in new engine and aftermarket volumes that we've seen in the past three decades. And finally, I would say that we're transforming our aftermarket and customer services business. It's really going to be a change that's driven by our customers. We're moving to a fleet management model that really has Pratt and Whitney making sure that the customers fleet stay operational. Yeah, I mean really in capital intensive businesses like this or really any critical asset, there's a lot of concerns about ensuring that you're thinking about things on a more predictive basis to avoid, you know, unscheduled interruptions. So how do you see predictive and really predictive and real-time analytics transforming Pratt and Whitney and helping you solve some of these challenges? That's a great question and we're really excited about it. So we're expecting the use of predictive analytics to really change everything across Pratt and Whitney's business value chain. The partnership with IBM was launched with an initial focus on driving deeper predictive capabilities for our aftermarket business. Let me explain. Today we have enormous amounts of data that comes off our engines. This data comes from our engine build history records, from our repair records, from our on-wing health monitoring system and things like warranty. Up until recently, we've looked at that data, but the analysis has really been more descriptive by nature and not complete, I would say. We could see what happened to a motor when it flew, how it was performing, and we had some indication of health, but it was still like looking in the rear-view mirror for us. So what we're transitioning to with our IBM partners today is an ability to look ahead and be predictive with regards to engine health. That ability to predict an engine event that might cause a delay or cancellation, I hope it didn't happen to anybody coming in here today, but the ability to look at an event that might cause that kind of a situation at an airport or even more focused on if there was an event in flight, that's truly an evolutionary capability. And we'll take it beyond engine predictives on the health side. We're looking at fuel burn analytics with IBM. We're also tailoring repair work scopes by segmenting flight regions and geographies. So the ability to use big data to bring real value to the fleet is exciting at Pratt & Whitney, and we're really focused on it. Optimizing our customers' in-service experience with our engines is the current focus, as I mentioned, but we're also looking at predictive analytics, how we can use it to really go after disruptions in our supply chain. And that goes directly to that large volume ramp-up that I spoke about. We're working opportunities to be predictive with regards to schedule assurance and product quality. So the asset itself and the supply chain, and really this is direct benefits that gets passed to your clients. And as you can see by, you know, there's not an empty seat in the house here, so you definitely did your job on making sure everybody got here up there. I can't quite see, but I'm sure... Look, you know, this is... We see really big data in analytics, not as a technology quest, but it's really about the business outcomes. And it really is very much about the line of business, getting the outcome reached with a close partnership with IT. And thinking about this from the problems that we're solving in is really the right transformational activity. So how have you embraced this strategy with your lines of business? Yeah, that's right on. From my perspective, there has to be a strong commitment from the business to make the transition from a descriptive analytics model to a predictive model. It's a big shift. It's absolutely not solely an IT initiative. The business has to be willing to be committed, and they have to change the way they think about their products and services. You have to re-engineer your business processes. It literally has to be a paradigm shift. At Pratt & Whitney, we have strong commitment from the leadership team to make this shift, but I'll tell you, I can't sit here and say that the work is all done. There's still work ahead of us that we're focused on. The IT team has been working with both IBM and our internal business partners to get this launched. And Bob, I'm really proud of the work that we've done with IBM to create this partnership at Pratt & Whitney. This is a great example, I think, Bob, of how IBM brings innovation to us as a company. Yeah, look, you know that we're humbled and honored to be on the journey with you, and that we are developing something of great value jointly here. Look, can you talk a little bit about partnering with IBM on the project? I mean, this was a broad initiative across our consulting services and our technology space. So can you just give us and give the audience a perspective on what it's been like to work with us? Yeah, you know, we took a look at this, Bob, and in order to get to the opportunities we wanted to get to and get to them quickly, we really believed we needed an accelerator on this journey of big data. Pratt & Whitney had been studying engine data for decades and identifying fleet-wide trends. But it was clear to me that we needed somebody to come in and help us to accelerate from descriptive to predictive analytics. After we came to that conclusion, we looked at the marketplace, and IBM stood out at having the deepest and the most richest portfolio of big data analytics technology within the industry. So the facts seem pretty convincing. I know that you guys are investing literally billions of dollars in big data analytics, and you've got thousands of consultants in this space helping your clients make this transition. So it was the accelerator we needed to move deeper into big data, and Bob, I'm convinced we made the right choice by partnering with IBM. This is going to be a great partnership. Excellent, Larry. I can't thank you enough, and I can't thank you for joining us at Inside Enough as well. Everybody, round of applause for Larry. Thank you very much. Thank you, Bob and Larry, for that awesome story. As a nervous flyer, I'm really glad to hear that. That's made me feel much more comfortable. So throughout this week, I'm going to be hosting an ongoing series called Moments of Insight, where we'll get quick glimpses into companies that are using real-time insights today. And I'm joined first by Deepak Dodani, Vice President of Global Transport and Supply Chain Solutions for CIVA Logistics. Welcome, Deepak. Thanks for having me. Thanks, so tell us a little bit about CIVA. So CIVA is a leading global logistics company with more than 40,000 employees, 1,000 different locations in over 170 countries. So what we offer is a world-class set of supply chain solutions in freight management and in contract logistics. Essentially, these are end-to-end solutions from warehousing to distribution to manufacturing, air, ocean, and ground.  That's right. You buy a TV on Amazon, we're behind it. Thank you. You get smartphones, we're behind it. Nice. So that sounds like a huge amount of logistics. How do you manage all that? Well, at CIVA, our motto is creating value through impeccable execution, which means delivering through very disciplined design, implementation, and operational processes. And underneath all these processes, what we have is our flagship technology platform called CIVA Matrix. It's right there. Very cool. And to talk a little bit more on the platform, it's a best-of-breed technology platform that allows us to integrate and orchestrate all the customer supply chains to provide complete control and supply chain visibility. So I imagine a lot of data, of course, goes along with that. What kind of data are you guys handling? So there's a lot of unstructured content to deal with. You talk about order forms and bills are lading, proof of delivery. In fact, we have about 70 million documents in just one line of business. It's a lot of content we've got to deal with. Huge amount of unstructured data. That's crazy. So what was your solution? So at CIVA, we're undergoing a strategic move towards utilizing cloud-based services in our platform. So when it came to enterprise content management, we chose IBM Navigator. And that allows us to now expand our cloud-based services portfolio. And that gives us a leg up on predictive analysis and all the harnessing that these millions of documents that bring to us as far as delivering insight and proactively running our business. So the decision to choose IBM was a win for us and our customers in order to deliver their requirements on supply chain visibility and information. Nice. So you've collected all this data. You're now managing it. Can you share any of the insights that you've actually gotten out of that process? Yeah. So we've gone through this process from on-prem to cloud.  