GA 439 | Collecting and Interpreting Data with John Knotts

Measurement is really. It really dictates how well you can do continuous improvement. If you're not measuring something very well, it's very difficult to continually improve.

Today I'm excited to welcome John Knotts back to the show. Now, John is a Gemba Academy employee here. He's been one of our senior coaches and in fact, he's doing the majority of the heavy lift now for our green belt and our black belt certification program. And he's fantastic. And this is the third time I think he's been on the show. And today we kind of dig into. We got a little bit geeky and talked a little bit about. About collecting data and how to make sense of data. And so often when we're coaching candidates here on our Lean and Six Sigma certification programs, in the beginning we're trying to say, well, what problem are you trying to solve and how do you know it's a problem? And honestly, it's sometimes it's the hardest part of the whole journey is getting folks and getting some data collected, getting organized, getting into the correct type of graph or chart or whatever it is that we're going to use. So that's kind of where we spend. The majority of the of the conversation is on that topic. We get into also some things, like some of the top two or three tools that John feels that everybody should be using. I mean, obviously respect for people is always at the top, but in the end, there's some. There's some specific tools that we really all need to be able to wield accordingly. So we get into that and then right at the the end. So John is this prolific LinkedIn poster. He posts every day on LinkedIn. And I don't mean just like a little lame story. He's got like some deep thought, basically a blog article on the daily on his LinkedIn channel there for his personal profile. And so the other day he wrote one talking about being the Rock. And it was one of my favorite posts that he's made. And so I asked him to explain that one. So that's right towards the end where John talks about how to be the Rock and maybe a storm or whatever it might be. So some pretty good stuff. So show notes can be found over@gembapodcast.com just look for episode 439. Again, gembapodcast.com look for episode 439. Okay, enough for me. Let's get to the show. John, welcome to the show. How's it going?

Getting all the bugs worked out? Yeah.

Welcome back to the show. Yeah, we Had a few little technical difficulties with our new platform here. I think it was more hardware error on my end, but we got it worked out. So thanks for your patience, John. All right. Well, hey. Yeah. So this is the third time that you've been onto the shows. So we've had some fun in the past talking about horse farms and, and then more traditional getting Lean and Six Sigma stuff. But today I'm excited to kind of continue the story. But in case folks don't know who you are, John, before you do your quote, give us a little background on. On who you are and what you do for Gamba Academy.

Well, I obviously, John Knotts and I have been in process improvement and improving organizations for close to 33 years, since 1990. Got involved in the Air Force's Total Quality Management program and worked in a lot of different organizations. Worked as a external consultant with Booz Allen, internal with USAA, Fortune 100 company, and right now working with Gemba, helping out with the Lean Six Sigma training and certification program, doing a lot of coaching for individuals that are going through that program.

Yeah. Awesome. Awesome. All right. Well, you do, you know, at the beginning of every show, we like to get our guests to share a quote. So what do you got today, John?

Well, I thought it would be appropriate because we're going to be kind of talking about continuous improvement, but this is a Mark Twain quote that I kind of keep in my back pocket, that continuous improvement is better than delayed perfection. Yeah.

And in a way, it's like a dichotomy, like delayed perfect, like assuming that you can even get to perfection. Right. So.

Well, I think it's, you know, we're going to talk about measurement, and measurement is really, it really dictates how well you can do continuous improvement if you're not measuring something very well. And we'll talk about that. But if you're not measuring something very well, it's very difficult to continually improve. You'll do huge improvements because you really don't have a good measurement system in place and you're trying to get as much done as one of the problems I see over and over again with organizations that they don't understand the problem well enough and they end up taking forever to try to get everything done. As you've said in videos, boil the ocean.

Yeah. No, it's so true. I mean, you know, and all the folks that we're all, we're all coaching here, whether they're green belts, black belts, masher black belts. I mean, I always say that the hardest part of More of a traditional say like Domic style project. It's, it's D, you know, like defining the problem and like what problem are you trying to solve? How do you know it's a problem? You know, and I always tell people fixing and gets the easy part. You know, once we get into, analyze and improve, it's like that's the fun part. And I don't know it's necessarily easy, but it's. Boy, if you get the D wrong, you're doomed. Right. If you don't define a problem correctly, you're doomed.

