 When you want to go into a business, you want to go to an interview. Identify the problems they have. That is number one. Do research. Do research on who is the MD. Do research on what problem does this industry generally have. Right? There's someone in the world that has solved it. The industry probably doesn't know that. There's someone in the world that has solved it. Go understand that problem, how to solve it. Try and solve it. Get some data. You know data is everywhere. Look for data. Solve that problem before your interview. Right? So when you go to that interview, yeah, generally you say not to solve it. Okay, there's general problem around productivity. Okay, let me give you one problem everybody had. During the pandemic, everybody closed shop, obviously. Everybody was at home. There's one funny video. So did you see the video in the US where animals started coming to claim the territory? Say, oh, this is just less human is around. Let's claim a backyard territory. Anyway, but what happened was quite a lot of companies had a big issue. Right? Of course, we had to go home and so everybody had to work from home. But now you can see many people coming back. Why do you think they're coming back? There's a good reason to come back. I think for me is culture. You need to sustain the culture of the organization. But one big reason they're coming back is because of something they didn't have when the pandemic started. They didn't know how to measure productivity properly. Companies didn't know how to measure productivity. So then a manager says, oh, we are there. They're working. He's working on his laptop, but you can't see that he's on Facebook. So it's not really being productive, right? But they couldn't measure productivity well, and they still can't measure productivity. So they think you've been in the seat, sitting down is productivity. It's not. So think about that's one big problem. How do you solve this? Well, you get a measure for productivity. Now, one thing about maximizing business value. Can we skip through the introductions? Can we go to the slides on? There's just one skill you need to learn. One skill. So you go to the slide that's somewhere that says one skill. Sorry, the slides are not very clear. Okay, let's go to the next one. Good. One skill. One skill you need to maximize business value in a data-driven world. Just one skill. Can we go to the skill? Click. It is click, click, click, how to measure when it comes up. How to measure. That's the key skill you need. How to measure. I think there's a ruler there. I don't use a ruler at all times. Many people do not know how to measure. Just give you one. Productivity. Many businesses need to understand are we productive? If we are productive, that leads to incumbents revenue and so on. We can't measure things very well. And experts, if you bring five experts here, ask them a question. Give me the most important steps for doing it. All those five will give you different answers. Were they wrong? Maybe not. But the fact is those five give you different answers because of some biases we have. So how to measure? That's number one. How can we measure? How do I measure? Anything. And there's a book out I'll recommend to you. How to measure anything. I can't remember his name. I think Douglas. So think how to measure anything. Because everything can be measured. You just have to think critically. So I'm not going to talk to you about data analytics and all the in Power BI and Google analytics and stuff. Those are tools. They're just tools. You have to treat them as tools. The big skill you need to know is how to think critically on how do I measure this thing. And when I measure it, does this correlate with the outcome that I want? Another big skill in how to measure, you always need to know statistics. You need to know statistics. Not the complex or who read statistics here. I don't expect many hands. One hand on the back. Wow. Can we clap for that brave guy? But this guy as well. The way they teach statistics is terrible. It's terrible in the university. This is really, really terrible how they teach statistics. But it is such an important skill in business. Because currently I can tell you for free, most businesses are measuring things wrongly. We're just measuring things wrongly. I have a video online on how we measure, given an example of sales reps and how we determine which rep to fire. This unfortunately is the thing. Pandemic happens, you need to reduce your staff force. When you now grow, you get them back. So you have 1,000 sales reps. How do you determine who is performing better? So that's one problem. How do you measure performance? How do you do that? Think about it and if you have an answer, then tell me. How do you measure performance? One single metric to measure performance. Now back to the slides. Another thing is these are three big things that affect how we measure. Overconfidence, inconsistency, and the complexity around probability that we don't really like. So overconfidence. We're always overconfidence. We are, yes, Nigeria is one of the most confident countries in the world. We are super confident. Can you do this? Yes, of course. I haven't told