So a couple of tidbits. Maybe since this is Vegas, a couple of golden nuggets for you. So one of them is, you know, plan their taxonomy before you move that content. Okay, get your data hierarchy there. You're going to less pain in the future and you can get some intelligence later on. And the second piece of advice is get your document retention policy at the enterprise level drafted and approved. So this will help you from all the stuff regarding compliance and to then get to a lot less pain in the future. It's fantastic. So you just hit all three pillars, Bob talked about. You got a huge amount of unstructured data. But that really has changed. Now we see clients moving to cloud for key business reasons like agility, innovation, and really forging completely new business models. How do clients achieve success in the cloud? Well, first, they use it to integrate with mission critical systems and brand new systems of engagement. Second, they use it to harness all types of data and a full spectrum of analytics. But they must have security intelligence and analytics to protect data and their applications. And they need expertise to accelerate that deployment. Leaders and innovators who use cloud share common characteristics. Let's go back to those Generation D clients that I talked about before. Those Generation D enterprises are twice as likely to use cloud for data management as their peers are. And more than half of them are already delivering significant value through the cloud. And up to 80% expect to be implementing big data and analytics directly on the cloud. The bottom line is cloud gives organizations of all sizes the ability to innovate at speed. And innovation isn't just one and done. It's a continuous, continuous cycle of improvement. All roles in the organization from business leaders to developers to IT leaders have more power at their fingertips in the cloud. And they can rapidly iterate to bring the transformative power of data to all the corners of their enterprise and their extended value change whether that's customers, partners, suppliers, or really importantly as well, directly to their employees. IBM's as a service model lets organizations like yours achieve your goals quickly, easily, intuitively, and with security. For example, Sun Life Stadium, home of University of Miami Hurricanes and the Miami Dolphins, uses soft layer to improve the fan experience directly in the stadium. Webster Bank, a regional United States banking organization, uses IBM security capabilities to help prevent fraud before it even happens. Now Akamai, a company well known for accelerating the power of the web and famous for internal innovation, turned to cloud, our database as a service to rapidly deploy their globally distributed application environment. And finally, DestinationXSeller, retailer who wanted to help its customers see clothes as a shopping pleasure, not as a chore, used our cloud based incentive compensation solution to show its sales associates that they can be more successful by providing customers with a personalized shopping experience and selecting full outfits rather than just single articles of clothing. Now as I said earlier, in order to innovate successfully you need to have also a comprehensive approach to security. In an IBM survey, 61% of all organizations say that data theft and cyber crime are their greatest threat. People used to think that security was simply about establishing a greater perimeter around their organization, a higher wall, a deeper moat. But it's absolutely time that you need to change your thinking about this. And IBM has changed our thinking about this. In fact, we see cyber security as a big data problem. If you want to protect your data, your applications and your networks for malicious threats, you need first intelligence by using insights and analytics to build a smarter defense. Second, you need integration to develop a completely integrated approach to stay ahead of those threats. And third, you need innovation that spans across the cloud and across all devices. Now cloud coupled with this security and the right innovation around data is really going to allow us to cross this chasm of innovation between business and IT. The innovations that really matter to you and to me are those that help reap the maximum value of data that's all around us. And what do those innovations give us? They empower more people to solve more problems, they make more data available for everyone and they put data to work in more places. So look, let's start with the innovation that makes data readily available for more people to use and actually helps make data more actionable. And to start this particular segment, I'd like to introduce my colleague, Marcus Hearn. We're going to get that full measure in whether we needed it or not. Right. Yeah. Look, Marcus, welcome to the stage. Thank you. Look, you know, in conferences like this, sometimes we introduce people with their titles and their roles but I want to ask you a different kind of a question. What's your passion? So as an IBMer, my passion is client results. That's a given. I think that goes to the whole team. So the marketer, it comes down to three things. First is using analytics. Using analytics, everyone on the team using analytics. Experience and opinions are great, analytics backs it up. The second is the time to the insight. Get there quickly. Using analytics, it takes a year. Not really that useful. The third thing is actionable insights. Being able to do and find something that you can act on and know the result, improve the outcomes, critical. So what I wanted to talk today with you about and show everyone was what's in analytics. It's IBM's innovation that reinvents the analytics experience on the cloud. Excellent. Let's get started. All right. So we've got a bit of a previous setup. Make sure we transition quickly. We're going to do a bit of artistic license here. I'll play the role of an insurance marketer. You'll be my C-level executive. So I'll be asking for budget and stuff and showing you why I should get it. I brought my checkbook. That's right. So we've already logged in here and this is the home screen to what's in analytics. And you can see it's walk-up intuitive. We can see immediately what we can do. We can start from a story as a marketer or as a finance person in operations. I can even sort of cancel these out if I want to bear it down to just the things I'm interested in as a marketer. That would be here, improving campaign effectiveness, refining leads, and so on. Or I can dive in. I can explore my data, predict, and explain. So let's skip ahead. Now now I've got data that IT has provided for me. It's curated. It's centrally controlled. I know it's up to date. They've even integrated some Esri data in there for me. So I've got the geospatial stuff, the superfood, as Jeff calls it. So let's go over and have a look at that here. Now what's really cool and what hits you straight away is I know straight away how good that data is. And it's, you know, the 68's a good score. It's certainly better than me guessing. So we're already looking pretty good. And what's in analytics has done is it started doing some data preparation. It's actually found some fields that it should get rid of. It's got missing data. You can see here, two of them have constant values, probably names. They're not really useful. And then down here, it's even started finding things that are interesting. Maybe I should have a look at these things. So I could actually then go forward and accept these changes and improve this data score. We won't do it today for the sake of experience. You know, Marcus, 80% of the time on any analytics project is really burned up in that element of preparing the data for analysis and for insight. And with lousy data, you get lousy insight. So this is amazingly valuable right up front in that it gives you insight into the quality of that data that you're going to use for your analytics project. You're absolutely right. And that's across the board. I mean, just using Excel, people do deduplications, sorting, fixing phone numbers, everything, all the way up to a data scientist. And you're right, 80% of the time, that's a huge chunk of time. And there it is. It's already done. So I usually would have skipped it, but it's being done for