 Okay, we're back here live at the IBM IOD, Information on Demand Conference, hashtag IBM IOD. This is theCUBE, SiliconANGLE, and Wikibon's flagship program. We go out to the events, you start to signal the noise. I'm John Furrier, I'm Joe, my co-host, Dave Vellante, and we'd love to have analysts in here, and in this case, former analyst, James Covillus, welcome back to theCUBE. Thank you very much, John. Thank you, Dave. Pleasure to be with you again. Thanks for being at IOD. You're a thought leader. You are an influencer. You work at IBM, so you're out there in the front lines doing some great work, so. Thank you very much. Tell us, explain to the folks out there, I'm not about the show, because we've had some people come in, and obviously you work for IBM, but what is this fit, what is this vector in context to what's relevant in the market? Obviously big data in analytics is the hottest thing on the planet right now, and you got social business now emerging categorically here, but it has a couple different flavors to it, right, within IBM's context. But the messaging is simple, right? You got analytics that tries value, outcomes, social business is the preferred way of people going to operate their businesses. Yeah, engagement and all that, yeah. It's great stuff, new channels, marketing, et cetera, et cetera. Explain to them how IOD is fitting into these megatrends. Into megatrends. The hottest trends, why are customers caring about what's going on here? There's a lot of activity around customers. What does IOD fit into that whole bigger picture? Yeah, well you know the world has changed, the world culture has changed radically, and really in the last decade or so, and it's everywhere in the world, everything is now online and digital, increasingly it's streaming in terms of culture. Look what's happening to Hollywood, it's being deconstructed by the Netflix's of the world. Movies and TV and music, everything is delivered online now, all engagement, more and more engagements with your employer, with merchants, with your family everywhere is online, things like streaming media. So if you look at how the world culture has changed, I, well yesterday I spoke here on a topic that's near and dear to my heart called big media. It's the ascendance of streaming media, and not just the areas I laid out, but in education, like MOOCs, distance learning, we use it internally at IBM for our think Fridays. Ginny Rometti and the executive team, every Friday it's cloud or it's big data or whatever, we all need to get up to speed on. The world culture has changed. Now, analytics is fundamental to that whole proposition in terms of world culture. Analytics driving engagement. Analytics in terms of, you know in a business context, analytics a 360 degree view, and you have data warehouses and the master data, and you have predictive models to drive segmentation and target marketing and all that good stuff. You know that's been in business for a long time that those set of practices, they have become prevalent in most industries now, not just in say, retailing, you know, the Amazons of the world. They're pervasive across all industries. Big data is fundamental to that, you know, engagement model and it's social. Social in the sense that social is one of many channels through which businesses engage and through which many people engage. But social is assuming a degree of importance in the fabric of modern life that goes beyond simple engagement with brands and whatnot. Social is how people create. It's how they declare who they are. It's their identity. And so social in your personal life, we all know about Facebook and Twitter and everything else and YouTube, but social has revolutionized enterprise cultures everywhere. We use social internally. Of course we use our own Lotus Connections. Most large and even many mid-sized firms now use social for interactions among employees or throughout their value chain. So social business is about all of that. It's the B to C. It's the B to B. It's the E to E and employee to employee. All these different models of engagement. They all demand a number of things. Obviously the social platform. They demand the data of various sorts, structured, non-structured in shared repositories or cubes or marts or whatnot. It demands the big data platforms not only at rest but in motion, the streaming media to make it all happen in real time. So at IOD, if you see what the themes are this year and really it's been building for several years, cloud, everything social is running in the cloud now and more and more, not just public clouds but federations of public and private clouds. It's all about cognitive computing which is a relatively new term in the sense that it's achieved a certain amount of vogue in the last year or so which is really fundamentally it's an evolutionary trend. It's basically AI for the 21st century but leveraging unstructured data and machine learning and so forth and predictive analytics and all that. Well the whole world learned what metadata was with the whole NSA comments. No one's like. And then just to wrap it up, in memory, real time, blue acceleration, you need real time, you need streaming, you need collaboration and social peer to peer, user generated content, all of that. To make this new world culture really take off and IBM provides all of that. We recognize that's where the world's going. We've been orienting, reorienting all of our solutions around these models. Cloud, social, increasingly going forward and we provide solutions that enable our customers in all industries to go there and big data