson the leading AI platform. So what we announced wtih Watson Anywhere is you could now have it wherever your data is public, private, any public cloud, build the models, run them where you want. I think it's gonna be amazing >> data everywhere and anywhere. So containers are big role in This is a little bit of a deb ops. The world you've been living in convergence of data cloud. How does that set for clients up? What are they need to know about this announcement? Was the impact of them if any >> way that we enable Multi Cloud and Watson anywhere is through IBM cloud private for data? That's our data Micro services architectural writing on Cooper Netease that gives you the portability so that it can run anywhere because, in addition Teo, I'd say, Aye, aye, ambitions. The other big client ambition is around how we modernize to cloud native architectures. Mohr compose herbal services, so the combination gets delivered. Is part of this. >> So this notion of you can't have a eye without a it's It's obviously a great tagline. You use it a lot, but it's super important because there's a gap between those who sort of have a I chops and those who don't. And if I understand what you're doing is you're closing that gap by allowing you to bring you call that a eye to the data is it's sort of a silo buster in regard. Er yeah, >> the model we use. I called the eye ladder. So they give it as all the levels of sophistication an organization needs to think about. From how you collect data, how you organize data, analyze data and then infused data with a I. That's kind of the model that we used to talk about. Talk to clients about that. What we're able to do here is same. You don't have to move your data. The biggest problem Modi projects is the first task is OK move a bunch of data that takes a lot of time. That takes a lot of money. We say you don't need to do that. Leave your data wherever it is. With Cloud private for data, we can virtualized data from any source. That's kind of the ah ha moment people have when they see that. So we're making that piece really >> easy. What's the impact this year and IBM? Think to the part product portfolio. You You had data products in the past. Now you got a eye products. Any changes? How should people live in the latter schism? A kind of a rubric or a view of where they fit into it? But what's up with the products and he changes? People should know about? >> Well, we've brought together the analytics and I units and IBM into this new organization we call Dayton ay, ay, that's a reflection of us. Seen that as two sides of the same coin. I really couldn't really keep them separate. We've really simplified how we're going to market with the Watson products. It's about how you build run Manager II watching studio Watson Machine Learning Watson Open scale. That's for clients that want to build their own. Aye, aye. For clients that wants something out of the box. They want an application. We've got Watson assistant for customer service. Watson Discovery, Watson Health Outset. So we've made it really easy to consume Watson. Whether you want to build your own or you want an application designed for the line of business and then up and down the data, stack a bunch of different announcements. We're bringing out big sequel on Cloudera as part of our evolving partnership with the new Cloudera Horn Works entity. Virtual Data Pipeline is a partnership that we've built with active fio, so we're doing things at all layers of the last. >> You're simplifying the consumption from a client, your customer perspective. It's all data. It's all Watson's, the umbrella for brand for everything underneath that from a tizzy, right? >> Yeah, Watson is the Aye, aye, brand. It is a technology that's having an impact. We have amazing clients on stage with this this week talking about, Hey, Eyes No longer. I'd like to say I was not magic. It's no longer this mystical thing. We have clients that are getting real outcomes. Who they II today we've got Rollback of Scotland talking about how they've automated and augmented forty percent of their customer service with watching the system. So we've got great clients talking about other using >> I today. You seen any patterns, rob in terms of those customers you mentioned, some customers want to do their own. Aye, aye. Some customers wanted out of the box. What? The patterns that you're seeing in terms of who wants to do their own. Aye. Aye. Why do they want to do their own, eh? I do. They get some kind of competitive advantage. So they have additional skill sets that they need. >> It's a >> It's a maker's mark. It is how I would describe it. There's a lot of people that want to make their own and try their own. Ugh. I think most organizations, they're gonna end up with hundreds of different tools for building for running. This is why we introduced Watson Open Scale at the end of last year. That's How would you manage all of your A II environments? What did they come from? IBM or not? Because you got the and the organization has to have this manageable. Understandable, regardless of which tool they're using. I would say the biggest impact that we see is when we pick a customer problem. That is widespread, and the number one right now is customer service. Every organization, regardless of industry, wants to do a better job of serving clients. That's why Watson assistant is taking off >> this's. Where? Data The value of real time data. Historical data kind of horizontally. Scaleable data, not silo data. We've talked us in the past. How important is to date a quality piece of this? Because you have real time and you have a historical date and everything in between that you had to bring to bear at low ladened psi applications. Now we're gonna have data embedded in them as a feature. Right. How does this change? The workloads? The makeup of you? Major customer services? One piece, the low hanging fruit. I get that. But this is a key thing. The data architecture more than anything, isn't it? >> It is. Now remember, there's there's two rungs at the bottom of the ladder on data collection. We have to build a collect data in any form in any type. That's why you've seen us do relationships with Mongo. D B. Were they ship? Obviously with Claude Era? We've got her own data warehouse, so we integrate all of that