 Hello and welcome. My name is Eric Fransen. We would like to thank you for joining us today for this webinar, A Production of Dataversity with our speaker Jim Kobielus of IBM. Today Jim will be discussing cognitive computing in the mobile app economy. Jim is an industry veteran and serves as IBM Big Data Evangelist, Senior Program Director for Product Marketing in Big Data Analytics and Team Lead of Technical Marketing at IBM Big Data and Analytics Hub. He spearheads IBM's thought leadership activities in Big Data, Hadoop, Enterprise Data Warehousing, Advanced Analytics, Business Intelligence and Data Management. He works with IBM's product management and marketing teams in Big Data. Kobielus has spoken at such leading industry events as IBM Information on Demand, Hadoop Summit, Enterprise Data World and Strata. He has published several business technology books and is a very popular provider of original commentary on blogs and many social media. Welcome Jim and thanks for joining us today. Thank you Eric. Great to be here. Nice to be speaking with you all out there in smart data webinar world and so I'll get rolling. Well, you know the whole topic of mobility just permeates every aspect of our existence increasingly. I have an iPhone, I have a tablet, I have a laptop and I'm sure that many of you have this in a wider range, even a wider range than I do of mobile devices. A lot of people have wearables now, things like Fitbits and so forth. So mobility is the way of the present and the future on going and every aspect of our lives including the economy. But ten or more years ago and I was still an industry analyst, I broached the topic of what I call mobile commerce at that time with various people in the business world and they looked at me like I had a third eye in the middle of my forehead like what is that? I said well that's coming. We're all going to be buying and selling and shopping and browsing for products and services through our mobile devices before long and it's like oh yeah, yeah, that's going to be a long time coming. Well that time has come and it's now. So the whole notion of a mobile app economy is mainstream and clearly we're all doing it. I've got 10 zillion apps here for the various retailers and other merchants that I do business with and I'm sure you do too. So what I'm going to talk about today is not just the mobile app economy and clearly we at IBM are doing plenty in that regard. We have some fairly important initiatives including, you probably saw last summer we announced a partnership with Apple. Apple is our partner on building a Watson-enabled application for the business world for mobility on the iPhone and the iPad and other iOS platforms and that just sort of scratches the surface. What we're doing here in mobility in Watson is a key piece of the overall smart mobility story and Watson of course is a big data platform in the cloud and it's built on the technologies, sort of technologies that's called increasingly cognitive computing. Today what I'll be doing is explaining the role of cognitive computing, what it is first of all, what is value, how it's built upon and how it differs from just big data analytics generally. And then what I'll be talking about is the whole topic of smart mobility as enabled by cognitive computing in the cloud. And of course I'll bring Watson into the discussion but it won't be, don't worry, it won't be a product pitch. It'll be an educational and informational, hopefully inspirational talk for you, with you for the next 45 minutes or so. So recently I was on a panel at the Enterprise Data World. I was managed and hosted and sponsored by the good folks at the University. As in Washington D.C. we had a wonderful talk. Me, Steve Ardieri, Adrian Bolden, Matt Sanchez, all around the various topics under the heading of cognitive computing. And I can't advance the slide here. Eric, can you advance the slide? Sure, you want to click on the tab that says Dataversity up there. Yes, yes, very good. Here's my title slide. Thank you very much. I was on the wrong tab, folks. I'm not too swift. So nonetheless, cognitive computing in the mobile app economy is a hugely important topic. Here we go. Smart mobility, as I was saying. Smart mobility is increasingly a requirement of everything. On the move, we want to make the best decision at all time and do the right thing at all times based on what? Based on the circumstance where you're at, what time of day it is, who you're dealing with, and in terms of who you're engaging with, whether it be a retailer, whether it be somebody in your social group, and so forth. You want the best data and the best analytics about that entire situation, what's come before, what's likely to come in the near future, what's happening now. Ideally, you want that delivered to you in the palm of your hand, 24 by 7, and you want expert advice and guidance on what you should be doing. Should you be buying this product? Should you be aware of this particular circumstance or issue like a traffic jam that may be 10 miles up the road on the path that you've mapped out on your GPS? You sort of want to know the issues before they become showstoppers and it's not just issues in terms of driving and in terms of should I buy or not buy this particular product, but it's issues in terms of security issues, your personal security, increasingly when we're talking about wearable devices. You want to sort of get a good sense for whether in any environment you're at risk in terms of crime rates in the area that you happen to be in or weather issues that may be cropping up and you're not even realizing because sometimes the weather system moves too fast for you to be paying attention to. So you sort of want guidance from the smart brain in the cloud about where you should go and what you should do in real time. It's all about mobility. You need this decision support tool wherever you happen to be. Mobile adoption, mobile devices themselves clearly, I don't have to tell you, they're everywhere and they're becoming even more ubiquitous thanks to wearables. I don't yet have a wearable, but my friends who just visited from out of town do have their wearables. They