Hey, Tom Stewart here. Our guest today is Aja Holiday. This is Smart Business Moves. It's Wednesday again. Woohoo! It's Wednesday, halfway through the week. No kidding. I'm going to be off for the rest of the week, so it's Friday for some reason. Oh, that doesn't even count then. How are you, Aja? Yeah. What are we going to be talking about today? I'm talking a bit about what I'll be talking about at convention in a couple of weeks, but mostly about data-driven decisions and how to use what you've done in the past to get you where you are and to change things to take where you are to a better future, to a more profitable, successful future. Talking a lot about how to use data in a profitable way, and not just looking at numbers to look at numbers, which is something I have fallen into. Or not to look at numbers at all. Or not to look at numbers at all. Yeah. To have actions in place that you don't even need to use the numbers. It's like, how are things going? Good. It's like, yeah. And? How good are they going? Please measure that. Sales are up. Really? And, you know, it's hard for us, though, to speak in numbers.  Things are better, things are worse, things are good, things are bad, things are okay. It's really easy to fall into the idea of, like, using how you're feeling. You're, you know, what you're intuitively feeling about a situation to decide how that situation is actually going. Whether you feel good or bad about it and not really knowing, I guess, if it's actually going good or bad. You know, we're used to talking about our feelings more than our... We can't really measure our feelings, but we can measure business. Yeah.  Okay. Or that's awesome. Okay. But, you know, throw the number in here. Speaking of numbers, you know what today was, Aujo? You know what happened today? No, it's my brother's birthday. Okay. Well, that's cool. You had a birthday party yesterday, didn't you? Yep. So, you know, the Federal Reserve, which is kind of the government entity that basically decides interest rates, you know, ended their meeting today and they announced another 75 basis points, which is like three quarters of a percent increase in their lending rate, which means all lending rates are going to go up and they're trying to slow the economy down to fight inflation. So, what does that mean to us? If any of us are borrowing money with a variable rate loan, your loan just went up again today. And they're telling us they're going to be raising it another 75 points, I guess, in six weeks and maybe another 50 points, six weeks after that, then they don't know. There's somebody who's buying a house in the next three weeks. Yeah, that's some stuff to definitely take into account on all sorts of choices that we're making right now. And, you know, for the last 30 plus years anyway, the smart money was when you're like getting a mortgage or whatever, get a variable rate mortgage, because interest rates are low, they're going to stay low and you'll pay less per month than what you have in the fixed rate. I think that's flipped now, though. Oh, yeah. But you're going to pay, you're going to pay, you're going to be more for that fixed rate mortgage than what you would in the past. You know, what you can hope for, and I've done this for a while and been through it, go ahead and if you have to pay more interest now, great. Right. Interest rates will eventually come down in a few years and when they do, you refinance it all. Exactly. I think somebody said to me, you know, date the rate, marry the house. Somebody said to me, I don't know if that's a common thing, but that has been stuck in my head. Never heard that, but yeah. Yeah, date the rate, marry the house. So I'm working on finding some rate dating right now. And if the house continues to appreciate and value when you refinance, you can like take money out of it and do other things with it. If you want to, there's all kinds of options there. Yeah. I'm looking forward to it. I mean, I'm terrified because this is a very different world than what I, you know, I bought my first house two years ago and it's very different in those two years. Yeah. It can be a, it can be a little stressful, but so, you know, it'll, it'll all work out and I guess I've found to believe that it's never as bad as you're afraid it's going to be. Sometimes, you know, it's, it's not as great as you hope it would be either, but you know, it's certainly within a range of, of acceptable. I can agree with that. I can get on board with that. So you've been developing your, your, your, your presentation for the ArcC ISSA convention, which is right around the corner, isn't it? Maybe three weeks? Two weeks, two and a half weeks. I mean, if it's your Friday, it's two weeks out. Okay. Well, fair enough. I'll have two weeks to get ready. I guess. And I do have some work to do. What's your, what's your presentation on? So yeah, working on data driven decisions, really, really trying to focus on the idea that, you know, you can really use the facts of where you've been to help with where you're going, I guess. And one of the things that I really liked that I've been kind of like really mulling over is the power of predictability and kind of the power of, you know, we talk about consistency, especially in cleaning services and what that does to us on our technician side, our employment side, on our client side, like the more consistency that we have, the better things are. And predictability leads into so much of consistency. Like when you can predict, you know, what you are doing, what you're currently doing and what that's going to do for your future and how that's actually going to, you know, by the choices that you're making today, what those results are going to be in the future. It makes this type of work a lot more enjoyable, a lot more. There's a couple of words you threw out there. One is consistency, you know, in a convention, I'm going to be giving a presentation on consistency. So help me out. I need to know what is consistency? You know, in cleaning service terms, you really talk about consistency of same technicians going to the same house at the same time, on the same day of the week, doing the same level of cleaning each time, the same quality of cleaning each time. And it can keep kind of diving down further into how much consistency you actually want. Or I mean, whether you want it or not, people crave it. So for me, consistency can be so many different things. You say people, who are you talking about? What people? Some companies, more than anything, a lot of companies, you know, either they value consistency so highly and they're like, all right, we said the same team to the same house every single time. And other people have thrown in the towel on that because we went through that major great resignation where nobody kept a job for longer than a couple of weeks.   We don't care about it anymore. But really, even if you're not promising it, if you're able to do it, you're able to get so much more out of your technicians and out of your clients. And that consistency helps so much. There's a whole spectrum there on one end of it. Yes, I guarantee you, you're going to be getting the same person every time we clean your home from now till death do us part, which would basically be over promising because, you know, and on the other end of that spectrum would be, no, you're going to get a person, a different person. Somebody will be there. If you're lucky, if you're lucky, somebody will show up. Yeah.   