me and now I can see what's happening. So let's go to the next step here. And we've got our data. I am going to go into a customer value analysis. Because as a marketer in this insurance company, I've got a campaign going, get the West Coast, trying to get all these people to renew their insurance policies. And I've got some results. So this is what IT is actually providing me. So Watson Analytics has provided some suggestions. Here's some suggested analysis. That's great. Somewhere in there is probably something that would be useful, but what I want to show everyone is that I can just type in a straight-out question in plain language and get the results I really want. So while we're at it, let's see. I want to see what is the... Wait. How should I type this? What campaigns are my customers? What campaign... Here we go. What offers and campaigns are my customers responding to? See, just to prove it's live. I actually did all of that on purpose so that everyone can see we're actually live here with the software. So what I've got is not really what I'm after. What campaigns and offers... Ah, you know what I'm going to ask instead? I apologize. What is the rate of response to my... What is the rate of customer response to my campaigns and offers? You're just showing off that the thing is analyzing the question as you type it. I am kind of doing that. And also, there we go. So this is the one I want right here. And you can see it's got a number of others that are very relevant, somewhat relevant. But we're going to dive straight into this one here. So the integrated natural language processing allows me to start thinking about the questions I want to ask of my data, not by looking at patterns in the columns already, but really by posing a question directly against the data that I've collected. That's exactly right. And as you saw through my somewhat messing around there, it iteratively goes back and hits and hits. And what it's done now is another really cool thing, is it's come back with these results in a visualization that is appropriate for the results that I've got. So rather than me jamming sort of fields and then choosing a visualization I think looks good, it's giving me the most appropriate one. We've got some fairly simplistic answers here, so that's fair enough. But what I want to show you here too is this data tray. The curation of the data, the refinement continues. I can actually go in here and choose. I can filter. I can say let's just look at married people. Let's look some of the geospatial, suburban, urban, rural. I can continue to do this iterative process with the data. So we can close that up. I can also do this I really like. I can do it through the interface. So instead of responses, I can look at education. I can look at employment status, right? And right on the fly, it's going to change. Let's go back to responses here. All right. So as a marketer, what I'm interested in is people who actually do respond. It's not really much point looking at the people who don't. Although, you know, arguably I could later on and sort of work out why not. But for now, and what we see this immediately apparent, offer number four is a dud. Offer three is not so great either. Offer two is the winner. Offer one is pretty good too. But let's drill into offer two here, right? So we're going to drill across and we're going to look at this by state because you're on the West Coast. And we can see, all right, California, Oregon, Arizona is not far behind and it's keeping the visualization. So here's another cool thing. As you drill through and as you drill around, this intelligence behind the visualization keeps up with you and it's made some recommendations based on my new view. And you can see here, I can switch to a map, which makes it readily apparent where I'm succeeding, right? There it is, California, Oregon. Here we are in Nevada, not doing much to lift our game, but we should maybe try and improve that one day. Let's switch back because I want to go into some more things. I've got a lot of things to show you, not a lot of time. We're going to add some rows. So here, I'm going to add a row about policies because I'm interested in what are the policy types that these people have that are responding to us. And here's another cool thing. You see down here we've got policy type and policy, one's auto insurance, one's homeowner. And what it's done is it's bundled them up into a rollout variable automatically. So it's derived new information from my data. Something that you would have needed a data model to do for you if you were sitting at the desk in most enterprises. I've got to say I would never have thought to do it as a marketer.  So what, 1970, something like that. So we pop this in and what it's then going to do, people at the math finally hit them, it's going to show us these different types of policy. So you see here, so we've gotten even better. Now we know it's people with personal auto policies. They're the ones that are responding. So I can drill in here, drill down and find out, okay, I've got the policy type. There are the policies, policy number three. So just like that, I've gone through data access and preparation. And I've managed to find out when it comes to the campaigns, which ones are succeeding, which offers a succeeding, where they're succeeding, and the policy holders we're succeeding with. So that budget you're giving me, I'm going to turn the ROI right up. I'm going to do a great job. All right, well, let's get to work with it. This is where it gets really cool. This kind of relates back to what they were talking about just before. Are you on the customer there? This is where we get predictive. And yes, I've jumped across. But if you go back to the welcome screen, you can see very quickly how I predict and explain. And what we've got is the same customer set here. And inside this data set, I have a customer lifetime value. And what this is essentially the revenue and the cost balanced out to then give us a value statement about them. So what I'm interested in is, okay, I'm going to really kick it out of the park here with my marketing. I'm going to do this great campaign. I'm going to have this great offer. I'm going to get great results, but I'm going to hit customers. They're going to give us great revenue, great returns. All right. So I want to know how do I identify these customers? They're going to have good lifetime value because they're not at the end of it yet. So you can see we've got a predictive model here. And this is our marquee graphic. You've got the target, the outcome you want to know about in the center. So I'm going to go back to that outcome. So right here, I can see number of policies. 46.6. That seems fairly intuitive. I immediately would think, okay, the more policies we can sell someone, the more customer lifetime value we're going to get out of them. But many times it's a combination of factors that give you the strongest result, right? Absolutely. And you see, you're absolutely right. And what we've got here is it's already done that. It's made a suggestion and more accurate and interesting insight. And I won't read it out. I am more interested in getting super predictive. So down here at the bottom, I can turn it up. I want to get the most predictive model I can out of this. And that's all I had to do. And you can see exactly as he said. It's like you've seen this before. We've got the most accurate predictor is a combination of fields at 66.7%. That's pretty good. So as a marketer, you just now used sophisticated predictive analytics against the problem you're trying to solve to understand what are those factors that really going to affect customer lifetime value without having a statistical model or a staple at your side? That's exactly it. I wouldn't know a lick of data mining. And I've just done it all. I've done this predictive analytics very well. And it gets better because now I can drill. You can see this decision tree.  