through our sequel engine. That thing gets to your point around. Are you gonna organize the data? How are you going to curate it? We've got data catalogue. Every client will have a data catalogue for many dollar data across. Clouds were now doing automated metadata creation using a I and machine learning So the organization peace. Once you've collected it than the organization, peace become most important. Certainly, if you want to get to self service analytics, you want to make data available to data scientists around the organization. You have to have those governance pieces. >> Talk about the ecosystem. One of the things that's been impressive IBM of the years is your partnerships. You've done good partners. Partnership of relationships now in an ecosystem is a lot of building blocks. There's more complexity requires software to distract him away. We get that. What's opportunities for you to create new relationships? Where are the upper opportunities for someone a developer or accompanied to engage with you guys? Where's the white spaces? Where is someone? Take advantage of your momentum and you're you're a vision. >> I am dying for partners that air doing domain specific industry specific applications to come have them run on IBM cloud private for data, which unleashes all the data they need to be a valuable application. We've already got a few of those data mirrors. One sensing is another one that air running now as industry applications on top of IBM Club private for data. I'd like to have a thousand of these. So all comers there. We announced a partnership with Red Hat back in May. Eventually, that became more than just a partnership. But that was about enabling Cloud Private, for data on red had open shift, So we're partnered at all layers of the stack. But the greatest customer need is give me an industry solution, leveraging the best of my data. That's why I'm really looking for Eyes V. Partners to run on Ivan clubs. >> What's your pitch to those guys? Why, why I should be going. >> There is no other data platform that will connect to all your data sources, whether they're on eight of us as your Google Cloud on premise. So if you believe data is important to your application. There's simply no better place to run than IBM. Claude Private for data >> in terms of functionality, breath o r. Everything >> well, integrating with all your data. Normally they have to have the application in five different places. We integrate with all the data we build the data catalogue. So the data's organized. So the ingestion of the data becomes very easy for the Iast V. And by the way, thirdly, IBM has got a pretty good reach. Globally, one hundred seventy countries, business partners, resellers all over the world, sales people all over the world. We will help you get your product to market. That's a pretty good value >> today. We talk about this in the Cube all the time. When the cloud came, one of the best things about the cloud wasn't allowed. People to put applications go there really quickly. Stand them up. Startups did that. But now, in this domain world of of data with the clouds scale, I think you're right. I think domain X expertise is the top of the stack where you need specially special ism expertise and you don't build the bottom half out. What you're getting at is of Europe. If you know how to create innovation in the business model, you could come in and innovate quickly >> and vertical APS don't scale enough for me. So that's why focus on horizontal things like customer service. But if you go talk to a bank, sometimes customer service is not in office. I want to do something in loan origination or you're in insurance company. I want to use their own underwriting those air, the solutions that will get a lot of value out of running on an integrated data start >> a thousand flowers. Bloom is kind of ecosystem opportunity. Looking forward to checking in on that. Thoughts on on gaps. For that you guys want to make you want to do em in a on or areas that you think you want to double down on. That might need some help, either organic innovation or emanate what areas you looking at. Can you share a little bit of direction on that? >> We have, >> ah, a unique benefit. And IBM because we have IBM research. One of their big announcement this week is what we call Auto Way I, which is basically automating the process of feature engineering algorithm selection, bringing that into Watson Studio and Watson Machine learning. I am spending most of my time figure out howto I continue to bring great technology out of IBM research and put in the hand of clients through our products. You guys solve the debaters stuff yesterday. We're just getting started with that. We've got some pretty exciting organic innovation happen in IBM. >> It's awesome. Great news for startups. Final question for you. For the folks watching who aren't here in San Francisco, what's the big story here? And IBM think here in San Francisco. Big event closing down the streets here in Howard Street. It's huge. What's the big story? What's the most important things happening? >> The most important thing to me and the customer stories >> here >> are unbelievable. I think we've gotten past this point of a eyes, some idea for the future we have. Hundreds of clients were talking about how they did an A I project, and here's the outcome they got. It's really encouraging to see what I encourage. All clients, though, is so build your strategy off of one big guy. Project company should be doing hundreds of Aye, aye projects. So in twenty nineteen do one hundred projects. Half of them will probably fail. That's okay. The one's that work will more than make up for the ones that don't work. So we're really encouraging mass experimentation. And I think the clients that air here are, you know, creating an aspirational thing for things >> just anecdotally you mentioned earlier. Customer service is a low hanging fruit. Other use cases that are great low hanging fruit opportunities for a >> data discovery data curation these air really hard manual task. Today you can start to automate some of that. That has a really big impact. >> Rob Thomas, general manager of the data and a I groupie with an IBM now part of a bigger portfolio. Watson Rob. Great to see you conventionally on all your success. But following you from the beginning. Great momentum on the right way. Thanks. Gradually. More cute coverage here. Live in San Francisco from Mosconi North. I'm John for Dave A lot. They stay with us for more coverage after this short break