you what to do. Trust me, I can do it. So that is confident over confidence. This is a good thing, but unfortunately, when it comes to data, we need to be more accurate. How many of you have used CharGPT? How many of you here? If your hand is not up, please today, go and start using it. You have to go and start. Go to OpenAI, get a free account and start using it. This is the most intelligent code human in the world, right? But it can be very stupid at times. Who has experienced food stupidity on CharGPT? Yes, so you can't just 100% rely on your tools. But how does it do what it does? It has all the knowledge of the world, frankly speaking, all the books, the whole Internet in its head. And then it now knows how humans basically answer things. Humans have neurons in their head, all these neurons and stuff. And if you train yourself, it gets better and better and better. It's something that doesn't stop growing. Every part of body stops growing, but those neurons keep on getting new connections and stuff. So that's how neural networks happen and that's how CharGPT works, right? So it can give you funny answers at times, just like me human beings, right? We didn't listen very carefully. Then we answered, we gave you some silly answer and looking at this guy. He's still talking. Have you finished? Okay, that's not what I asked. Okay, sorry. What did you ask? Why didn't you listen? So same thing with CharGPT. Sometimes it doesn't really understand what it is you are saying or asking and gives absolutely terrible answers, right? But sometimes it also gives answers and continues to fester the error. Do you notice some people, when they make a mistake, they just continue and continue and continue instead of just standing back? Oh, I've already spent 10 million on this. No, it was work. You spend another five million. You spend another, it's really sad. But that is how we humans work and that's also how this new chart engines work. We can go back to the slides. So these three, inconsistency, right? In fact, the most powerful metric, the most powerful metric of performance in the world is how do you measure consistency? Right? That's the most powerful metric. So when you tell how good is this person or that person, it is consistency. Oh, how good is this person in sales? That person made like five million last month. And that's the highest you've ever had. Does that sound like someone that's very good? Yeah, he made five million last month. The highest sales they've ever had. Put your hand up if you think that is an excellent performer. I made five million. Now, what's up with you guys? Hands up, hands up. Come on. He's a good performer, right? Okay. They know there's a three question. You guys are two smarts. Okay, anyway. So he made five million last month. That's fine. What did he make two months ago? He made one million. What did he make three months ago? He didn't make anything. He was ill. Right? What did he, what's he making next month? Probably he's going to make another 500,000. So you have 500,000, zero, two million, five million, one million, one million, 100,000, 100,000, 10,000, 10,000, 10,000. That will give someone a heart attack. What is this guy bringing to the table tomorrow? We don't know because he is what? In consistent. So you can be average and consistent. You're far better than someone that's all over the place. Right? And that's what risk is. So business is all about risk. How do we measure risk? Right? And how do we manage risk? Data can help us a lot. We need to understand how to analyze. So let's go to another slide. The complexity and probability here is a simple thing you could do. So I'll just put some, for marketing, for example, measure social media impressions. Now, what do we mean measure social media impressions? Is the number of impressions correlated with how many people buy that product you're selling? So as impressions go up by 20%, does that revenue go up by 10% potential revenue? That's correlation. That's another big word. Correlation. You need to know how to measure correlations, which is what charting it is doing. That's all it is doing. Check it. If we say the word word comes after the most, let's put it there. What word comes after the people? The people are, I think, yeah, so it just, that's all it does. Give you an experiment. Everybody take your phone out very quickly. Let's do a quick experiment on data. Take your phone out. Go to WhatsApp. Everybody go to WhatsApp. You're going to send a message to someone you trust because the person will say something is wrong with you after the message. So go to WhatsApp. Everybody go to WhatsApp. Very quickly. Pretend you're going to send a message, right? I want you to type, you choose which one you type. Let me say I. Type I. I. You don't say I space, right? Type I space. I space. Have we done that? Don't send yet. I space. Can you see three predictions? Shout out the prediction in the middle. Shout it out. Okay. See what you're going to do. You're going to click that prediction 30 times. That one. Just click, click, click. You don't need to read. Just click, click, click, click 30 times. And when you have done, if you think you are brave enough to tell