I don't know if this is where you wanted to go with her. But I'm curious. I'm giving a presentation. I need to know this. Yeah, we'll help each other out. Yeah. No, I mean, so I, I, I see people have both of these mindsets either, you know, we're super consistent. And that's what we promised. And that's what we're working towards. And others that are like, we just don't do it. We don't care. We don't do that. And what I think people don't realize is how much consistency matters. And whether you're promising it or not, everybody else craves it. Your technicians crave it, your clients crave it. And the more that you're able to make that happen, the better your, you know, bottom line is at the end of the year. The less money you're spending on training, the less money you're spending on churn of clients and technicians and, you know, and the less time you're spending scheduling because everything's already set and ready to go. I mean, the returns on consistency are, I don't even know how to measure them. Honestly, I haven't, you know, how to measure the return on consistency is, is a whole project on its own. But there are massive returns on having good consistency. If you think about it on the, what's the, what's the opposite of consistency? I don't like birds. But, you know, I would argue variability. I would argue you can say change even and there's a whole body of, there's a whole body of work out there. There's a lot of people who make a living helping people deal with cope with change. So change and variability is stressful for people. I mean, there's the joke of like change is hard, but really change is hard. Okay. So it certainly, certainly makes sense that if you know what to expect because you've been there done that and that's important to your technicians. That's important to your clients. Everybody's happier. Yeah, I feel better just thinking about it. I'm looking forward to that, my presentation now. There you go. You're welcome. I mean, I'm like, I've always had a passion about consistency and it was really funny how it started to come up as I was, you know, writing down my thoughts for the data driven decisions is that that consistency kept popping up in a slightly different way. The consistency of action and as far as working towards goals and I may essentially talk about our core KPIs and a lot of people have different KPIs that they're tracking and stuff like that. And we know what good things can do and what bad things can do like if there's too much of the good and not enough or too much bad and not enough good that you're working your way in the wrong directions and you want to go. That's the level of consistency that comes from consistency to get predictability and that little marrying of those two concepts was really interesting. That if you're able to really get everybody on board internally that now we're not talking about technicians and clients necessarily now we're talking about what does this look like. What else can consistency do for you internally and how does that help your predictability of your projection. And I, you know, somebody, I don't remember who said it or where I wherever where even got this but the idea of working towards like using data as starting with observation working towards calculation and then getting action off of that and that becoming like your cycle of prediction prediction of your success and of your growth. And we've got observation down you know people have built spreadsheets and spreadsheets and spreadsheets off of their cleaning services and off of their results and where they're at. And then some people get stuck there. And then what I've always done is I've gone into the heavy, heavy level calculation of, okay, well, what does this mean if we go here and how does this change if we do a little bit of this and just like really working on calculating, like, what, what impact. What what numbers impact each other go together. And how changes on any of those could help change the bottom line. And then final thing being action. Okay, now what do we do with this. Now, now that we know what can happen. What are the things that we need to do to make that happen. And by doing that consistently, you're really able to really have some really big changes on on your business. If anybody watching wants to hit us up and chat with a question we'd be glad to help you with that. That being said, we get questions all the time, you know, Tom what's your favorite made central report. And, you know, I love all my kids the same right, you know, I don't really have a favorite. But it does make me wonder it's more of a like an observation exercise where not even to the point of trying to reach some conclusion from the data and then taking some action on it it's getting ready if you're playing, you know, house cleaning jeopardy.  So, do you have an example in mind of how this progression from observation to calculation to action might might look at a cleaning business. Yeah. So, really, I think the some of the easier ones to kind of, or the ones that maybe not as easy, but you can look at like technician turnover customer attrition, and I don't even know just like total home, something like that and the homes that you're cleaning, just to gain a couple of our KPIs, but you can look at those through the past and you can find trends, right, that's just observation, where depending on where you're located, you might see, you know, if you have a heavy summer life that your technicians turnover and your attrition, both the jump at that point, they both go high, because people start leaving and running around over the summer and they work together in tandem at least and then they kind of come back up during cool year and winter and what not depending on where you're at. By knowing that that's going to happen, you're able to start helping calculate how you out, outweigh that, what do you need to outweigh that how many people do you need to hire additionally to outweigh that turnover or that attrition, or how many extra sales you need to get to outweigh that turnover or that attrition, and then the next year when that starts coming up you now have, now you have calculations of how many need to do, and now you've got the action in place of alright, in April, we need to bumper sales up really high and we need to bumper hiring up really high using whatever methods work in your area and outweigh both that jump in attrition and that jump in technician turnover during that time so that when you come back in the fall, you're starting much higher than you have in the previous years, because that just steps you into a cycle, it breaks the cycle.  So the example you're giving sounds like you're using data to predict what's going to be happening in the future. Yeah. Think about that. If we if we could like predict the future, how powerful that is. Oh yeah, it's a lot of things that can be predicted. We know how, how numbers, you know, once you have a level of data that you can use of where you're at, you can easily show where you're going to be in a year if you stay at that same place. And that's, I mean, it's nice to be in an industry that it is. It is a little bit easier to do that. But yeah, we can, we can do a lot more than we think if we're not just stuck in the day to day putting out fires if you're actually able to take the time to observe and calculate and do an action that will change what you're doing. So the example you gave, you know, probably any of us who have run our business for for a while for for a couple years, a few years have noticed trends. Maybe they're, you know, there's hunches can't really, you know, at that point can't put a number on it, but you know, it seems like But the observation is a good place to start. If you think that you see a trend, maybe that's where you want to start, you know, pulling some data together and do some calculations to actually verify your, your assumption. Yeah, so I always talk about the difference between long term data driven decisions and short term. So short term helps with your long term but short term really comes from questions like, why is this happening. Is there a link between this and this, how long has this been happening like being able to go back and seeing when it started and help it trying to figure out if you can find out what is the problem. Like those are always the questions that seem to start that sort of