is fundamental to all of that. As we say, where computer science meets social science, that's always been Silicon Angles kind of mast head view but to unpack what you just said from the market relevance, you mentioned Netflix, we saw Amazon coming out their own movie, they're going to go direct with their own programming. So that speaks to the direct business model of the web was originally pioneered as hey a direct business model, cut the middleman out but now that dimension has been explored so that's kind of what you're saying there. So that's cool. The end user piece is interesting, you mentioned social. So what's your take on the end user orientation? What's the expectation? Because you got social, you got at rest, you got in motion, you got learning machines providing great recommendations, you got the Watson kind of reasoning for people. So personalization, recommendation engines, the sea change, attention, time, currency, big data, all those buzz words. What is the expectation for users in the future? Right now we're moving into this new world where hey I can self serve myself monologue based that information from the web, now it's all coming at everyone in real time, the alarms are going off as Jeff Jonas says. What is that preferred user experience? The direct business model, people get that, I think the business is to see that, but now the end users are now at the center of the value proposition. How does, what's the role of the user now they're participating in the media, they're also consumers of the media, and they now have different devices, so what's the- The sources of data, so fundamentally yeah, the role of the, with the consumer's expectations now is always, everything's always on, everything is always online, everything is all digital, everything is all real time and streaming, everything is all self service, everything is all available in the palm of my hand, and then the back end infrastructure, the cross channel infrastructure, you know users don't care about individual socials, they really don't, they don't really fundamentally care about Facebook, or Twitter, or whatever you have, they just care that their experience is seamless as they move from one channel to another, they're not perceived as channels anymore, they're simply perceived as places or communities that overlap to, in a dizzying array of socials, thus social is where we all live, and thus social increasingly is mobile, increasingly mobile is, you know, the user expects that the handoff from my smartphone to my tablet, to my laptop, to my digital TV set, and so forth, that it all happens through the magic of infrastructure, that it's being taken care of, and they don't have to worry about that handoff, it's all part of one seamless experience. You know they always just say in the search business, it's the intersection of contextual and behavioral, and now you take that online, behavior is community, and contextual is context to what people are interested at any given time. So many long tail distributions at any given time, so do you see the new media companies, the new brands that might emerge? I mean, there's all the talk about Marissa Mayer kind of turning over Yahoo, and you know some say putting lipstick on a pig, but is that just an older brand trying to be cool? Is that what users want? You're talking about big media before, right? Big media, just user experience, we're like, we're small media, but we got big ideas. But the thing is the outcomes, right? I'm a small fry in big blue, so I'll go figure. Are the outcomes still the same? Companies still want to drive sales for their business, sell a product, provide great value, users want to find great content and find people? I mean the same concept of the old web, search, find content, consume it. Do you have any vision on how that environment will evolve for a user? Like is it going to be pushed at me? Do you see it, a new portal developing? Is, I mean, Facebook's kind of a walled garden, but don't care about that. What's your take on that? The future vision of a user experience online. User experience online, future vision. In many ways, I think, let's talk about internet of things, because that keeps coming more and more into the discussion. It's not so much that, you know the user wants a seamless experience across channel, across device, all of that, but a big part of that experience is the user knows that increasingly they'll have some confidence that whatever environments, physical environments they're in are being, they're obviously there's privacy implications and surveillance here, are being monitored and tracked and optimized to meet their requirements to some degree. In other words, environmental monitoring, internet of things, in your smart home, you want to configure your smart home so that every room that you walk into is as you're moving there, even before you get there, has already been optimized to your needs, that ideally there should be prediction. Oh, Jim's walking into the bathroom, so turn the light on and also start to heat up the water, because it's 10 o'clock at night, Jim usually takes his bath around this time. You sort of want that experience to be handled by the internet of things. Like Nest, these new tools like Nest. So essentially then, my user experience is not just me interacting with devices, but me simply moving through environments that are continuously optimized to my needs and needs of my family. The whole notion of autonomous vehicles, your vehicle, if it's your personal vehicle, then you want to always optimize the experience in terms of the heat setting