>> Live from Times Square in New York City, it's theCUBE, covering IBM's Change the Game, Winning with AI, brought to you by IBM. >> Hi everybody, welcome back to theCUBE's special presentation. We're here at the Western Hotel and the theater district covering IBM's announcements. They've got an analyst meeting today, partner event. They've got a big event tonight. IBM.com/winwithAI, go to that website, if you're in town register. You can watch the webcast online. You'll see this very cool play of Vince Lombardy, one of his famous plays. It's kind of a power sweep right which is a great way to talk about sort of winning and with X's and O's. So anyway, Daniel Hernandez is here the vice president of IBM analytics, long time Cube along. It's great to see you again, thanks for coming on. >> My pleasure Dave. >> So we've talked a number of times. We talked earlier this year. Give us the update on momentum in your business. You guys are doing really well, we see this in the quadrants and the waves, but your perspective. >> Data science and AI, so when we last talked we were just introducing something called IBM Club Private for data. The basic idea is anybody that wants to do data science, data engineering or building apps with data anywhere, we're going to give them a single integrated platform to get that done. It's going to be the most efficient, best way to do those jobs to be done. We introduced it, it's been a resounding success. Been rolling that out with clients, that's been a whole lot of fun. >> So we talked a little bit with Rob Thomas about some of the news that you guys have, but this is really your wheelhouse so I'm going to drill down into each of these. Let's say we had Rob Beerden on yesterday on our program and he talked a lot about the IBM Red Hat and Hortonworks relationship. Certainly they talked about it on their earnings call and there seems to be clear momentum in the marketplace. But give us your perspective on that announcement. What exactly is it all about? I mean it started kind of back in the ODPI days and it's really evolved into something that now customers are taking advantage of. >> You go back to June last year, we entered into a relationship with Hortonworks where the basic primacy, was customers care about data and any data driven initiative was going to require data science. We had to do a better job bringing these eco systems, one focused on kind of Hadoop, the other one on classic enterprise analytical and operational data together. We did that last year. The other element of that was we're going to bring our data science and machine learning tools and run times to where the data is including Hadoop. That's been a resounding success. The next step up is how do we proliferate that single integrated stack everywhere including private Cloud or preferred Clouds like Open Shift. So there was two elements of the announcement. We did the hybrid Cloud architecture initiative which is taking the Hadoop data stack and bringing it to containers and Kubernetes. That's a big deal for people that want to run the infrastructure with Cloud characteristics. And the other was we're going to bring that whole stack onto Open Shift. So on IBM's side, with IBM Cloud Private for data we are driving certification of that entire stack on OpenShift so any customer that's betting on OpenShift as their Cloud infrastructure can benefit from that and the single integrated data stack. It's a pretty big deal. >> So OpenShift is really interesting because OpenShift was kind of quiet for awhile. It was quiest if you will. And then containers come on the scene and OpenShift has just exploded. What are your perspectives on that and what's IBM's angle on OpenShift? >> Containers of Kubernetes basically allow you to get Cloud characteristics everywhere. It used to be locked in to kind of the public Cloud or SCP providers that were offering as a service whether PAS OR IAS and Docker and Kubernetes are making the same underline technology that enabled elasticity, pay as you go models available anywhere including your own data center. So I think it explains why OpenShift, why IBM Cloud Private, why IBM Club Private for data just got on there. >> I mean the Core OS move by Red Hat was genius. They picked that up for the song in our view anyway and it's really helped explode that. And in this world, everybody's talking about Kubernetes. I mean we're here at a big data conference all week. It used to be Hadoop world. Everybody's talking about containers, Kubernetes and Multi cloud. Those are kind of the hot trends. I presume you've seen the same thing. >> 100 percent. There's not a single client that I know, and I spend the majority of my time with clients that are running their workloads in a single stack. And so what do you do? If data is an imperative for you, you better run your data analytic stack wherever you need to and that means Multi cloud by definition. So you've got a choice. You can say, I can port that workload to every distinct programming model and data stack or you can have a data stack everywhere including Multi clouds and Open Shift in this case. >> So thinking about the three companies, so Hortonworks obviously had duped distro specialists, open source, brings that end to end sort of data management from you know Edge, or Clouds on Prim. Red Hat doing a lot of the sort of hardcore infrastructure layer. IBM bringing in the analytics and really empowering people to get insights out of data. Is that the right way to think about that triangle? >> 100 percent and you know with the Hortonworks and IBM data stacks, we've got our common services, particularly you're on open meta data which means wherever your data is, you're going to know about it and you're going to be able to control it. Privacy, security, data discovery reasons, that's a pretty big deal. >> Yeah and as the Cloud, well obviously the Cloud whether it's on Prim or in the public Cloud expands now to the Edge, you've also got this concept of data virtualization. We've talked about this in the past. You guys have made some announcements