 Hi, this is Dave Vellante and we're back. We're at 590 Madison Avenue, and we have a cube alum in he chose saw is here and In he thanks very much for spending some time. It's always great to see you. Oh good seeing you Dave Yes, so we saw each other last week in California the big data big data management announcement Oh, yeah, very exciting. We've been covering that like crazy on Silicon Angle and Wikibon So take us through what you guys announced and what customers are saying about it It we're really excited and the feedback has been tremendous since the announcement last week Wednesday It's really about speed and exploration big data at the speed of which businesses are operating And so what we're doing is we're really enabling clients to consume it better and more So one of the big things that we announced was big sequel capabilities and our infos here big insights Because we found out you know what clients were having a hard time adopting it They didn't really have the skills in-house to get this stuff set up quick enough That was one piece another piece of the consumability. We also announced the IBM pure data system for Hadoop And that is in an appliance like simplicity so that it's really a data load ready system for big data Projects within under four hours. I mean that's what we're committing to clients. So a lot around Consumability performance speed exploration. Yes, so we just had Steve Mills on and we've been talking about the synergies across his lines of business So it's interesting to see you here as a big data analytics, you know person supporting the flash announcement talk about that a little bit Yeah, IBM is really investing in flash at a incredible level So we announced today our strategy around one billion dollars of Investments in research which encodes development acquisitions as well as other Optimizations that we'll do both in the hardware storage as well as software Layers the other aspect which is exciting is this kind of marries into some of the announcements that we made around DB2 with blue acceleration So blue acceleration is our new dynamic in memory Capabilities which allows you to take advantages of things like you know active compression So now you can have more compressed data that can take advantage of kind of the advantages of flash or taking Advantages of more parallel computing capabilities and some of the hardware trends that you're seeing in flash and systems design Let's talk about a little bit about the big data space. You're obviously, you know really immersed in that area IBM has just done a tremendous job Taking its analytics business and really driving to what we've cited as a number one status in the industry. So What's going on there? You're seeing the sort of old world DB2 in the new world to do come together You're seeing real time in memory take us through help us squint through all the you know machinations in that business What's going on? Well, you know what it is is it's actually sort of a confluence of things coming together, right? Clients are saying, you know what they're new types of mobile applications Some clients want to access things via the cloud There's higher degrees of power shifting to consumers and all of that is try driving kind of fundamental changes in how we do entire systems architecture design and software design Now if you think about it from a data standpoint Really what clients want to do is just really get better insights faster and The investments that we're making are enabling clients to do that and some of the new technologies and the ability of bringing all data together with the new technologies is much more affordable than it's ever been and It's just kind of an exciting time to get clients all started and and some of the use cases that we're discovering So one of the big themes that you hear is bringing real time to Hadoop and it's there's an interesting debate going on I was talking to Stefan last week at the announcement Stefan from datamere who's who's he's an early-day Practitioner, oh, yeah, one of the predecessors for Yeah, they help build the build out for you So and he's essentially saying you know what Hadoop is is really designed to be batch and and you know the the rest of The world, you know, we'll we'll connect to it But a lot of people disagree with that a lot of people are really trying to make Hadoop look more real-time What's your take on all this, you know Well, I have a very strong point of view on this because that's why I'm asking you You know what real-time is is all relevant right relevant to the type of workloads and types of transactions That you want to run and types of analytics you want to run So I've been really the only vendor that has what we call streaming technology the ability to consume All sources and all types of data in true real-time So and when you think about Hadoop the capabilities of to Hadoop is really offline batch, right? And the and the design point is to leverage sort of the distributed and memory and file system Capabilities of that kind of architecture with stream computing. It's a completely different design of being able to sense and respond I mean when I think about Hadoop think about the kind of the things that you do in your head, right? You're in a mode of deep reflection You want to sit back think about think about what happened during the day think about what happened during the years Versus in your in-sense and respond mode if something happens the last thing you're doing is actually thinking Deeply at that moment. You're naturally responding to based on instinct prior experience all under less than a second So those are kind of very different Requirements in terms of their architectural design So I think pushing that in terms of the Hadoop system is going to be a real stretch the stream since digging