and the entertainment adjustments on the media center in there, always to be tailored to your specific needs at any point in time. But also, let's say you take a zip car, you rent a zip car and you've got an ID with that company or any of the other companies that provide those on-demand rental car services. Ideally, in this scenario, that whatever vehicle you rent through them for a few hours or so, when you enter it, it becomes your vehicle. It's completely customized to your needs because you're a loyal customer of that firm and they've got your profile information. This is just a hypothetical. I'm not speaking to anything that I actually know about what they're doing. But fundamentally, ideally, any on-demand vehicle or conveyance or other item that you lease in this new economy is personalized to your needs while you're using it. And then as it were depersonalized when you check it back in, so the next person can have it personalized to their use as long as they need it. That's the vision of a big part of the vision of customer experience management. Personalization, not just of your personal devices, but personalization of almost any device or environment in which you are operating. So if I ask one question, I don't know if anyone says a question. I want to ask one more question on the user experience. Came on Twitter from a big data, Alex, says, while you're on the subject with James. Hi, Alex, I know who he is. Great, great friend of the cube. Thanks for the tweet. Too bad we didn't have our crowd chat open. We can get the chat going there, but why not talk about AR and augmented reality? I mean, obviously, Internet of Things is now not the palm of your hand. It could be on your wrist or on your clothing. The wearables and all that. On the glasses. And just gave out three invites to Google Glass. So this is, again, another addition, augmented reality, a software paradigm as well. What does that fit into that? What's your take on augmented reality? On augmented reality, oh, augmented reality, okay. So augmented reality is the, which I don't use myself. I've just simply seen it demonstrated in plenty of places. So augmented reality is all about layers of additional information overlaid on whatever visual video view or image view that you happen to be carrying with you or have available to you while you're walking around in your normal life. So right now, conceivably, if this is an AR setting, I would, environment or enabled device, I would be able to see, for example, that, okay, who's in this room in the sense that, who has declared that they are in this area of Mandalay Bay right now? And why, what specifically are they doing to the extent that they allow that information to be seen? And oh, of these people here, which of these people, if any, might be the person I'm gonna be speaking with at 430 so that if they happen to be in this environment, I can see that. I can see that they're, to some degree, they may have indicated status, waiting for James Covellis to get done with the Wikibon people. Oh, that's kinda cool, so I'd see that overlay and then I walk to other parts of the convention center. I might also see overlays as I walk around like, oh, there's a course down, several rooms down, that I actually put in my schedule. It's gonna start in about five minutes. I'll just duck into there, because it reminds me through the overlay. That's the whole notion of personalization of the environment in which you're walking around in real time, dynamically and contextual, in alignment with your needs or with your requirements, or in alignment also with these, whatever data those environment managers wish to share to anybody who's subscribing in that context. So that's context-aware, that theme that I've been talking about here, context-aware. So essentially, it's a public space that's personalized to your needs in the sense that you have a personalized view that dynamically updates. Dave, that sounds like CrowdChat. Are we running a CrowdChat right now? CrowdChat's an overlay, a social space is a public, a set of social networks. Yeah, tailored to the needs of the group. Yep, that adds value on top of that data. So James, I gotta get your take on something. So we had Merv on yesterday, great guest. Merv Adrian, one of my great bugs from my analyst days. And he was on last week at Big Data NYC. We did our own little event there at Don Coincident with Hadoop World. So Merv said, well, we're just entering the trough of disillusionment for Big Data. You gotta love those Gartner communications tools. I mean, they are genius. And I get a lot of credit to him. But he said that's a good thing because it goes left to right. So we're making progress here. Okay, great. But I'm getting nervous. Internet of things, I love the concept. We done work on industrial internet and a smarter planet fits in there. So I love it, but I'm getting nervous. Here's why. I look back at a lot of the promises that were made in the BI days. 360 degree review of the business, predictive analytics, a lot of things that we're now talking about in the Hadoop Big Data movement that were actually fulfilling with this new wave that the old wave really wasn't able to fill because it got sort of distracted doing Sarbanes-Oxley and reporting in balance scorecards. So I'm nervous. He's old school now. And when he references something that was hot in the mid part of the 2000 decade. Okay, go ahead. Okay, we had a guy on today talking about balance score questions. Well, we just talked about CrowdChat. That's the hottest thing in 2013. It's the first time in like five years I've