short term data driven data driven decisions. You're asking those questions, you go and find the numbers, and then you're able to start making changes on those to see what's going to do the most. And then that turns into long term data driven decisions because now that you were able to do some short term testing on what works best in your area in your business with your culture. Depending on what you're working on. Now you're able to say, okay, by doing this, I know that we're going to see an increase or decrease in whatever way you're trying to see it good or bad, a positive effect in this direction. And if I'm able to change it this much, you know, where would that put me. So when you say short term, is that kind of like an experiment just to verify your assumption. Pretty much yeah, like a monthly to quarterly nothing longer than quarterly typically of testing pretty much, because we have things that can change our, you know, our results. We all know different things that we can do we can throw more money at our ad words we can post more indeed ads for technicians we can, you know, higher do sales people we can do different things that are going to change those numbers some of those things are going to be better than others for your business. Not the same things aren't always going to work for everybody. Some are some are very much like a, you know, 100% for everybody but those are very rare. Don't think there's many of those things that are 100% effective for everybody in the same way. So you've got to determine effectiveness of your actions before you dig deep into them I think. You know, hey, I'm down with numbers.  Sometimes I wonder though, you know, can you drill too deep on the numbers. And, you know, where do you get to a point of diminishing returns how, how much data do you need and how precise do you need to be if if another degree of precision are you going to be making a different decision taking different action. And I don't could you speak to that a little bit. That's a really good point. I do think that there is, you know, the time to value is very important on when you're dealing with numbers and data because you can go too deep and go too far.  So what can I, what can I grab that will be the same, it'll be coming from the same source using the same information every single time with as little effort as possible. What's the easiest way for me to get that information and the easiest and quickest really because you don't want to spend forever getting the minutiae of everything when it's going to be almost the exact same number of what you can get from a high level summary of the same information. So, you know, if I'm finding myself, I might dig deep for a second to determine reliability, but I will go out as far as possible from that reliability definition to whatever to like as far as possible from that. And still feel accurate or still feel, still feel on par with it. I don't know if I have a precision percentage precision amount that I use but you know I might dig in a little bit further take a small sample size and make sure that that's good and once I know that that feels good to me then I'm off to the races with the original number. So does it makes sense to be thinking about the action that you're going to be taking based on the data and what impact does that action have on your overall business.  And if I spend more time getting more data more precise data, is that really going to change my action and if the answer to that is no well, I don't need to build another spreadsheet.  Then I think I've got enough data to know what I need to go but you know it's the stakes are high so let me go ahead and maybe dig a little bit deeper. Does that make sense. Yeah, I mean, there's definitely I mean it is it's a return on, you know, the diminishing returns on how how far you want to go. And for a lot of things it's not I mean even if the potential outcome is really high. I don't think it. It still may not be the, you know, super beneficial to go super deep on it either. It, I'm trying to think of like an example of I'll give you I'll give you one maybe. One of the things that that that we we measure and put a lot of emphasis on and made central is customer turn customer turnover. What is your monthly loss rate for your recurring customers. 5% I guess is a nice round number that is probably average. I was kind of looking at some of your some of your presentation that you did for our last live event and I think for main central users it's a fair amount lower than 5%. But I think 5% is the industry average. So if you do the math on that, you know, you can say well gee 5% a month that means I'm getting 20 months worth of cleaning out of that customer. And if I'm getting 20 months of cleaning and say on the average are getting two cleanings a month and you know, obviously I'm pulling these numbers out of the air. I'm you know, but I just want to do that more precisely but that's roughly 40 cleanings on the average I get for every new recurring customer and say if every customer pays on the average $150 per cleaning was that $6,000. Say my cost to get sold is 50%, then that's $3,000 in gross profit. I'm going to make off of the average recurring customer over the lifetime of that customer. So how much money am I willing to spend on ad words to get a new recurring customer if I can get $3,000 on average. Would it make sense for me to spend $2,000 and ad words for recurring customer. But the numbers would say well I'm still going to be netting a thousand. So I'm going to spend a ton you know. The problem is it coming in the problem is going out. Explain that what are some of that what are some of the the other the problems with that logic. Why would I not want to spend $2,000 to make three. $2,000 and you turn around and give me $3,000 I'm going to keep giving you $2,000 as fast as I can right. Yes. Okay. Yes. So it's I think it would also be a measurement of what would make the biggest difference for your business as a whole that you know if you're already putting out. You know $2,000 does doubling it really still feel you know you're not starting at zero typically at that point.  That that plays a part in it and then how much would it change the lifetime value of that customer if the attrition level was lower if your customer turn was lower. That if you were able to pull the turn down lower instead of just doing more money at getting more people.  Without, you know, having to put more money out there in a more predictable way. So, you know, one of the things averages are important numbers we make a lot of decisions in our business based on on averages and for good reason you need to know that but there's a lot of detail that gets lost in the averages. If you look at that 5% churn, what you're going to find is there's a lot of customers that quit your service within the first couple of months, a lot of them. And there's an occasional unicorn that'll do business with you for 20 years. So, the average might be 20 months, but if that's because you've just got a couple that stay with you forever. Okay, for the ones that you spent a lot of money on that you lose real quick, you're not going to get your return on them. And for the ones that are going to be with you for 20 years to get you back to that 20 month average, you're going to be waiting 20 years to get your $3,000 that you were anticipating to get for you. Yeah, exactly. That's two thousand and out of words. The things that turn around very quickly. So, you know, that's an example where if the stakes are high and you're spending a lot of money, you might want to. The averages can lead you to make bad decisions sometimes. I think it's really good to pay attention to, you know, like to constantly be looking at your, not constantly, but regularly be looking at those numbers for your most recent period of time. You know, I think we can usually assume that that customer that's been with us for 20 