 Live from the Frederick P. Rose Hall, home of Jazz at Lincoln Center in New York, New York. It's the Cube at IBM Z Next, redefining digital business. Brought to you by headline sponsor IBM. Welcome back to Jazz at Lincoln Center, everybody. This is Dave Vellante. We're here in the Big Apple. Bob Picciano was here. Bob is the Senior Vice President of IBM Analytics and he's also joined by In He Cho Sa who runs strategy and business development for that group. In He, Bob. Welcome. Thanks, Dave. Thanks for having us here. Good to see you guys again. More importantly, thanks again for coming to this important event. Pleasure, big. We're seeing the rebirth, you know, mainframe meets mobile. I mean, a lot of great, really strong messaging. You gave a talk today in the general session. Why don't we recap that? What were your key messages? You know, obviously the big theme here is one of them is bringing analytics and transaction systems together. You talked about that. Yeah. Fill us in on what you're talking about. Yeah, that's really, you capitalized on the key message which is, you know, this is a platform where we focused on a deep set of architectural advancements that were going to allow us to pull analytics into the Z platform. So as I talked to you guys, we were at Insight. I talked about us now being in what I call the inside economy. The cognitive error is ushering in a set of IT value creation opportunities and a new inflection point with that value creation is going to be based on insight, not just on the codification of business process and rolling it out through the enterprise in the form of applications. It's now about what I can learn about the applications, what the transactions, what I can learn about customers and what I can learn about my products. So there's no better platform in the world to take that vantage point from than System Z. It's where the world's majority of transactions go through mobile and traditional transactions and it's where the majority of the world's data sets. So if we can integrate analytics into that platform, then we have the best opportunity to affect what I call right time analytics. Understanding in that business moment whether I need to take action as a potential fraudulent transaction as opposed to sampling things after the fact, whether I can really deepen the system of engagement by understanding what the next best action is for that client, I can do that in that platform on a real-time basis, better than any other platform in the market. So Enhi, you've been living this world now for quite some time, right? We see you at the events like this, we see you out at things like a dupe world. What are you seeing in the client base just in terms of that trend of wanting to bring analytics and transaction systems together? What's achievable prior to today and what are people trying to accomplish? Yeah, what Bob was talking about in terms of the inflection point is just coming together of, if you think about it, new set of capabilities, new economics that are available for clients and then you're marrying that with domain expertise plus new sets of data like the partnership we have with Twitter to kind of be able to make decisions and take actions on those decisions in the moment. I mean, that's what we're really trying to enable for clients and I think what clients are seeing is new possibilities, new possibilities for their business models, new possibilities in the way they kind of define and engage customers and what we want to do is ensure that they're successful at every part of that journey, whether or not they're starting in terms of point capabilities or whether really they're thinking about holistic transformation of their entire enterprise. So we're seeing the cycle compressed. You mentioned sampling. We all know we've lived it with fraud detection. It used to be months and then it went down to days, now it's hours and are we at a tipping point now where sampling is dead? You're actually doing, you could call it in time, I think, or right time, which I guess is before you lose the customer, before you get ripped off or before you make the payment. If you're a insurer or a care provider, do you want to know before I make that payment or before I process that claim whether or not the patterns associated with how this has come about could potentially be fraudulent? How do I know the customer better so I understand when that is the case from the standpoint of those insurers but also in financial transactions just keeping up with the regulatory mandates of terror identification, organized crime identification, the regulations associated with what analytics to drive into the system are a real handful. So we're integrating those into solutions that continue to learn about those regulations that drive them into a solution stack and apply to sophisticated analytics in that business moment. So we're increasing the agility of the organization to learn as well as the way it learns on the transaction. So what we had, Inhia, we had an inflection point now where we'll look back and say, okay, here we are. In January of 2015, IBM made the Z announcement. That's when the industry really compressed that gap and is the industry going to follow this now, right? In 2009, Paul Merritt said, I'm going to build a software mainframe. Everybody wants the mainframe, right? Now let's go back and look. Are we at that tipping point now? And what's different between what IBM is delivering and what the rest of the market's delivering? You know, let me start with the kind