heard anybody mention Sarbanes-Oxley. Well, kind of saved that whole business for you. Thank you, Enron. So anyway, so what I'm nervous about is I've seen a number of waves over the years where the vendor community promises a vision, great vision, great marketing, and then all of a sudden something hotter comes along like internet of things. It says, oh no, this is really it. So my question to you is help me in my mind, close that dissonance gap. Are these two initiatives, the sort of big data analytics for everybody, putting analytics in the hands of business users? Yeah. Sort of complimentary to the internet of things? Is internet of things just the new big trillion dollar market that everybody's going to go after and forget about all those promises about analytics everywhere? Help me spread through that. My job is to clarify confusion. You know, if you look at the convergence of various call them paradigms, there's a lot of, big data analytics is one of them right now. Clearly there's cloud, clearly there's social. Clearly there's big data analytics in mobile and there's something called internet of things. So some talk about smack. SNAC, Social Mobile Analytic AKA Big Data Cloud. If you add IOT of there, it's smack-y it. I don't think it works or smassy it, but fundamentally if you think about internet of things, it's all about machines or automated devices of various sorts. Probes and your smartphone or whatever, servers, or even the autonomous vehicles, those are things that do things and they might be sources of data, they are. They might be consumers of data. They might conceivably even be intermediaries or brokers or routers of data. What I'm getting at is that if you look at big data analytics, I always think of it as a pipeline, all data. It's like data sources and data consumers and then there's all these databases and other functions that operate between them to move data and analytics and insight from one end to the other of the pipe in a conceptual way. Think of the internet of things as, well, a new category of sources of data, these devices, whether they be probes or monitors or your smartphones and new consumers and all those same things are probably gonna be, many of them, consumers of data and there's message passing among them and then the data that they pass might be passed in real time through streaming like InfoSphere streams. It might be cached or stored at various intermediate databases and various analytics performed on them. So think of, I like to think of the internet of persons, places and things. Persons, that's human endpoints, consumers and sources of data. That's all of us, that's social. Places, that's geospatial. You think about it, the internet of geospatial, geospatial coordinates of data and analytics and then there's things. Automated endpoints or hardware, even from macro to nano devices. So there's just a new range of sources and consumers of data and new types of analytics that are performed and new functions that can be performed and outcomes enabled when you, as it were, stack internet of things with social, with cloud, with mobile. New possibilities in terms of optimization in real time throughout the smarter planet. If you think about the smarter planet vision, it's all about interconnected, instrumented and intelligent. Instrumented, instrumentation, that traditionally it suggests hardware instrumentation. That's what probes are. Sensors and actuators, that's the internet of things. It's a fundamental infrastructure within smarter planets. I love that, thank you for clarifying. I could write a blog post out of that and I think I'm very well made. Now, I want to follow up and bring it back to the users. I know smack. I'm sorry, go ahead. Smack, I thought you were going to say storage. You don't know smack. MapReduce, analytics and query or something. We sell smack on the cube. So I want to bring it back to the users. So we had a great conversation yesterday. Actually, last week, Abhi Mettah was on, I don't know if you know Abhi Mettah, and he said, look, why aren't there any, where are all the big data apps? He said, you need three things for big data apps. You need domain expertise. You need algorithms, which are free, and you need data scientists. And I'm like, oh, we'll never get there. Are algorithms really free? Well, there are open source. That was his argument. Yeah, I mean, he's saying. Yeah, sure, they're open source. If people charge him for algorithms, big trouble. It was his point, I think. Oh, sure. And then we had a discussion yesterday about how in the early days of the automobile industry, the forecast was, this is problematic. The gap to adoption is, there just aren't enough chauffeurs. So the premise that we were putting forth in the discussion yesterday, I don't know who that was with. Was that with Judith? Anyway, it was good. Look, we've got to figure out a way to get analytics in the hands of the business user. We can't have to go through a data scientist or some business analyst. That's not going to work. We'll never get an adoption. So what's going to bridge that gap? Is it the things you talked about before, all these cool solutions that you guys are developing? The project NEO that you announced today, Visualization, is another piece of that. What puts it in the hands of guys like me that I can actually use the data in new and productive ways? Yeah, it will self-service business intelligence and visualization tools that are embedded in the very experience of using apps, for example, on your smartphone. Democritization of data science down to all of us, you need