years is probably not going to leave us anytime soon. So don't really need to worry about taking them into account on most things because they're bought. They're so bought nothing that you're very few things that you're going to make decisions on are going to change their business with you. As long as I see their doctor occasionally and they're fairly healthy, you're good. We've got a few of those, dang it. Yeah, you're typically good on that front and not very few things that you're doing are going to affect their business. So really focusing on what matters and that's part of that intuition thing. Intuition I think also can be skewed by averages too. If you're taking an average on something and you're taking that average over a long period of time and that's what you're seeing all the time. Your intuition is probably going to follow that. You know, what you believe is happening is just going to follow that average. And so it's really important to pay attention to what your, what data you're taking, what timeframe you're taking. If there's any blips along that timeframe as well. So sometimes even over the time, over a time period when you're talking about putting in a lot of investing into something, you might want to just do more than just averages. Looking at how something has changed over time as well. If you are going up, going down on that particular front, so average lifetime value of a customer, are you going up or is it going down? Because yeah, if you're going to throw $2,000 of your ad words, but the lifetime value of your customer is actually trending downwards, and your return is actually smaller than what you thought it was. So sometimes looking at that too, or it could be bigger if your lifetime value is actually trending up. Yeah. And looking at it in two different time frames, like if your three month turnover rate is higher than your 12 month turnover rate, even if it's fairly low, you know you're going in the wrong direction and it's beginning higher. Yeah, and I'm a big fan of year over year comparisons. And some people haven't been taking that information for that long, but year over year comparisons are huge in predicting where you're going to be at this time next year. So tell me about that. How does that work? I, so I've pulled, I started, when I was with All Star, I started pulling all the things in 2018, I think was the first iteration of that. And to get, you know, by all the things, for those of us who might not know what that world is, what are all, and it sounds like a bag, an everything bagel. It is. It's an everything bagel for cleaning service leaders. So all the things at the time was, you know, getting weekly data points for sales, quality, technicians, revenue, cancellations, I think that's everything payroll. Pretty much all of the major pieces of the business taking a, you know, weekly aggregate of all of those things to see how things were going over time. And we started doing this in 2018. And this pretty much exists and made central now is the set by week is almost the exact same thing. It is extremely close and it's almost that one more thing on there and that's coming. So that is that's very close. And it just gives you a really good quick reference. And it's that upper level data point, right? We were taking the summary upper level data point of all of this information and being able to see how they relate to each other. What is their commonalities that any of this stuff happened at the same time? Throwing them all up on different crafts of over a period of time and getting to see that. All right, for the three years that we've been doing this, tracking all of these numbers on a weekly basis. What, what's the same and what's different? And, you know, being able to see revenue trends around summertime, that's where that example came from. Being able to see technician turnover trends also around summertime. Let's see here, you're able to, you're able to make decisions on the next year. So once we found those patterns or saw those patterns, then, you know, the next year we were able to start trying to outweigh them. Didn't work out perfectly, but it helped. You can do it with, you can do it with almost anything, anything that you're tracking on a weekly basis or even just on a regular basis. You can kind of see where you're at year over year and being able to take advantage of those. Oh, when you're looking at like year over year and hopefully you're seeing growth year over year. Is it a percentage that you're looking at?  I like for that case for those year over year changes and like trying to find those patterns. I don't even look at dollars or percents. I'm looking at visual trends of, you know, if I'm looking at 12 months for the last two years, two 12 month periods or even two, it would be favorable for three years, three 12 month periods. I'm just trying to see if there is similarities in the trend lines across those, assuming that there's growth. So assuming that as the years get further more present, they're bigger, but do they still have trends? Are they still going up and down in the same way across the year that they were three years ago, even just at a larger volume? And if you're at a larger volume, sometimes those changes can mean a lot more. So when you say trend line, that makes me think of a graph. So are you just looking at like a spreadsheet with columns and numbers or are you creating graphs out of this so you can see the data in a different way? I am so visual, I'm definitely creating graphs. And I think that's actually a really good point on data driven decisions that is that you do need to put it into a format that you relate to. Very few people are going to be able to scroll down a column of numbers that look vaguely similar across 169 weeks. Is that three years, something like that close to that? 172? It could be like 156, I believe. 50 times 3 is 150 and 3 times 2 is 6. Maybe it's a leap year, maybe it's a leap year, you know, I don't know. Alright, cool. So 157. Similar numbers, right? They're not going to have major drastic changes. Maybe the top and the bottom will be different, but just scrolling across them. You're not, a lot of people aren't going to be able to see those changes. Some can, I'm not one of them. I definitely need to graph it. I charted something, some way to put it into a visual method of me being able to see how is that changing. So what you're, you know, explaining here, it seems like that once we start getting into the analysis part, the calculation part, it sounds like that. Spreadsheets play a role in this. This isn't as simple as just looking at a report out of Made Central or QuickBooks or anything else.  For most people, yes. I think that that also becomes more of a necessity, the larger people get as well. It might be a little easier to see it on a smaller scale to see some of those changes without putting all of it into a spreadsheet and playing with it. I don't know. And I know, like I try not to put too much focus on that. The biggest thing that's why I like graphs is that those most people can understand graphs or at least see the ups and downs in a good or way or a bad way. So, yeah, you know, plugging that information into a spreadsheet, looking at it in various ways. And if not finding outside sources that can help with that. Yeah, I wanted to go there. So not everybody knows how to use Google Sheets or Excel. Certainly at the level that you do, Aja. So do I, does one have to be, you know, a top gun of fifth degree black belt and spreadsheets in order to do this? I mean, is there a basic skill level that one needs? Should I even bother, you know, if I'm, if I own a cleaning business, should, and I don't know how to, you know, do spreadsheets. Should I even bother to learn or should I just hire somebody?  