 and I'm the Executive Editor of Data Diversity. We'd like to thank you for joining November's installment of the Monthly Data Diversity Webinar Series, Enterprise Data World. This webinar series is designed to give our Enterprise Data World conference attendees education year-round, a conference we've been producing in partnership with Dama International now for nearly 20 years. Enterprise Data World will be held this year in Austin, Texas, April 27th through May 4th, 2014. Today's webinar is sponsored by Target and we'll be discussing make big data work for you with Morton Middlefart. A couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. We'll be selecting them by the Q&A section in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag Data Diversity. As always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and any additional information requested throughout the webinar. Now let me introduce to you our speaker today, Morton Middlefart. Morton has over two decades of experience in developing and managing business intelligence solutions. Currently, CTO and Chief Product Visionary for Target. Morton holds an MBA from Henley Management College and two PhDs from Rushmore University and Albor University in Denmark. Morton holds seven U.S. patents and 25 worldwide for his technical developments in business intelligence and analytics, placing him among the top 1.8% of all active inventors. In his third time, Morton is an avid skydiving instructor and enthusiast with more than 15 airplane jumps and several big jumps to his name. We have a video of one of those base jumps that inspired a blog Morton wrote for Data Diversity on our site, and I'll be sure to send everybody a link to that in the follow-up email. And we're very lucky to have him here with us today, and with that, I will give the floor to Morton. Hello and welcome. Thank you very, very kind introduction, Shannon, and I'll be very happy to be here and very happy for everybody checking in here. Thanks so much for coming. What I'll be spending about 45 minutes on talking about is how to make big data work for you. In other words, there's a capital work and there's a capital you. And what I really want to make a point about here is about how to practically make big data work in a way that is manageable, not just for enterprise Fortune 500 companies, but also for Shelby, the little guy, the medium-range enterprise and a smaller one, because there's really a lot of things you can do if you can just stop to think about how to intelligently use the data. The key takeaway that I'll be pushing today is that we should understand how our huge ability to adapt is more superior than computing. And then we should use that ability to adapt as a way of building that with the technology we have available in all of the runner organizations and to put this highly competitive into the future. So I'm going to start out talking a little bit about the general idea. So if you're just trying to push a button here one second. So if you're a human being and you're under pressure, there's a threat around you, well, one thing that you have is to simply flee what's chasing you. Another option is that you have no illusions with the systems that I'm going to be explaining about today. There's a lot of facts about that challenge or conquering that fear in all the runner organizations. What I'm using here is both the target logo but it's what it's referred to as the ODA loop, the cycle of continuous observing against the world or observing yourself on what's going on and then deciding what to do about it and then importantly to put it into action. Think about it. That can actually be defined by a number of the BI disciplines we have today available. I think in general, the idea is that people reporting and utilizing a separate discipline or creating a dashboard or getting an agent work, I would argue that in fact, it's all about integrating and the speed with which you can travel this ODA loop. The reason for saying this is based on some work that was highly successful during the Korean War in which the American F-86 Saber was up against the United States in the 15th century. A guy named John Boyd was probably the forefather of the Top Gun School as we know it. He worked with the concept of working in this very high cycle between observing against the world, looking from the cockpit of the plane, what was going on. If something was going on, if you saw a dot in the horizon, you would orient yourself, you would figure out what was going on. Based on that, you would make a decision, say engage or not, and based on that, you would put it into action. The interesting thing with the success of this concept was really that the MiG-15 at the time was explained by better definition. It could fly higher, longer, and faster but the F-86 Saber had distinct different processes. It had a rounded cockpit that gave the pilot more visibility and then it had some hydraulic steering that allowed the action phase to fly slightly different. So in other words, by working this concept of the OVA loop to go as fast as possible in the cockpit of the pilot compared to the one he was competing with, then theoretically, the Americans successfully made the F-86 Saber beat the MiG-15 in the middle of the Korean War. This concept has then been widely applied in many forces today. I have to say that what inspired me was an article a little over 10 years ago on how business review and renewal warfare that really