the right tools. You need the tools that the new generation of people, like my children's generation, just adopt and they're just attuned from the cradle to working with data and visualizations and creating visual analytics of various sorts. Though they may not perceive it as being analytics, they may just perceive it as working with shapes and patterns. Doing stuff. Yeah, doing stuff, yeah. So playing around in a sandbox, I love that terminology. Data scientists work in sandboxes, which is data that's the marts that they build to do regression analysis and segmentation and decision trees and all that good stuff. The fact is your sandbox can conceivably be completely on your handheld device. With all the visualizations built in, you're simply doing searches and queries. You're asking natural language questions. You're looking at the responses. You're changing your queries. You're changing your visualizations and so forth to see if anything pops out at you as being significant. Playing around. It's a simple matter that these kinds of tools, such as IBM, Cognos and so forth, enable everybody to become, as it were, a data scientist without having to make that their profession. It's just part of the fabric of living in a modern society where data surrounds us. People are gonna just start playing with data and they're gonna start teaching themselves all these capabilities in the same way that when they invented automobiles, and it wasn't Henry Ford who invented them. It was invented in the late 1800s by engineers in Europe and America. We didn't all become auto mechanics. There are trained auto mechanics, but I think most human beings in the modern world know that there's a thing called an automobile that has an engine that needs gasoline and oil and occasionally needs to be brought to a professional mechanic for repair and so forth. We have, many of us have a rough idea of something called a carburetor, blah, blah, blah. In the same way that when computers came up after World War II and then gradually invaded our lives through PCs and everything, we all didn't become computer scientists, but most of us have an idea of what a hard disk is. Most of us know about something called software and things are called operating systems. In the same way now in this new world, most of us will become big data analytics, geeks practical to the extent that we'll learn enough of the basic terms of art and the relationships among the various components to live our lives. And when the stuff breaks down, we call the likes of IBM to come and fix it. Or better yet, they just buy our products and they just work magically all the time without fail. Conversion and comfortable with the concepts to the point at which you can leverage them. And what about visualization? Where does that fit? Visualization. Visualization is where the rubber meets the road of analytics, because it's where human beings, how human beings extract meaning, insight, fundamentally. It's like, yeah, you extract insight in lots of different ways. You do searches and so forth. But to play around it, to actually see a heat map or a geospatial map or a pie chart or whatever, you see things with your eyes that you may not have realized were there. And if you can play around and play with different visualizations against the same data set, things will pop out that the statistical model, just seeing the raw output of data mining or predictive model or statistical analysis, those patterns may not suggest themselves in rows of numbers that would pop out to an average human being or to a data scientist. They need the visualizations to see things. Because in other words, when you think about analytics, it's all about the algorithms that are drilling through the data to find those patterns. But it's also about the visualizations. You need the algorithms and you need the visualizations and, of course, you need the data to really enable human beings of all levels of expertise to find meaning. And fundamentally, visualizations are a lingua franca between non-expert human beings and expert human beings, between data scientists. Visualizations are a lingua franca. Hey, look what I saw. What do you think? That's the whole promise of tools like concert. For example, we demonstrated this this morning. It's a collaborative environment. It's sharing of visualizations and data sets and so forth among business analysts and the normal knowledge worker. What do you see? Here's what I see. What do you think? No, I don't see that. Here's another visualization. What do you see there? Oh, yeah, I think I see what you mean. And here's my annotation of what I've, the broader context that I've, here's what I've, oh, this is great. That's the whole notion of humans deriving insight. We derive it in socials. We derive it in teams that Dave might be adept at seeing things that Jim is just absolutely blind to, or Nancy might see things that both of us are blind to, but we're all looking at the same pictures and we're all working with the same data. It's part art. Yeah, it's art. So let's talk about some plumbing conversations. One of the things that we noticed we were at the Splunk conference this year. Splunk came out of nowhere, taking log files, making them manageable, saving time for people. So the thing that comes out of the Splunk conversation is that it's just so easy to use. Their customer testimonials are overwhelmingly positive around the area of, hey, I just dumped my data into the Splunk box and good stuff's happening. I can search it, it gives me insight. It saves me time. So that's the kind of ease of view. So how does