Absolutely.

So, well, so with that I guess foundation there, let's dig in a little bit on data collection. Because we're many organizations, even when they say ah, we don't measure things, they actually do. I mean a lot of companies are probably more data rich than they realize they are. It's just that they don't, they might not collect it correctly or they might not organize it correctly. So if we started there with like, maybe even this like frequency of data collection and the style of data collection, like what do you recommend when, when people are kind of getting going with this?

When we go through project selection in the defined phase of a process and we're talking about how to find a project, the biggest advice I give is find something you're currently measuring. But when it comes to data, because if you're currently measuring it, you've just eliminated a long piece of your project because you have to collect enough data to be able to make a decision. We don't make a decision off of the last piece of data. And so here's the, the advice that I give to people is that if you're measuring and reporting something on a monthly basis now let's say how many, when you, when you talk to coaching clients, how many data points do you think somebody needs to have a good trend?

I mean, I mean, a lot. I mean, honestly, I mean, well, you know, minimum.

What would you need? I usually say six. Yeah, well, I mean, two is a trend.

Yeah. Officially, I mean, I think officially even on our videos where, you know, we're, we're, we're more snobbish even than that in, you know, trying to get, you know, 15, 20, 30 data points. But it's just sometimes it's not even realistic if, especially if they're the beginning of the thing, they're like, we don't track it. Okay, we'll start tracking it right now, you know, and can we get daily.

Or think about that. Let's, yeah, let's see if we. What happens if you're doing. If you're collecting something on a monthly basis?

Yeah.

If you needed 30 data points, it's a long. All right, you can work on this in two years.

Yeah.

So but that's the whole thing. Even at six. Even at six data points, to be able to tell, you know, like, first off what's going on and then. Did I make a difference at the end? Six data points. Okay, get me at least that much. That's six months. If you're looking at something monthly. And most businesses look at things monthly.

Yep.

You know, they report out their revenue, they look at their profit margin. You know, it's like always a monthly, and that's way too late. So getting something that's monthly into a weekly basis, you just turn six months into two months. Really six weeks. But I tend to say get two cycles. So if it's monthly, then get two months. That way you see month over month. And if you get something daily, you need six days. Typically that'd be two weeks. That's amazing. You can shrink your time to make a decision from six months to two weeks. And if you're doing something that's like hourly in a day, you would know if you've made a difference. And you know, like, you could go a week and be like, okay, that's good. But if you're doing continuous improvement, and this is, I think the thing about that quote that's so important. If you want to do continuous improvement, you want to break your problem down to a tiny pieces using all of the tools, Pareto and ANOVA and SPC and all this analysis, tree diagramming things and understanding where specifically is this problem occurring? What can I do about it? And then putting one solution in place. And if you are collecting data daily, in two weeks you'll know, okay, I made a difference.

Yep.

Or I didn't. And I need to go back to the drawing board. And that's where. That's why I think what happens with this delayed perfection is that we don't collect stuff fast enough to be able to make any significant improvements. So instead of doing very small improvements, we try to do everything. And the kitchen sink.

Well, and I only think like when we're dealing with monthly data, you know, some people, People will say we only have it available monthly. I can only get it monthly.

So.

Well, you know what, it's being tracked somewhere every minute, you know, every day. It's somewhere. It's in a system. So we might need to get our IT architecture friends involved. And you Know, pulling it out of ERP systems or whatever it might be. But you know, by definition, if you can get it monthly, you can get it weekly. And the other reason that I love daily and weekly data, hourly, even better. Right. But like once you make encounter, implement countermeasures, forget to find phase. Say we're in and we're in, analyze and improve, you know, and we want to test out some, implement some countermeasures and see how it works against those root causes that our team thinks might be driving the problem. Oh, you have monthly data, you're waiting three, four months just to see if he made it better. Right. And that's only three or four data points. You know, to your point, if you want six, then you know, wait another couple months. So. Whereas if you have weekly or daily data, it's much faster. Not to mention the problem with monthly data is that, you know, the way things were going six, seven months ago might not even be relevant anymore. You know, we might have.