And this one's actually a bit complex. I mean I can zoom in and I can sort of look at the number of policies. So obviously if they have lots of policies, they're going to be very useful to us. And it gets a bit more complex as I drill down the tree. So what can you do instead? This is getting beyond the black box of it all. I can see what's going on. I can get rules. Straightforward language. And I can see, look, number of policies. And here's something interesting. See, number of policies equals two is consistent across the board. So it's not a customer with the most policies possible. And I can see my top group. Highest maintenance. So they're paying premiums. They've probably got expensive cars or teenagers. They've got a collective policy. So they're on a family plan. And that's the top. So when I get my million dollars of marketing budget out of you for this, I know the offer I'm going to use. I know the state I'm going to go after. I know the policy holders. And I now know who are the people I want to get first on the list. I'm going to get the highest valued customers for us through that campaign. And then, you know, we can also look at I think people, this is a good way to do it too. You can see what are the strongest predictors in a word cloud. And one thing that's occurred to me in doing this analysis, which is quite cool as well, is that, yes, I should do this campaign to get these people to renew. And now I have a very targeted way to do it. But I've thought of a new one. Let's go after people with one policy and simply get them under two. That's a great customer. Another great insight. Marcus, thank you very much. Fantastic work. Thank you. Well, look, as I hope you all can see, IBM has truly reinvented the analytics experience in the cloud, letting you gain insights faster, easier, and more instinctively than ever before. In fact, as we've showed this to many partners and clients, they've reflected back to us that they think IBM has really transformed this entire space. And this is what makes Watson analytics unique. It puts those sophisticated analytics in the hands of every person. Second, it allows you to discover insights through a natural language dialogue that you saw up here. And third, your analytics experience is now unified into an intuitive problem-solving platform. Now, speaking of putting data to work, please join me in welcoming Vice President of Big Data Integration and Governance, Inhe Cho Suh. Welcome to our main stage. So I'm going to ask you the same question What's your passion? I'm absolutely passionate really about how data helps our clients solve their toughest business challenges. I genuinely believe that data is going to be transformative for every industry, organization, and profession, and cloud just accelerates that transformation quite on this. I agree. And Inhe, you know, I share that passion, so why don't I let you get started? Thanks, Bob. You know, my own children remind me every day how cloud and data are naturally part of our clients. Actually, at the dinner table, our sons often talk about joining each other's servers, which is crazy, right? And creating worlds and places on online games or sharing photos with friends or space-timing with their cousins. By the way, Jacob and Noah are only seven and three years old, right? So I've been at IBM over 16 years and have had an unbelievable opportunity to really interact with thousands of companies and clients about how they're leveraging data, really thinking about the cloud, and the business challenges that they've got. And what I want to do today is actually share a story that compiles many of these client experiences. And more importantly, the innovative solutions that we've built in the labs and are going to debut here today. So let me introduce you to company ABC. They're global. They've got people in offices all around the world, multiple locations, but they've got a lot of employees. Their employees can't access the data that they need in their day-to-day jobs. It's difficult for different roles across the organization to leverage all data. Let's take a closer look at kind of three roles and three people in the organization. First, we just saw Marcus's life completely transform with Watson Analytics. But you can imagine before Watson Analytics he probably spent more time looking for data than getting to play with it or even analyze it. Let's go to Megan. So Megan is our application developer. And she's getting more requests to build mobile apps. But she doesn't really have an easy way to access and integrate that data services in the applications that she's trying to build. Let's go to David now. So David's our IT engineer and data architect. And he's often frustrated by what the volume of requests that he gets. There's probably shadow IT teams in his organization. And then more importantly, he's also getting apps sort of dumped on his lab that don't properly incorporate data and sometimes even violates governance rules. Does any of this sound familiar to you guys? Yes? Okay, good. So no one really within this company actually feels like the data is working for them. So what I want to do now is really look into the solutions that will make data work for them and how their lives are going to start changing today and starting right now. So let's go to Marcus. Marcus at the coffee shop, right? So just think about this. Remember, he was frustrated by using spreadsheets to gather data manually. Now, he can use Watson analytics. But there's also a secret sauce inside Watson analytics, which is in the UI experience itself, he also has access to powerful self-service data refinement capabilities. When I talk about data refinement, you're taking kind of raw data and making it more useful before you do and apply analytics to it. So what do I mean? He can do things like send and shape that information. He can automatically improve how the data quality is going to be improved within the service and within the application. He also has access to libraries of data sets. And this is all in his application experience. So no separate UIs and no IT requests. I'm absolutely thrilled to announce IBM DataWorks. It's our new cloud-based data refinery service that makes relevant data available to everyone. And it offers a set of things like data acquisition services, cleansing, matching, and security services. And this is all embedded kind of in the Watson analytics experience. So Marcus can get the benefit of refining data all by himself. By the way, we've exposed DataWorks capabilities in BlueMix for our developers and IT engineers. So let's go to Megan now. So Megan's in our office. She's our application developer. She's used to writing apps and what text editors and probably development tools. And remember, she had difficulty deploying database applications and databases for mobile applications. So now Megan can go into BlueMix, which is IBM BlueMix is our open cloud-based platform as a service. It's built on cloud foundry. But it's where you can actually easily compose apps. You can pick a routine, a set of services, and really just get started. So here, let's zoom in. Megan can see some exciting new services that we have. So I want to announce several new offerings that we've got. So first, I'm excited to announce IBM Cloudant Local. It's our mobile database as a service that's now available as an on-premise offering in the privacy of your data center. Many of you guys already may be familiar with Cloudant, which was really in our public cloud. Now, what Cloudant enables you to do is for developers to create and innovate faster. It scales elastically on the cloud. It also handles multi-structured information, types like JSON, full text, geospatial data. And also in the Cloudant experience, one of the things that Megan can do, she can click a button very quickly and we can move that data into actually a new breakthrough analytic warehouse. So the second item that you see on the screen, and I'm excited to announce, is IBM-DB. This is our agile cloud-based data warehouse. It's self-service. It's got the cloud agility aspects, but we've also built in analytics and security inside. It also has powerful in-memory capabilities for high performance. We're trying to break the silos between operational and analytic systems. Now, I've already mentioned dataworks. What we've done is, within BlueMix, we've actually added additional services so you can do things like cleansing profiling matching that Megan can also easily integrate into our apps. Now, the question is, how does she put these three things together? So let's look and say that Megan has to actually write a mobile application for restaurant takeout orders. She can use Cloudant to manage kind of the app's data wherever she's working. And then with a click, what she's doing is we're converting a JSON schema to a relational warehouse and dataworks moves the data to dash-DB for predictive analytics of future restaurant orders. And now Megan can have essentially additional data refinement services to our app. She can begin to analyze what restaurants people prefer and then predict kind of where they want to go next. So now let's go to David our IT manager. So remember David, right? He