IBM getting to that scenario? Cause you guys have some good products. We've got on the platform side, but you also have some older products, legacy or Lotus, other environments, collaborative software that's all coming together and converging. So how do we get to that environment where it's just that you just dump your data in and let it do its magic? What's the- Yeah, create, load, and go. That's the very proposition that we provide with our peer data systems portfolio. Peer data system. And big insights, right? Peer data system for Hadoop and so forth. Big insights. You know we have an appliance now. We have PDH. So that's the whole create, load, and go scenario that of course Bob Pitchiano and Les Wretchen and others demonstrated on the main stage yesterday and today. So we do that. And we are simple and straight and easy to use and so forth. That's our value prop. That's the whole value prop of an appliance. You know, simple. You don't need a ton of expertise. We pre-build all the expertise patterns that you can use to derive quick value from this deployment. We provide industry solution accelerates for machine data analytics on top of big insights to do the kinds of things you're talking about with Splunk's offerings. So fundamentally, you know, that scenario, we all, we, and we're, you know, we have many fine competitors. We offer that capability. Now in terms of the broader context you're describing, we're a well-established provider of solutions. We go back more than 100 years. We have many different product portfolios. We have lots and lots of customers who have invested in IBM for a long time. They might have our older products, our newer products in various combinations. We support the older generations. We strive to migrate our customers to the newer releases. When they're ready, we don't force them to migrate. So we make very, we're very careful in our roadmaps to provide them with a migration path and to make it worth their while to upgrade when the time comes to the newer features. Okay, so I got to now change gears to the shiny new toy conversation, which is, you know, we love that in Silicon Valley. What's the shiny new toy? And there's always an emerging markets when you have seed changes like this where there's a whole new wave comes in, creates new wealth, old gets disrupted, new takes over, whatever that conversation goes. But I got to ask you, okay, we're all through the IBM landscape that you're overlooking with big data and under the hood with cloud, et cetera. There's always that one thing that kind of breaks out as the leader, the leading toy, the shiny object that people gravitate to as, and I'm honest, I won't say lost leader because it's not about giving away free. It's the product that goes, wow, this is the lead horse in this game, right? So what is that? What is the IBM thing right now that you're doubling down on? Is it blue acceleration? Is it insights? Is it all, point to the few highlights right now that's really cutting through the new soil of? Yeah, we're developing our own rip-off version of Google Glass, thank you. But you know what I'm saying, it's always like, I mean, I'm going to say shiny toy, but there's always that sexy product, wow, I want that. I want all customers saying I want that product, which leads more, obviously, to lift for other products. Is there one, is there a few that you can talk about that you've noticed anecdotally as other than specific data, but just observationally? A shiny toy for the consumer market or for the business market? Business market, business market. Okay, that's everything. Yeah, okay. Is it Watson? Is Watson the draw? Is it, what's the headline now? I'm looking for the lead dog here. What's the lead tech? There's always one in the emerging market. Well, you could say, put you in the spot here. Well, you could say that the funny thing is the whole notion of a shiny new toy implies something tangible when the world has gone more and more intangible than the cloud. So we are moving our entire portfolio, begin the big data analytics solutions into the cloud. Cloud first development going forward are the core principles for the pure data systems portfolio and the like. So the shiny, the shiny new thing is- The new concept could be shiny new concept or a new paradigm. Yeah, but the shiny new thing is the cloud. The cloud is something pervasive and the cloud is something that really, multiple form factors. It's not very sexy, but customers want flexibility. They want to acquire the same functionality either as a licensed software package or running on commodity hardware. We offer that for our big data analytics offerings or as an appliance of one sort or another that specialized particular requirements or as a SaaS cloud offering or as a capability that they can deploy in a virtualization layer on top of IBM or non-IBM hardware. Or they want the ability to mix and match those various deployment form factors. So in many ways, the whole notion of multi-form factor flexibility is the shiny new thing. It's the hybrid model for deployment of these capabilities. On-prem, in the cloud combination thereof. That's not terribly sexy because it's totally abstract, but it's totally real. Yeah, I mean demand-wise people could be, hey, that drives my business. Because when you go to the cloud, I mean that's where you can really begin to scale seriously beyond the petabytes. The whole notion of big media, it will exist entirely in the cloud. Big media, I like to think is the next sexy thing because streaming is coming into every aspect of human existence where stream computing, a