 on buying lists and understanding your behaviors. When do you get that second piece of mail to make sure you're walking through the door at Macy's or Lease Shopping Online? And there's a science to the colors they use. Charity companies, non-profits, have told me that orange works. If you want to get somebody to write you a check, put a little orange in the mail, and for whatever reason, orange makes you write more checks. I mean, that's true, they've told me this. Fonts matter, personalization that's gone on mail matter. All that has an effect. It's really a data-driven exercise. And that's what we got to kind of get our arms around. How do we keep people in the mail making it relevant so that when you come home from work, what's in your mailbox is stuff you want to look at, not stuff that's not of any interest to you. That's when we start winning the game. Making decisions earlier in the process is a big part of this, moving from reporting to prescriptive. Our approach has been one that says that the CIO organization on the left side, we manage the technology and data. That's our role. And somewhere in between, there's a bridge between the different disciplines in the organization, whether it be finance or operations or marketing. But you can't think that you own this analytics. You will lose if you try to take this thing over. And then you're gonna bring all the analysts in the company under one roof, under one umbrella. And I bet you that Colin could talk to you about his experience talking to customers. That is not the right solution in many ways. You want to creep you away there. So you don't want to have, I have enterprise agreements with MicroStrategy sitting all across the organization. We probably have about seven of them. Is that financially the best way to go? I know anyone from MicroStrategy in the room, I love you guys, you do good stuff. But I probably should have one agreement with a company like that. And I think you gotta find that middle ground on this so that your business owns the outcomes, they own the data, and you're bridging the solution and providing a better solution. This one is part of my evangelist story. Data's a corporate asset, data's a corporate asset. I gotta try to drum that in so that we can get governance out of the data. We call a post office five different things and different data silos. I don't know, I'm gonna come up, get way outside and say, how about a zip code? Everybody understands that. Why don't we just call a post office a zip code? But we have five different numeric and named equations for post offices. So when you're trying to do multivariant analysis, you can't say, tell me what's going on in 21014 because it's always different. So you need some governance and you need to realize the way you start that is to make data a corporate asset. And it's a partnership, I already beat that up, I won't beat it much longer. You gotta instill an analytics culture. And frankly, it wasn't that hard in the post office or the postal service because people have been using reporting for years. You give operations people more data to use to do their job better. I mean, I tell my guys, it's written on the wall, is the best way to do it is add value to their day. The guy that's running that Boston plant up the road, that is a tough job. And the more data I can give him to do his job better, will make him start adopting this world and getting himself immersed in an analytics culture. And the skill base, this is in the room. So there's a joke, I could tell jokes, right? I heard it from a guy from HP. So what's the difference between a data scientist and a statistician? Salary, just that's it. So sorry if I insulted anyone, but it was, you know. No, but the challenge is that it's, look, Wall Street Journal on Saturday had a big article on how hard it is to get data scientists. So you need these skills. I mean, so one of the things we're trying to do is hire right out of school and we'll train people and stuff. Look, we have world-class companies like HP and Accenture helping us with this Deloitte. We have a lot of people, Teradata, MicroStrategy, everybody is part of our world. And they're all helping us do this. And, but in the long run, you need to get those skills in your organization. You need to train people to do this. And this is a busy eye chart and I don't expect anyone to read it, but that's the kind of work that's gone into understanding from both a people and a data. What's your data solution? What's your people solution? What's your technology solution? What's your cultural solution on this whole journey? And you gotta fix every one of those boxes. It's not as easy as it sounds. And one of the big things that we kinda focus on is what problem are we solving? I'm telling you, in these early journeys, I've watched people solve the wrong problem. We say, don't solve a problem, solve the problem. There's a difference between those two. And it's one of the challenges that you have to solve for when you talk about the complexity. For us, I mean, this is just one example. One size does not fit all. These are three different systems, applications we built in the last year and a half. And you can see how diverse the solutions needed to be. And mostly dealing with speed and volume, you know, the three Vs gave us different solutions to different challenges. And I think that as, I don't know if that's a solution for everybody, but that's been our kind of experience that you need to have multiple solutions on this. And I think that this one is where we take those 561 systems, I just put a few of them on the bottom. We love our acronyms as much as you do and get it up into an analytics platform that runs Haven and Splunk is a big tool of ours in the cybersecurity space. And SAS is big in the operations and we're an SAP shop over in the HR side. And MicroStrategy obviously is the visualization. And the visualization becomes an important piece. And then we're serving data back to all our internal customers and our external customers. And that's kind of where we're getting to. We're mostly there. When you think about it from a, you know, so we're just standing up at this point, you know, we're not trying to solve for unstructured data. We haven't finished solving the structured data yet. But once we do that, we have to have solutions across the enterprise. We're standing up a data lake and Hadoop clusters. We've done some high-performance computing and we're not the Defense Department or NSA, but we found solutions that needed in-memory high speed when I talk about scanning a package in Guam and bringing a response back in 20 seconds from a database in Minnesota. That's where we're using in-memory high-performance computing. We're very interested when I talk about mapping the GPU and the general purpose GPU. That's kind of helping us understand kind of how to visualize map when you're getting 10 breadcrumbs a second from 230,000 devices. And you want to display that on maps where people are so you can make decisions relative to maybe picking up packages or making sure people are staying on time. And I think that's one that's emerging. And my takeaway on that is our job is never done. These smart people in HP and many of you smart people in the room are gonna keep coming up with new solutions. So be nimble on some of that because you're gonna keep having to adapt to the emerging