originates from this concept of John Boyd. What I was thinking then was then to say, why don't we just thinking of technology as tools? Why don't we think of it as an extension of our way of conducting leadership and management? Why doesn't this excellent leadership and management excellent execution of strategy and applied technology to that? And really the result that you see here on the screen, if you then map the technologies that were available at the time in the early 2000s for sure, but the idea of having them integrate very tightly from the operation phase and analytics as an actual extension of the dashboard report or agent before they get it into the decision phase. Let me just tell you a few words to this because this was really the recap moment that I had for a little over a decade ago. Here, there cannot be any decision based on any report or dashboard unless there's an analytical conflict between the two. This is simply from the fact that if you have a learning organization that learns from its mistakes, whenever something breaks down on the production line, you fix that and then hopefully it's a new problem. I would say it's the same problem coming up again and again, you probably get fired. In other words, you have to analyze hoping if you're learning from your own mistakes. And this is something that you cannot go directly from a report or from a dashboard directly into concluding what to do about it. It has to have some analytical component. Another thing that's important to say here is that that analytical component has to be user-driven. It can be something that is predefined by the IT staff. It has to be something that the user is completely in control and shall we say his leadership cockpit because one key guy knows about every problem, every opportunity of the organization, say many months ahead or years ahead. This is definitely something where you as a leader or a capable manager would be wanting to analyze. Again, let's just, without going too much more into this here, just say that think about yourselves out there listening today. How many of you are working in an organization where there's a detachment between what you see on a report and then if you want to analyze it, you have to kind of like recreate that and put it into another system. To me, it has to be a very fluent, very dynamic process to make you move from one phase to the other. And that's really what we're striving to create for more than a decade now. So the journey that we're proposing and now it's where we kind of like start to get a little bit into the data that's around you, really that the journey starts out very operational. Let's say we need to produce a certain number of reports or we need to have some dashboard or whatever you want to do. The number of data sources involved in that process will just be one ERP system, maybe adding to that a CRM and similar systems. But we start out small with a pretty well-defined, internal number of data sources. So from that point on, once we simply, and this first phase here is all about just giving users access to as large an extent as possible, self-service themselves within what we, you know, additional BI that we've seen for the past decade or more. Now, the second phase, as I was talking about before, is much more interesting. We start not to see BI and analytics as separate disciplines, not when we see the output of BI and analytics as a report or one pie chart or whatever that may be. This is when we start to have an interactive learning cycle through the ODA loop with the disciplines I mentioned on the slide before that we need to integrate, you know, one interaction, whether that be one click or one swipe on the iPad or maybe as small as possible, making ourselves from one to the next with the intent of putting things into interaction. I should also acknowledge the leverage that we put in the ODA loop. It's based on some interesting stuff I'm getting back to, but it's all about, like, the more fact you have, the more informed you are about a decision, the more likely, if you know the right thing to do, you are to drive it into action. And then what we want to do is that we want to allow users to put decisions into action with as much confidence as possible behind them. Now, after this point, let's say, you could say that the phase two is just the BI way that you know it, but still doing BI as an analytics to some extent. Now, the next phase, and that's really what we're going to be talking about, tools and approaches to do today, is when we start exploring new data sources, data sources that are not necessarily from side of our own organization, data sources that are not necessarily our own at all, but they're still very interesting clues about how to compete against our competitors in whatever they are set out to do. So this is a presentation here. I'm basically just taking everything else for granted, and then we're jumping to say, I want to highlight and go into the data-driven culture and look at what's driving there. So we want to know that this is important at all. Why do we even have this conversation here? We look at the traditional technologies that are working out there, is that we're having all these, I mean, this is like a very classic thing to say, especially if you're in a tech field already. We all almost know that the next phase is going to be smaller, faster, cheaper. We know that we're going to have more storage capability. That's going to have more