have their Fitbits and so forth and so on. And so I was just looking at what they were showing me in terms of fits into their lifestyle currently, not into mine yet, but I believe before long things like smart watches and smart glasses and so forth might serve my personal needs. All of our needs are changing. Our lifestyles, our cultures are changing. We need and we demand they're using a wider range of connected devices that are wireless from the get-go and as the Internet of Things pushes its way into our lives in the form of sensors and actuators and so forth that are embedded in our mobile platforms. When I say platforms, our cars, buses, planes, trains, but also into our homes and into our smartphones and the like. Clearly, there's going to be trillions upon trillions of connected devices within our lifetime that will be brought into, need to be swept up into a larger mobile analytics, mobile guidance, cognitive mobile environment to give us guidance because quite frankly, we can't, for human beings, we can't possibly be following the data, the low-level data in terms of the entire situation that we happen to be in in terms of mapping out all future likelihoods against what has happened in the past and then plotting out our guidance, our next best actions ourselves. We need help from the cognitive tools that are in the cloud that can be looking for these patterns, patterns of opportunity, things that we may not realize that are out there that we should be doing or considering and of course the likelihood of threats and other circumstances that might prevent us from achieving our desired goals. So in the mobile app economy, we have now the possibility and the very real practical capability to optimize every decision we make, every interaction we engage in and every transaction we make from the best data and the best analytics statistical models in the cloud that are relevant to our specific needs and situations at any point in time. They include geography, time of day, our own personal circumstances, what others in our situation have done or experienced in the past. We have the benefit of the best data and really the best guidance at any point in time available to us seamlessly in the context of wherever we happen to be. And that's through mobile applications, smart watches and connected cars and in-home smart devices like smart thermostats. So we'll go to the next slide. Here we go. So the mobile app economy is driving transformation across the world at large and especially in the business world. Everybody in business now has a mobile device and that increasingly becomes or devices and that becomes their primary tool for doing work. In fact, I use my iPhone all the time in my job and I've got access to all my corporate email and my calendars and everything else and plenty of other resources that are available to me securely through my iPhone back to all of IBM's tools for its employees to be as productive as possible and I don't know how I could go back in time to before the day when I wasn't able to access all this information at a moment's notice from wherever I happened to be and I'm sure you have very similar lives. So the mobile app economy is critically important in business. Mobility is primary. 91% of mobile users keep their devices within an arms reach 100% of the time and mine actually is about a good four feet away from me and I watch it like a hawk. Usually I have it in my pocket when I'm on the road, of course. You know, insights from mobile data provide new opportunities for us all. In the consumer world, you know, 75% of mobile shoppers take action after receiving location-based messages. The research has shown and I think this is going to grow and I think the in-store couponing, the real-time wirelessly is becoming, will become a standard part of the shopping, the brick-and-mortar shopping experience. As the brick-and-mortar all was, increasingly the retailers, and what they do is they're really reorganizing the entire experience of their customers across both virtual and physical channels and making everything highly virtual in terms of real-time opportunities being presented to you wherever you happen to be, including, you know, contextualize within the context of where you happen to be within a particular retail outlet in what aisle, near which end cap. And based on what you've purchased recently or what you might have in your cart at the very moment, being able to provide you with a contextualized offer right there in the moment that may alert you to something that you didn't realize is there that might suit your needs and so forth. So I think real-time couponing driven by data and analytics in the club will become ubiquitous. Mobility is all about transactions increasingly. Mobile commerce, because it's so easy, because it's always there in the palm of your hand, because the sheer power of notifications and alerts and targeted next best offers and so forth, mobile commerce in many ways will become a predominant way that we do a manner of business. And really mobile is all about creating a continuous brand experience where users have, you know, multiple screens and really multiple channels and they expect the retailers and other businesses in general to provide a seamless experience across these different channels so that, you know, the backend cloud is fully aware of what you've been saying to the call center and what you've been doing through your interactions through social channels and ties it into your behaviors within the stores and so forth and ties that all into a seamless experience so that the experiences you have in each of these channels are optimized to those specific channels and are aware of what you've been doing in the other channels on a 24 by 7 basis. Clearly, the multi-channel experience is critically important and you really need powerful data, powerful aggregation and powerful statistical analysis and predictive analysis to do that right so you don't bombard people with spam and so you're always aware of how people sentiment in terms of to the extent that they prefer to do business with you through one and only one channel, you should have that data available and linked