changes that take place. And finally, mobile. Everything we do, we sit there, we say, we have a mobile application for this when you think about the visualization of the data. So we talk about it way back when this is just a, when we move from the world of supercomputers and craze all the way to where we are now with really high-performance computing and trying to do things in big ways. So I think the time has never been better to move data around. Ingest remains a big challenge for us with all this data. The I.O. is always a tough thing to solve for and multiple solutions have represented themselves. So some closing thoughts so you can go drink. Get ready to hear a lot of comments. So these are some of our fun ones. So I have been guilty of saying, run that again, that can't be right. So you're gonna hear some key statements. Run it again, that's unbelievable. No way. Huh, that's the compliment. When you give this to an operations person and they look at the data and go, huh, that's good news. The negative to that is like, holy crap, you know? So, and then my favorite is Kamitsu and that's a lean term that we've invented. And that's when you look at data and you have a moment and you sit there and you just say, you can't make this shit up. It just, it doesn't make any sense. It must be wrong. It's not the process. It's got the data's gotta be wrong. That's the change that you have to go through. So room full of engineers, one of the things that we've tried to be agile, just like in our development and our analytics, is don't let perfect be the enemy. Go get it out there, let the people use it, let them tweak it. It'll get better over time. Let them get the immediate use of this data. Don't think it has to be perfect before you publish it. And the other thing is I talk about governance, but you manage information. That's what I do for a living. It's gotta be credible. You will lose all, you will lose the organization. If they find out the data, the analytics you're providing is incredible. So take the steps and the time to make sure that the data's credible. I'm a government agency. This is especially appointment, but I think this, I've worked with a lot of companies and bureaucracies are everywhere. And it's not necessarily at the leadership level. I mean, we have, we're like any other kind of service under desk all over the United States. John's trying to get him out for years now. And so you gotta hate the bureaucracy because they're gonna fight you on this. Because it's my data, knowledge is power type scenario, right? So if I'm the only one that knows this, my job is secure. That's the bureaucracy you're gonna fight with this. I'm honestly, my finance team is a daily fight. They do sampled costing. And I said, I have census data, why would you sample? I mean, your statisticians in the room, if you had census data, wouldn't you use census? Well, your census doesn't match my sample. So then I scratch it. All right, so who do you think's right on that? I'm gonna flip a coin, but it's like, but that's the bureaucracy you're gonna fight because there's an organizational people that spent their careers in the postal service, not only doing sampling, but testifying to it at a regulator. So you're not like standing in front of a court, a regulator raising your hand saying, I solemnly tell, it's where to tell the truth to make you believe that everything you do is perfect. Because otherwise you're lying and that's, you know, you go to hell with something like that. The other thing is it's not the people, it's the process. It's focused on the process. The people will take care of themselves. Get laser focused on the process that you follow. And my marketing spin on this as an old marketing guy is as we talk to people, it's more, better, faster. We're gonna give you more information. We're gonna give you more tools. We're gonna give you better tools. We're gonna give you better information. I'm gonna do it faster than you even need. And that gets them on your side because they don't wanna let go. But if you sell it more, better, faster, you will get there. So final thoughts, be curious, stay curious. You're in this room because you're very curious. Ask a lot of questions, challenge everything that they can put in front of you and assume nothing anymore. There's no reason to it. There's more data than we need to to fix that stuff. So with that, I wanna thank you for your time. I know I stood between you and the reception. Have a great conference. I think it's a great venue and really, as I was wandering in and out of some of the sessions, there's some outstanding content. So congratulations for being here and thanks for the opportunity to share our vision with you. Jim, I mean, come on, that's not enough. Let's go, let's go, that's it. That was amazing. Now, you know, we say that the HP Vertica Big Data Conference is different, right? It's special. Think back to this morning and what you saw and what you heard. Think back right now to what you saw and heard and everything you've heard today. Are we right? Is it special? Is it different? Yeah, yeah, it is. Okay, so what do we have? Actually, Jim was wrong. He does not stand between you and cocktails. I do. Yes, and I always get the pleasure of following the best act first in the morning, after TARC, now after you. You and I, Colin, are gonna have to discuss that going forward, but okay, my job is to remind you that right now, and you don't have to wait till five, it's just a question of how quickly you can get across to where the partner zone was in the Galleria where cocktails are being served. If you make it by five, they'll be waiting. If you get there earlier, they'll be waiting. Don't miss it. Tomorrow morning, starting at 7 a.m., we have food, coffee, and also lots of smart people to talk to. Don't miss that. And then tomorrow morning at 8.30, right back here in the grand ballroom, we are going to bring that curious little setup that you might have seen outside of the ballroom called the Cube right here on stage. That is a live web streaming platform with some pretty cool analysts that are gonna come and not only share their point of view, but engage you. We're gonna put HP Vertica Big Data Conference on the Cube live tomorrow morning. So come with your favorite, tweet, post, tap, question, whatever you like, device, and be ready to join CrowdChat because it's gonna be really cool. Now, one last thing before we go to cocktails. During the track sessions today, we had to adjust the rooms. Why? Well, just like we should have predicted, if we'd had good predictive analytics, the technical track overflowed. Too many people wanting to talk to the brainpower of Vertica. We moved it to a bigger room. But you know, there's a room downstairs in the partner zone that's called the Dev Lounge, or, well that's the acronym, it's the Developer Lounge. It's the Developer Lounge. Tomorrow between 9.30 and 3.30, the creative people who created that t-shirt and are wearing that t-shirt will be available for you to talk to anytime you stop by. The best practices bar, which is really just a stop and chat with some of our smartest corporate sales engineers and field sales engineers is available for you there. So don't miss the Developer Lounge, don't miss the best practices forum bar, and most importantly, don't miss the open bar that starts right now. Thank you very much.