 Should we, should we sally forth and continue to find out what's going on? Yeah, do you have any? You might be asking yourself, what did I just find? In actual environment. It is very dark in here. Why are we here? I don't even know what here is. Why not? We should measure something so we know where to go. I would like to know where I am. That's a good start. Well, we need data. We do need data. So, where does data come from? Like, especially in an organization. It's from a unicorn. Thanks. I don't know. It's impossible to have a serious conversation with him. Because it's so magical. Okay, fine. He's right. He's not wrong. Data is magical. So, and what does it do? It brings you into the light. It brings me bright ideas. Oh my gosh. We're never going to get through the video on data. So, where does data come from besides unicorns? Oh, wait a minute. Look at this. Pull up. Waterfall. Count the droplets. One, two, three. Seven, four, five hundred and twenty-three gallons. Yeah, that's a lot. Gallons are important when we're talking about waterfalls. It's moist. It's moist. That's good. What a good word. Yeah, moist. So, we should take data on how many people don't like the word moist. We could. We could. Probably a lot of people still in the cave. Yeah, it's moist in there, people. Right. So, that's really moist. Exceedingly moist. There we go. Measurement, right? So, we're talking about that's really moist. That's a little bit moist. That's kind of dark. Like, what good does that data like that do anybody? It's kind of redundant. It's completely inaccurate. Like, everybody interprets a little a lot differently, right? So, we need to get into something that's a little bit more strict, right? In terms of measurement. So, like I was asking you earlier and you're popping off with unicorn comments. Where does data come from? Ideally from a target, something we want to measure. From something we want to measure, right? So, measurement's the key. So, measurement straight up is what's going to tell us, what's going to produce the data that we're going to use in the organization to make decisions regarding policy and procedure and how we're going to go forth with the business. The question is, what data? What measurement? How, when, where? All that fun stuff. But, what are we looking for? Well, we've got indirect measurement, right? We've got direct measurement. Yes. All of those things are going to relate to something that we're going to put out there. Some type of goal or something like a key performance indicator. Surprise, surprise. Right, so we're going to tie all that stuff in together. And we're going to, as we tie all the measurement, the data collection, and then we're going to look at the validity of that data. As we go through all of those pieces, then we're going to start to add all sorts of other stuff including convergent validity, multiple data sources on the same thing. Right, because we don't want just one, my employees did everything they were supposed to do. Well, how do you know, Brad? Did they tell you? I have check boxes that say so. Did they fill them out or did you? I did. Well, that's pretty good. Yeah. You should see their check boxes. I didn't watch them though. I just checked boxes. Even better. You should have seen that. Right, you didn't actually track it. So we're going to get into all of those other types of data, all of those pieces that are related to it in terms of the validity and all those fun things. And we'll be able to draw conclusions and make goals and do task analyses and all that stuff is related to data collection and measurement when we're in the OBM Center. And then building the quality of that measurement, or not building, but evaluating the quality of the measurement. Right, so then we're going to get a convergent validity. That's like, okay, are you scoring your people this way? How are the people scoring themselves? Those two things are divergent. Like, if they don't agree, like, I'm doing great, boss. And you're like, no, you're not. That's going to be a fun business. We have an issue. Yeah, exactly. So we need to look at all of those pieces. This is where, you know, maybe we should tell our people over here. Oh, maybe. This is where research methods comes in. So when you were skipping all those research methods classes and doing whatever it is you needed to do to survive your program, you should have been paying attention to all of this stuff because this is where it really becomes useful. In terms of OBM, right? It's super useful for you guys to stuff and practice. They're going to find out it's not that different. It's just bigger. I wouldn't even say it's bigger. Right, true. You're just going to pick a target. You're going to measure it. And then you're going to find another target and measure it and hope those two things agree. And if they don't, that tells you a story. You got a place to work. Ta-da. Yeah, so I don't know. Qualitative, quantitative. We're going to do everything we can. Obviously, I'm going to beat up them. It won't take long. Because it doesn't have any value whatsoever. And you're like, yeah, it does. Yeah, it's very rational. Yeah. I'd like to keep things empirical around here if we get the rationalism. Cool? Yeah. Let's move on and dive into that. Sounds good. All right. All right, let's go.