Exactly. Well, in reality, if you're doing something monthly, see, I always believe that you should get a cycle. And a cycle in a monthly type environment is really a 13 month rolling because you want to see January versus January, you want to be able to compare month to month, year to year. And unfortunately sometimes we work on things, this is where not in a process improvement certification, we don't want to do this here, but you still have to improve things. Things like the budget process, the end of year, the end of quarter, which only happens occasionally. So you know, like you're not going to wait around and say, did I make a difference six years from now on something. So there are, there are exceptions obviously to this, but when you can get to the data faster and every process like end a year, there's an end a quarter, there's an end a month, and there's a bunch of process steps that are very similar. And if you can streamline those similar process steps and just not worry about the stuff that only happens once a quarter, once a year, then you can really affect things faster. And that's what it's all about. It's getting to things faster.

Okay, so we spent some time on frequency. What about like the type of data? What are you, what are you looking for there, John? Is there any type of data or, or measure that you're, that you're wanting to see or you're advising folks to, to examine?

There's three things that I think are imperative to improving a process. There's a lot of data to run an organization, but when it talks about improving a process, you should be looking at the volume that's coming into the process or the volume that's going through the process. How much work are we doing? That tells you what your workload looks like. And you can look at that day over day and really tell, like, how busy are my people, how busy are we? And when the spikes happen, what do we do about it? In a certain extent, a lot of organizations, when they're working on a process, that process is within a bunch of processes. They're not in control of what drives the volume. So it really is a reactive, it's a lagging indicator. But when you know the cycle time of your process along with your volume, now you truly understand what it takes to do that per accomplishment, and you can start working on that. And the last thing is defects. So if you're looking at your volume, your cycle time and your defects, you have the ability to continually improve things, remove those defects and make that process faster so that you can do more of it. And you should be able to see that if you're looking at that data.

Right. I know you're a fan, just because I know you and I work with you of trended data. A lot of times we'll see organizations with the, what do you call it, like, kind of like the stoplight style chart. And it's very common. Right. You'll see it on a lot of dashboards and project plans or whatever it might be. In some cases, maybe they're okay, you know, kind of giving a status of how things might be working but. But really shouldn't be trying to manage our business with it. At least I don't believe so. So like, what's your thoughts on like trended data and the importance of that?

Well, what happens when you look at something in a month over month view, and especially when it's just a color on a chart, people react to red. Oh my God, what's going on? And we know that processes go up and down. You know, it's just, you know, it's common for things to be at a high or be at a low. And it could be during the type of year. You know, this could be just a cyclical type behavior or even the time of month. It could be a cyclical thing.

Right.

That just happens. But what happens is when people only look at the last data point and typically that's in a color, then they react to that and oh my God, we got to solve this. I can't tell you the number of times that, oh, we we got a lot of defect or we got a lot of customer complaints that went to the CEO. So we got to do something about this problem. And when you look at the overall process, you go, this is not a different, any different than the number of customer complaints you get on a regular basis. It's just that they went to the CEO. So suddenly it was like it hit a red. Yep. So when people manage that way, it is so hard to try to improve things because you're just chasing what commonly occurs.

Very emotional as well. Right. Lots of emotions. It could be we're either happy and we're so awesome because we're green or you know, fire everybody. We're red. We need a new team, you know. Yeah. Whereas, yeah. It's the whole concept of common cause variation versus special cause variation. You know, if we don't, we can't see a signal from the noise, then, you know, we're, we're, we're really doomed. I'm wondering something that also was, it's interesting when I, when I'm working with my, my candidates is, you know, obviously, and I know, I know you do the same thing and so does Steve. One of the things we do early on when we're looking at a data set is trying to determine what type of data are we dealing with. Is it parametric or non parametric or normally distributed versus not normally distributed? And if it's not normal, you know, we typically go with the median and the range. And if it's normal, we'll go with the mean and standard deviation to describe that data. Have you seen folks kind of get in trouble with that? If maybe they have a, what we would call not normally distributed data? Maybe it's lead time or some data like that that's going to be naturally skewed and they all of a sudden want to start talking about the mean and a standard deviation. You're like, whoa, whoa, whoa, whoa, whoa. You know, have you seen that a lot in your experience?