It's like, you know, I can spell Excel. That's about it. So what am I supposed to do? If you don't put your time into it, find outside help for that. I definitely, again, time to value the time of learning how to even get to that base level. Because, yeah, I do think that there's a super base level of Excel work that you would be able to use to take something like the staff by week report. For the last six months, plug it into a spreadsheet and make some graphs off of it and be able to see some of those trends. That is a step above basic. It's a little bit more than a sum formula, but mostly, mostly self-explanatory and especially G Suite Sheets. The Google Sheets for graphs and stuff like that is actually a lot more self-explanatory than Excel, but shouldn't be too hard. But if you can spell Excel and that's about all you feel like doing or all you're really wanting to be capable of doing right now, time to value. Your time is way more valuable spent on the things that you're good at. Do not spend time trying to figure out how to use Excel to make these things happen or be able to look at these things or, you know, play with these things. Find another way to do it. Either somebody within your business that already exists or hiring without people that can literally just manipulate information for you. So they don't have to tell you what it means. You can probably, once you see it, you can probably know what it means. But yeah, there's a lot of different ways to do that. Time to value is huge. Don't spend time doing things that you're not valuable in. So do you, if you're not valuable in it, I mean, imagine you can find people on Fiverr. I mean, there's probably a lot of people out there that are really good with spreadsheets that would love to crunch your numbers for you. Oh, yeah, for sure. There's, I think, what's the other one? Upward. Upward, yep. GOO is the one that's been around forever, maybe not as popular, but it's got a lot of people there too. Yeah, I think I've, I've looked into a lot of those when I was looking for extra help for me for just kind of getting the base level of things done before I wanted to do the big extra stuff. Like, I don't want to add all of these, these formulas here where somebody else I can do that for me so that I can do the fun things that I want to do. But yeah, definitely, there's lots of other opportunities for that. Because there is a level of every business is different and you're going to have to look at your numbers knowing your business a little bit more than somebody else looking at it who doesn't know your business. I love your model, though, it's like observation, calculation, action, it starts with the observation. So, well, what, you know, what, what spreadsheets do I need? What do I need to build? Well, that depends upon what you're observing, right? Yeah, what are you, what are you seeing? What is it going as expected or what numbers are higher or lower than you'd like them to be? It all starts there. Why is this happening? Now, there's some, you know, nuts and bolts kind of blocking and tackling parts of running a house cleaning business that, you know, if you're using the right, you know, the right software to manage, you're not really having to crunch a lot of spreadsheets for a lot of this. You notice your payroll to revenue is creeping up. There's, you know, you're not charging a month as much as you need to or you're not managing your labor as well as you need to. And there's kind of a progression, a flow chart, if you will, in terms of how to address either one of those. You very well might not need to hire somebody from Upwork to build a spray sheet for you. You just need to do the hard work of managing your business. And also understanding how different things relate to each other. You know, really understanding that your payroll to revenue, you know, we used to be able to tell when we were training and how many people were you're trading like to the number off of that payroll to revenue number. That took some time of just watching it and understanding it and knowing that we are that was important to our bottom line or what we are working on. And knowing when not to worry about something too. That that comes into play. That's an observation thing is like, okay, well the numbers bigger. Okay, before I spend a lot of time trying to figure it out how many people did I did I train that month. Or that week.  Yeah, August is so great every year. Why is that? And those things matter. September always feels like a rough one because it comes off of to two big months. And suddenly September feels like shrunk, but it's just because of the number of work days. Or if you're looking at your P&L like looking at a quick box and it's like my gosh my payroll really blew up this month as well. You had five pay days that month compared to four pay days and most months and that'll mess everything up to critical thinking on those things are really important. You can't you can't stop at just, Oh, this is bigger or smaller than it was. A lot of the why is this happening. What's the link between this and this. So, in Chicago, and two and a half weeks, I guess, you're going to be doing a deeper dive in this discussion. Yeah. And if you're going to convention, awesome, we'll see you there. If not, and you're interested in going, I want to know more about it. I'm dropping a link in chat. You can go to the RxC website is a show and it will take it to this page and you can register if you want to see the topics we got our friend Liz who. Oh, Liz isn't here today. No, she's trapped. Just just miss Liz. Matt is going to be taking us through discussion here seven steps the cleaning business can follow about to get results. Here's Aja and this is on Monday. This is where we're going to be doing the data driven decisions and this is going to be recentation is going to be fun you definitely don't want to miss that. And there's cool stuff going on Tuesday and Wednesday as well. Take a hard look at this we hope to see you there so we've got just a couple minutes left. Is there any final thoughts you think that we should wrap this up with. I mean, I just want to say I'm super excited for convention because it's my first time going. Yeah, I've never gone before so I'm like, I'm super excited. I think I showed up for a day and somebody let me their past just to kind of watch walk around the trade floor for a little bit but that's the most that I've ever seen of it so. Where was that do you remember. I don't. Where's it been recently. In Las Vegas the last couple of years. Oh, then maybe it wasn't even that maybe it was a different. No, I don't even think it was that I haven't been to Vegas for an industry event before. It was in Dallas a few years ago. That would have been it. Okay, I think that one was it that I was in. We went to or was it an awesome. No, it had been Dallas. That was a while ago. Occasionally it's in Chicago. I don't think it's been in Chicago for the last few years but it's in Chicago this year. I think it was the Texas one say I think that was when. And that would have been a while ago. That would have been 2017. That's my gas. That's my gas. I think I saw the show floor for about 30 minutes and that was my extensive convention so pretty excited about it. I am excited to get a little bit more of the whole thing to see some people I haven't seen in a while and no first big industry event in the last couple of years. I'm pretty pumped. Sweet. Yeah, it's going to be fun and it will be here before you know it. You're telling me I've got I've got a lot happening right before then. Yep. Never a dull moment. Not a single one, but yeah, no feeling good. Very excited. So we'll go ahead done and call this a wrap for today. As you know, as always, you're awesome. You're my favorite spreadsheet expert.  So it's my passion. Let me tell you. We'll see you a couple weeks in Chicago. Smart business moves here every Wednesday, five o'clock Eastern. So don't see you before we'll see you again this time next week. Again, I just thank you everybody take care. See you soon. Bye bye.


