 We would want to, first of all, thank you to our sponsors, to our sponsors for their sleep, LBS, can we then hand them over? And then also D-Brown from Salty, who is my team. That's D-Brown from Salty, we have Immanuel. Yes, Immanuel is always taking videos, he's an amazing group. And we have Wali, another one of you too. Yes, so there are two. So that's David Dave, Wali and Immanuel. Thanks, so once again we will have to kind of put them together and do this across. So to introduce LBS, I'd like to welcome... Yes, thank you. Good morning everyone. Good morning. So this is LBS for those who are coming here, who are here for the very first time. I say welcome to you. Two things about LBS. I'm here because I've been known by my father's really, I've not been known by my father's really, his sister, all sort of things. In the nutshell, LBS is a management development, a company, I use the word a company. All you have to do is to develop managers for Nigeria and Africa at large. And that's why you see that the whole of the programmes we're doing here are geared towards leadership and management. We've been around for the over the last 27 years. We have an alumni base of over 6,000. So I can actually tell you that there is virtually, about over 7% of the CEOs you can talk about in Nigeria who are faster than LBS. Yeah, currently there are about 3 governors who are alumnus of LBS, current governors. I mean, the least is industry. In terms of a product of RIM, they are basically putting too soon. So you have a degree programme and a monthly degree programme. The degree programme is the MBA. The MBA is further between two different groups. So you have the professional MBA, you have the full-time MBA, you have the executive MBA, and then you have the modular MBA. It's all the same, but the difference is in terms of flexibility. Flexibility says that for full-time MBA, you have to be here for 18 months. For the professional MBA, first of his kind is going to be starting next February. As for full-time MBA, you need to have a minimum of three years of experience. And then we realise that there are people who don't want to leave their jobs. Even though they have a three-year experience, they still want to work at the same time. It's cool. And then you have to do the professional MBA. So you don't have a seven-year experience. You have three years and a half between three to seven years of professional MBA. You spend 14 months without the executive MBA. And that's when you have seven years and a half of the manager experience. It's the same as well as the member of the modular executive MBA. Now on the team side, we have the executive, as I said. That's a non-degree programme. So you're a finance person. We come down to, I mean, obvious tips. Financial, modelling, world view, doctor belt, budgeting and forecasting. You have a lot of things. We come up with some programmes. One, two, three days, maximum five days programme. And then we also have what we call the executive programme. So it's a multidisciplinary programme. All of them are made up. So I'm not the general manager for instance. I have an accountant report to me. I have a marketing person report to me. I have a feature report to me. I need to be able to understand. Even though I might not be a complete professional director, I need to understand what that person is saying to me. So an accountant report is, I can't think of anything else. I need to be able to understand the numbers and say, this is what that person is saying. Am I not necessarily here to know that? Marketing person report to me. I need to be able to understand what he or she is saying. So we have programmes with that regard as well. And then there are set criteria for these programmes. So we have a programme called My Advanced Adoration programme. This programme is for high-flyers and direct-spective organisations. Direct-spective organisations, they are one or two people. You can see that this is the future of the company. And then beyond the technical skills that these guys have, you want to really approach them all. Because, I mean, really that leadership skills. It goes beyond just, this is what I can do. You need to, as you rise up the level of your career, need to be able to manage people. I then introduced that programme called My Advanced Adoration programme. There's a programme called Scenium Management programme too. Sorry, I'm not using my slide. I'm not going to just talk. That's my kind of person. I don't know that she's using this slide. So for Scenium Management programme, you have to have seven years of experience and all that. So we have a lot of programmes like that. The key thing about 10 years is that you summarise your experience with us whenever you come in here. So the confidence one, in terms of the number of people here, it is 7 to 9. It is a small class. You can check out 10 years on financial kinds of content. It is greater than 79 of all the decisions we've made. And then we have the group outreach. Actually, about just less than 5% of the business will be involved. That has the GAC, CSB and the hand-bar ring. So we have that. Sorry, what about you? How do you see it? A particular association of colleagues, a association of colleagues, a group of business, something like that. Who are you? It's a group of colleagues. That's a fine group of people. So we know about that. We all get that. So we have those opportunities. We have rules and petitions. So again, aside the confidence I've just said, one thing you can check is the connect. So like I mentioned, just mention it. Mention the CEO's you need. I can tell you there are three or four things here. And so you can't out-true the power of the network here. And that is actually a strange thing. So welcome on board. Thank you for coming. You have a wonderful day. We're going to start, we're going to do one more video. So LBS, we've seen a lot of LBS stuff. For us in deep ground, just give us a quick intro. Both of them are fine as well. All right, so this is our session. So this is our sponsor here. The other team, our other teammates. So just wait for us. So we do training in deep ground to consult and improve. And really what we do is data. Anything data. We just focus on data. So we like data a lot. We think that data is a new goal. And we just want to, it's not just a new goal, new world. So I want to use data, help people use data, that they really use data to answer any kind of query they may have. So everybody keeps hearing about machine learning and AI. Yes, AI, machine learning, AI, machine