of the problems that are kind of really wanting to tackle but also innovate around. And what they're saying is, look, my industry is changing on many dimensions and the speed at which I need to change and iterate different aspects of my business, those decisions are not going to be static decisions. They're not investment changes that they're going to make for three, four, five year investments where they're not going to be able to iterate on it. What we're really thinking about consciously is that analytics journey for every client. Say, okay, based on your business agenda, how are we going to enable you with the right set of capabilities, right set of analytics, right set of domain expertise and solutioning so that you're really successful. We really want clients to be just accelerated in the outcomes that they actually want to achieve. And I would say that's fundamentally different. And we have not only the breadth of capability but quite honestly, the real passion inside to say, okay, some of these problems that we want to solve are hard, right? It's fighting crime, but you know what? There are different ways of fighting crime. Energy and distribution of energy and you know, if you think about the economic, the energy crisis availability globally, it causes very different dynamics, right? If you think about with oil prices and so forth right now, if you think about knowledge base, I mean, the entire, we're thoughtful about what does it really mean for academic institutions and the labor force for every industry and every country to rethink about the skill of the population mixes and thinking about the investments that way. So I think there's a level of thoughtfulness that IBMers bring to the table every day in their jobs that are really unique that make up our possibilities to transform clients. So, Bob, I know we don't want to get too much into the inside baseball and plumbing of IBM, but speaking of clients, you've reorganized in a way typically organizations are built around client needs. You're seeing, right, but you're seeing industries digitize, they're transforming, you're seeing all kinds of disruption. Talk about the organization and specifically some of the details, but what it means for customers. Yeah, so what it really think it means for clients is our ability now exists to simplify what was an IBM matrix before. And it really is focusing in on what outcome the client is trying to achieve and the level of skill and expertise about that industry, about the domain of problem and about the capabilities that service they're that challenge for the client. Now before from an IBM perspective, we used to hear from our clients, you have the right stuff, but sometimes it takes you a long time because it has to come from multiple teams. And sometimes there's things that have to be assembled throughout those teams in a commercial structure in order for us to be able to get to that solution. So what we're focused on now with the new organization is the ability to start with that consulting level engagement about what's transforming about the industry, about their profession, about their position in the marketplace, externally around them, how new competitors are emerging. And that's a very consultative engagement. So tell me about my industry and what's transforming and how analytics provide me a better opportunity to service my clients and build a new marketplace. All the way through the solutions that then fulfill that set of requirements and the products and capabilities that really deliver the great technology that allows us to do this at scale. So you're embedding industry expertise and knowledge into the analytics group and other groups, presumably, I mean, right? It really is a pivot point from the standpoint of more generalized products and middleware as we think about the analytics market to industry-specific and domain-specific solutions that address problems like, say, financial services that's interested in risk, in financial performance management, in threat and fraud, in capital intensity, and being able to put all of those things together in the domain for that particular industry. So, Indhi, I have an observation. I wonder if you could test it on you. So, the reason I like that is because, you know, industries used to sit in their own swim lanes and now you got Apple is in banking or, you know, finance, you've got Amazons and media. So it seems like there's a lot across correlation, across industries, and data seems to be one of these conduits, the pipeline that allows people, and data scientists don't like to be trapped in an industry, so what are you seeing? I mean, is that a valid sort of premise? Yeah, that's a valid observation. I mean, I look at data as, like, the connective tissue, and we've been actually talking about this notion of fluid data, and one of the reasons we've innovated the way we did with DataWorks, which is our cloud-based, really data refinery and data curation set of capabilities, is that, you know, data really isn't static. I mean, it only gets, the value increases only the more you use it, essentially, and the more you use it means, you know what, people need to have access to it, you need to know what's available, you need to know when, at different decision points, you're making it, you're putting it in the context, right, for the right decision and impact, and so I would say data is definitely a connective tissue. What's kind of happening right now in terms of the industry shifting is that people want to do this much more in a self-service