Ladies and gentlemen, Sir David Hattenborough. It is evening in the data center. Most of your IT staff have already made a hasty retreat. They are hopeful that on this evening they will enjoy some peace, and that no clarion will sound. So far, this evening is a quiet one. Back in the fields, here we can see a wild Kubernetes. This Kubernetes cluster is blissfully unaware. It is currently grazing, among other instances, looking for some delicious YAML to consume. This YAML provides the Kubernetes with all the nutrition it needs to perform its daily activities, and it guides the herd's actions of carrying workloads. But the cluster shares its environment with many cloud-native projects, and others. A pride of developers arrives, hungry for idle resources that it can use for its applications. While the cluster remains unaware, the developers are quite active. They function on a different schedule. Now, while the developer starts functionally blind, looking for purchase in this vibrant and robust ecosystem, it is wily and creative. Eventually, it adapts to its environment, but it may find the footing unstable and it can be fraught with perils. This pride is currently searching to sate its hunger and knows that when others are not using the cluster, many additional resources may become available to them and their applications. These developers, who previously fought for easier access to the systems, bring along some YAML of their own. Some of them carry it along in helm charts, only ever wanting to change one or two variables. But for others, it is the ease of deployment they care about, and for them, it is carried forth by this river named Argo CD. Argo maintains the health of this valley and ensures all the YAML is nutritious and up-to-date. With Argo's help, they are able to consume many more resources than if they were to sew their YAML by hand. But with a ravenous appetite, the developer says, I want it all. I want to test quickly, so I want as many resources as you can give me. Through use of a quick one-liner in Bash, developer makes copies of their application, tens, sometimes hundreds. They then copy and paste within the Argo configuration and with a good push, they are off and running. But what's this? With the influx of all these new development, QA, and pre-production deployments, a problem arises. These nodes are full and can consume no more. But what happens next is truly extraordinary. To protect itself from the developer's resource consumption, the Kubernetes relies on one of its greatest strengths, its scales. In doing so, it is able to bring many more resources into the herd and respond to the developer's insatiable appetite. While scaling provides more resources, it is not magic. It cannot help with everything. In this case, because of the constraints upon the cluster's design specifications, there are no more internal IP addresses available for nodes. The allocated pool has simply run dry. The overconsumption from the developer's hunger begins to reach a boiling point. And suddenly, new nodes and new deployments are beginning to fail. As the coming chaos unfurls, alert manager begins to take notice of these problems. The group of Prometheuses, known as a gang, sends out a warning cry to alert whoever is listening. They shout about node failures, about there being insufficient CPU and memory to fulfill all the requests, and even about indecipherable errors from the upstream cloud providers. With the Prometheuses' cries crossing a threshold, the clarion has rung. The IT staff is spurred into a defensive formation, huddling around and protecting the cluster. The pride of developers, of course, will collectively proclaim no knowledge of what has transpired this evening, though the Git logs will tell us otherwise. Meanwhile, some of the nodes may have succumbed, but through sheer numbers, enough make it through to ensure the survival of the applications as a whole. The next day, after meeting upon meeting, quotas are being set, and Open Policy Agent, or OPA, is being installed. The previous approval process that was loosened is reinstated, so the developers can no longer check in changes to the Argo configuration without oversight or at least a thumbs-up. They have paid for their indiscretions, but this particular problem will not arise again. The developers will have to be even more crafty to set their hunger tomorrow. The previous cluster managed to keep damage to a minimum because it was protected. Between DEX and Gangway, handing authentication back and forth, the Prometheus alert managers constant eyes, and later, OPA enforcing its rules. Controls stood guard to keep watch over the herd. This, however, is Nigel. Nigel is a brand-new on-prem cluster, the first of many, just released into production on a Monday evening for an adoption test. The original design team, however, had foolishly decided while building non-production clusters that since the access keys were only given to GitLab and the administrators, they felt that they did not need to worry about additional authentication or login patterns. This, as will surprise no one, was not corrected before being propagated to this newly launched production cluster. An unprotected cluster, however, attracts other visitors with more nefarious intent. Birds such as these will take advantage of any easy meal that they can catch. As soon as the cluster was turned loose into the wild, this gaggle of red teamers was acting from a remote location with swift and efficient. They have found that the Kube config and admin keys are embedded in the image for the custom GitLab runner, and it points to an API endpoint that is accessible from anywhere within the corporate network. These are design decisions that never should have made it out of testing. So their attack is merciless. As with all birds of prey, the carrion, the detritus, the potential for disease to spread and infect others in the biome has now been identified and corrected. If this had been a real attacker, by morning there would be no secure nodes left in this cluster. No pod would be safe. If the internal corporate network were compromised or the actors otherwise had access on the inside, the entire cluster would be lost. Unfortunately, with no elastic cloud capacity behind it, it would have no room with which to scale. So by the time they are done mining bitcoins or whatever they're doing, this cluster would have simply run out of resources. The herd would be picked clean. What's more, once compromised, the flock can also be expected to drink deeply from any and all storage buckets they find behind this cluster. They would simply drink this data lake completely dry. Luckily for Nigel, the red or offensive security team makes a formal report and the blue or defensive security team has now responded. They had very little knowledge of the danger they were in, but have now been made aware of the terrible design decisions that were made long before these clusters were ever thought about being put into production. Once made aware of the threat, they are forced to be reckoned with. They will implement strict authentication and authorization tied to the corporate LDAP service. They will bring in another avian challenger, Falco, to mine the threats and keep watch over the herd. Significant rules will be created and processes will be changed. But first, they need to tend to their GitLab runners. This was created as an upstream problem which they have enshrined themselves in the registry of Harbor. If they do not fix the danger that was checked into that image, it will only be a matter of time before a feast like this presents itself again. Instead, keys will now be encrypted and then stored as secrets for the runner to decrypt at runtime. And new network segmentation will be put in place. Quite a lot of work for day two of production. But what's this? While the Infosec team is implementing new segmentation and separation policies, a new VPC and new cluster have arrived. Meet Bertram. Bertram is the latest release even though it only has 12 months until the end of its life. Many will not even consider it feasible until it is mature. But by then, the growing herd will have left it to die. Will this young, upstart Bertram take its opportunity to build the dominant cluster? No. As part of the segmentation, this new cluster lives solely for PCI compliance and has even gone through a completely separate and quite rigorous qualification process. While it may drink from the same lake and it may