learning. All of this is data. Because really you have data in the past, which is trying to understand or help advise you on data for the future of them. So really that's what's happening. You have machine learning algorithms. It's like you don't do detailed analytics of the past and then come into some insights that you can use for the future. The more data you have, the better. So I see some people, when they're doing a reporting, very large data, they get a little bit timid like those. It's a bit too much. It's not the better. So anything you're doing, please, when they say, I'm going to give you, I need you to do a final report on the last four years. Here's your annual data. Tell them to give you daily data. Daily data. Get the lowest granularity. You know what I mean? Granularity of data means how detailed it is. Get the most detailed, not the least detail. Get the most detail of your data. And then from that data, it creates your reports. So really typically that's what we do in most of our queries. Financial modeling and stuff. And we have related quite a few bodies. There's one I like, I do financial modeling training mostly. While Pali does more Excel and Power BI. I do Power BI as well. And so does data. For me, I like modeling because there's a financial modeling institute. I've heard of that. Financial modeling institute. Anybody got it? Okay. Are you doing the exam, or are you doing the exam? I am working. Are you writing the next one, October? No. Okay. Okay, we have a lot coming up on the couple of weeks if you are writing it. So financial modeling institute is a body that has a set of the trial to help people become good in financial modeling. So it's like CFA. It's like ICANN. There are three levels. So anybody who financed here? Anybody? You know who you mean? If you financed with you, Pianel, Balanchiste, at least. Only a few people. Anybody collect a salary? Everyone? Yeah, so that's your Pianel. So you have a Pianel about Balanchiste. I hope you know that. Each individual here has a Pianel about Balanchiste. Yeah, yeah. In fact, some of us will say, look, draw your Pianel about Balanchiste and it's amazing. Yeah? Who owns your equity? As individuals. Who owns your equity? You do. You don't. You don't own your equity. Equity, you know when you start a business, you break your equity? They start a business in your parents. It's not a business. It was you. Yes, they brought you in. All the money they spent on you until you were 18. That's it. Yeah, if you're an orphan, somebody will suspend money on you. Whoever spends money to get you to 18 years old, that's equity. Which is why when they call and they say, please come and do this, and they say, can you come? Okay, I'm coming. You come. Equity. Yeah? That sounds like dressing. So how be I am? How be I go again more? And how be I am in Nigeria? There is this user group, RBI user group, PBI user group.com slash makers. I think if you saw the website. So I hope all of us have tried to register to this group. All right. Try PBI user group.com slash makers. So that's the official group for this meetup. And worldwide events like this is happening. And how be I is a data analytics tool. It's a bit like Excel. I mean I was using Excel. I'm sorry, I want to say who does not use Excel? You don't use Excel. You don't believe in Excel. Excel has used them in bits. No, seriously. Excel uses you first. Oh, you know. The moment you start using Excel, it's the last. Then one day you discover people come and you start using Excel. Who knows who you look up as now? Who has used Excel? The new Excel. That's another example. It's there, right? If you have the ones that are called 3, 6, 5, 6. So what Microsoft did, right? Because they were trying to build an analytics tool. So by the way, the session has started. I will give you a breakdown of how it's going to be. We have one hour. It's already started. I'm going to give you a general guide, general overview of how it's going to be. Right? So again, let's find the things. Great. So I'll give you the history of how they are. I think when you want to learn a new technology, you need to understand how these people built it. You need to know what the idea behind it is. And then what were they trying to solve? If you can get into that, then you may be able to use it much easier. Right? That's how I learned Excel. So my learning of Excel is... What exactly were these guys thinking? Now if you crack that code, any new thing they bring will not surprise you. Because you know how it connects, right? So I'll attempt to give you a small history of how it works. And so when you now see the demo, when we're going to have the demo, you need to start making sense. To really start making sense. Okay? Right. How many of us have actually built a report with how they are? You built something yourself with how they are? So just put your hands up where you want to see. One, two, three, four, five. Okay. So the majority of us haven't built something with how they are. You've probably heard of it. You used it to click, click, click. You haven't built three. So those that have built, please, I don't think if you want to sit together, it's good to spread, but let's not destroy the class. It's nice for you to spread around. If you can. I don't know if there's any free seat. Because these are two gurus here. Let's sit together. No guru. Self thought. Yeah, guru. Just take it, claim it. Okay, don't worry. So you built a system of skill in talking because one thing about the user group is you're not learning just from one person. We're learning together. Yeah? Everybody has unique experiences. And those unique experiences is what you use in parties. Everybody has unique experiences. Everybody has different ways, completely different ways. Right? So that's what we're going to do. Please make sure if you haven't already, just have this somewhere right written down somewhere so you join it later. But by the end of today, we hope that all of you are joining this group. Because once you join there, there's data for you to download. I'm going to use data today. And we're closing at 12. So on the dot of 12, we're closing. So there's data there. If you join, you need to download. So if I may ask, can you just raise your hands who has downloaded the data? Who has downloaded? Very few. The data that's when you join this group, the data are very unique. So if you join the group or go there, see the latest shared file. Somewhere on that's the homepage. And you just download all the data. So let me see if I can go on. Here's a