and discovery way. So historically, you know, it was very linear in thought that says, hey, I'm going to store some data over here, I, you know, here's some schemas that I'll set up, here's some queries that I want to run, and you know, we're going to pre-define the answers that you can search against. Now people are saying, well, you know, as I start to kind of play with different data sets and I have different questions, it just drives your thinking in a different dimension, and you're able to do that if the data's more fluid, and that's what we're really enabling. I think also that, you know, and he talked about the fluidity of the data, it's also being able to continually incorporate new data to improve the context and improve the sense of understanding of the landscape that affects the outcome. So data is a very elastic concept, and more and more organizations are concerned about the exogenous data, data that exists outside their firewall that they know that needs to be combined with data that they do trust to correlate a better outcome, identify a better demand signal, understand a particular customer better. So like we mentioned before with the relationship with Twitter, and the last time I talked to you guys it was on the eve of making that announcement, so I had to bite my tongue a few times, it was hard, because I really like to share a lot with you guys, but thanks for understanding. When you can see now that how we can use that voice of the client, of the world, to really improve how every business decision is made, and that really is that fluidity of data in very different concepts in terms of the rate that it moves, the ability to extract signal from that fast moving data, but also to incorporate broader and more diverse types of data. You know, it's interesting, because I remember that the night before we were talking and you guys couldn't tell us and we were excited, because we love Twitter of course, we live there. We had Nate Silver on at one of the conferences, and his premise was the data is not there inside of Twitter. Of course, those are fighting words to us, and so up to now, of course, that was a year and a half, two years ago, and maybe he was right at the time, but it seems like there's really been an evolution there. What are you seeing with the Twitter relationship, and what kind of signal are you able to extract? I do think that there's an aspect of Nate's point that I think will always remain valid, which is that it's the combination of that really critical sentiment data that exists with a lot of gravity, but being combined with other things that you know about your product, that you know about your customer, that you know about your marketplace. So I think any one axis is never sufficient to really solve a profound business challenge, and I think Nate is, was right, and will always be right, but the gravity around the validity of that Twitter data has done nothing but grow and become more relevant. And it's an incremental data source that just fundamentally didn't, you didn't have access to before. Yeah, and I think this was like a few years back. I think the New York Times even did a documentary around the news is Twitter, the Twitter is news, I might have gotten that reverse, but the notion of that is it's actually a class of data that is unique because of the ability to operate much more in real time. Like it is one of the driving fuel data sets, I would say, for sense and respond applications. You know, you've had Jeff Jonas here, right, talking about context computing. You marry that in terms of understanding entity analytics, and you marry real time stream computing processing power, both from an ingestion and sense and respond kind of way, and you put in data sets like Twitter, then the ability to sense the news or sense points in time in the moment to operate not just predicting a future occasion because of prior history, but actually earlier and earlier detection in the moment. So weather is a great example of something that impacts many supply chains, it impacts the insurance industry, but what if you marry that with images that people take outside and say, wow, I'm sitting in the eye of the hurricane, or wow, here's the health storm that's actually coming down right now. You marry that real time with data sets that these enterprises have that operate their core business processes, and then you can process that in context. We're entering this moment of being able to provide this decision-engineering capability in real time, to impact it. And the Z event here is a great example of it. We're now able to do in transaction analytics in the moment at a scale that was not possible before. Yeah, I think you mentioned earlier that inflection point, and the importance of what we're talking about here was to take that platform that was so well positioned to be really the important catalyst of that programmatic era of computing. To really be the platform that was trusted for the codification of all business process and logic and further applications and help industrialize an IT generation. And now as we hit that inflection point around the inside economy, what are the things that we had to do architecturally to the platform to ensure that it was going to be capable of the kind of demands that it was going to be required? Single instruction, multi-data, parallel vector operations, increased accessibility of large memory footprints, more diverse workloads, from a Zeo Linux standpoint, moving Hadoop into the platform, blue acceleration of DB2 for in-memory