there. But let's put a double click on that a little bit. What's it all about? >> Data virtualization been going on for a long time. It's basic intent is to help you access data through whatever tools, no matter where the data is. Traditional approaches of data virtualization are pretty limiting. So they work relatively well when you've got small data sets but when you've got highly fragmented data, which is the case in virtually every enterprise that exists a lot of the undermined technology for data virtualization breaks down. Data coming through a single headnote. Ultimately that becomes the critical issue. So you can't take advantage of data virtualization technologies largely because of that when you've got wide scale deployments. We've been incubating technology under this project codename query plex, it was a code name that we used internally and that we were working with Beta clients on and testing it out, validating it technically and it was pretty clear that this is a game changing method for data virtualization that allows you to drive the benefits of accessing your data wherever it is, pushing down queries where the data is and getting benefits of that through highly fragmented data landscape. And so what we've done is take that extremely innovated next generation data virtualization technology include it in our data platform called IBM Club Private for Data, and made it a critical feature inside of that. >> I like that term, query plex, it reminds me of the global sisplex. I go back to the days when actually viewing sort of distributed global systems was very, very challenging and IBM sort of solved that problem. Okay, so what's the secret sauce though of query plex and data virtualization? How does it all work? What's the tech behind it? >> So technically, instead of data coming and getting funneled through one node. If you ever think of your data as kind of a graph of computational data nodes. What query plex does is take advantage of that computational mesh to do queries and analytics. So instead of bringing all the data and funneling it through one of the nodes, and depending on the computational horsepower of that node and all the data being able to get to it, this just federates it out. It distributes out that workload so it's some magic behind the scenes but relatively simple technique. Low computing aggregate, it's probably going to be higher than whatever you can put into that single node. >> And how do customers access these services? How long does it take? >> It would look like a standard query interface to them. So this is all magic behind the scenes. >> Okay and they get this capability as part of what? IBM's >> IBM's Club Private for Data. It's going to be a feature, so this project query plex, is introduced as next generation data virtualization technology which just becomes a part of IBM Club Private for Data. >> Okay and then the other announcement that we talked to Rob, I'd like to understand a little bit more behind it. Actually before we get there, can we talk about the business impact of query plex and data virtualization? Thinking about it, it dramatically simplifies the processes that I have to go through to get data. But more importantly, it helps me get a handle on my data so I can apply machine intelligence. It seems like the innovation sandwich if you will. Data plus AI and then Cloud models for scale and simplicity and that's what's going to drive innovation. So talk about the business impact that people are excited about with regard to query plex. >> Better economics, so in order for you to access your data, you don't have to do ETO in this particular case. So data at rest getting consumed because of this online technology. Two performance, so because of the way this works you're actually going to get faster response times. Three, you're going to be able to query more data simply because this technology allows you to access all your data in a fragmented way without having to consolidate it. >> Okay, so it eliminates steps, right, and gets you time to value and gives you a bigger corporate of data that you can the analyze and drive inside. >> 100 percent. >> Okay, let's talk about stack overflow. You know, Rob took us through a little bit about what that's, what's going on there but why stack overflow, you're targeting developers? Talk to me more about that. >> So stack overflow, 50 million active developers each month on that community. You're a developer and you want to know something, you have to go to stack overflow. You think about data science and AI as disciplines. The idea that that is only dermained to AI and data scientists is very limiting idea. In order for you to actually apply artificial intelligence for whatever your use case is instead of a business it's going to require multiple individuals working together to get that particular outcome done including developers. So instead of having a distinct community for AI that's focused on AI machine developers, why not bring the artificial intelligence community to where the developers already are, which is stack overflow. So, if you go to AI.stackexchange.com, it's going to be the place for you to go to get all your answers to any question around artificial intelligence and of course IBM is going to be there in the community helping out. >> So it's AI.stackexchange.com. You know, it's interesting Daniel that, I mean to talk about digital transformation talking about data. John Furrier said something awhile back about the dots. This is like five or six years ago. He said data is the new development kit and now you guys are essentially targeting developers around AI, obviously a data centric. People trying to put data at the core of the organization. You see that that's a winning strategy. What do you think about that? >> 100 percent, I mean we're the data company instead of IBM, so you're probably asking the wrong guy if you think >> You're biased. (laughing) >> Yeah possibly, but I'm acknowledged. The data over opinions. >> Alright, tell us about tonight what we can expect? I was referencing the Vince Lombardy play here. You know, what's behind that? What are we going to see tonight? >> We were joking a little bit about the old school power eye formation, but that obviously works for your, you're a New England fan aren't you? >> I am actually, if