connectivity in terms of bandwidth and so on and so on. No news there. However, some of the other things that seem to be driving this system as well is the fact that the human behavior, the very unpredictable factor in this year, really, is making some impact as well. Human behavior suddenly decided that even though we spent the last hundreds of years trying to guard our privacy, now still we decide to take all our inner private secrets and put them on the social network in the past five to ten years. And to me, that's a very interesting thing to see, that now we're basically going back to what's where we were in the Stone Age, where we're sitting around the campfire and everybody knowing about everybody. And we're trying to almost create that same situation again using technology. So the willingness for humans to really plug in their heart, so to speak, in the computer system is not something that you would be able to walk past similar to technology if you're looking at technology ten years ago. Another thing that's interesting here is, of course, this is an odd thing here in the States, because I believe that there's a lot more opportunity in regards to open data. There's a lot, we are further away here than down that line, than you, for example, in the United States for some institutions to open up and share data that we can use. What about your business? There's no doubt that some of these data will give valuable insight. There's also, if we know what we're looking for, I should add to that, because the amount of information that's relevant here is going to be way smaller in percentages than if you look at your systems inside your organization. It means that your volumes need to work way, way bigger. At the same time, some of these, the way that things travel, for example, on Twitter and other social media, is the new relevant in the moment, but it may not be like in an hour or two ago. At least I found out a little newsflash on Twitter two hours ago, saying that there was two hours until my talk, and people were still welcome to join. I'm pretty sure that in an hour or two, that treat is going to be completely relevant. And so on. So many of these things, like you pick them up right away or you don't at all, and then they're irrelevant. And then it's the last school of thought, but maybe over time this collaboration will be pretty searched. I'm going to argue that it's going to be one or the other. I think it's going to be a little bit of both. But I think that it's going to be such that it's, if you're a company or a company, you need to know what's going on in both. And then finally, and this is one of the things, I mean, this is the benefit of starting to program commercial software more than two decades ago, is really that if you look at how technology seems to expand and contract, you look at web browsers. We started out having three browsers in the beginning. There was, of course, Escape competing, mainly with the Internet Explorer, and then there was Opera. And then everybody started using Internet Explorer up to the extent of 97% of the entire number of users. And now, when we develop software, we need to take at least five different browsers into account, primarily driven by mobile device. And when you think about it like this, first, connection then expansion. Similar to the model that we applied with server platforms, it's like we started on the mainframe, then we went out and said, well, maybe we could use a PC as well and do some of the work there. Then we decided to use a PC as a server. And then now again, we're going towards maybe cloud-based architectures where we're going to do more stuff in the cloud. And then we're even going to see, as I'm writing down at the bottom, that some things we like to start driven by the need to high-performance analyze stuff that we're closer towards the hardware again, away from the cloud, which is one of my theories, because I think that since we need to analyze so much data, we really need to think about visualization and the elasticity of the cloud. If we need 100% of the resource all the time, then maybe it makes sense, at least for some systems, to work on hardware that you control and maybe even optimize the hardware towards specific tasks of analytics. Well, whether that works or not, of course, is not the main point here. But I just think it's very interesting how these cycles work. The last one was like, when we started doing databases in the old days, first program flat files, then simply program the database before we program the actual application. Then there are the relational databases. There are standard databases. And if you didn't use like a Microsoft or a Google or DB2, you were an idiot. So in other words, you should kind of like build on that. With analytics, we then have the multi-dimensional databases. That kind of like still live and do well for some certain tasks. On the other hand, we see the more you want to go in memory, the more you want to do specialty type of analytics, the more it gets to be okay to do your own database. Now, suddenly, you're not a ridicule for programming a database. Now, it's okay as long as it's for that purpose. And then going forward again, the file, if we look at a Hadoop system, it's really a flat file system again. So we have these cycles that I think are interesting. But anyway, also by the way, let me just put a quick word. So if you go to the blog and find this post that I did a while ago, you'll find all