lot of people who focus on big data think of volume as being like big headline, oh god, we can go to petabytes and exabytes and all that. Yeah, it's important. Some really fixate on variety. All these disparate sources of data, and now we have all the censored data, and that's very important. We have all the social media and everything, all those new sources, that's extremely important. But look at the velocity. Everybody's expecting real-time instantaneous, continuous streaming. And everything we do, all of our entertainment, all of our education, surveillance, everything is completely streaming. I think ubiquitous streaming to every device and everybody themselves continuing to stream their very lives everywhere all the time is the sexy new thing. Yeah, Dave and I talk about running data. We coined that term running data, what, four years ago. So I gotta get kind of a thought leader. They're watching us and we're watching us. We're streaming data right now from these experts. Yeah, see, your guys are streaming. This is big media. So why don't we get your thought leader perspective? Here's some thought leader mojo around the hashtag data economy. Now you're moving into a conversation with C-level folks and they said, James, tell me, what the hell is this data economy thing? So what is the data economy? In your words, kind of like, I mean, obviously it's a mindset or anything else. What's your take on that? We've been discussing that internally and externally at IBM. We're trying to get our heads around what that means. Here's my take as one IBM or one thought leader. By the way, the trick of being a thought leader is just to let your own thoughts lead you where they will. They turn around, we're all my followers. Yeah, seriously, hopefully they won't lead you too far astray where you're out in the wilderness too long. That's an important topic people are talking about because people are trying to put the definition around data economy. Can you actually have a business construct around? Yeah, data. Here's my take on the layers of the meaning of data economy. It's monetizing your data. The whole notion of monetization of your data. Data becomes a product that you generate internally or that you source from externally but you repackage it up and then resell it with value add. The whole notion of data monetization implies a marketplace for database products. When I say data, I'm using it in the broader context of it could be streaming media as a very valuable category of data like whatever Hollywood provides. So there's a whole notion of monetizing your data or providing a marketplace for others to monetize their data and you take a transaction fee from that. Or it also means in more of a traditional big data or data warehousing BI sense, it means that you drive superior outcomes for your own business from your own data through the usual method of better decisions on trustworthy data and the like. So if you look at data monetization in terms of those layers including the marketplace, including data driven outcomes. In many ways, the whole notion of a data economy hinges on everybody's realization now that the chief resource for betterment of humanity, one of the chief resources going forward for us to get smarter as a species on this planet is to continue to harness the data that we ourselves generate. People start with data as being the new oil but oil was there before we ever evolved. But data wasn't there before we landed on earth or before we evolved. We generate that so it's our own exhaust. It's our own exhaust that's actually a renewable resource. Data exhaust from exhaust to gold, that's what we say. Data is the data exhaust that's good. If you can harness it and put it together as Jeff Jonas has the puzzle pieces. The picture, the big picture, the smarter picture, the smarter planet. So on the final question, I want to wrap up here for our next guest, but what's going on with you these days? Talk about what's up with you. You're very active on Facebook. You have a good following. I've got a birthday coming up. What's happening? I'll make sure I say a big birthday for you on your Facebook page. Thank you very much. But what's going on in your life? Obviously, you're working at IBM. What other things are interesting? What's on your mind these days when you're at leisure? Are you hanging out? What are you thinking about the most? What are you doing with your things with your family? Just share with us, see what's going on. Well, I hang out at home with my wife and drink beer and listen to music and tweet about it. Everybody knows that stuff. What kind of beer do you drink? Whatever is on sale. I'm not going to say where we buy it, but it's a very nice place whose initials are TJ. But fundamentally, my mind is an open book because I evangelize. I put my thoughts, my work thoughts, and my personal thoughts out there on socials. I live completely on, but not completely on socials. I self edit. But fundamentally, the thought leadership I produce, the blogs and whatnot I produce all the time, I put them out there for general discussion and I get a lot of good feedback from the world. And including from inside of IBM, I just try to stretch people's minds. What's going on with me? I'm just enjoying what I'm doing for a living. Now people say, Jim, you're with IBM. Aren't you an analyst? I'm still doing very analyst style work in a vendor context. I'm a thought leader. Oh, I tried to be. Being a thought leader is like being a humorist. It's a statement of your ambition, not your outcome or your results. Yeah, you can write jokes to your