Not only, not only do you see it, but unfortunately, sometimes the statistical programs lie because they're fooled by the data. If you don't look at the, if you don't look at the charts that the data is looking at. And that's the thing I always say, you know, when we, when we're walking through analysis and we're, we start with talking about charts and graphs, what we're doing is that we're doing inferential statistics on our own. We're looking at the data in a chart and we're making decisions or we're making assumptions off of what we see. But you've noticed that in every test that's run, every thing that we do, it spits out a chart. The computer is looking at those charts, but it's looking at the data behind the charts and it's saying, this is what I see. But sometimes I've seen processes where there's three separate processes. So you have a histogram that goes up and down and up and down three times. So it's got three peaks in it, but it looks like, according to the data, because there's overlap of the numbers, it looks like a very normal process. And it'll say, oh, this is a normal distribution. And it's not. It's actually three different processes in one. What's going on? And it's just not clear where the break is. But until you actually look at that true chart and histogram, you wouldn't see that. And there can be sometimes when you have numbers that are really far out and they're throwing off your overall, it can really mess up things, too. So you have to look at things from both perspectives. And that's what I think a lot of people struggle with.

Yeah, what's your two cents on that? You just mentioned an interesting one that you'll see a lot is you'll have two or three super huge outliers. Something crazy happened, you know, a year ago or whatever. What's your, your sense when someone's kind of chartering up their project? Do you want them to drop those off or do you want them to keep them in or what? How do you handle that?

So when, when, typically when people are starting out and they're trying to learn this stuff, they don't really know a whole lot about charting anything. So they have data. And when you look at it, you'd be like, I kind of know what's going on here. But to jump ahead and start to start, teach, you know, certain things, you're like, okay, why don't you go back to the descriptive statistics and just run the basics and look at this and see what's actually happening? Because I'm bad. You got some really high numbers in there that are, you know, outliers, you know, is a thing. You know, everybody teaches measurement systems analysis and they teach gage R and R and attribute agreement analysis. Now, those are tools that, you know, are everywhere. The thing is, is that we pull data out of computer systems nowadays. They'll put iPads or tablets on machines, or the machines are actually collecting it via infrared scanners. Or, you know, motion scanners, or you're pulling information out of, like a human resource information system data with start and stop times and dates and all this. And it's, it's giving you this data. And we download reams and reams of data, which is great because we have all kinds of data to work with. The problem is there is no MSA for this. There's no way to test it statistically. So I always tell people. So I call Gage R and R and attribute agreement Analysis, a quantitative measurement systems analysis. But when you're pulling data from a system, you need to do a qualitative msa, which means you need to pull it into some kind of spreadsheet and you need to sort your data columns that you're going to be doing analysis on, and you need to look for anomalies, anomalies that you wouldn't normally see. You'll run your analysis and those big numbers or those blanks won't show up, but they could be affecting things. So when you do your analysis of that and you sort it ascending, descending, you'll start to see gaps that appear. Like, wow, where'd that number come from? I had a guy that was working on a process improvement, and they were collecting downtime, and they ran into two situations because of the tablet that they were collecting it on. There were way too many reasons for downtime. So everybody was just selecting other. They weren't scrolling through all the reasons, they were just selecting other. So he ran a Pareto analysis and he had two other columns, the one that Pareto automatically creates when there's too many items, and the other column that everybody was selecting and the other. They were both on both ends of the spectrum there. And this is stuff that's happening in. It's happening in this data that we get more and more from systems, and we need to make sure that the data that we're using is valid.

So when you think about, there's a lot of tools available to us as continuous improvement practitioners, um, and we got videos on pretty much all of them. Like. But are there, are there any that you really think stand out from. From some of the other ones, Some tools that folks really should be, you know, looking into and using as they're trying to improve their process when it.