group.com slash legos. Come up and download. And for those that have downloaded, you can have a flash with the gesture so that we can move on. So I'm going to continue anyway. While you guys try to download just the listening, we'll need the data until the next like 25 minutes. So just keep on trying to download. This is the user group. If I come down here, you should see latest shared files, September 22. Yeah, latest shared files. So once you click on this latest shared files, you'll be able to click on download form. Probably enough, I think we're people releases. So even if you're not a member, you can actually download it. So you click on this download form. So the key thing is download form. How many of us attended last month's live lab? Last month's live lab. Nobody's? Cool. It's cool because every lab, let me tell you how the labs are going to run. Okay? What we plan to do with the labs. Obviously, we plan to teach people that are not kind of learning to use it. Right? Giving you a good framework. Yeah? But also, if you attend every single lab, we're going to, we're starting off, what we're, the data we're using is world data. Okay? We're using world data, something the whole world. So we're going to read it on the whole world. So last period, we said, okay, let's look for a proxy. So we're going to look for metrics and proxies. Interesting. So most organizations really need to build a data culture. And it's not just organizations. Data is everywhere. So if you're not literate, if you're not literate with data, there's a big problem. So you need to be literate because it is soon term that you literate. If you can't work with data. And also, you also need to learn how to code. Yeah? How many of us here know how to code? Who knows how to code? Okay? If you guys are lying, exactly which code? The moment you type the formula in Excel, you are coding. Okay? Most people don't know that. Excel is a programming language. And why I started Excel is because the data, the story of how power can never be told without Excel. So Excel is a programming language. Yes? When you open Excel, the biggest difference between Excel and every other software in the world is that Excel shows you your client. So every other software has a content. You open SAP. You do username password. Open any software. username password. You log in and then you see something. Isn't it? When you see that thing, you do certain things, certain things happen. When you don't do it right, an error message comes up. Isn't it? That's how software works. Because the program I have shown you a front end, it might never allow you or you'll never allow you to touch the back end. So again, Excel is completely different. When you open Excel, you are seeing the back end. During break, I want to do something for us. But we welcome you nonetheless. Alright. So when you open Excel, what you're seeing, please, is the back end. Okay? So programmers understand this more. When you just see the back end, how many cells in a sheet? Who knows? Watch out with us. How many cells in a sheet? One video. One video. That's the rule. That's the rule. How many left? 8,500. 16,000. 16,000. 16,000. 16,000 generate 4,000. Yeah, they put that in. Excel, a single sheet, has 17 billion cells. The Excel that you can use 10 sheets. Isn't it? You go, generate the sheet, prepare for the sheet, match the sheet, that's the rule. It was hopeless with Excel. Unfortunately, the whole world isn't like that. Because we're using Excel as a database. When actually, all Microsoft created a database in that public sheet, and nobody likes all of it. Access. To give you access to data. So Excel, what you are thinking, as I told you already, you have to understand how we are, understand what we were thinking, building this thing. What you are thinking is, hope, who creates a world, something for them to write letters, and something like that. We call it world. We create something for them to present. You know, they can use videos, audio, text, and all sorts. We call it PowerPoint. Unfortunately, you can use PowerPoint like world. Just that. Yeah. Then, they also created an analysis tool. So this analysis tool, we need data. Let's think of it later. It's not as complex as SQL. That's your access. Well, we didn't get access that well. But Excel, it was just nice cells, plenty cells. You can type anything in there, it takes you four hours. And then, when in the world, abuse is Excel. Right? They've abused it so much. And so it's not really nobody's using it. It's full potential. Because the key thing here is, yeah, a lot of trouble. I'm in trouble, maybe like, 20 years. So the data, it's not really strong. So the key thing is data. Everything starts from here. Okay? Everything starts from data. Are we together, guys? Yeah. Is this data or data, please? I always think. What is data? What is data? Data. Data. What is data? Data. Data. Data. So it's data. What is a data? So, data first. Data first. Data first. Okay. So data really is the starting point. Right? Now, for those Excel users, so the back end is there for you to use. Anyhow, you use it in the last five. But you need data. So if you really want to use Excel efficiently, you have data, and then you have a report. Your report connects to that data. You also have a control sheet. So all you guys that have Excel files more than you should. It's inefficient. Right? It's really inefficient. Now, this data, or data, whatever you call it, really, you have two uses for it. The other half used for the data, for the past, and the future. Right? Now, when you analyze the data in the past, you need to have structured it in a setting where you can maximize the use of it. So Power BI, when it came, which was for analytics, Microsoft was like, we need to build analytics, too. Then they looked at the ecosystem and they saw a billion Excel users. I think they're almost more than a billion Excel users. The most popular software in the world is Microsoft Word. The second is Excel. Yeah? So an Excel is the analysis tool of the world. It's the BI tool of the world. Regardless of what BI tools are out there, the most number of people will use Excel for analysis. You can't deny that. Right? And because it's not so well used, because IT guys are so structured, they take one error and enter an error everywhere. And then they see these Excel guys building all sorts of interesting books, scattered, or structured, very, very, or ruling, as far as I'm concerned. So they don't consider Excel a real programming tool. But it is. You can build everything in