you saw the games this weekend Pat's were in the power eye for quite a bit of the game which I know upset a lot of people. But it works. >> Yeah, maybe we should of used it as a Dallas Cowboy team. But anyways, it's going to be an amazing night. So we're going to have a bunch of clients talking about what they're doing with AI. And so if you're interested in learning what's happening in the industry, kind of perfect event to get it. We're going to do some expert analysis. It will be a little bit of fun breaking down what those customers did to be successful and maybe some tips and tricks that will help you along your way. >> Great, it's right up the street on the west side highway, probably about a mile from the Javis Center people that are at Strata. We've been running programs all week. One of the themes that we talked about, we had an event Tuesday night. We had a bunch of people coming in. There was people from financial services, we had folks from New York State, the city of New York. It was a great meet up and we had a whole conversation got going and one of the things that we talked about and I'd love to get your thoughts and kind of know where you're headed here, but big data to do all that talk and people ask, is that, now at AI, the conversation has moved to AI, is it same wine, new bottle, or is there something substantive here? The consensus was, there's substantive innovation going on. Your thoughts about where that innovation is coming from and what the potential is for clients? >> So if you're going to implement AI for let's say customer care for instance, you're going to be three wrongs griefs. You need data, you need algorithms, you need compute. With a lot of different structure to relate down to capture data wasn't captured until the traditional data systems anchored by Hadoop and big data movement. We landed, we created a data and computational grid for that data today. With all the advancements going on in algorithms particularly in Open Source, you now have, you can build a neuro networks, you can do Cisco machine learning in any language that you want. And bringing those together are exactly the combination that you need to implement any AI system. You already have data and computational grids here. You've got algorithms bringing them together solving some problem that matters to a customer is like the natural next step. >> And despite the skills gap, the skill gaps that we talked about, you're seeing a lot of knowledge transfer from a lot of expertise getting out there into the wild when you follow people like Kirk Born on Twitter you'll see that he'll post like the 20 different models for deep learning and people are starting to share that information. And then that skills gap is closing. Maybe not as fast as some people like but it seems like the industry is paying attention to this and really driving hard to work toward it 'cause it's real. >> Yeah I agree. You're going to have Seth Dulpren, I think it's Niagara, one of our clients. What I like about them is the, in general there's two skill issues. There's one, where does data science and AI help us solve problems that matter in business? That's really a, trying to build a treasure map of potential problems you can solve with a stack. And Seth and Niagara are going to give you a really good basis for the kinds of problems that we can solve. I don't think there's enough of that going on. There's a lot of commentary communication actually work underway in the technical skill problem. You know, how do I actually build these models to do. But there's not enough in how do I, now that I solved that problem, how do we marry it to problems that matter? So the skills gap, you know, we're doing our part with our data science lead team which Seth opens which is telling a customer, pick a hard problem, give us some data, give us some domain experts. We're going to be in the AI and ML experts and we're going to see what happens. So the skill problem is very serious but I don't think it's most people are not having the right conversations about it necessarily. They understand intuitively there's a tech problem but that tech not linked to a business problem matters nothing. >> Yeah it's not insurmountable, I'm glad you mentioned that. We're going to be talking to Niagara Bottling and how they use the data science elite team as an accelerant, to kind of close that gap. And I'm really interested in the knowledge transfer that occurred and of course the one thing about IBM and companies like IBM is you get not only technical skills but you get deep industry expertise as well. Daniel, always great to see you. Love talking about the offerings and going deep. So good luck tonight. We'll see you there and thanks so much for coming on theCUBE. >> My pleasure. >> Alright, keep it right there everybody. This is Dave Vellanti. We'll be back right after this short break. You're watching theCUBE. (upbeat music)

(upbeat music) >> Announcer: Live from Las Vegas it's The Cube, covering IBM Think 2018. Brought to you by IBM. >> We're back at IBM Think 2018. This is The Cube, the leader in live tech coverage. My name is Dave Vellante. I'm here with my co-host Peter Burris, Moe Abdulla is here. He's the vice president of Cloud Garage and Solution Architecture Hybrid Cloud for IBM and Tim Davis is here, Data Analytics and Cloud Architecture Group and Services Center of Excellence IBM. Gentlemen, welcome to The Cube. >> Glad to be here. >> Thanks for having us. >> Moe, Garage, Cloud Garage, I'm picturing drills and wrenches, what's the story with Garage? Bring that home for us. >> (laughs) I wish it was that type of a garage. My bill would go down for sure. No, the garage is playing on the theme of the start-up, the idea of how do you bring new ideas and innovate on them, but for the enterprises. So what two people can do with pizza and innovate, how do you bring that to a larger concept. That's what The Garage is really about. >> Alright and Tim, talk about your role. >> Yeah, I lead the data and analytics field team and so we're really focused on helping companies do digital transformation and really drive digital and analytics, data, into their businesses to get better business value, accelerate time to value. >> Awesome, so we're going to get into it. You guys both have written