When it comes to continuous improvement? Continuous improvement means that everybody is improving things as they see problems, means that they have the basic abilities to detect and to make improvements. They're empowered to make improvements. So when it comes to teaching the worker, you know, like the person who is in charge of this process. Maybe there's like eight things that they do, and I always share three things. One is the control chart. Teach them how to enter their data and then put it into a control chart and let them. And teach them how to read it. What are you seeing? And when should you do something about this? Teach them a Pareto chart. So if they have multiple things that are going on and they have multiple defects, put those in a Pareto view and you can build these things in an Excel file that all they have to do is enter the data. And third, and this one's probably the more difficult is a box plot, because a box plot will show you truly the differences between what's going on versus where a Pareto just kind of puts it into a rank order. And I'll tell you, I did this with usaa and we were working. There was eight processes or document processing activities, and we taught the team how to identify what was a defect. That's always an interesting discussion because they don't see them. They don't see things as defects. They just see them as part of the process. But once they understood it was a defect, then they just counted them every time it happened. Put a little line on a chart, check sheet, and then at the end of the day, put that data into this Excel spreadsheet for the day. And they did that. And I let them just go. And I taught them how to read the charts and everything, and I let them just enter it for three months. Working on other things, came back and I looked to open up the data, and I looked at it, and like, half of the problems that they had had almost disappeared immediately. I was like, right. I wonder if they're counting the data anymore. Maybe they stopped. So I went, I asked like, hey, what happened? Like, oh, well, we just fixed the problem. They recognized that the problem exists. They could see it. They picked up the phone and they said, hey, we keep getting this this way, right? Why is that happening? Oh, we didn't know that. And let's, let's fix that problem. And they fixed the problem, and it pretty much went away. But that's when they see it. And those three charts show you that. Of course, as a practitioner, I mean, there's so many things that you got to bring to the table, but when it comes to continuous improvement, we want that done at the, at the line.

Yeah, you mentioned, you know, control chart, Pareto chart. You mentioned check sheet in there, different types of graphs. One of our more popular courses, it's actually in the. In the school of Lean. It's not even in our school of Six Sigma. It is that seven QC tools course. You know, where we're going through graphs, check sheet, Pareto chart, cause and effect diagram, scatter diagram, histogram and control chart. Yeah, control chart. And yeah, man, you know, it's like, you don't have to get too crazy here, people. You know, just. Just get in there and, and start studying your data and adding context to the data. That's, that's the power of the control chart, right. Is that we're adding context to it, you know, with those control limits. And maybe you throw in the moving range graph at the bottom if you want to get even fancier, you know, but boy, it's a powerful tool. Just that. That simple tool right there.

Well, and usually you're just looking at, you're looking at things like volume of work, you're looking at the number of defects. These are counts.

Yep.

So they're pretty basic. You know, it's not like you get to get really fancy with this stuff. And I try not to get, you know, when I'm just teaching somebody how to. I'm not going to talk about like, oh, well, you need to figure out whether you need a P or U or C or a. Wait a minute.

I feel like your distribution is plenty. No, we need to look at. It's like, what. You know, I'm just trying to get you to put that. Give me some weekly data here. Mankurtik, man.

Who cares?

You know? So. Yeah, no good stuff, right?

Yeah, that's one of the things I don't teach. I don't teach people that are on the front line how to do it.

Well, you know, there's a, there's a. And there's a place some people will do it. Right. And. But yeah. Does that frontline worker need to be worrying about their, their CP and cpk? Probably not. Not. Not on a daily basis. And that's. Yeah. Depending on what they're doing. But. But probably not, you know, so.

Yeah, but I tell you when it's funny because people will say, oh, I'm working on a Six Sigma project. What's your Sigma level? Well, I don't have one. Right. And you're not working on a Six Sigma project. @ most, you're working on a design for Six Sigma because your Sigma level right now is zero.

I don't have any data. Okay. Definitely Sigma project.