look similar, its paths will not cross with the other clusters. Now, when the predators return, they will find this prey difficult and hardened. Not exactly what they were after. Despite all the new measures put in place after a laborious and disastrous day one, the herd cannot, however, think themselves completely safe. These birds of prey will continue to circle, watching from afar, forever searching for their next meal. Bumper. By now, you have surely decided that this world is one worth exploring. But you may be wary of the threats looming around you. The world of cloud native, however, is not all predators in danger. There is plenty of fun, excitement and even leisure to be found here. The excitement that awaits you is just part of a much larger ecosystem. Here though, in this unforgiving land, it can be difficult to get one's bearings, especially with interesting denizens competing for your attention. It is easy to get lost, even with a map. Luckily for all of us, there are guides aplenty. The cloud native locals are not just friendly. They are incredibly hospitable. They are always eager and happy to help, because it is the people that make this community so vibrant. They can help you understand service meshes, for example. Or maybe some of the Indigenous Github's platforms. Or perhaps you may need help with something ubiquitously problematic, like DNS. It's always DNS. And let's not even get started on how many denizens there are that call observability their home category. Why, the monitoring section alone is so densely populated, it is hard to tell those taking action from those who sit by in idly watch. But many of them can bring you peace of mind and help you sleep at night. But there is even more in motion here. There is also a hierarchy among the projects, vying for dominance and contributions. With enough market acceptance, some of them will graduate while others may be left in the sandbox. But perhaps you would like to join them in this sandbox. There are dozens of projects here that would be happy for some of your attention and would just love for you to come and play with them and contribute. Once you have your bearings, you will find a wondrous world stretched out before you with projects ranging from security to database scaling, CI, CD to network management and from cross-cloud control planes to cleaning up those cloud workloads. On the human side, there are many various meet-ups, conferences and other gatherings. The denizens, sometimes congregating simply to discuss how much they love to congregate. You will always find an outstretched hand here from one of these ambassadors. While the real Savannah is full of endangered species and faces many crises, the cloud-native ecosystem is healthy, thriving and growing. Whatever part of this ecosystem you're self-in, you can rest assured that there are people willing to help you on your journey in events to make it informative and enjoyable. That's all for this evening and we hope you will join us next time as we take a trip to the clouds. Thank you, Sir David Emper. So, it's really cold open on that talk. I thought it would be fun to just sort of go in. I had a couple people suggest it and what would normally be the intro slides here we put at the end. So, who am I? Why did you even come to my talk? My name is Noah Abrahams. I am the open-source advocate at Stormforge, which means I am split between open-source program office and dev advocacy. If you want to know more about Stormforge, find us at the booth. A talk is not the place for talking about that. I'm a CNCF ambassador. I run the Meetups out of Las Vegas and I've been doing this cloud thing for quite a while since about 2008 and I've spent the time bouncing back and forth between low-level hands-on work, being DevOps or SRE, being the guy that broke the thing because I didn't realize that wasn't namespaced. Whoops! And being much higher level and having conversations like this to broader audiences. Why did I make this absurd talk? This talk started from a tweet, actually. It was a tweet by Chris Nova who said, what would your entrance music be to stage for a keynote at KubeCon? And I have a feeling you all can guess what the music was that immediately popped into my head because I've hummed it like 15 times through the course of this talk. You all know the non-words. Because I love nature shows and I think getting in touch with the natural world and getting in touch with what's around you, it just helps ground you as a human being. But that evolved from the theme music be to how would you build a talk around that? Which eventually morphed into how would you get Sir David Attenborough to come give a talk like that? Which the answer is you don't because he's very busy. So I had to do it for him. And we did that because it's another set of analogies. There's no lack of nautical analogies, that's for sure, in the Kubernetes environment where you can build buildings with concrete foundations building up stacks and layers or shipping containers or spider webs or any number of them. And I wanted this one to be something that people could associate with to show you some starter patterns. So now when someone says Prometheus, you don't think Prometheus as the monitoring solution, you think Prometheus, so you're welcome. That's all in the back of your mind now. We wanted to call it a couple patterns that we're good to avoid too. The flip side of why did I make the absurd talk is why did I make the talk absurd? Partly because I think I'm funny. Less of you do now than when you walked in that door. Also I really wanted to just do an impersonation on stage. So thank you for letting me get that lifelong dream out. But I did it so you'd remember it because we have to have those associations. We have to have something that we build upon as we fumble our way through this incredibly vast ecosystem. And if you've got anything to associate it with then it helps you learn the projects, the people, the ecosystems, helps you learn everything. So that's why I did it.  I hope you all had fun. A few points to remember just as you're all getting up to run out and go to lunch, which I'm sure will happen very shortly. This is a living breathing ecosystem. The same way that the savannah is full of plants and animals. This ecosystem is full of people and projects. And the projects are just more people. So always remember there's people under the hood for everything that you do. And it's important to care for these resources. One analogy that you'll see in a lot of places, people talk about pets versus cattle. That was obviously an analogy written by someone who never had to take care of cattle. There's an idea that some things are disposable and some things you just don't give attention to. Well, as I just pointed out if there's things you don't give attention to suddenly your entire system can be compromised. So treat things well. Give them respect. And as I mentioned just a moment ago that everything is built on people in both technology and in naturalism and conservationism and things. Teamwork is important. You have to be able to work together as a group. You have to be able to go hand in hand and go down this road happily together. We don't do this alone. None of us gets through this without the help of at least a couple of thousand other people that are here today. Cooperation is far more important than competition in this industry because we're all relying on open source projects. I also want to take the moment to point out that the work we do is tied to the physical ecosystems. The number that we had in a previous report is that every year data centers create over 100 million metric tons of CO2 and if you don't pay attention to the resources, if you don't pay attention to scaling, if you give your devs unfettered access to the system and allow them to spin up 8 billion new instances it doesn't just harm your wallet it harms the planet. So keep that in mind. That happens. It's real. And you know, I mean this was tongue in cheek. It was meant to be fun. It was meant to be silly. But these years are serious. So don't forget them. Apologies to Sir David Attenborough for my terrible impersonation I'm sure. Apologies to the National Geographic Society for using an orange or yellow rectangle to public broadcasting and to viewers like you. Thank you all for coming to my talk. Very image heavy. Any questions? I'm going to take that as a no. Do we have any questions from online? Thank you all for coming.