of this elaborate a little bit more. So I'm not going to tire you with that, but it's just to give you the resource. And if I have to pursue it to do one thing after this, see Kevin Allen's TED Talk about the algorithm shape of the world. It's an art talk. And it's a really eye-opening thing about analytics and the extent to which analytics is applied, especially in the financial sector, but the magic is it's going to happen to every sector. The financial sector, insurance are just going to be the first ones if you want my guess. So we're going to do this more and more. And this is also like my main resource thing. I think that we're going to see analytics getting closer to the hardware, even be integrated in the hardware going forward because that's really what's happening in the financial sector. The cost of the cable, when it gets in, like where you locate your server on Manhattan, it makes a difference now in terms of these algorithms. So this is just the beginning and we're going to see that some of us as well. Now, going to watch big data. I just took a couple of clips here about some of the conversations that I've been happy to engage with online. And one of the things that I think is the most important one is that the big data seems to be a big business property where big data itself to me is really just a technological property. Big data itself doesn't do anything. It's how we use our analytics in general to solve different problems. And I think that of course we as vendors, of course, have been contributing greatly to kind of make confusion about this. But again, to me, it's just important that now we're all here. We're talking about big data. We're talking about opportunities there. So let's move on because everybody here are already on the same page as this. Well, I mean, maybe that's a naysayer on this topic and then we can talk about that during the Q&A. One of my main points, and these are just some of the numbers that's probably more than IDC that really tries to figure out how much data we have. But one of the interesting things here to me is just the fact that the amount of data is already huge and it's exponentially growing. And just like this, if your company is growing 45% annually, only the big data, even if it's just a fraction of the data out there that are important to you, then if you get to watch some of the data you need to see with your organization, to compete with your organization, I'll come from the outside and the big data that you don't have any control over. So in other words, I think one of the classic definitions about big data, again, this goes back to saying, well, big data just defined as some kind of data defined with a technological perspective and I agree with this. I think that big data is a much different problem than about how to put it on a server. Big data is a challenge for leaders and managers in the sense that it's not a well-meaning amount. And if they tried, they wouldn't be able to download the entire Internet on their servers and figure out what's going on. They would have to take some examples, some clues, and then they would have to use those to figure out what to do. So in other words, big data is managed really. A lot of control is in the sense that more data that you need to compete with are coming from the outside of your organization. And in other words, big data that you do not own, that you do not control, and therefore, can really ensure that you can have tomorrow. All you can do is just use it in the right environment. And unless you're Google and a few other companies in this world that can download the entire Internet, this holds for most people. Another thing to think about is that if you run on an ERP system inside on your internal data, you can behave as an accountant. You can take the profit, and then you can start deducting that and figuring out where the profit is coming from, regenerating it, losing money, and so on and so on. With that data, since you want to download the entire Internet, you need to cover the theory thinking, I think this year is a good idea. I think that we have to test this idea and see if there's some evidence in the data. In other words, we'll be working on samples of data. And just to take anything away from this session today is that stop sampling data, grabbing some data sets that are relevant, based on whatever you know about your business, and then use that to see if it works for hours if it doesn't, try something different. Try to start building a huge Hadoop cluster and find out what you store, all the things that you can, what to do with that. And then, the device is more and more capable, so the ability to do more interactive analytics is increasing. On the other hand, as we take down, and so on and so on, it's getting more and more, and it's going to be a barrier of the data you need to compete. And if you don't control them, they can shut down the data feed tomorrow and we will not be able to do anything about it. Now, for the purpose of demonstrating how we can do stuff like this, I have a video I want to share with you, and this is the first time I'm going to show you. So bear with me. And what happened here is that we really did an inverted data warehouse, meaning that it's a lot of analytical autonomous clients that load data in memory. The picture here is really inspired from a presentation I saw of big data from the large Haldan Collider at CERN, in which 99% of the data that they get from servers