Yeah, yeah, exactly. And that's where it's always funny because, you know, they don't realize that. And we even teach like getting to sigma level at analysis. And really if you need to know what your sigma level is up front, that should be part of your define. If you don't know that, then your first job is to figure out what's going on. How can I measure defects versus the volume and you know, just get to a basic dpmo.

Exactly. And that's why we're not purist on Six Sigma lean or whatever. It's like just let's figure out what problem are you trying to solve. How do you know it's a problem and let's go on and do it. And you might not need a single so called Six Sigma tool or you might be mostly the traditional lean approach. However, let's throw in a measurement system analysis because you're using those calipers over to measure those parts on the daily, you know, like are you any good at it?

It.

You know. You know, and so that's why I'm, I feel like it, this, these, all these camps, you know, the lean guys, Six Sigma guys, you know, like oh my word. You know, it's like, you know, just stop it. You know, like just make sure that you're, you're learn it all and that way you can use what you need to use when you need to use it, you know. And yeah, use as fewer tools as possible to solve your problem. That's a beautiful project. Right.

And, and if you get the problem to be really small, you can get them done, you can solve things quickly.

Yeah, especially when you got that daily data. Right.

Exactly. And that's, I think that that's like the biggest thing to walk away from training is to understand how to make big problems, little tiny problems and solve them quickly. And that's, I mean kind of how our, our program is a little bit different is because we're not like, oh well, you've got to save, you know, 150,000 or 500,000 on this type of project. No, we want to see you apply this methodology and learn how to break down problems to understand them and solve them.

Right.

If you can do that, you can do that with anything.

I feel like, and I did, I know when I was going through way back when my black belt projects and all the rest of it, you feel this pressure like that. This is my one chance, you know, to you know, make a mark here. I got one black belt project, you know, and this is it, you know. But no, you, it's not it, it's the first one, one of hopefully hundreds and hundreds of projects that you're going to run over your career, man. You know, so don't put so much pressure on yourself. So.

Yeah, in the military, we used to have a term called jeep. It means just educated enough to pass.

Yeah.

And wherever you were, whatever you were doing, you were always. You come in as a jeep. You'd be like the first jeep staff sergeant or the first jeep on the. You know, in the assignment. Yeah. But you didn't know anything and you had to learn. And I. I always look at that. I'm sure you've probably seen people who have certification programs that are run by individuals with a lot of experience who just want to prove how smart they are and how little, you know. Yeah. And it's just. That's so destructive, you know, like, come on, it. These people are going through this for the very first time. Yep. It cannot expect them to be experts at this. They're learning and they will continue to learn and it'll take years to be able to. I still look a lot of things up because I don't remember, like, how to do that exactly.

I know you've heard me say this before, but let's bring out a book stuff and found blog articles that I wrote 10 years ago with the answer. I was like, oh, man, how crazy is that? You know? But you're right. I mean, I remember going through some training again way back when. I had some super smart folks that were teaching me. One gentleman was this big PhD, statistics, blah, blah, blah, you know, and he got up there and he's just, to your point, just showing off, man. I don't know. And I remember sitting there, like, lost, but, like, sort of like humiliated, but I wasn't about to raise my hand, you know. And so I remember thinking, man, when I'm teaching people, like, I figured it out on my own after class, you know, I did figure it out, the topic, but it wasn't because of that person. But I remember thinking, I'm just never going to make someone feel like that. I'm going to do my best to never make someone feel like that, you know. And anyhow, I feel like we're kind of biased, I guess, on Academy, but I feel like we really do that, you know, we try to help people where they are and. Yeah. And not make it so overwhelming.

I've seen a program once where there was a director and she went into the board. Okay. You know, they have the board, and there's like five people on that board who've been around process improvement forever. And then they look at all of the tools that you're required to use, and then they just pick them apart. And she came out of this thing, she's a director, and she came out crying. And we were in the midst of kind of like re engineering the entire program. And that became a mantra for us is a respect for the employee. You've heard that before, right? Respect for the employee. We're not going to treat people like that.

Yep, exactly.

That's not. It's not right.