Hi and welcome to the session. I am Purva and I will help you with the following question. Evaluate the definite integral sin x plus cos x upon 9 plus 16 sin 2x where limit is from 0 to pi by 4. Now we begin with the solution. We denote this integral by i. So we have i is equal to integral limit is from 0 to pi by 4 sin x plus cos x upon 9 plus 16 sin 2x dx. This is equal to integral limit from 0 to pi by 4 sin x plus cos x upon 9 plus 16 into 1 minus sin x minus cos x whole square dx. Now I will tell you how we have got this second step. Now sin x minus cos x whole square can be written as this is equal to sin square x plus cos square x minus 2 into sin x into cos x right by applying the formula of a minus b whole square which is equal to a square plus b square minus 2 into a into b. Now this is further equal to now we know that sin square x plus cos square x is equal to 1. So we get this is equal to 1 minus and 2 sin x cos x is equal to sin 2x so sin 2x. So we have got sin x minus cos x whole square is equal to 1 minus sin 2x. We can also write this as this implies sin 2x is equal to 1 minus sin x minus cos x whole square. So in this first step we replace sin 2x by 1 minus sin x minus cos x whole square to get this second step. So you have understood how we have got the second step. This is equal to integral limit from 0 to pi by 4 sin x plus cos x upon 9 plus 16 minus 16 into sin x minus cos x whole square dx right which is further equal to integral limit from 0 to pi by 4 sin x plus cos x upon 9 plus 16 is equal to 25 minus 16 into sin x minus cos x whole square dx. Now we put sin x minus cos x equal to t. So put sin x minus cos x equal to t. Now we differentiate it. So differentiating we get after differentiation sin x gives cos x. So cos x and after differentiation minus cos x is equal to sin x so plus sin x dx equal to dt. Now when x is equal to lower limit that is when x is equal to 0 then t is equal to putting here 0 in place of x we get sin 0 minus cos 0 that is this is equal to now sin 0 is equal to 0 minus cos 0 is equal to 1 which is further equal to 0 minus 1 is minus 1. And when x is equal to upper limit that is when x is equal to pi by 4 we have t is equal to sin pi by 4 minus cos pi by 4 which is equal to now sin pi by 4 is 0 and cos pi by 4 is also 0. So we get this is equal to 0. So when x is equal to 0 we have t is equal to minus 1 and when x is equal to pi by 4 we have t is equal to 0. Putting all these values in i we get i is equal to integral limit from minus 1 to 0 dt upon 25 minus 16 t square. Now multiplying and dividing the denominator by 16 we get this is equal to 1 by 16 integral limit from minus 1 to 0 dt upon 25 by 16 minus dividing here by 16 we get only t square so t square. This is equal to 1 by 16 integral limit from minus 1 to 0 dt upon now we can write 25 by 16 as 5 by 4 whole square minus t square this is equal to 1 by 16. Now we know that integral dx upon a square minus x square is equal to 1 by 2 a into log a plus x upon a minus x we know this formula. So by applying this formula here we get 1 by 2 into a that is 5 by 4 multiplied by log a plus x that is 5 by 4 plus t upon 5 by 4 minus t and limit is from minus 1 to 0. Canceling the common factor 2 we get here 2 so further this is equal to 1 by 16 into we can write 1 upon 5 by 2 as 2 by 5 into log 5 by 4 plus t upon 5 by 4 minus t limit is from minus 1 to 0. Again we cancel the common factor 2 from numerator and denominator we get here 8 so this is equal to now 1 upon 8 into 5 is 40 into log 5 by 4 plus now the upper limit is 0 so we put 0 in place of t upon 5 by 4 minus 0 minus log 5 by 4 plus now the lower limit is minus 1 so we put minus 1 in place of t upon 5 by 4 minus minus 1 this is equal to 1 by 40 into log 5 by 4 plus 0 is 5 by 4 upon 5 by 4 minus 0 is 5 by 4 minus log 5 by 4 minus 1 upon 5 by 4 minus into minus is plus plus 1 this is equal to 1 by 40 into log now 5 by 4 upon 5 by 4 is 1 minus log 5 by 4 minus 1 is 1 by 4 upon 5 by 4 plus 1 is 9 by 4 this is equal to 1 by 40 into log 1 minus log now we cancel out 4 and 4 we are left with 1 by 9 so we get 1 by 9 and this is further equal to 1 by 40 into log 1 minus now we can write log 1 by 9 as log 1 minus log 9 and this is equal to 1 by 40 into log 1 minus log 1 plus log 9 right now cancelling out plus log 1 and minus log 1 we are left with this is equal to 1 by 40 into log 9 so we get our answer as 1 by 40 into log 9 this is our answer hope you have understood the solution take care and have a nice day


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Let's get into what you guys need to do number one You must be subscribed in order to earn your chance to win if you are not subscribed You my friend will not be able to enter the raffle. It's not gonna happen, baby This is what you need to do You need to email cars in Cribs Raffle at gmail.com and when you email that email you need to put this in there a Screenshot showing that you are subscribed to the channel You also have to put in both cold words. There's two cold words There was a cold word in today's video and there was a cold word in Monday's video Which was two videos before this video So the two cold words and you also need to tell me what is your YouTube name? Whatever you write comments in whenever you comment all that stuff you need to put that inside of the email Let me say it again because some of y'all don't listen You need to Send an email to cars and cribs raffle at gmail.com You just sent a photo showing that you are subscribed to the channel You need to send both cold words and you need to send your YouTube name So you can go on things with your thing a comment below and let me know if you understand what I'm saying right now Because a lot of times people don't be paying attention to everything you need to know some of y'all I didn't even know what the cold word was I said it clearly in the other video. It was clear today I don't know what's going on, baby. Pay close attention or you're gonna be missing some of this money I'm just telling you right now now when you guys send this email Make sure that you send everything in one email Let me say it again when you send the email make sure you send everything I told you to send in one email So if you email before on Monday and tell me the cold word you have to do everything to do So all those emails are they don't even apply to anything You guys have until Friday 3 p.m. Central Standard Time Chicago time. That's my time 3 p.m. Central Standard Time or CDT whatever you want to call it 3 p.m Then we are going to do the live raffle. So Listen to what I'm saying. I really want you guys to win some of this money But you have to pay close attention to what I said and if you didn't hear what I said You need to hear it again with why in the video listen it again Because if you do not do exactly what I said, guess what you will not win none of these dividends I'll just look that's all you gotta do, baby. It's very simple and very easy. So you can get this cheese Gee, I hope you guys enjoyed this video again If you are not subscribed make cheese man scribe one give me a thumbs up and do not skip those ads and lock it Tomorrow because that's bad why you message bad the 2023 is a big bag season. We'll keep on give a reason I see you guys on next one. We go every glory for you already know it is a decay and I'm on my way