Excel. There's nothing you can't build. If I show you one or two things, yeah, you won't be able to see, but I'm not going to show you. So anything you can build out there can build in Excel. Now, so they took this Excel, okay, so we have all these one billion Excel users. Let's build, let's build that. Let's make it easier for them. Let's hear what their problems are. So they created this thing called User Voice. I don't know if anyone has heard of User Voice. We heard of this tool, something out there, where you can go and complain about Microsoft products. Have we heard of User Voice? Okay, so User Voice is there. And you go there to say, look, I don't like this feature. I don't like that feature. And what Microsoft decided to do is, okay, we're not going to build Excel by 2018 or 2019. So we're only going to build based on what people want. And how do you know what people want? Let them complain. It can be easy for them to complain. Our engineers will go to the complain board and say, what complaint is at the top? Well, I said, oh, yes, this is also my problem. What has been voted to the top? And then they do it. So that's why they discovered okay, people are complaining about data. We don't know how to kind of do their reporting data. And they're like, well, we created Get External Data. So people can go and Get External Data and use it for their report. But people are not using it. Okay, what can we do to make life easy for these folks? Let's then connect directly to their data, which is maybe SAP. Which of those data sources do you have? Um... SQL. We have people access on people. And then some people, I call the whole database, they just have text parts. Right? Yeah? Yeah. Yeah. Especially when you ask IT, IT, please give me some data. So now, can you give access to our database? Because it's security. Yeah, IT security. Security policy. So ideally, you should connect to your data source. And as the data source is growing, your analysis in Excel is growing. Right? So that's what they did. They just invented two where because you need to connect to SAP and because HR has the data and one Excel file, there's only the Excel file HR has that data. Then another person has data in text file. Another person has data somewhere and there's a data on the internet with something. And that's how you be able to report all this data. So let's give you something called a data model. Okay? Let's give you the ability to build a data model. So what's a data model? You just have data everywhere and we are trying to bring them together. And that's what we do in Excel. The moment you move from data to report, that thing in between is you kind of doing a struggle and punching your data to make a report. Because that's all we do. Data report. That journey between data and reports. Sometimes it's very nice, sometimes it's tedious, sometimes it's terrible. Right? So that data model allows you to bring in... Welcome. We bring in Excel, bring in data from SAP, bring in data from SQL and connect them together. Right? Now, of course, they have already built access. Access could do that. But people don't like access. So it's like bringing access into Excel. So how many here use Power Pivot? We hand out if you use Power Pivot. Okay? If you don't, please write it down somewhere. Power Pivot. Yeah? So Power Pivot is the data model I'm talking about. So they put the technology for you to connect to multiple data sources. So it's Power Pivot. When you have Power Pivot, it's a data model and it's SQL. It's SQL. It's a complete animal and it's a different application connected to Excel to help you connect to all the data sources in this world. And connect to Facebook. To connect to Facebook, send data from Facebook, take data from SAP, take data from here, take data from there and build a data model. So when you now build that data model, you will now be able to answer questions around it. Yeah? We answer questions around that data model. But there's some rules around how you build it. We're going to see those rules. Okay? Now, once they did this in 2010, by the way, guys, they invented it in 2010. We're in 2011. We're in 2020 almost. Right, 10 years ago. So they invented that. But they didn't market it. I don't know why they kept it a secret. So once they invented it in 2017, there has to be a language that you can speak to this data model. You must be a language creator. Because Excel has its own language. All the difficulties that's what we're writing is Excel language. Anything you put into itself is Excel's language. So they actually invented language for this. Can Sean shout out the name? Yes. That's. Yes. That's. So that's a language to talk to this data model. So in Excel, you can now write it on the language and then you can get things like same period last month versus actual, versus targets. You can bring in targets. So all the things you do in business, you can just use these dots, write it, and then bring it into your report. And that's it. You know, you can go to sleep. When the new data comes, when the new data updates in your SQL and Excel, even a text file inside your folder, you take a new text file and drop it inside your folder. Your reports are ready. So they were happy with this for a while, but then they now got complaints from the Excel community that, yes, it's very nice, very beautiful. But the issue is all our data, we can't connect to SAP, IT will not allow us, right? We can't connect to this. IT will not allow us. We could connect to this, but there's so much restrictions in our connection. So we ended up having Excel files. So most of our reporting is done with Excel files, thousands of Excel files. Some organizations have hundreds of thousands of Excel files. So think about it, especially if there's anybody in IT here. Yeah, IT. Yeah, so think about this. IT says, look up guys, IT says, don't connect to this, right? Security issues, isn't it? New to security, we can't allow you to see everything here. Yes? But what do you want? I want this column, I want all our sales personnel for the last three years. Okay, I'll send you that as a text file, isn't it? So they send you a text file, which you put in your folder, and every single day they send you a text file for the last three years. Guess what you now have in your folder? Everything they send you, they don't want to give you. You have it. So the security policy thing makes no sense, right? They don't give you the entire thing inside text files. They don't give you text files, text files, text files. So there's a complete policy issue, but