blue in the face, but if nobody laughs, then you're not a successful comedian. Likewise, I can write thought leadership pieces till I'm blue in the face, but if nobody responds, then I'm not leading anybody anywhere. I'm just going around in circles. So my ambition on every single day is to say at least one thing that might stretch somebody's box a little bit wider. And I think IBM is smart. They've been in social while, the content markings about marketing to individuals with credibility. So I love the analysts. I love all my buds, like Merv and everybody else. And I'm sort of a similar cat, but there's a role for ex-analysts inside of solution providers. And we have any number, John Haggerty we have, we have Brian Hill, another ex-forrest to write. You know, it's a big industry, but it's a small industry. We have smart people on both sides of the equation, solution provider and influencer. You know my line? 100 people, 99 seats. And you know, I suck up to my superiors at IBM. I suck up to any analyst who says nice things about me and hosts me on their show. And that's what's going on in my life. I'm just a big suck up. Well, we like to have it in the cube. Looking forward to doing some crowd chats with you, our new crowd chat application with you guys. Lock you into that immediately. It's a thought leader haven that the crowd chat, as it turns out. Dave, what's your take on the analyst role at IBM? Let's do a little analysis of the analysts at IBM. What's your take on this situation? I think that the role that IBM has put James in is precisely the way in which corporations, vendors should use former analysts. They should give you a wide latitude platform and not try to filter you. And you're good like that. Guess what? I do the usual marketing stuff too, the traditional, but I do the new generation of thought leadership marketing. And there's a role for both of those. To me, marketing, if I said it once, I said it 100 times, marketing should be a source of value to people. And it's so easy to make marketing a source of value by writing great content or producing great content. So that's my take on it, John. I mean, your marketing is a great explainer. You explain the value to the market and thereby hopefully for your company, generate demand hopefully in the direction your customer is buying your things. But that's what analysts and influencers should be explainers. It's, you know. Well, I mean, Dave, I mean, as influencers, as influencers that we are with the Q here, here's my take on it. When you have social media of direct full transparency, there's no, you can't head fake anyone anymore. Those days are gone. So analysts, bloggers, people who are head faking, journalists, head faking the audience, the audiences will find out everything. So to me, it's like, it's the metaphor of when someone knocks on your door at your house and you open it up and they wanna sell you something, you shut the door in their face. When you come in there and they say, hey, I wanna hang out and I got some free beer and a big screen TV, you wanna watch some football, maybe you invite them into the living room. So the idea of communities and direct marketing is about when you let them into your living room. You're not selling, right? You are creating value. Tell you what I do. I drop, I try to drop smart ideas into every conversational context throughout socials and also at events like IOD. So a big part of what I do is I thought leadership marketers, not just write clever blogs and all that, but I simply participate in all the relevant conversations where I want ideas to be introduced and oh, by the way, I definitely want people to be aware that I am an IBM employee and my company provides really good products and services and so forth. You know, that's really a chief role of an evangelist in a high tech solution provider. That's one of the reasons why we started CrowdChat because the hashtags can get so difficult to go deep into so we created CrowdChat. Let's go deeper and have a conversation and add some value to it. Yeah, it's, you know, think about earned media as it turns and kicked around, but in communities, the endorsement of trust, earning a position, whether you work at IBM, people don't care, hey, he works at IBM or whatever, if you're creating value and you maybe have some free beer, you get an entry, but you win on your own merits. You know what I'm saying? At the end of the day, the content is the on merits and I think that's the open source paradigm that is hitting the content business, which is community marketing. If you're painting the ass, you're gonna get bounced out, right, out of the community, or if you're selling something, you're gone. So you guys do a great job, really admire. You guys are awesome. Thank you, James. I really love what you add to the IOD experience here with this corner and all the interviews. It's great, great material. Well, thanks for having us here. We really appreciate it. We learned a lot, it's been great. You guys are great to work with, very professional and the product's got great, great looking portfolio. You're hitting all the buttons there. So hitting all the checkboxes. So this is theCUBE, we're right back with our last interview coming up shortly with Jeff Jonas. He's got some surprises for us, so we'll see what he brings to his A game. Apparently he told me last night he's bringing his A game to theCUBE. That guy is seriously smart. I'm a huge Jeff Jonas fan. He's a rock star to me. We love him on theCUBE. He's a tech athlete like yourself. We'll be right back with our next guest after this short break.