to their customer profile and available in the cloud to across all the channels so that you're not bombarding them say with offers in the store when they've clearly indicated that they don't like that. That's the sort of thing in terms of the mobile app economy that you need to have the big data resource, you need to have it the policies and so forth tune it to personalize the one-to-one experience to a fine degree and if we adopt the Internet of Things for more and more mobile commerce and other transactions we can intensify so as more appliances say in your home become IoT endpoints like your refrigerator increasingly you'll do more ordering from these appliances so it's all about mobility and the various permutations of mobility will continue to expand the mobile app economy is reshaping enterprise IoT clearly engaging mobile user experiences or everything application developers now principally the people who are now entering the field of app dev are focused primarily on mobile application design and really developing to multiple form factors on the mobile side is the way to go and in fact it is the standard practice now you can optimize the experiences for the features of the iOS and the Android and the various desktop platforms or traditional platforms as well in one seamless development cycle and that you can use DevOps techniques to ensure a seamless and frictionless development of all these disparate interfaces across a using a common set of methodologies to ensure that you can move towards more of a versionless development capability that continues to iterate improvements to this multi-channel interface through A.B. testing and real-time experiments and the like APIs and frameworks for mobile app development are critically important in this regard APIs and frameworks that enable you to design creatively design these experiences in a way that doesn't require the developer to write low-level code but it can in fact design these experiences as dynamic and visual and engaging and immersive these mobile experiences are nothing if not immersive and should be seamless in that sense they should just fit in they should just blend in just to the way that people live and work and think this all needs to be cloud-scaled clearly we're talking about millions and eventually trillions of devices across multi-channels infrastructure needed to manage these devices as well as manage the very complex development cycle of DevOps across all these disparate mobile devices can get very large in terms of not just the number of devices and the variety of them that are managed but in terms of just the need for real-time agility and scale in terms of managing the underlying data and metadata and so forth to be able to support a continuous development cycle across different mobile apps and channels so fundamentally we're moving towards more of a scrub iterative scrum I should say scrum development paradigm we have small teams that are moving very fast, highly focused, highly skilled development teams that where UX is critically important mobile UX really a hacker-like or a hacker way a Facebook-like development approach that's focused on short, frequent and incremental release cycles where smart mobility intersects with cognitive computing is in the notion of the guidance in the cloud big data is fundamentally important cognitive computing is really all about helping you, the user the individual think better and to think more powerfully based on leveraging the best data and the best analytics so fundamentally the smart mobility experience is an always on experience it's always optimized with the best data and analytics it's always engaged through social business and social channels in your personal life in your business life of course it's always mobile it's a wireless and untethered it's to use the parlance that we at IBM introduced with the Smarter Planet program it's always instrumented, always intelligent and always interconnected and it's always untethered so it's all about all of these design imperatives are critically important to seamless, frictionless mobile commerce in this new environment so really what cognitive computing is it's one way of looking at it it is artificial intelligence but it's our cognitive computing is artificial intelligence for this new century the 21st century and at the century where big data has come into its own in a major way big data and analytics are fundamentally different now so a new force in our lives that really had just had not really developed to this extent in the 20th century we've of course it's a steady unbroken growth of course in the amount and the variety and velocity of data but what we've seen in this century is that we've moved into a new era of computing called the cognitive systems era and I'll lay out the historical timeline in just a moment but fundamentally cognitive computing is AI for big data unstructured big data where it's AI that is geared to automating the ability of systems to handle the conscious critical logical attentive reasoning and evaluative modes of thought just to help us all think better you know, think that's the old Thomas J. Watson motto well it's all about thinking machines but it's thinking machines that learn from data and learn from interactions with people and then adapt their algorithmic behavior of these machines to these inputs without explicit programming and let me underline without explicit programming because when we look at this new era wait for my slide to advance here oops, go back oh my other guy but I've deleted that slide regardless, I'll talk to it we are now in the cognitive systems era and what this is is it's a step beyond the programmable systems era that we've been living in and we still are very much embedded in where if you look at a hundred years ago all processing, all execution logic on information systems was hardwired it was mechanical and then eventually electromechanical in nature you had to actually build it into the hardwiring the logic of the systems that were doing the processing we're just talking about tabulating machines and so forth that's the era that IBM was born in before the First World War well clearly we