that's not what we're going to do. You just need to know that the policy issue around IT that you need to deal with, because it's better for you to connect directly to this, right? Guess what you mean? And IT can see, oh, Emeka has just connected. You can see. Okay, Emeka, you're not supposed to connect to this. Why do you need customer data? No, no, no, no, there's not many people in this department that can customer data. Don't get clearance. Get clearance, you do. That's how to work. But unfortunately, we have the data in text files, or Excel files, right? So we connect to Microsoft that, look, the data is in Excel files. We need another way to kind of get it. And then I once read it to you guys. We've already invented that thing, like 20, 30 years ago. It was called an IT app. It was also the IT app. Extract, transform, and load. Good, so its ETL is Extract, Transform, and Load. Now this is a very important thing. You're going to go and extract data, target, clean it, and then load it. But Excel people the world over have been doing that manually in Excel for years. You get data, you remove columns, you remove enteros, you remove this, you remove that, you do this, you do that, it's concatenate, join this, join that. You become a guru in Excel. Doing a very unproductive thing. Cleaning your data. We do it a lot. 80% of our time we spend cleaning data. Not analyzing data. Right? And power bear is there to change that. So Extract, transform, and load. So we need a tool. Extract, okay, well we'll put the thing together and then create a new tool and put it in Excel. Call what? Someone shout out to me. Power query. This is your best friend. Guys, power query. So power query is your best friend. Because this tool will go and connect to any data source and clean it. When it brings it in, it can decide, oh, do you know what? I want data for only the north east of Nigeria. When you get data for the entire Nigeria, you can tell power query to remove the rest and just give you north east. Yes? So that thing you were doing manually in Excel, you're not using this tool to do it. And the benefit is, as you are doing it, it is recording what you are doing. And when you want to do that same thing tomorrow, guess what? It has already been done. You don't ever need to do things twice. So if you want to do it inefficient, if you do something twice, just stop. I'll give you a web page. You can go through it and go and ask the question. If you do anything twice in Excel or RBI or whatever, stop because it's inefficient. There's an inefficiency there. Kind of query inefficiency. So power query, if you don't like anything else, once I'm learning power query, it can save your life literally. Seriously. Now, if you remember, data model is called power people. We use the language for that. So they invented a new tool, a new application in Excel called Power Query. It also has its own language. It's called M. By the way, it was called M. M was the code name for the language when they invented in 2013. Then they tried to change into Power Query language. After off-road fighting, community, they went back to M because M sounds cool. M was meant to be a mash of their machine things talk. But that's the language M. So it's kind of a difficult language to learn. The nice thing is you don't need to learn M. Maybe a little bit. And you'll do a little bit of M today. But when you go into Power Query, you're saying, hey, you see it like Excel. I don't want this column, right? Click, release, isn't it? When you right-click and release the column, Power Query is right on the code somewhere. When you look at the code, you'll be like, what kind of graphic code is this? So you're right-click, release. But I don't want this to do this quickly. So you're just clicking buttons to clean your data or that. So that's Power Query idea. So we have Data Model, which is when Power Query cleans all this data, cleans the Facebook data, exactly what you want. You will give it to Power Pivot. It cleans SAP data the way you want it. Gives it to who? Power Pivot. Clean the other one. Gives it to Power Pivot. Power Pivot is the data model. So your data model gets all this data from Power Query, connects them together. Then you run your report. When you finish building your report, when that data updates your report as updated, as simple as that. So in your Excel that you have now, you just have your report connected to all these data sources through Power Query. You've built a data model and all you're looking at is, oh, has data come in? Okay, what's that number looking like? Interesting. Well, time to think of something. Do you know what? If it rains, do we make more money? Which is what I told the client once. I asked the client, because I'm trying to get them to use this. I asked the client a simple question. I said, you sell these products. I said, how well does your product sell when it rains? They're like, what kind of question is that? I said, yeah, how well does it sell when it rains? So that you know, okay, if the rainy season is very heavy this year, this is the likelihood of what we're going to sell. So our budgets should be this and that. So that would be interesting. So what do you think you need to have to be able to answer that question? That's one answer. Yes? I'd say you need to have historical weather data. Good. So you need historical weather data. Keyboard data, yes? And then you also need the sales. So they have that already. So you know, that's what you are doing as a business. They have that. So the new thing is historical weather data. Can I give you my hand, please? So what do you have to do? Remember the data model? The data model has data from S&T. Maybe that's where the sales figures are. Data from maybe HR about the names of the sales team. They have from HR. And data from HR connected into your record. Now someone asked the question, how well do you sell when it rains? And what is that exactly what you need? You need data, historical weather data. So you just need maybe a daily basis. They want the two persons general, the 2018, the 2018, all the locations in Nigeria. For this location, what was the presentation? What was the rain? This rain or not rain? Once you have that data, what do you do? You bring it to a data model. You put it there and then you plug it. It's like you're taking the cable and plugging it. You plug it into your report or your model. And guess what? Now you can analyze everything when it rains. That's how powerful it is. So you need to learn the basics of how you build this data model, how