us what WhatsApp said, let's do it. So just click, click, click like 30 times. And then someone should read what WhatsApp gave us, what data gave us. So who has the mic? Can we go on to read? One person at the back. Maybe that lady there in the middle. One person here. Okay. Okay. So one person at the back. So one person, you know, lady here. Okay. That's it. Can we pass the mic? Any mics? Pass the mic. So you will hear some funny things. So let's hear what you can leave your hands up. You can quickly get to shout out. Tell us what's your AI or your data generator is from. Can I give the mic? Can I give the mic? Yeah. Okay. Who has their hands or someone at the back has their hands up. Someone at the back first. And then, okay, let me just come down. All right. So let's listen. Hi. Good morning. My name is Ola Tumiya Blasif. So from my keyboard here, I have been a little too busy for a week. But I'm glad I can help with my phone screen for a while. And then I'm going back home. And then... Okay. Zina, I have to go to the bank to pick up my money from my bank. And then... Okay. Mine stays. I'm so happy to see my Instagram. I'm so happy to see that you're having fun with family. I hope you're doing good. I miss you. My name is Keshifu. I am not sure if you have any questions or concerns. Please listen to the plug-in settings. I'm working on something. So yeah. So that is what's up using your data. Obviously, we know that guy is a very hard worker. So it's using your data and then looking at correlations and looking at doing some statistical analysis, right? What usually comes after I? What usually comes after I am? What usually comes after I am? Good. It just predicts. And the models are getting so good. Let's quickly go. I want to tell you one method that you should learn to be able to do all these things well and progress with business. I know my time is almost up. Let's go to scientific method. I'll leave this. I'll send you this. I want to watch this short one-minute video on what this scientific method is. Hope it plays. The scientific method is about ordinary people doing ordinary things. That includes you, me, and other scientists in the world. The scientific method is just a process or step taken to produce reliable results to answer a specific question. Maybe you think you don't use a scientific method in your life, but I can guarantee that you do. For example, imagine you wake up on a Saturday and you couldn't find your cell phone. That's an observation. Then you do a little research by thinking about the last time you had it. You suspect that it might be in the pocket of your pants from yesterday. That's a hypothesis. And when you check your pants, you're doing an experiment. But science and life don't always go as planned and you find no cell phone in your pants pocket. So the second observation leads you to think again and recall what else you did yesterday. You remember that you put your cell phone in your backpack during school. So you decide it must be there and you go and check. And lo, there it is. Life can continue and you're so happy that you share the results with your best friend and explain why it took you so long to text them back. Science. Can I go to the next slide? So this is the scientific method. I'm going to go back again. Observation, research, hypothesis, experiment, conclusion, share results. That whole process that you go through every day when you're trying to solve a problem. That is it. Observation, research, hypothesis, experiment, conclusion, and you share your results, right? So in the data analytics guides that are in visualizations, that's sharing the results, right? Hopefully they get the correct visual so that when people see they instantly understand what's going on, right? So observation, research, hypothesis, experiment, conclusion, and sharing your results. So whether you write science or not, you're going to add, you need to learn the scientific method and use that to understand how to measure things, right? Understand how to measure things and then how to present those things. Let's go to the next slide. Right, solving problems. Next, next, next. Right. So this is how most corporate solve problems are always discovered. They do data generation, then they get data first. They usually have a software. That software, unfortunately, they need that software to talk to 10 other software. So they download data and most of the preferred software for downloading it to is Excel. Then they're not clean the data, kind of go through the data or clean it up, mash it up, mixing it up with different things, getting a particular measure out of the data. Then they go and start presentations on PowerPoint and they present to management on PowerPoint. And then they brainstorm on those numbers presented. Unfortunately, most companies spend maximum 20% of their time on that brainstorming. That's the most important thing, brainstorming on what's going on. They spend a lot of their time cleaning the data, getting the data, collecting the data, not thinking through what the data is saying, right? So your job is when you get to all those companies or you're already in those companies, try and eliminate that backlog, that