available So that will allow you to process data in real-time before you even persist it Oh, yeah, absolutely. It's it's an ability to actually stream the data in just the data as it's coming out Understand kind of the anomalies of what's happening and then take action very quickly And what's exciting about is we're also enabling things like being able to take The modeling work you do in SPSS those operators could actually reside in stream So even as the data flows in you can optimize even the models in which you're predicting and analyzing some of the behaviors And patterns and then optimize that into and and automate that into your workflow and process Yeah, you guys have a lot of the pieces. I mean you've got the Cognos piece you mentioned SPSS You've got the Informix piece, which gives you time series. You've got your own Hadoop distribution now and big insight streams I mean your portfolio is really unmasked in the business Do you feel like you have all the pieces and now it's really about executing and going to market? Doing some organic development seeing what else pops up and maybe acquiring that if necessary Are you guys where you want to be with your analytics business? Yes, to all of those statements all of the above in terms of the investment Of everything that we want to do and I would say we're never satisfied. Why because What drives IBM is a natural curiosity for how clients are really leveraging this stuff And I would say even over the last two years. We've learned a lot. We've learned a lot about like the five Primary entry points in which clients are getting started around big data We're also learning kind of as clients get into it You just don't know necessarily how they're going to reapply some of the analytics or the way some of the decisions are going to be optimized Or fundamentally how it's transformed the entire delivery systems and business models for companies So we're sort of on the early stages I definitely want to stay in at the head of the pack and the goal is to continue to innovate Yeah, so what I really do like about IBM is you guys talk about business outcomes. You talk about business value But I'm an analyst so we'd like to also talk about the platform and the competition and so you're seeing I mean everybody coming out with a Hadoop distribution John Furrier announced the other day that silicon angles coming out with a Hadoop distribution You know it's talking to cheek but So There's a lot of discussion around okay. Will there be a red hat of Hadoop will there be? You know that's interesting. I think one of the constraints around Hadoop It's a lack of skills around understanding map bar understanding the different types of Capabilities whether it's H base hive so forth The the thing that you're going to start to see more vendors provide like IBM is big sequel capabilities And the reason is is why most clients already have existing sequel skills in house they want to be able to tie back to their existing investments and So my view of this is it's going to continue to evolve But more importantly most clients say you know what I've got a lot of data in my enterprise I've got a lot of existing skills in my environment I want to be able to leverage and get as much value out of the existing investments as possible While I start to innovate and do new things so IBM's often criticized because of its you know huge services business Everybody says I'll be in the throw a bunch of services at me But in the Hadoop world in the big data world people need help Actually figuring out how to monetize data what data sources can I use what's my new data architecture look like I've said I see IBM services business is a huge differentiator, and then we've sized the market about 50% is services again You don't like to talk about it too much I think there's a sensitivity there, but to me services is like a secret weapon that you have that complements your your rich technology portfolio Can you talk about the role of services a little bit? Oh, yeah, so service is a huge piece I think what clients are really doing today at least in some of the big data projects for more sandbox side type projects Especially in terms of the Hadoop capabilities and because they don't have the skills in-house They really do need services services to implement services to think through the types of analytics that they want to build as well as some of What we call analytics accelerators, right? The types of analytics you want to do around social media data or machine-generated data Around tel telecommunications, right certain types of annotations certain types of data models analytic models Certain algorithms that you start to create and you need a lot more SMEs to do this not just in terms of the traditional You know computer science engineers, but actually applied math and applied mathematicians and business analysts So you're gonna see that in improved Kind of dynamic in terms of the teaming and and the services required both in-house in terms of the cultural change and skills as Well as service practitioners and I see a lot of global systems integrators Really expanding to say, okay How do I partake as as part of the big data market differently than maybe historically done because of the new technology base in this business? It's in such flux I think San San Palmasano said that no matter what business you're in you're gonna get commoditized and And that's happening in services. It's certainly happening You know Mark Andreessen said softwares eating the world will open source software is eating the software world And IBM is just you know continues to stay ahead of the pace and move fast so congratulations on all the progress that you've made I'm glad to hear that you're not done in heat. Thanks. Oh, far from done Thank you, and this is the cube right back