was frustrated by things getting dumped on his lap. So now David, he wants to create a data warehouse in minutes and not have to worry, right? So he's going to do that in dash-DB and you can see David is deploying a data warehouse himself. No more waiting for infrastructure. Now, in addition to that, what David is also able to do is within dash-DB and also in data works, we've added capabilities like matching customer records. Within data works, one of the things that we want to enable is some of our unique capabilities around big data probabilistic matching. It's a matching service that runs on Hadoop and what we've done is he can identify customers within his, let's say, data lake or data reservoir, right, to add to dash-DB. We're also innovating, not just in the structure world but really in the unstructured world and the new generation of cognitive applications. So I'm thrilled to announce IBM Watson Curator. That's right. It governs content collections for use within Watson and so that data stewards and knowledge workers can access, kind of, you know, categorize, curate that information to make it more useful for everyone. So here you can see a knowledge worker using Watson Curator to review, govern, profile and improve content collections. Now David has enabled self-service access and you can imagine life has completely now changed at Company ABC. So starting today they can use these cloud-based data capabilities to make data work. I'm excited to announce that you can get started today with IBM Data Works, IBM Dash-DB, IBM Cloud-It, Cloud-It Local as well, and IBM Watson Curator. By the way, these are in addition to the Hadoop analytics as a service that we have as well as the geospatial streaming analytics capabilities and the SQL-DB services all in Bluemix. These solutions allow everyone to innovate. Now ABC Company can leverage the capabilities that we have for hybrid cloud for self-service data capabilities, but really to give more people more access to more data. Every role is going to be empowered with data. Business analysts, knowledge workers, application developers, DBAs, IT and data stewards. Look, the tale of the ABC Company isn't just a nice story. It has a point. Think on this question for a second. Who are Marcus, David and Megan really? I would say that they're all you.  I challenge everyone in this room to sign up today on Bluemix for these cloud data services right now. IBM is empowering you to make data work. Thank you. What Enhi just showed adds a rich platform of data services on the cloud. But that's not all. Earlier this year we announced geospatial analytics and time series database on Bluemix. These services enable developers to create new products and services with exciting business models fueled by the Internet of Things. Continuing that IoT theme we're going to have some exciting announcements on stage tomorrow in the general session. Now also, DashDB, our agile data warehousing solution within memory database analytics is available for you to try right now. I think that IBM is uniquely positioned where the only company that provides servers are our own Hadoop distribution, our own SQL database, no SQL database, data refinery, data security privacy, and in-memory database. And that's just, that's not even a complete list. We're the only one delivering this full spectrum of analytics on platform as a service. Now we didn't stop there. We provide a rich and extensive portfolio of software capabilities that are best in class across business functions with more than 30 solutions available we're committed to help you seize Cloud for your business. More people, more data, but what's next? We need the ability to access this data on any device, no matter where we are. We've just seen Inhi announce Cloud local, but I really want to reiterate the power of Cloud. Cloud and no SQL database is enabling customers to create new systems of engagement to deliver their business as a service digitally to people via their phones, via the technology they wear, the service they travel in, and the goods and services that you produce for them. It's the only database as a service that supports hybrid Cloud environments, which means that you can choose where you want to put your data based on the regulatory and privacy requirements that you set. Now while Cloud is about building the next generation of mobile and web applications anywhere, we also need to access content at any time and any place. So speaking of content, I know that many of you in the audience have already decided about the 2015 season of House of Cards, and since I knew that Kevin Spacey was going to be here on Wednesday, I decided it was going to take a couple minutes and work up a script for his episode, and since I'm traveling all over the place all the time, I had to work on the script anywhere, on any device at any time, and I'm going to show you how I was able to do that. I'm going to bring on stage Ian's story who's going to help you see how I was able to do this. So Ian, come on out. I realized I made a mistake. I have another change I need to make in the script, so I'm kind of having to do it on stage here this morning, but I'm hoping you can help me with that. Sure thing, Bob. I'm happy to help. What do we need to change?  Instead of writing House of Cards, I wrote House of Cards. So I can't submit it like that. We don't want that. So let's make a change right away and fix that. This is IBM Navigator, running on SoftLayer. I'm going to sign in, and we're going to just find your script. So I'll come in, and you know, instead of going through all the scripts that you've written, I'm going to just find this particular one that's got the word cars in it. And so we'll run a search, and we'll find, yep, sure enough, there's a document that says cars. So let's fix it. So I'm going to click on the document and just check it out. I'm at the top where we've already downloaded it. And let's just open it up and take a look. And sure enough, House of Cards, right on the title screen. So let's fix that. House of Cards. And we'll go back over to Firefox. And let's just check that back in. So when I check it in, you'll see that I get the chance to show all the metadata. So this isn't just basic consumer file sharing. This is file sharing for the enterprise. I can add metadata and so forth. Files, encryption, security, et cetera, all running on SoftLayer. And so let's just grab that document here from my hard drive and check it in. So, now that it's checked in, I presume maybe you'd like to have it on your phone. Yeah, I'd love to have it on my phone. Oh, you just happen to have my phone here. We've got your phone right here. Exactly. Let me go ahead and let me open up the Navigator app and I'll type in my super secret password here.  So let me look at the synced items, right? Because we just synced it. There it is. My Pitchiano Productions. There we go. House of Cardscript. Let me open that up. Oh, look at that. There you go. The change is made. Well, that's ultimately simple. Ian, thank you very much. Appreciate it. Absolutely. All right. I think I'm now ready for Kevin. He'll be here, you know, on Wednesday. So I hope you stick around and see all the exciting things this week, including a great set of things with Watson tomorrow and also with Kevin on Wednesday. Now, this seems like a silly story, but as we heard earlier from Civa Logistics, they have tens of millions of documents, bills of lading, invoices, et cetera, all of them that need to be available for other clients anywhere, anytime. With IBM Navigator, we can collaborate on that content securely and we can manage any document. It provides security policy, auditing capabilities and a trusted environment on software. This is enterprise, collaborative, filesink and share. To help you get started, we've actually added select presentations from this conference and we've made it available on Navigator for all of the registered conference attendees. So this is truly information in more places. Now, our mission here at IBM is really to help our clients solve their business problems. Above everything else, it's about helping you achieve the business outcomes that you seek. Our innovation, both on the cloud and on-premise, is about helping you bring more data and more insights to more people in more places for competitive advantage. Now, as I said earlier this morning, this is a different moment. When it comes to data and cloud and analytics, this is about a critical inflection point.  