books. We're going to get into the Field Guide and we're going to get into the Cloud Adoption Playbook, but Peter I want you to jump in here because I know you got to run, so get your questions in and then I'll take over. >> Sure I think so obvious question number one is, one of the biggest challenges we've had in analytics over the past couple of years is we had to get really good at the infrastructure and really good at the software and really good at this and really good at that and there were a lot of pilot failures because if you succeeded at one you might not have succeeded at the other. The Garage sounds like it's time to value based. Is that the right way to think about this? And what are you guys together doing to drive time to value, facilitate adoption, and get to the changes, the outcomes that the business really wants? >> So Tim you want to start? >> Yeah I can start because Moe leads the overall Garage and within the Garage we have something called the Data First Methodology where we're really driving a direct engagement with the clients where we help them develop a data strategy because most clients when they do digital transformation or really go after data, they're taking kind of a legacy approach. They're building these big monolithic data warehouses, they're doing big master data management programs and what we're really trying to do is change the paradigm and so we connect with the Data First Methodology through the Garage to get to a data strategy that's connected to the business outcome because it's what data and analytics do you need to successfully achieve what you're trying to do as a business. A lot of this is digital transformation which means you're not only changing what you're doing from a data warehouse to a data lake, but you're also accelerating the data because now we have to get into the time domain of a customer, or your customer where they may be consuming things digitally and so they're at a website, they're moving into a bank branch, they go into a social media site, maybe they're being contacted by a fintech. You've got to retain an maintain a digital relationship and that's the key. >> And The Garage itself is really playing on the same core value of it's not the big beating the small anymore, it's the fast beating the slow and so when you think of the fast beating the slow, how do you achieve fast? You really do that by three ways. So The Garage says the first way to achieve fast is to break down the problem into smaller chunks, also known as MVPs or minimum viable product. So you take a very complex problem that people are talking and over-talking and over engineering, and you really bring it down to something that has a client value, user-centered. So bring the discipline from the business side, the operation side, the developers, and we mush them together to center that. That's one way to do fast. The second way-- >> By the way, I did, worked with a client. They started calling it minimum viable outcomes. >> Yes, minimum viable outcomes means what product and there's a lot of types of these minimum viable to achieve, we're talking about four weeks, six weeks, and so on and so forth. The story of American Airlines was taking all of their kiosk systems for example and really changing them both in terms of the types of services they can deliver, so now you can recheck your flights, et cetera, within six week periods and you really, that's fast, and doing it in one terminal and then moving to others. The second way you do fast is by understanding that the change is not just technology. The change is culture, process, and so on. So when you come to The Garage, it's not like the mechanic style garage where you are sitting in the waiting room and the mechanic is fixing your car. Not at all. You really have some sort of mechanical skills and you're in there with me. That's called pair programming. That's called test-driven, these types of techniques and methodologies are proven in the industry. So Tim will sit right next to me and we'll code together. By the time Tim goes back to his company, he's now an expert on how to do it. So fast is achieving the cultural transformation as well as this minimum viable aspect. >> Hands on, and you guys are actually learning from each in that experience, aren't you? >> Absolutely. >> Oh yeah. >> And then sharing, yeah. >> I would also say I would think that there's one more thing for both of you guys and that is increasingly as business acknowledges that data is an asset unlike traditional systems approaches where we built a siloed application, this server, that database manager, this data model, that application and then we do some integration at some point in time, when you start with this garage approach, data-centric approach, figure out how that works, now you have an asset that can be reused in a lot of new and interesting ways. Does that also factor into this from a speed aspect? >> Yeah it does. And this is a key part. We have something called data science experience now and we're really driving pilots through The Garage, through the data first method to get that rapid engagement and the goal is to do sprints, to do 12 to 20 week kind of sprints where we actually produce a business outcome that you show to the business and then you put it into production and we're actually developing algorithms and other things as we go that are part of the analytic result and that's kind of the key and behind that, you know the analytic result is really the, kind of the icing on the cake and the business value where you connect, but there's a whole foundation underneath that of data and that's why we do a data topology and the data topology has kind of replaced the data lake, replaces all that modeling because now we can have a data topology that spans on premise, private cloud, and public cloud and we can drive an integrated strategy with the governance program over that to actually support the data analytics that you're trying to drive and that's how we get at that. >> But that topology's got to tie back to the attributes of the data, right? Not the infrastructure that's associated with it. >> It does and the idea of the topology is you may have an existing warehouse. That becomes a zone in the topology, so we aren't really ripping and replacing, we're augmenting, you know, so