data, all that manipulation or manipulations they do with the data. Eliminate that. So we spend time on real stuff, which is brainstorming on those numbers. And you can automate it. You can see what's what's up just automated for you. Send a message, obviously. It needs more input from you than I. Right? You know, it's really good when you see some options that are really excellent, isn't it? I've seen that. Yeah, so it's using that data from you. Now, next slide. Let me, I have a slide where I've shown the framework for problem solving in Excel, love Excel. So I've given an example there, but I want to go to one quote at the end. Can we go to the end? Hopefully, we're going to see the quote. Okay, this is also important. Let me talk about this. Biases. One thing about chart GPT and all those other software out there is they're using our data. Our data is biased. It is biased. We are biased human beings. Do you all believe me that you're all biased? Yes, some of you say, no, we are all biased. If you want to prove it, go to this book. Read this book, Thinking Fast and Slow. Who has read it? Thinking fast and slow. Go and read that book to humble you. You will see how biased you are. And which means many decisions will make are biased. So they're not optimized decisions for making money. They are biased. Right? So if you check the board, board of directors of most companies don't have ladies, don't have women. They're mostly men. How many, what's the percentage of women in the population? It's like, let's say 50%, right? So how do you know what your customer, 50% of your customers want? How do you know how to sell to them if you don't have that customer in the room? So the data basically says that, right? So when women are more women in a board, those companies actually make more money. It's not, it's proven. When there are more women in the board, there's no women in hand. A lot of those heavy lifting is done by men, but men have decided to stop doing that. Do you know who they've employed? Robots. They now do robots and stuff. They've made robots to kind of do that. But it's important that we understand our market, right? Understand your market. I know that if your market is not represented in the decision making process, you will not maximize your money making, which is what companies need to make money to pay bills and stuff, right? So back to the number one skill you need, which is what? How to what? How to measure. You need to learn how to measure. It's nothing to do with beautiful data, analytic cloud, analytic, etc, whatever skills. No, it is a thinking process and to help you think of how to measure, use the scientific method. It's a very good method to use. There's one method we use. We are known for excel a lot, doing a lot of excel training. I've been doing that for like almost seven years and modeling and stuff. So we created the coin one methodology for using excel. We say a good cook always dices vegetables rapidly. That's how to remember it. A good cook always dices vegetables rapidly. Can I go to the slide where we explain what that means? And you can take that, but I know I need to leave now. This bias data, go online and check for something called the bias codex, cognitive bias codex. You see what this slide is. Very important slide. These are all the biases we have. We will go check this out later. The cognitive bias codex, CODX. You see the image? And then you can check that out. But read that book of, what book did I mention about fast, thinking fast and slow. Very important book. I mentioned another book today you need to get. How to measure anything. Very good. Go to the next slide. I need to conclude, I conclude with this statement from Professor Stephen Hawking. He said, our future is a race between the growing power of technology and the wisdom with which we use it. Let's make sure that wisdom wins. Right? Thank you very much, everybody. Okay, so before you maintain, you started about saying, as going into interviews, you should then have to solve the company's problem. So if you are a healthcare personnel and you're going to a hospital, let's say the general hospital, so how do you then have to look at how to solve the problem? Do you feel like, take a survey into the hospital corner, or how can I reduce patients' accidents and all that? Hold the mic, so we'll have a conversation. So the thing is this, right? It's not solve the problem of that hospital. There's a lot of data on problems that hospitals have in general. If you go online, it's okay, what's the top five problems hospitals have? All right? You'll list it up. You actually have GPT going, so okay, so how do you solve these problems? Tell me. Then you can then pick that out and say, okay, typically I have discovered from my research that hospitals have this, this, this, and this problems and major problems that people have. I don't know how many of them we've solved in this hospital, but I have good ideas around how to solve this and this, right? I don't know if you'd want to. Awesome. Thank you so much, David. Thank you very much. Please give David a round of applause for today, please. You have a plaque. Just to follow you, thank you so much.