still have a lot of execution logic that's burned into chips and the firmware and the like but after World War II we moved into the software programmable era where the execution logic was increasingly programmed in higher-order languages from machine languages to assembly languages to 4G and so forth COBOL and Fortran and then Java, C++ and beyond what has happened with the last really 15 years since the start of this century is that there's less and less need to program the execution logic into applications because the applications are now able to derive their logic directly from data from statistical models against big data sets statistical models for data mining and segmentation and so forth that look for patterns, look for correlations and outliers and anomalies and trends and so forth to help then the algorithms what they do increasingly is apply is develop predictive logic machine learning and so forth predictive logic that is derived from the data itself is being used to drive more and more applications so what we're talking about is recommendation engines that might recommend, for example, that you buy this book or that CD based on your past buying behavior based on what people like you have bought or recommended and so forth this is predictive logic and increasingly this kind of logic is built into all manner applications that can understand you or what you're likely to find interesting or fun or acceptable or not unacceptable able to find that rapidly and recommend to you those options that might be best suited to your situation, your needs that's fundamentally how Watson works and that's really fundamentally how this new generation of information systems works it's all about helping you make better decisions by providing you with interactive guidance based on data so thinking, you know, cognition is thinking and really thinking machines have been around for a long time clearly that's the whole notion of a computer but what we have now is, you know, cognition, automated cognition derived from the data itself and statistical models derived from the data that's helping us to address the time-honored questions that of course have been the heart and soul of business intelligence and now we have essentially a new era of business intelligence that's driven by cognitive computing Watson analytics, for example essentially a new generation of business analytics decision support tool in the cloud that's powered by cognitive computing machine learning and the like so really fundamentally cognition any cognition, cognitive computing system exists to answer the following core questions what is happening what is happening based on what we're seeing now in terms of the data what has been going on why did it happen you know, you do the historical analysis and then you use it to understand why that happened and then what is likely to happen under various scenarios predictive analysis and modeling and then to help you ultimately to decide what action should I take prescriptive analysis through decision management decision automation and the like so the new era is increasingly focused on decision automation and decision support decision automation through guidance from the automated next best action logic embedded in systems and decision support built from highly visual highly interactive reports and dashboards and guidance in line to various applications this is what cognitive computing is all about so here's a slide I was thinking of it's the one that was out of order but nonetheless hardware processing logic gave way to software explicit program processing logic gave way to data derived processing logic none of the prior eras are really over of course we have hardware processing logic and we have software explicit logic and now we have an extra layer of highly contextualized data derived processing logic available to our cognitive systems so you can develop fresh insights algorithmically from fresh streams of big data so Watson fundamentally is a kind of computing cloud in the premier kind of computing platform fundamentally it understands natural language and human style communication in terms of interacting with humans to learn from your responses and adapt its behavior adapt its algorithms to to fine tune the results that it delivers to you and to people like you on the next turn of the crank as it were on the next interaction or on the next query we saw that on Jeopardy of course Jeopardy was that particular showcase four years ago was tuned to that particular circumstance a game show but Watson has been rolled out into a broad range of business and commercial applications in very different areas from Jeopardy but it's the same underlying principle it's able to communicate with you the human being using human style communication you can have text to voice like we showed on Jeopardy you can have any UX you wish you know on top of Watson it generates and evaluates evidence-based hypotheses so it uses machine learning to be able to develop alternate hypotheses of any given subject domain be it medical be it multi-channel retail and whatnot and then it takes fresh data and determines to what extent one hypothesis is better fitted to the data than another and then it ranks the results of your query to buy their degree by Watson's degree of confidence in whether they specifically address what you're requesting or specifically anticipate what you might be requesting fairly soon the whole notion of predictive analytics it adapts and learns from you in terms of if you found one answer better aligned with what you were looking for than another Watson factors that into its dataset and continues to tune its algorithms so that in the future it can better field queries of whatever sort it was from you and people like you in circumstances like you that's the power of cognitive computing Watson understands you and engages with you it learns and improves over time it helps you discover new datasets that you may not have been aware of because Watson has big data in the cloud Watson has a massive corpus of data petabytes and so forth you can load in your own corpus if you wish or you can use any number of open source data corpus corpore