we may augment an on premise warehouse that may sit in a relational database technology with a Hadoop environment that we can spin up in the cloud very rapidly and then the data science applications and so we can have a discovery zone as well as the traditional structured reporting and the level of data quality can be mixed. You may do analytic discovery against raw data versus where you have highly processed data where we have extreme data quality for regulatory reporting. >> Compared to a god box where everything goes through some pipe into that box. >> And you put in on later. >> Yes. >> Well and this is the, when Hadoop came out, right, people thought they were going to dump all their data into Hadoop and something beautiful was going to happen right? And what happened is everybody created a lot of data swamps out there. >> Something really ugly happened. >> Right, right, it's just a pile of data. >> Well they ended up with a cheaper data warehouse. >> But it's not because that data warehouse was structured, it has-- >> Dave: Yeah and data quality. >> All the data modeling, but all that stuff took massive amounts of time. When you just dump it into a Hadoop environment you have no structure, you have to discover the structures so we're really doing all the things we used to do with data warehousing only we're doing it in incremental, agile, faster method where you can also get access to the data all the way through it. >> Yeah that makes sense. >> You know it's not like we will serve new wine before its time, you know you can. >> Yeah, yeah, yeah, yeah. >> You know, now you can eat the grapes, you can drink the wine as it's fermenting, and you can-- >> No wrong or right, just throw it in and figure it out. >> There's an image that Tim chose that the idea of a data lake is this organized library with books, but the reality is a library with all the books dumped in the middle and go find the book that you want. >> Peter: And no Dewey Decimal. >> And, exactly. And if you want to pick on the idea that you had earlier, when you look at that type of a solution, the squad structure is changing. To solve that particular problem you no longer just have your data people on one side. You have a data person, you have the business person that's trying to distill it, you have the developer, you have the operator, so the concept of DevOps to try and synchronize between these two players is now really evolved and this is the first time you're hearing it, right at The Cube. It's the Biz Data DevOps. That's the new way we actually start to tell this. >> Dave: Explain that, explain that to us. >> Very simple. It starts with business requirements. So the business reflects the user and the consumer and they come with not just generics, they come with very specific requirements that then automatically and immediately says what are the most valuable data sources I need either from my enterprise or externally? Because the minute I understand those requirements and the persistence of those requirements, I'm now shaping the way the solution has to be implemented. Data first, not data as an afterthought. That's why we call it the data first method. The developers then, when they're building the cloud infrastructure, they really understand the type of resilience, the type of compliance, the type of meshing that you need to do and they're doing it from the outside. And because of the fact that they're dealing with data, the operation people automatically understand that they have to deal with the right to recovery and so on and so forth. So now we're having this. >> Makes sense. You're not throwing it over the wall. >> Exactly. >> That's where the DevOps piece comes in. >> And you're also understanding the velocity of data, through the enterprise as well as the gaps that you have as an enterprise because you're, when you go into a digital world you have to accumulate a lot more data and then you have to be able to match that and you have to be able to do identity resolution to get to a customer to understand all the dimensions of it. >> Well in the digital world, data is the core, so and it's interesting what you were saying Moe about essentially the line of business identifying the data sources because they're the ones who know how data affects monetization. >> Yes. >> Inder Paul Mendari, when he took over as IBM Chief Data Officer, said you must from partnerships with the line of business in order to understand how to monetize, how data contributes to the monetization and your DevOps metaphor is very important because everybody is sort of on the same page is the idea right? >> That's right. >> And there's a transformation here because we're working very close with Inder Paul's team and the emergence of a Chief Data Officer in many enterprises and we actually kind of had a program that we still have going from last year which is kind of the Chief Data Officer success program where you can help get at this because the classic IT structure has kind of started to fail because it's not data oriented, it's technology oriented, so by getting to a data oriented organization and having a elevated Chief Data Officer, you can get aligned with the line of business, really get your hands on the data and we prescribe the data topology, which is actually the back cover of that book, shows an example of one, because that's the new center of the universe. The technologies can change, this data can live on premise or in the cloud, but the topology should only change when your business changes-- (drowned out) >> This is hugely important so I want to pick up on something Ginny Rometti was talking about yesterday was incumbent disruptors. And when I heard that I'm like, come on no way. You know, instant skeptic. >> Tim: And that's what, that's what it is. >> Right and so then I started-- >> Moe: Wait, wait, discover. >> To think about it and you guys, what you're describing is how you take somebody, a company, who's been organized around human expertise and other physical assets for years, decades, maybe hundreds of years and transform them into a data oriented company-- >> Tim: Exactly. >> Where data is the core asset and human expertise is surrounding that data and learn to say look, it's not an, most data's in silos. You're busting down those silos. >> Exactly. >> And giving