you use power to clean up your data. Okay? So that's all of this. So now after inventing power querying in 2013, it was working nicely, then they decided, I think around 2016, I think around 2016, that this tool is really cool. Let's take power purports and that. Let's take power querying and that. And let's create a new visualization tool called Power BI. So that's how Power BI was born. So taking this one, this one, join it together and form it Power BI. Now if you look at Excel, Excel works with cells, isn't it? Cells. I want you to think about this. Excel works with cells. Because the easiest way to know Power BI is to know Excel. Because they built it for many Excel users. So when they were building DAX, for example, the language, you know this language DAX, when they were building it, they said, okay, how do Excel users do song? They used a function called song. They could have decided to use a function called add. They didn't. They used song. How do Excel do their averages? It's a function called average. So most of the functions in Excel, the names, are the same as this new language called add. All right? So it's deliberate. Just so that they can use the same, people can think the same. But how you should think, when you're using Power BI as this, Excel works with cells. Power BI works with tables or columns. So take note of that. Excel works with cells. Power BI works with tables or columns. And when I say Power BI, I mean DAX. Because you're going to be writing DAX. That is the language of Power BI, yes? So Power BI works with tables or columns. So in your mind, when you're thinking in Excel, it goes to this times this. You cannot do that in Power BI. You can't say this times this. You have to think in a table form. Or have a table that has various prices. And have five products. And have five prices. Then have a table. Another table that has quantity of product sold. So you bring a beginning in table form. I have five prices. And I have five quantities. What I want to do is now say total sales. How do I get total sales? Think about how to visualize before looking at it. Five prices, quantity. And I want total sales. Think about it. Don't visualize in your head. Five prices like this. Five quantities like this. What is my total sale for this company? So bring in Excel form. In across each, okay? What do you do? What do you do? Cross fly. And then you now sell. Okay, interesting. You are going row by row. Isn't it? Think about it. So you do a row by row. And then you now, you know, you now sell it. Isn't it? So there are certain things you need to know. There are certain terms you need to know in part. Right? Certain terms. Now if you have a table. Let's assume we have the table. Which is price, quantity. And then somebody infinitely created sales value. Isn't it? So they've already done the modification. So now I have how many columns? Three columns. I still want to know the total sales for that company. How many are there? Price, quantity, sales. How about the total sales for that company? Sound the last column. Cool. So sound the last column, isn't it? So if the last column is called sales, it's equal to some sales. Isn't it? So that's exactly what you do. It's called some sales. And then when you are now doing your reporting, and you want to report by product A, you carry product A's name, put it there, and say hey, what was the sales? You put it there. So you see the sales for that product. You pick another product's name, put it there. Pick the sales, put it there. You see the sales for that particular product. There's something called, what I just told you, it's called filter context. Give me certain terms. You will gradually keep on learning. Hopefully, if you come to our monthly meetups, you will be learning all these terms. Filter context. So there's a context around this. You'll see a real quick report very soon. You'll understand it. Filter context. So whenever you're building reports, how many of you have used PivotTable? In Excel. PivotTable. Okay, let me rephrase. Who doesn't use PivotTable? Who has never used PivotTable? Okay, good. So if you remember it's PivotTable, you need to introduce Excel as well. Okay, so PivotTable definitely won't have to use PivotTable. So PivotTables is really not Excel. It's a tool that works with Excel. But for those that know PivotTable, you understand what I mean. You drag something to PivotTable, you will see it listed. You drag a file into PivotTable, you see it listed. PivotTable uses filter context. So when you look at a PivotTable, whatever you have on that table is a context. That's a filter context. You decide to put something in a slice side. You've changed the context. But we're going to see that live. Okay? So to that PivotTable, filter context. Now remember I said if you have a price, you have quantity, isn't it? Hello guys, one last thing. You have price, you have quantity. Before you can get the total sales, unfortunately we have a lot of work to do. Price, quantity, first of all, you have to change price times quantity. Write it down, isn't it? Then the next one. Price times quantity. Write it down. Price times quantity. Write it down. When you write all of those things down, you're now soft, isn't it? So those things you are doing like this, it's called growth context. Let me write that down. Growth context. So to understand Power BI, is to understand working with tables. And for it to work with tables, you need to understand filter context. And you need to understand the growth context. It's good you have that general idea. You're not going to be an expert today. But if you have that general idea, you know that, oh, this thing I'm doing is going to have to do like this, right? In your brain, growth context. You know this thing I'm doing, I just want to solve this. I want to average this column. You just solve it to average it. And when you bring it into your report, you use the filter context to see what it wants. So Power BI is a mix of this and this. And they now brought it together into a tool. Instead of using cells to navigate your reporting, I would just say use PowerPoint. Think about PowerPoint. PowerPoint has a slide, right? So PowerPoint has a slide. PowerPoint doesn't have cells. I hope you know that. It's just a slide. So PowerPoint has a slide, and then you bring things, you bring some shapes, you bring some things in. That's really what Power BI is. Power BI is like a slide. And then you have built your data model by data model, please. Power keyboards. Sorry, sorry, sorry. So you build your Power