So you've all been sitting back and you've watched the morning messages about the inside economy and what's really happening. And I'm going to ask you a question. I really want you to answer, are you ready to seize this moment? Yeah? Are you ready? Are you ready? All right. All right. Well, look, it sounds like you guys are pretty engaged and we're going to talk about engagement. We're going to talk about our client stories on engagement and to get started, I want you to hear about some of the client stories. We want to service a customer the way that the customer chooses. So if she chooses to shop with her phone, we're going to be there. If she chooses to shop online, we're going to be there. If she chooses to come into the store or experience us in all three ways, that's part of our job and our relationship. It's individualized. It's specific to you, the customer. We used to offer products to market segments. Now we offer products to individuals. We want to talk to the segment of one, generate a personalized bank for each individual. Big data generates that possibility and big data generates that reality. Your very own bank made with that. Man, how cool were those demos? It's awesome, right? I love this little Mr. Wizard setup we've got going on here. And I think, aside from the fact that Watson Analytics might be putting me out of a job soon, I thought that was so powerful, so interesting. I also felt like when Inhi was up here, like we should all chant, we are all Megan. We are all David. I very much feel that way. But yeah, right, thank you for the one slow clap. Exactly. Thank you. But I wanted to come out here to tell you that we started the day talking about data. We started out with the first thing, then we moved on to the future of cloud. But now we're going to turn our focus to the third imperative that's changing our world as we know it, engagement. As many of you know, mobile and social are redefining customer expectations at the point that they interact with us and the value that they expect to get from us. They expect personalized experiences. I don't know if you guys have had this feeling before, but I recently downloaded an app on my phone and it was clear really quickly that it wasn't personalizing itself based on my location. And I got angry. I was like offended. I was a great app. Maybe I should download this on my flip phone in 2008. No thanks.  This is really what our customers are starting to expect from us. They want to have personalized, individualized engagement. And that's now possible with big data and analytics. So for more on how you can capitalize and this huge opportunity, it's a pleasure to introduce senior vice president, global business services, Bridget VanCrologen. Good morning. This is an amazing event and the energy feeling in the room is incredible. Actually even behind the room, the energy is pretty amazing. So just before I came out here, I was handed a note by our social media staff and I actually thought it was something that you'd be very interested in learning about. Apparently the social activity from this room alone this morning, the tweets, the Instagram posts, updates on Foursquare, Facebook, exceed the volume of all the digitized data from all the video surveillance in all Las Vegas casinos in this past month. And even more incredibly, surpassed the combined volumes of all the alcohol and aspirin consumed in this hotel in the last 12 hours. Obviously not including you lot, because I know how virtuous you are since you got up at 7 to be here. So none of these are STEM professionals. Okay, so this morning, we've covered cloud and data and now we're going to talk about engagement. And I'm going to suggest to you that there is something really interesting happening at this intersection where big data meets individual engagement. Because when you think about that intersection, there's something of a bit of a paradox going on in this world where data is massively available. And that paradox is, even though it's massively available, you're going to need to generate much more of it. Much more of it. Second point I'm going to make is that the arrival, and Jake mentioned, of the long-awaited market of one, finally got here. But in fact, that has turned out to be just a stopover on the way to the real destination. And third, the consumerization of the experience for professionals in business and in every industry. Now, let me give you one quick example that actually highlights those three themes that I... And I believe you will hear this throughout the next few days. The evolution of work, the transformation of a job role, terrific new business value at the level of individuals. Let's say you were flying here from Paris and connecting to JFK. But your departure was delayed by 90 minutes, and you knew before you left the ground that you were going to miss that flight and miss that connection. Now, imagine a world where your flight crew could walk the aisles while that flight was heading over the Atlantic and you had some devices and apps for every passenger's itinerary.  Versus what you normally go through when you land. Now, that is just the transformation of work, of a specific job role and new value that an airline could create for its customers like you and me. And that kind of work and that kind of engagement is coming. The spoil, you'll see some of it in our partnership with Apple. And I'll come back to that in a few minutes. But you will see it as you walk through the next few days. Now, let me take these three themes one by one. So, if data, as you've heard this morning, is the basis of new competitive advantage, and I believe it is. I think we're all here because we do. How are we to think about what is becoming recently possible? Are we just solving old problems? Or unlocking a fundamentally new possibility? Is it the standard view of big data? Rationalizing massive data warehouses? Or finding the needle in the haystack in tax or medical claims to see patterns of fraud? There is tremendous value in all that. No question about it. But what is becoming more newly possible is very, very different. And in the most important of ways, it's intensely personal because it's around the behavioral and attitudinal data that reveals the whole human being.  Think about it this way. If we agree that the primary quest of business is all about the use of data to create these more intimate, more individualized relationships, then you and your organization can either create access to that information on individuals, or you're actually shut out of it. You either have an open channel to all that information on preferences, choices, behaviors, attitudes, actions, expectations, or it goes to somebody else. And that's going to be a function of the front-end engagement you do with individuals. And it'll decide whether it starts or short circuits a cycle that fuels insight to process design, product offering, changes to operations, services, and sales. In very new, real ways, I think this will be the new form of ERP, Enterprise Resource Planning. It'll be the source of the next great wave of business change and the new backbone of our businesses. Which sets up my second point. For years, we've been talking about the proverbial market of one. And I don't know about you, but for most of us, it was a bit of an empty promise, right? An empty promise. At best, we were addressing large, demographical categories because segments were really the best that we could do. Not anymore. But, but now that it's here, it turns out that that market of one is really just a way station. It's just a way station identifying and even reaching me or you as a segment of one is really hardly as important as engaging each of us at a level of actual intimacy. And that's what each of us really wanted to do and where we wanted to go all along. Which is wonderful. But to reach that goal, I think we have to be very precise on how we think about it. There's design and then there's engagement. They're closely related, but they are not the same thing. There's design, beautiful, elegant, presentation, fun, intuitive interface. And then there's engagement, which describes the complete experience and it's entirely dependent on the astute use of data and analytics in context and personalized. Engagement needs context. So, back to the quest of contemporary business and the value at the intersection of data and engagement, we at IBM are pouring 100 million to the build out of 10 new studios around the world in our global network of IBM interactive experience, which at age ranks as the world's largest digital agency. Please tweet that if you don't mind. So some of our early and ongoing work has been with organizations that wanted to open up and enrich the experience of live events like Masters Golf or Wimbledon. And I actually think they were the forerunners or predecessors of what some of this engagement is going to feel like. So one example is last year we launched a Wimbledon app with a feature and an experience that you can't get even if you're right in the front row of center court. 