the prescription to do that. >> Exactly, yeah exactly. >> I think that's what Tim actually said this very, you heard us use the word re-prescriptive. You heard us use the word methodology, data first method or The Garage method and what we're really starting to see is these patterns from enterprises. You know, what works for a startup does not necessarily translate easily for an enterprise. You have to make it work in the context of the existing baggage, the existing processes, the existing culture. >> Customer expectations. >> Expectations, the scale, all of those type dimensions. So this particular notion of a prescription is we're taking the experiences from Hertz, Marriott, American Airlines, RVs, all of these clients that really have made that leap and got the value and essentially started to put it in the simple framework, seven elements to those frameworks, and that's in the adoption, yeah. >> You're talking this, right? >> Yeah. >> So we got two documents here, the Cloud Adoption Playbook, which Moe you authored, co-authored. >> Moe: With Tim's help. >> Tim as well and then this Field Guide, the IBM Data and Analytic Strategy Field Guide that Tim you also contributed to this right? >> Yeah, I wrote some of it yeah. >> Which augments the book, so I'll give you the description of it too. >> Well I love the hybrid cloud data topology in the back. >> That's an example of a topology on the back. >> So that's kind of cool. But go ahead, let's talk about these. >> So if you look at the cover of that book and piece of art, very well drawn. That's right. You will see that there are seven elements. You start to see architecture, you start to see culture and organization, you start to see methodology, you start to see all of these different components. >> Dave: Governance, management, security, emerging tech. >> That's right, that really are important in any type of transformation. And then when you look at the data piece, that's a way of taking that data and applying all of these dimensions, so when a client comes forward and says, "Look, I'm having a data challenge "in the sense of how do I transform access, "how do I share data, how to I monetize?," we start to take them through all of these dimensions and what we've been able to do is to go back to our starting comment, accelerate the transformation, sorry. >> And the real engagement that we're getting pulled into now in many cases and getting pulled right up the executive chains at these companies is data strategy because this is kind of the core, you've got to, so many companies have a business strategy, very good business strategies, but then you ask for their data strategy, they show you some kind of block diagram architecture or they show you a bunch of servers and the data center. You know, that's not a strategy. The data strategy really gets at the sources and consumption, velocity of data, and gaps in the data that you need to achieve your business outcome. And so by developing a data strategy, this opens up the patterns and the things that we talk to. So now we look at data security, we look at data management, we look at governance, we look at all the aspects of it to actually lay this out. And another thought here, the other transformation is in data warehousing, we've been doing this for the past, some of us longer than others, 20 or 30 years, right? And our whole thing then was we're going to align the silos by dumping all the data into this big data warehouse. That is really not the path to go because these things became like giant dinosaurs, big monolithic difficult to change. The data lake concept is you leave the data where it is and you establish a governance and management process over top of it and then you augment it with things like cloud, like Hadoop, like other things where we can rapidly spin up and we're taking advantage of things like object stores and advanced infrastructures and this is really where Moe and I connect with our IBM Club private platforms, with our data capabilities, because we can now put together managed solutions for some of these major enterprises and even show them the road map and that's really that road map. >> It's critical in that transformation. Last word, Moe. >> Yeah, so to me I think the exciting thing about this year, versus when we spoke last year, is the maturity curve. You asked me this last year, you said, "Moe where are we on the maturity curve of adoption?" And I think the fact that we're talking today about data strategies and so on is a reflection of how people have matured. >> Making progress. >> Earlier on, they really start to think about experimenting with ideas. We're now starting to see them access detailed deep information about approaches and methodologies to do it and the key word for us this year was not about experimentation or trial, it's about acceleration. >> Exactly. >> Because they've proven it in that garage fashion in small places, now I want to do it in the American Airlines scale, I want to do it at the global scale. >> Exactly. >> And I want, so acceleration is the key theme of what we're trying to do here. >> What a change from 15, 20 years ago when the deep data warehouse was the single version of the truth. It was like snake swallowing a basketball. >> Tim: Yeah exactly, that's a good analogy. >> And you had a handful of people who actually knew how to get in there and you had this huge asynchronous process to get insights out. Now you guys have a very important, in a year you've made a ton of progress, yea >> It's democratization of data. Everyone should, yeah. >> So guys, really exciting, I love the enthusiasm. Congratulations. A lot more work to do, a lot more companies to affect, so we'll be watching. Thank you. >> Thank you so much. >> Thank you very much. >> And make sure you read our book. (Tim laughs) >> Yeah definitely, read these books. >> They'll be a quiz after. >> Cloud Adoption Playbook and IBM Data and Analytic Strategy Field Guide. Where can you get these? I presume on your website? >> On Amazon, you can get these on Amazon. >> Oh you get them on Amazon, great. Okay, good. >> Thank you very much. >> Thanks guys, appreciate it. >> Alright, thank you. >> Keep it right there everybody, this is The Cube. We're live from IBM Think 2018 and we'll be right back. (upbeat electronic music)