keyboards and stuff, you build that data model that's connected to everything, blah, blah, blah, blah, blah. Then you now have a slide. All you do is now grab maybe a chart. Bring the chart in. Take cells, put it in the chart. And you now have cells, feel it. Take product, put it in the chart. Now sales by product. You understand? That's the difference between Power BI and Excel. Excel is cell, cell, cell. Power BI is like PowerPoint. So Power BI is a combination of PowerPoint, Excel, and the whole data model. Power is power. That's really what Power BI is. And it's so powerful. Because at the end of the day, you publish your report online, and your users can only see, you see one version of your report. Just one version. They go online and see one version of your report. So everybody in the organization can see one report. Currently, when you finish your beautiful report yourself, someone should tell you, how do you share it? You share it by email. Ninety-nine percent of the world shares the report by email. And guess what happens when you email it? The manager opens it. Oh, wow, wonderful. Oh, he didn't stop my name right. Let's change his name. Save. The moment he saves, he has a different version than you. He now falls into somebody else and doesn't send the call for it. By the time the day runs out, that your report has only become 40 in everybody's desktop. That is terrible. And everybody does this. Final. Final, final. Final, final, final, final. They get time. They don't put in days. Listen, that is what we do. Everybody in the world does that. So we're kind of eliminating all of that and say, look, just have one version of the truth. Yeah? So that one version of the truth. Since people want it, you need a way that people can see it but not change it. I mean, the change is changing in this place. So that's why you have one drive for business. That's why you have SharePoints. Yeah? So try and put it there. Everybody shares it. So now in Excel, you see, you see all the people that are working on this part. See the pictures? Yeah. Yeah, if you share it properly. Right? But the best thing is just publish it online. So if you publish it online to powervi.com. You get it, payyourpowervi.com is the Microsoft site. You can either publish Powervi reports there or you publish Excel. You can even publish Excel there. So everybody sees it, plays with it, but you are the one that's changing it. And if you give somebody else rights to change it, they can change it. But just one file. Not 1,000 files or one file. When they were investigating Enron, if you know Enron, most of the investigation was around Excel. Excel files. So it was Excel files that brought out all the arguments. They could hide their things in the sources of the data that's got this one. Yes, what? What I told you already. Everybody yes, what? No, you can't talk to this. But yes, what? You said it's to hold that as what? Text files. So they had all these massive Excel files all over hundreds of thousands of Excel files and told the actual truth of what happened. So we're going to read the full report on Enron. You will see that. Believe this story. It sends thousands of Excel files. And we throw all the information there. So that's the nutshell. The story of RBI, how it got to RBI. Questions, questions, questions, questions. Before I jump into the demo. Questions. Questions, questions. So tea break is there that I see some people are hungry. You know, I can see some hungry faces. I'm sorry. We'll have a tea break at the end. So questions. If you don't ask questions, you're going to ask each person a question. Questions. So just a general question. Yes. In terms of the transition, how does Pablo do it? So Pablo is a different software. In a different software, just... Pablo does exactly what Pablo does. Pablo is an excellent tool. It's exactly almost the same as Pablo, right? It's just a different technology, right? And a different company running it. So it connects all the resources to Pablo. But the only difference to me, and I have a history with Pablo, because I... We have two guests that are going to do something for us later. Thank you. And I'll tell you why. So now I'm not there. This is Pablo there. Clicking is always at the bottom here. Thoughts spot has kind of jumped into the leader quadrant. So the top right is called the leader quadrant. The top left is called challengers. Then you have niche players, and you have vision hackers. So Microsoft is up there. And I think the main reason is the rate of updates is ridiculous. So the updates power BI every week. And it's not just small updates. Big, big, big updates every single week. Let me show you the updates for just September. So what happens is power BI desktop, which all of you have, is what we call the local plant. So that's like you excel in your desktop, ready to build something and then pop it. Okay, that's power BI desktop, which you have. That power BI desktop, they wait for a month, gather all the updates they've gone on, the power BI on, and then feed it into power BI desktop once a month. So this is the number of updates. This is the update that came last week. So these are all the updates. This is just one month on power BI. So what do they do? Reporting color, color and text classes and things. So they allow you to do more things and things. You use JSON, it's a language called JSON. You use JSON to create your own thing, directly in power BI. So you have a thing for your company to build your own wonderful things yourself, right? And they make it easier for you to do that. Then their personalized digitalization came on pain before visual. So currently there are default visuals to the right, especially some visuals. You may not like your writing custom visual for your line chat. If you don't like the default visual, you can remove it now before you put it into like a remove it. Right? So see all the updates. Quite a lot. This is just one month. And this is not the full base. This is just a summary. Okay? So summary for all month. There's no application of company out there that can compete because guess what the updates are based on? I mentioned it earlier. It's a number. User voice. So we have employed, we have basically employed researchers, 1 billion researchers worldwide to complain and complain and complain and complain. There was complaints at the top. That's what they do. Before they used to have all these, yeah, how can we improve this? Let's think less. Let's do a solving. No. This one is, what have they said we should do? They just completed