360 degree walkthrough from the changing room to the court, 80,000 beautiful high-def images of the grounds in time-lapse video, along with live information on matches. But even more important, we analyzed eight years of Grand Slam data, 41 million points and found patterns of player performance and brought that insight to a real-time second screen. This app was downloaded to 2 million devices last year, mobile, and that same approach to intensive data beautifully presented in context is being applied to our work with clients all over the world in retail, consumer products, telco, banking, automotive. And what's interesting is not any of the fans love the app, the players love the app because it gives them insight they never had into what they do and what their competitors do. Very powerful, very contextualized. So think about this, whether that would be in the mall in your car, at your desk, on a construction site or at 30,000 feet, all with a mobile device. The next thought around engagement is immersive, right? Relevant, contextual, but immersive. Digital experiences, technology that can actually infuse the physical world with digital interactions of all kind. Here's a great example. We worked last year with Jaguar Lambrough, but they turned to us to create a new kind of car buying experience to extend their brand and their reach. We built with them virtual show rooms that they can pop up anywhere. Order shows, airports, shopping malls, by the way, this interactive experience was the hit of the Paris order shows. So here it is. Tablets let visitors configure the full range of car models and options. The car drives with full engine sound. You move gestures, you see what he's doing, leaning to turn the vehicle. He pushed the trunk down to close the trunk. You can slam the door, take a virtual test drive. In fact, there are only cars that you can safely drive in text. Sensors detect arm and body gestures. You can see pointing at these small circles called information spots, which gets my pointing to it and leans again to rotate the car. And finally, he swipes to open the hood, heard something I would never do, but he actually expects and explores the engine. Some of you might like to do that. And by the way, he feels when he gets into it that it's driving and the car is rendered 60 frames per second in real time, 10 times the detail of equivalent generation video games. That little virtual experience was used by thousands of people around the UK and the European continent. And what JLR actually told us is not only was it tremendous for them in actually building pipeline, getting people to know and like their cars. It gave them invaluable data about how people like to configure their products, how people experience and research, buying the vehicle, what they think about, what they combine and what their reactions are. A whole new digital way of thinking about and engaging with their consumers that they could never have dreamt of. That's what immersion will mean for us in this idea of engagement. Then the next expectation I want to touch on is speed. Battles will really be lost in minutes, even seconds. So on one level, that's very obvious to you all, the battle for loyalty. And we all know that, right? We have lots of data. Just think about every one of you. Your tolerance for waiting is so much shorter than it used to be. You expect instantaneous. That is your expectation. So at one level, we can say we need to be responsive to the individual, measured by time, and the quality of the response. But there is a whole new different level. How do you design experiences that actually play on and satisfy that impatience and the expectation for immediacy? Here's one. Nationwide, first online banker in the UK, they came to us to develop a mobile app to pioneer a new level of innovation and experience, all aimed at loyalty. Now, here's how this plays on this impatience and need for immediacy. The innovation here that we built with them was impulse saving. Now, for those of you that are shoppers in the audience who love shopping and know impulse buying, impulse saving. The day it launched was the number one finance app in the UK, and within 10 days had a five-star on both Android and Apple. I'm going to show you the client's YouTube video on the app, but as you watch, just look for two things that, again, I think anchor in this engagement thought. Skipping the annoyance and the irritation of a sign-on, if all you want to do is check your balance. And by the way, in the first two months, the quick balance was used five million times. And secondly, the impulse saving, which, by the way, last week won the Innovation Award for user design and experience across the whole of the UK in every category. So take a look. Once after that launch, individuals had already followed their impulse to save 1.6 million pounds, 25 pounds at a time on average. And really interestingly, we now know that the most popular impulse savings days are Fridays and times are 6 a.m. to 7 a.m.  But it definitely says that engagement, these cycles where you get customers to engage on these kinds of things create deeper data and insight that leaves your competitors playing catch up. Now, that kind of disruptive value is one of the major design points of IBM's work with Apple. And here I want to ask you to think beyond the app when you think about what we're trying to do here for our clients and for industries and for organizations. The higher level concept we have is scaling the collective intelligence of the enterprise down to individuals, to the level of single individuals. And so we watch the progression from mobile to social as a consumer phenomenon. And we've seen that changes in consumer behaviors have actually altered entire industries. And we can now start to think about that as the consumerization of the entire business experience. Because the expectations that each one of us have as consumers and as customers of expecting transparency, immediacy, fun, they're going to become our expectations as employees. Now, did I say we can bring some more fun into the workplace? I did. And I don't even think it's going to be optional for enterprises that want to attract the best talent. That's coming. But it hasn't happened yet. Despite BYOD and despite the fact that every single one of us is walking around with about 100 times more computing power in our hand or our pocket than the average satellite in orbit today. I've seen estimates that say about 60% of mobile devices used in business are doing nothing beyond email, personal calendaring, productivity, such as instant messaging. And that's fine and it's wonderful and it's helpful, but it's hardly transformative. And we know that widespread adoption is being slowed by some very real issues. Integration with back-end processes, device management, and of course the security of the data, the device and the application. Our partnership with Apple takes those reservations off the table. As it stands today, we're all pretty hard-pressed to find real business applications which have been developed from the ground up for mobile and are able to address the concerns of CIOs. But at the same time, we're also hard-pressed to find enterprise-scale apps that have been developed to leverage the native insight and capability of mobile and iOS. This fall, that changes with the first suite of four apps that share four characteristics. Four will come out to start with and we'll scale this to 150 over the first half of next year. And they're going to have four aspects. They're all aimed at a specific industry priority and a specific job role. Think about the flight attendants we talked about, insurance claims processor, retail associates. Secondly, they're all powered by analytics and they will integrate with core enterprise processes and data. The collective intelligence of any enterprise will be there in the hand of every employee. Third, each one is designed for mobility, not reversed engineered for a mobile device. And finally, they'll all unlock an amazing new possibility in the way work is done. What happens when you design for mobile first? At the most basic level, you can restore the balance of power. And you can see this