and columnar data world-class onto the platform, our Cplex optimizations, SPSS predictive modeling directly into the ZOS. So now, this platform has been well known for transaction processing, and nobody assails it for that. What do they say? Typically, well, I'd move the data to other places to do the analytic processing. It's expensive, it increases security risks, it increases latency of getting the insight, while now being able to put the athleticism into that one system that handles the transactions and the analytics, that's really a new value proposition. Well, the integration is key, right? Because like you say, people want to spend time on insights, you guys talk about outcomes. Mills had a great quality, so ever since memory's been around, or ever since memory's been around, there's been data and memory, and memory databases have been around, and it's not anything new. But what is different now? It almost feels like it's coming full circle back to this sort of integrated approach, and people want to spend time doing other things. Yeah, well, I think from an IT cycle, I love to talk about technology, and I think it would be easier for me to say, yeah, it is, and here are the technical reasons, and this is what we're doing in the automatic tuning, linear algebra, software layer that we've built into the engine, or the mathematical acceleration salt in the subsystem that we put in, but that's almost too easy. Really, we got to talk about the client value, and I think that's where it really needs to be resonating with our listeners here, is understanding that if this is the platform where transaction processing continues to run, and where mobile transactions are continued to back end, then this should be the platform where with new analytical capability, we should be able to increase the value of that transaction, increase the intimacy to that client, the value of understanding that client to the institution, and to minimize the threat fraud and risk that could occur pre-payment, and understanding more about all those things. Yeah, Dave, just on, in addition to that, what you had said earlier about different industry and different industry players partnering that you wouldn't necessarily expect, part of the innovations that we're talking about accelerate those types of joint innovations, because now you don't have to waste the time trying to either get the data where it needs or put the analytics where it needs to be put, because we're thinking inherently about the workflow and the flow across applications that transform processes, right? And so now, whether you're in the energy space, whether you're in finance, or whether you're in retail or healthcare, clients can begin to share. I mean, one of the most interesting scenarios recently was looking at the work we've done with eHarmony, but also there are interest companies that are looking at predictive models for even if you had the best offer in front of someone, why don't they respond? Well, matching algorithms say, hey, you've found a great mate, but why don't people respond, ideally based on the match patterns? These companies are coming together to say, hey, do I actually really understand, not just putting something in based on traditional segmentations and models, but really getting at micro segments and cohorts and classes that say, hey, I actually have a better comprehensive view of your context and the way you kind of engage with me and others, and I want to enhance that experience. And that's what we're enabling, and we're doing it at an economic level and a performance level. Last night at dinner, Ron Perry, I don't know if you know Ron, a company called Radix, so he started a software company, helped transform the airline industry, sweeping his floor of x86 systems, bringing it into mainframe, but the, which is interesting in and of itself, but the more interesting part is the transformation that's happening in the airline business. They don't have, the data is bespoke, it's led by whoever does the ticket. That's, it's like the clearing house for all the revenue, so nobody really knows what goes on at the back end. His vision is that, for example, airlines could become a form of retailer because you've got a captive audience right there, but it's all about the data. Examples like that are just incredible. That's a common pattern. We engage with a lot of clients who have opaque aspects of either their channel or of their customer's identity, and they're really striving to look at the best ways to be able to fill in more of that picture, to move from a pixelated image into a high fidelity, high definition or ultra high definition image of what it is they're trying to understand about the market opportunity or the individual. We've talked a few times about context computing, which is the way to increase the focus of those things and doing it by also understanding how those signals change over time and revisiting some of the past decisions that were made so that we can formulate a better picture going forward because what you learn, what you know, you can't unknown, but most systems aren't able to go back and actually relearn based on that new knowledge our context computing solution around G2 and identity insight and what we provide in the way of our I2 threat and counter fraud solutions actually provide that degree of learning so that they continue to provide that improving fidelity picture going forward to do the important work that it does. You know what's interesting to me about the market, thinking about the market