wants to do their Haldan collisions is really without. And the sooner you can throw away data, the better. And using the theory that you can get off a crew and see where it goes, you behave like a scientist as an accountant wanting the sum of all crews and then see which one is bigger. But you can actually push the crew very closely back to the source and have also a very interesting parallel analytics system. And really my main point is that you can do something that is operationally and strategically useful using somebody else's data if you just kind of like poke around and get the data out. I used to call this big data for the little guy because it's something that requires no more than some powerful PCs, maybe servers, but nothing more than that. So this is one clean example that we've been using ourselves actually. Another thing that I think is an interesting question that most companies probably would like to know about is that what are you most looking for before you enter your business? Or, of course, also, what's the way to the friends that they've done business with you? And these here things are not just running alongside the same territory. I was always showing what, for example, the followers that I have on Twitter and hopefully some of you are here today. Thank you very much for coming. Let me know about what fans on Twitter are concerned about. And that's also, of course, then also talking about leadership and interesting to me, too. That would also be if I look at my Twitter audience what they would hope me to do. Again, stuff like that I think is very interesting for all of us, really. This is an example of another product that's an add-on to the Target BI suite. What you see here is the yellow line or the yellow dots going downwards. It's the Google trend for searches on intelligence on a global scale. And the orange is the trend of search for analytics. And on top of that, the purple ones are searches for data. So if you were to develop new stuff later that you were in my position, would you do business intelligence or do something big data analytics, at least in order to write the waves of the future? Let me show you a quick example and a video as well in which we actually take not just Google trend data but we take the data on the line from Google and then join that with our internal systems like Renew and stuff. And it's just to show you the potential of using big data samples with your existing data on the fly-off course. Here's a quick example. It's the same application just showing where you have like one trend of revenue and then you take their samples from the Google trend and then join them. I should mention, of course, that it's possible to call your Hadoop high and all that stuff as well. And my position is that you start out sampling, start making the discipline of just taking a new data set, trying it out for a day if it doesn't work, use it if it does, and then make that discipline something really live because the really important thing here, and I think this is what we forget when we try to solve everything with technology, is our huge ability to theorize, to come up with new ideas. And that is far superior than computing's ability to just cross all the numbers and figure something out. So if we can come up with the right theory and test that against the data, that's a much better way to exploit big data than trying to download them all. But then, of course, we can support technologies like that. Now, I cannot help but say that the consumption part of big data is one of my main concerns. I just want to show you a quick sample of where I see things going advice-wise because, as I said before, devices are getting smaller and smaller and more and more capable. How do we then interact with the big data? And you already saw how in the cross-bone technology we just draw up technology in, and this year would actually work on the data that I demonstrated on cross-bone in the previous video. So, in other words, what I want to say with this video here is that everything you see in this video will also be available on top of the big data. Again, it will require that somebody figure out which samples to use, but the idea that we should be able to use a mobile device interacting with the big data is also an important thing for me to come across with here. And I'd like to encourage everybody, all these technologies that you've seen so far in this entire presentation, available if you're at a university or you want to play around with it, feel free to come visit our target.com slash lab, and then you can connect with us and, you know, download demos, you can get free licenses for your dynamic purposes. So just come in there and play along if you'd like to try some of this year on your own. Just kind of like the end of this presentation is the following dream. How many of you have had the following dream where you fall and fall and fall and then right before you hit the ground? I can guarantee you that close to 74.5% of you have had that dream. And that alone is not really that surprising. What I find interesting is that we found by interviewing skydivers that the more skydives you have, the more it is that that dream changes, that the dream continues after you hit the ground and you just like, I mean, you still fall without a parachute or anything, but the dream continues. So this is pretty interesting. And that's one of the reasons that it's a learned thing. It was something that was in our brain