vividly in the example of the retail industry where more than half of store associates actually believe their customers are better informed about products than they are, better connected to what they're trying to sell than they are themselves. We've become so focused on meeting the demands of online customers for information that we've lost sight of the people walking the floor. Imagine what a beautiful world it would be when retail associates have real-time access to inventory and an accurate view of the store's history with that individual. And they know just who walks through the door. This new hyper visibility to everything that's happening around us, whether that's the movement of a person, a piece of equipment or inventory or a downed power line, radically augments our capability to introduce new levels of serving, great efficiencies through optimization, and guides our movement intelligently. Helps us produce a new level of security and safety. And who could need this more than first responders? The first responders to fires, floods, earthquakes, crime, they're trained to go in, and they train to do it accepting the risk, including the risk of what they can't see or can't know. You can imagine the possibilities for analytics applied to information, streaming from cameras, accelerometers, GPS, and eye beacons on their phones, moving with that individual responder, removing blind spots, and assessing risks for first responders and potential victims alike. The value of this kind of situational awareness is absolutely incredible and self-evidence, and its applications cross over to countless industries.  So I'll close on this point. And it's about how all this is shaping up in what I see in the attitudes and opinions of the clients I talk to every day. And I think that something very different is happening and decidedly optimistic. It's decidedly optimistic. There's an emerging sense that the real question and agenda is all about, what's my next possibility? What's available to my enterprise now that wasn't before? And that is a remarkable difference. From existing problems to the next great possibility. And that attitude is coming to life at this intersection where big data meets individual engagement. I think we really are standing at the threshold of the next great era of business change. Moving from back office automation using data to an agenda defined by data and technologies for every one of us to exploit it in the moment. I think this will be remembered as the next great transformation of work. And we're not going to do that by ourselves. But with our clients and our partners and all of you here, we can aspire to changing work, to making it more productive, more experiential, more transparent and just smart off for everyone who engages it to unlock that next level of great possibility for this world we're in. And I think that is worth our best thinking and our best efforts. So I hope you enjoy that thinking and those efforts and engage with us in them over the next few days. Thank you very much. Giving up for Bridget, that is awesome. Yeah, give it up one more time. Yeah, I couldn't help but think when I was watching that. One time that that actually happened to me when I was on a plane that got delayed and when I landed, lo and behold, my phone lit up to tell me that in fact I had been rebooked because I'd missed my flight and it felt wondrous. And actually that moment and hearing everything that Bridget was talking about reminded me of the Arthur C. Clarke quote that any sufficiently advanced technology is indistinguishable from magic. And that's I really think what I felt when watching that presentation is that we're approaching this age when this all feels like magic. So thank you, Bridget, for sharing that with us. It's an exciting initiative. It's really impressive to see how this is changing, how and when work gets done. I mean who better to do this than Apple and IBM? Two heavy hitters, so I think that is really just fantastic. Now this morning we began the journey on how to take those first steps by exploring the role of data, cloud and engagement. But obviously this day is just beginning and there are countless ways that you guys can all get involved here at INSEC. So first up, keynotes run throughout the day in the Mandalay Bay ballroom. And those are your chance to dive deep into all sorts of technical areas, business analytics and more. So please go check those out. They are fantastic. For those of you who are developers out there, stop by Dev at INSITE where you can actually work hands-on with some of IBM's coolest new technologies. So go check that. You don't just have to watch keynotes.  And now when we talk about taking action, we really mean it. So as many of you know, I and my company, DataKind, are very passionate about volunteering and using data for social good. And of course, so is IBM. So while you're here at the Expo, you can volunteer to help stop hunger now. The INSITE team is partnered with this amazing aid organization which provides millions of meals to families all over the world. Our goal this week is to help them prepare more than 125,000 meals. I know, sounds like a lot, but it only takes 30 minutes of your time and it feeds a person for two months. So come on down and help out while you're here. Now tomorrow morning, we'll be back here in the event center, but we are gonna be starting a bit earlier. Session starts at 8 a.m., so mark your clocks, get your alarms ready. And we'll continue to explore this new era of data-driven insights. Some of the things that are in store for you is that we'll be exploring how Watson and the advent of the Internet of Things are already at work transforming entire industries. And of course, we're gonna have founding chairman of Kayak.com, Terry Jones joining us. So don't miss that. He's gonna have an awesome talk about something really cool that he's been working on. Now, of course, while you're here this week, we really want you to join in the conversation. Not everything that happens in Vegas has to stay in Vegas. So please tweet your comments and insights to hashtag IBM Insight and stop by the Insight Go Social Lounge. I'll leave you to decide what should stay in Vegas. Please don't tweet those things. But with that, I want to thank you guys all for a wonderful start to Insight 24.2 and Insight 2014. Have a great day, and we'll see you back here tomorrow at 8 a.m. Thank you all. You can't read your customers' minds, but what if you could read their faces? Marketing intelligence startup Inviso uses predictive analytics to interpret consumers' facial expressions the moment they occur. So Inviso can help businesses know what consumers want because it's written all over their faces. Click to learn more about how Inviso uses the IBM Big Data and Analytics platform, Watson Foundations. We never know which way the wind will blow. Or can we? Vestas, the world's largest wind energy company, uses stream computing to know exactly where to place their wind turbines. More precise ROI forecasting and lowered costs were for Vestas a breath of fresh air. Click to learn more about how Vestas uses the IBM Big Data and Analytics platform, Watson Foundations. What if you could drive every decision, fuel every process, and inform every interaction with fresh insight? Create new value in every aspect of your business and compete on speed to action. The opportunities are here, now. Are you ready to achieve outcomes that have been beyond your reach? Here are three ways. First, what business person doesn't want more insight? To keep you in the lead, IBM is reinventing the analytics experience so every business user can problem-solve more easily to improve customer loyalty, optimize operations, and manage risk. Second, who doesn't want to move faster? Defined analytical solutions can solve problems and drive your business forward, which is why IBM has pre-built analytics solutions with cloud-first options and can enable developers to create data-rich mobile applications that engage customers and harness the transformative power of data, including the Internet of Things. Finally, to help quench your thirst for more context and data, no matter its source or style, to drive the best decisions possible, IBM is enabling business people to more easily acquire and provision trusted data in the cloud as they need it, making data more available than ever before.  Don't just ask why, think why not, envision your better tomorrow today. To learn how to accelerate speed to insight and act with confidence, please visit ibm.com slash big data and analytics.