opportunity here is the market for you is huge, obviously, the team is enormous, but the market for your customers is way bigger than your market. I mean, it's- The global economy. Right, it's the global economy. I mean, you can't even quantify it, it's so huge. So it's the practitioners of data that are going to really cash in a big way. People, I guess, kind of get that, they talk about it. What percent of the clients are really prepared for that? Oh, we've seen a big step function improvement. I mean, Inhe has done a lot of work to champion the formation and broadening of the role of the Chief Data Officer and we held our first Chief Data Officer about three years ago and there were a few people that came and they didn't have Chief Data Officer roles but they sounded a lot like what we were kind of creating as an archetype. We did the same event last year, you know, ourselves and our colleagues from GBS and it was a packed house. I mean, there were 350 many brand new CMOs, some of them reporting into IT, many of them reporting into the line of business. I said CMO, I said CDO, some of them reporting into the CMO, some reporting into the line of business, some reporting into IT, some reporting directly to the CEO. So we've really seen a blossoming of the important disciplines that are really, you know, embodied into that role and a greater increase of focus of what needs to happen to the data governance and data understanding in order to give organizations that important leg up on data. So we actually, last year, time flies, it's so fast. We've actually brought the community together and our Institute of Business Value actually just published a document on the new hero, the hero of big data and analytics, which is the chief rising role of the chief data officer. And one of the interesting insights, one of the CDOs actually said was, yeah, I was proactively recruited for this job, I'm hired now, I don't exactly know what I need to do because I don't know where I am supposed to learn some of the best practices models and he goes, I guess I'm going to have to learn and so one of the things we did was sort of convene some of these experts who wanted to get together and, you know, we're humbled by the fact that they're allowing us to be part of that conversation in a way that says here, we want to figure out a way to document your learnings as you're progressing so we can actually help the broader industry excel at putting the right skills, you know, it could be skill-based, it could be technology, it could be investment, it could be degree of risk, but the reports actually available, we just published it publicly, I'll send you the link. I'd love to see it, so I mean, we do an event every year with MIT on the Chief Data Officer, which is fantastic and really within healthcare, within financial services, within government, I mean, it's exploding. I mean, I would think that the vast majority of those types of organizations are going to have a CDO, you know, let's say within the next three or five years, are you seeing it seep into other industries and how rapidly and what's the driver? Well, I mean, the driver is that understanding that data is the new oil in many respects, right? It's the world's new natural resource, it's abundant and needs to be refined and organizations are at the same time drowning in it, but they really don't know how to extract the value. So I think that message is permeating, it's become, you know, a contemporary challenge for businesses of all sizes. And one of our great friends, you know, are the people of data kind, right? And they've done amazing work and, you know, Jake Porway and his team really looked to help organizations that are data rich and data science poor. That's his statement, data rich and data science poor. And, you know, his work is almost philanthropic and that he can connect, figure out how to help those organizations build some athleticism. And he does that with very, very small organizations, sometimes municipal offices, small businesses that really need the help and are doing meaningful work and just can't bridge that gap. So it exists in the smallest business all the way to the largest enterprise. So big year for IBM coming up and this is probably, if not the one of the most exciting parts of IBM, the analytics business. So I'll ask you both, sort of summarize the sort of the event, trucks are pulling away, the bumper sticker question. What's the bumper sticker on January 2015, the Z announcement, the analytics piece? What's the quick snapshot? Oh, you're talking to the analytics guys up here. So, I mean, my bumper sticker really talks about the new architecture for the inside economy. And he, what do you think? I think the inside economy is something that everyone wants to have a share of, right? Who doesn't want top like growth and who also doesn't want to drive new value creation, especially in the way they engage. I mean, I'm really excited about 2015. I think a lot's going to happen in the industry, especially for every vertical domain space. But even our industry as providers of various services and class of capabilities. I'm excited. She drives a bigger car than I do. Yeah. Well, the digital transformation's underway. We're documenting it here. You guys are making it happen with your clients. Bob and then he, thanks so much for coming on theCUBE, it's always a pleasure. Thanks for being here. All right, keep it right there, buddy. We'll be back with our next guest right after this. 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