I think is the plural of that and Watson can really work with any data to answer almost any question and in pretty much any context and Watson fundamentally like kind of general has machine learning at its very heart and machine learning in many ways is the core IP within any cognitive computing platform fundamentally the kind of fabric uses machine learning to find patterns within data essentially machine learning are statistical algorithms that learn from data that abstract quantitative models from the data and from prior knowledge and enables data driven analysis inference pattern recognition and prediction so fundamentally Watson is tuned to big data and you can really throw any data that you wish at Watson and train Watson's algorithms to your proprietary data or public data or a combination thereof for it to refresh there we go Watson foundations is a term that we've introduced to describe our direction at IBM which is to bring kind of computing technologies into our entire portfolio of information management and business analytics tools both for the enterprise and for the consumer world public sector and the like so fundamentally it's all about when you look at big data that's the foundation of cognitive in Watson big data is increasingly heterogeneous in terms of the underlying platforms that are storing and analyzing it data warehouses and OLTP databases had to stream computing content management no sequel and like Watson's able to work with and pull data from any database of any sort and Watson's able to deliver the results of its analyses to any application be it business intelligence predictive analytics decision automation and the like so in many ways Watson is a unifying technology and thread across the entire IBM analytics portfolio and Watson is able to discover fresh insights for fresh data in real time it operate in real time it's built on an enterprise class had to platform IBM infrastructure big insights leverages the best of memory computing through IBM db2 with blue acceleration and it's able to stream its insights through IBM infrastructure streams or several other underlying data technologies and fundamentally you know IBM Watson's able to deliver its results its guidance transparently in the context of decision making scenarios and deliver this guidance out to your smart phone or really to any application you wish and with security and privacy and compliance built therein so what we've been stressing in the last year or so is that we have moved Watson and its tooling and the entire development and platform ecosystem into the cloud specifically for partner enablement into the blue mix cloud where you can combine Watson cognitive services with mobility services with big data services and with a wide range of other services that we make available now in the cloud specifically in our software cloud and accessed by developers through blue mix and we provide a development tools and apis and frameworks or different development requirements in the context of this portfolio so in many ways Watson is not just an enabler for all of our analytics offerings but it's also at the heart of our Internet of Things strategy and roadmap and move to the next one. So mobility mobility is a critical piece of the overall go to market message for Watson. As I've indicated, first of all IBM has been in the mobility space for years. We have the mobile first portfolio of mobility management middleware. We provide a broad range of consulting and professional services to help our partners and our customers to mobile enable their applications. And we really support the entire life cycle of development and production and management of mobile applications by our customers for the refresh. So mobile first enterprise is the name for the product family of all these tools for businesses that wish to mobility enable their applications. From integrate data to developing the models to instrumenting your business processes and applications for mobility enablement, testing the applications, scanning and certifying them, deploying them out to all the disparate devices that are in use within your organization or your value chain or in your customer environment. Then managing it all and obtaining insight on usage patterns and the like so that you can then tweak and tune your mobility applications to better meet your needs. And of course security is built into the entire portfolio. And you're able to as soon as it refreshes I'll show you. So you're able to mobility enable a broad range of devices and operating systems. What we've announced as I've said is that we have a strong partnership with Apple. We've delivered many applications since that announcement that are geared to specific industry markets to help our business customers to completely mobile enable themselves around the Apple platform. And we've gotten great reception from the market. Our four go to market offers which are mobile first for the iOS platform. It is the iPhone as well as the iPad. As well as the enabling middleware tools to manage it all. Applicate for enterprise Apple provides full support for the applications that we co-develop with them for the iOS platform for our enterprise customers. And of course as the mobile first supply and management capabilities we offer so that you can distribute and manage all these disparate devices using IBM tooling and IBM support. So the Watson ecosystem is also a very critical part of our mobility story for our customers. We have the Watson developer for those customers who want to leverage the full power of computing in their iOS mobility applications. The Watson developer cloud we have over a thousand engaged innovators that are building mobility tools on the Watson platform. So we provide them with tools and APIs and guidance and training and development and support their testing of their kind of applications on our platform. The Watson content store if an ISV wishes to bring data either their own data or for a fee data or even free data from various public domain locations so we can assist them through the Watson content store. It's a hub for accessing all this data in order to power Watson mobility applications. And we