from the beginning. And by looking at a number of examples, I recognize of course that we're talking hundreds and thousands of jumps. By giving the brain examples, we can change even stuff that was put in our brain from birth. So all the overwhelming evidence here in my opinion is that the act can indeed lead to failure. And I mean, if we can do this to stuff that was put in your brain from birth, we think we can do with learned behavior such as, well, I fear losing a deal as a salesperson, or I fear an organizational change, or whatever that may be. There's so much opportunity to use facts to apply this year. And all the taste is making the mistakes in the mind of course operating in the organization, making the act for the operation in the organization. I really want to share an even more exciting fact about our brains that goes back to the skydive because this previous example here requires hundreds and thousands of examples. This is more about how fast our brains adapt. I think that the most exciting thing that we have to show as human beings is the fact that our brain can adapt itself to a new situation in just one example. You give the brain one example and it's already learning. Can it adapt to a supercomputer no matter how powerful it will require hundreds, if not thousands of examples, more to learn? So the shortcut to really using our human abilities in this entire day-to-day technological equation is to use our adaptability. And we've done much better than that. I've basically forced two employees to adapt for the same time and let's see what they have to say. This is an opportunity to adapt. What happens when you present the brain with a skydive the first time is that you kind of black out and you don't really understand what's going on, at least not for like five, six seconds. And then the second time you send everything else and the sensory system of the brain has already adapted. In my opinion, it's the reason why we should really think about how we use ourselves the best as the quote-on-quote scientist, wondering about sampling of big data. Our ability to understand and to come up like send signals are superior to computing. And this is the shortcut if you're the little guy who's trying to get your hands dirty on the data. The example I showed you already where I use Google data against internal data or use Twitter data by sampling. I didn't download all of Google or all of Twitter. I had a theory that maybe these people would be interested in leadership or maybe this query would be something to optimize. And then I just tested it out. And Google, I tremendously reduced the amount of data I need to process. I pushed the query back to the region of the data and thereby got some very useful data and some very useful insights. So basically this concludes my tour of the data-driven culture. I propose that we deal with a lot of external data sources, a lot of a number of size of data sources that again, traditionally refer to as big data, but I would rather call them a big data to which there's a big challenge of trying to integrate or relate to it, so it's more of an adaption exercise than a technological exercise, in my opinion. So that's the company that I want to share with you. I would like to state to all of you, whatever you're doing out there with organizations, because I believe that the type, strategy in any market really applies. That's what I wanted to say. I'm very welcome to any questions, comments at this time. Thanks for listening so far. So much. This has been great. And I'll give everyone a couple of minutes here just to get their questions in the Q&A section in the bottom right-hand corner of your screen. So feel free to get in there. We definitely encourage you to. And one of the common questions are, and we've already had this question, is if the slides and the recording will be available. And I'll be sure to get that out within two business days. So by end of day Thursday, I will send a follow-up email with links to the slides, links to the recording. And there was a couple of requests for videos that you were showing Morton. So if you could send me those links. Great. So just let me know how to do that. So they're not shared as they're on the conference site now. They need to be kind of like put somewhere else. Yeah, just send it to me an email and I'll make sure and get it in the follow-up email out to everybody. Okay. Yeah. That's perfect. And everyone's so quiet today. I don't know if everyone's just getting ready for the holidays or what's going on. But no questions so far. So yes, everyone's getting ready for the holidays. Everyone's winding down. We've got a quiet because we're all in awe. This is a course of Morton. It's great. Everyone's just lying. They're just kind of observing information, Morton. So thank you so much for feeding back. I mean, it's really warming to the heart that when you're standing here just talking into a screen that you still thought that there was some useful in it. And I want to say that I'm trying to be as much available as I can be on Twitter, for example. So if people want to just like tweet something later on, I'll happily see if I can go to Twitter. I mean, there's a limitation to what you can do there, of course, in terms of length. But, you know, happily, I try to respond to people that, you know, come up with comments or questions and stuff like that to my best. Anyway. Oh, here we go. Here's a question for you. In general, how many