Well, good stuff, man. Well, hey, man, it's been fun, John, as always. I'm sure we'll do it again down the road, but keep up the good work. I know. The great work. I know you've got more candidates than me and Steve, I think, combined now. So appreciate all you've done for, for GAV Academy since you've come on because you sure have helped, helped our sanity levels.

Well, it's exciting and I think a lot of people get a lot out of it. So that's what. Yeah, I, you know, I think that's the most important thing is getting people the AHAs at the end of the project. I love asking the questions on what were the top things that you got out of this? What did you learn that was most important? Yeah, it's always different.

Exactly.

You know, that's what they're going to carry forward.

They're going to remember the AHA's, right. So, yeah, very good. Hey, if folks want to connect with you, I know you. You're busy on LinkedIn, man. You got some prolific LinkedIn post daily, right? Every day, seven days a week.

Yeah, daily. Every day, Every day. On business and success. I just, you know, want to share everything from, you know, personal or professional success, businesses, how to improve. It's interesting because I see people that say, constantly tell people what to do. So, like, yes, or just today I saw somebody is like telling people you need to document your processes. Well, no kidding, right? But how and why and what am I gonna do with it? And that's the, the thing that I try to always share every day is a little bit of how or why or what do you do with it. Because that's the biggest thing. It's like we can tell people what to do all day long. So if they, you know, it's John Knotts on LinkedIn.

The one that you. Every day, maybe yesterday, before I can't remember saw it was one of my favorites. You talked about being the rock. What was that? Give us a quick synopsis on that one. We'll link to it. I'll have Jessica link it up.

But this actually came because I was the superintendent of a commander's action group in Air Intelligence Agency. So all Air Force intel working directly for two star commander. And we were responsible for the auditorium. So they have big PowerPoint presentations up there and all this kind of stuff. And occasionally something would happen, much like the computer issue that you had coming into this podcast. So, you know, like they'll run into something like the light will burn out or it won't turn on or whatever. And of course we had a phone call and somebody was had immediate response to come in because it's the commander's auditorium. But these people would come in freaking out. And I'd always say to the nine people that worked in there, I said, there is no such thing as a PowerPoint emergency. We need to be calm, cool and collected and we need to just pick up the phone and make our phone call. We cannot assume their craziness. And that's, I think, you know, like being consistent. This is for like leaders, for employees, for just people being consistent and constant in what you do. If you make a decision you're going to do something, just do it and just keep plugging away at it. Sometimes you'll feel like you're not really going anywhere brilliant. And I can, you know, like occasionally you'll feel like, is this posting every day really making a difference? And then I'll get some because I send out when they allow us to do the birthday things, I send out happy Birthday every day to people that are have birthdays. And occasionally I'll get somebody to reply and say, I really appreciate your posts. And they've never, I've never talked to them before. And I'm like, okay, somebody's reading that I don't even know they're reading. So that's important. But that's kind of like being the rock is the rock in the middle of a stream is unmovable. It may get softened over time, it may change a little bit, but it's not going to move.

Yeah, well, and that's a little smoother. We need to be, you know, for glistening. Yeah, right.

Exactly. Looks better. Exactly. So we get better with age. Right. But yeah, that's what the post is all about. I try to do things like that on the weekends because it's more personal and yeah. Than professional. But yeah, I always try to, always try to share and I, I like engagement. I like people to get in there, tell me I'm wrong because I probably am. And I'd like your Input.

Yeah, yeah.

I mean, you know, like, sometimes I'll just post stuff that I really like. I'm gonna push some buttons because I want people to argue it.

Right, right.

Because you, you learn more when you get more sides of the story, more sides of the idea than just their own.

Yeah. I had a boss one time, back into my corporate days, he said, you know, there's like, I don't know, 10 of us in the room. We were all managers reporting to this person. And. And they said, the boss said, hey, if all 10 of you are agreeing, I got nine too many. You know, I'm like, oh, yeah. So. Yeah, so exactly.

Sometimes you want disagreement.

Yeah. Yeah.

All right, well, good.

Thanks again, John. It's been fun. And I know you got a busy. Probably got some coaching calls. I do here shortly.

So I do. I have one coming up.

All right, take care. Thanks, John.

All right, bye.

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