also have the Watson talent hub. This is an old number. We've got well over a thousand subject matter experts now providing a marketplace of skills necessary to build beautiful, elegant and highly useful mobility applications that leverage the best of Watson and of our cloud environment. So as I said Watson's IBM has been in the mobility space for a long, long time and we're recognized as a leader. We've helped transform nearly 4,000 customers with our mobility solutions. Since the last ten years we have made close to a dozen acquisitions of mobility, enabling tools and applications of different partners. We have over 250 business partners right now working with IBM to deliver mobile solutions. We have one of the deepest benches in the world of researchers doing basic research into mobility related to technologies and applications working on projects in close to 2,000 locations around the world. IBM has received well over 100 patents for wireless inventions and other mobility enabling inventions in the past several years. So it's just an ongoing focus of investment for us into all things mobility, leveraging big data and leveraging Watson in the cloud. And we have mobility experts working around the world in most of our labs. Because as you well know, mobility is a universal requirement of every country, every region and really every industry. And in the consumer realm as well as in the business realm. So we stay definitely very, very busy, continue to evolve our mobility capabilities and our portfolio and our partnerships. Because it all comes down to thinking, cognitive competing is all about helping us think about leveraging the best data in the cloud. And really when you look at what Watson is all about, it's not just a big brain in the cloud that's powered by statistical and machine learning models. It's that big brain helping your brain to get bigger. Fundamentally, Watson is just a premier research tool for us normal human beings going about our daily lives to research the best options without necessarily having to launch into a research project. It's just a matter of doing a query or another query or yet another query or as many queries as you wish in the context of whatever mobile application you're using to ascertain exactly what's going on in the world or that small piece of the world that you need to know about right now to have the best intelligence on. And that's what mobility enabling and that's what you need to know about and the one after that ongoing in real time wirelessly untethered. And so really think how can we help you do it better. That's the power of cognitive computing. And with that I close my remarks. All right. Well, Jim, thank you so much for a really interesting presentation. Great slide deck and really fascinating stuff. We did have just a couple of slides before we run out of time here. The first was you talked about the Apple partnership. Is there a time frame that you could share regarding support for Android devices or is that already in play in some way? Well, we've had support for Android devices from the start. I mean, since Android was all launched as an operating system so we continue to provide device agnostic support throughout our mobility portfolio. So we don't want people to think that because we've got a partnership with Apple that we're slacking off on support for the other platforms in the mobility world. Even if we wanted to, we certainly don't want to where customers would never stand for it. Of course we're going to continue to support Android to the fullest and invest in it. Okay. And then we'll take one more question in corporate training. Well, if you want a free playground to touch cognitive computing, I strongly suggest the freemium version of Watson analytics. Watson analytics we launched to this past fall. It's free to anyone in perpetuity. And to get the full power of Watson as a decision support tool that leverages interactive real-time self-service predictive models that are baked into a highly visual tool that any of us can pick up and use immediately. It's the best of business intelligence for this new era of unstructured data. And so if you want the power of cognitive, I think Watson analytics is the place to go if you don't want to have to pay anything for it. Of course if you want to search more data and you want some other features, then clearly there are premium charges there. But that's essentially a playground in the sandbox for the rest of us to get the speed on the basic technology. We also have technology showcase centers around the world for all things to do with mobility in Watson. I alluded to that in this presentation. And it's, you know, feel free to come online to IBM.com and specifically to look into the pages and the resources available on our Apple partnership and our mobility portfolio. You can get ample information and we'll be glad to engage you further. Big Data University is a great place to go. It's absolutely free to learn about all these technologies and from a developer standpoint it's a great place to get immersed in the convergence of all these technologies at a very practical level. And that's sponsored and hosted by IBM. It's available to anybody for absolutely free. So that's an intellectual playground. It also is a skills development opportunity for the rest of us. Great. Great resources. Thank you so much. And thanks again for this wonderful presentation. I'm afraid that is all the time we have for today. Just to remind everyone listening, the Smart Data Webinar series takes place on the second Thursday of each month at 2 p.m. Eastern, 11 a.m. Pacific Time. Details about all upcoming DataVersity webinars are available at DataVersity.net. And you should see on your screen now a little plug for an upcoming conference we have on the Smart Data Space. That conference will take place in San Jose, California, August 18 through 20th of 2015. The program for that will be announced several weeks from the recording of this Webinar. But if you would like to save those dates, there they are in front of you. Thank you again for tuning in to today's Webinar. Thank you again to Jim Kabilis. And I hope you all have a wonderful day.