scenarios who really need BAs to call system for reports instead of other mobile apps? Well, one second again. I just have to ask, how many scenarios? What was that? Yes. How many scenarios really need BAs to call system for reports instead of other mobile apps? How many just will use that technology? Is that a question? Is that a correct answer, please? Yes. How many business analysts to call system for reports instead of... No, no. I mean, I don't think that it is business analysts calling. I see this much more driven by end users. So, business analysts that have, like, the larger needs for analytics, they probably will be using something that's not that very different from what we have today. They will probably still have, like, a huge, you know, computer to crunch the numbers. Now, one of the issues that I'm trying to reach with the money is sales guys, just needing to know something specific about a specific situation he's in. So, he only uses his mobile device and he doesn't have the skill to just call a report at all. He's basically just requesting information that caters a given situation that he's put in. That's the scenario where I see that very likely. And by the way, I don't think that I've shown both analytics and a report. I think the most likely scenario is that people will be asking for something analytic saying, you know, the profit that we generated last year on this customer, or I want to know the Google trend of this word. You know, and then just get a reply that's, you know, start manipulating. And it's just because of the size of the device. But, I mean, think about this way. In which you need information while you're mobile. And I would say that we are more mobile because it would be nice if the system lets you know like through a warning that's wrong here even though you're not at work, which typically will be, you know, depending on who you are, only like a third of the day. Wouldn't it be nice if the computer called you and let you know when you had a problem if you could ask an analytical question back those types of scenarios that I envision mobility. But I will show, I predict that in a year you'll actually have this much more commercial application to relying on the underlying layer that is a pattern that this algorithm where you can basically type in everything. So, I mean, of course, time will show. But I think that you shouldn't see this as a replacement for a traditional business analyst scenario. We should do this as an extension of an ability that some people don't have today either because it's too cumbersome for them to do it or because mobility alone prevents them from doing so. Does that make sense? So, and I know there was an additional comment and the questioner said, yes, thank you. And so, absolutely. And it seems got another quick question here before we run out of time. It seems like a flexible approach. Is the adoption rate going up? A lot of companies have already got a lot invested in their current marketing technologies. Is Target on any Gartner Magic Quadrants? Yes. Magic Quadrants for Business Intelligence. I mean, we're not, you know, Gartner categorizes stuff across their way. So, they don't have something called an integrated business intelligence plus analytics platform. But that's not the most appropriate place to put us. But we are on the Gartner Magic Quadrants for Business Intelligence. I haven't seen it in a couple of years. So, I think I just answered the last part of the question, by the way. What was the first part again? Sorry. The adoption rate going up. Yeah. I would like to see that we see more and more customers doing this here. Yes. This year, I mean, the trend, in my opinion, is very clear that the traditional BI paradigm where you simply just analyze your internal data and just do it in a, shall we say, kind of like an accounting type of way towards where you do analytics on a much more than, I mean, first of all, doing analytics at all. Even if your accounting scheme can be a challenge and, of course, we've been dealing with that for more than 10 years, but you definitely see a want and an interest in the agency to just take an external data source or source that you just collected and then the fly tested how it analyzes and then figure out do I want it or do I not. And then if you want it, of course, think of it as a sandbox where you really rapidly figure out what to use and what not to. So, I mean, definitely we see that as being a very hot topic among the, both the existing customers we see, but also like the ones that we meet out there when we're, of course, competing as anybody else for business. So then, yeah. We are at a time, but thank you so much for this great presentation. There's a couple of comments in there from Paladin. I'll make sure you get those. And just a reminder to everyone, I will send a link to the recording of this presentation, the slides and the videos that Morton has provided throughout the presentation and everything else you guys have asked for, including. And you can find Morton on Twitter. It's at Dr. Morton, M-O-R-T-O-N. You can find him on Twitter. And again, I'll get that out. I can show my contact information here right again. Yeah. I love it. And I'll make sure to include that on the follow-up email. Thanks, everybody, for your questions and for participating in today's webinar. And Morton, thank you again for this great presentation. I hope everyone has a great day. Thank you for joining me. Thank you. Bye.