it. And they do it at a rapid, rapid pace. So it's going to be very hard for Tableau to compete. Which is why Tableau's share price unfortunately is crashed and they sold to who? To Salesforce. To Salesforce bottom line. And yes, they will be stronger with Salesforce but they're becoming niche products for that. Power BI is connected to everything. Including Tableau, you get the internal stuff. So that's my own thoughts, really. And again, so that's all. So next question. Next question. Another question, please. Yes. Now the first question is, how can we learn these two languages? Why things below structure of Power BI, in terms of ideas? Yes. What is better than what you learned there or what's that error? Well, here it is. Good. So they keep it to learn anything. I like trial and error. Trial and error is one of the best ways to learn. Okay? Trial and error is one of the best ways to learn. The problem is we don't try. So there's no error. Yeah? There are more errors to make than the best ideas. Okay? So what's your error rate? If you calculate how many errors did that make today? Keep that in very few. That means you're not really pushing yourself. Yeah? You need to create more errors. But not in a way like create the same errors. Different. Create more errors. Create the same error. Create the same error. Something is wrong. Probably need something. So creating more errors is key. So trial and error is perfect. But please, guided trial and error. Don't just trial and error. Guided. So what I'm giving you, as small as it sounds, this is the fundamentals, understanding of how they built this tool. So obviously when you're trying to get data, what do you think of first? How to get data from somebody? What are you thinking of first? What tool are you thinking of first? How to get data? Because I want to do this part here. How to get data? What tool are you thinking of? What tool are you thinking of? You should be thinking of half query. Okay? How to get data? How to get data? Power query. So what you need to do is a mind map. A mind map. So that mind map will help you. How to get data is power query. In your head, in your room, I'm using power query. Right? Mind map. So it's like your mind. You build the mind map of how you're going to do this tool. That's the first thing. Always use power query together. Now, unfortunately, the names are different. In Excel, power query is called get and transform. You have the little version of Excel. You see a section called get and transform. That is power query. In power BI, it's called power query. I think? It's called power query, right? Okay, so it's called power query in power BI. Okay? It's the same technology, guys. So if you learn this technology in Excel, it's exactly the same in power BI. No difference. In fact, you can build the whole thing in Excel. Open power BI. Floor power BI to Excel. Power BI will go into Excel. What I'm looking for is a model. If you find it's easy to look for power query, you just pull everything into power BI. That's the same technology. How many of us use cloud-based technologies? Who knows Azure? Okay, so Azure. So in Azure, power query is also in Azure. It's called data flows. So I want to give those names. So it's called data flow in the cloud. When you want to do power query in the cloud, it's called data flows. When you have data flows, that is power query. When you're here in Excel, get and transform. That is what? Power query. When you have power query in power BI, it is power query. Same technology. So that's one. So now you've sorted the data cycle. Yes? The next thing you need to sort is that your data. Your data must follow certain rules. What are the rules for how your data should look? Because before you bring in your data, it has to be structured. So you don't have empty rules like this. You have something scattered like that. It must follow a certain structure. Anybody here know what we call several modules? Yeah. In our training, we just find something called the seven modules. So you might not know what I'm saying. So can you look at the seven modules of data? How should your data be structured? So it's going to tell us just several simple rules. If you look at your data and it doesn't follow all those rules, something is wrong. So can you look at what? The software tasks. What should it be software tasks? Those software tasks are two tasks. Anywhere you wait. That's one. It may sound strange, but don't worry. No software tasks anywhere in your data. That's one. No empty columns in your data. So if you have a heading, it's not an empty column. The whole column empty. You have data here, empty column, another data here. No empty columns, yes? No empty rows. No empty rows. No empty rows. So no empty rows. There's a difference between data and report. Take note. Data is different from report. This is data or data. So no empty rows. That's number three. So three that you know as well. Who can remember any of the one? So he said no to-tas or to-tas. No empty columns. No empty rows. One row of headings. Your headings must be just one row. One row of headings. Right? One row of headings. Right? Your dates. Dates must be in a single column. Dates must be in a single column. Right? One column for dates. Only one column. Wait a minute. This must be in a single column. Then another one. No obstruction anywhere around your data. What do you mean by obstruction? You just type. Oh, good. Very good. Just some funny stuff you type around your data. Especially in Excel. No obstruction. Don't put any obstruction anywhere around your data. Then the last rule is every data type or data must have its own unique column. Just every data type or every unique data entry must have its own column. Now, I'm talking to Excel guys especially. What do we need? Every unit, every data type or every unique data must have its own column. Sometimes, look at the column. At the head, you see Lagos State or you see Nigeria. Then under, you see Lagos, Abuja, Cameroon. Then under again, you see Cameroon. On the same column. So that column contains country data and states data. They have to be separate columns. So those are your seven golden rules. Once you have those rules, your data is ready. Once your data is ready, nearly everything else follows. But I'm told we need to go for t-brake now. So when we come back from t-brake, you are starting your demo. So today, then you will generate a framework of how we're working. Every month, we're going to take one element, one or two indices from the world. Okay? One or two indices from the world. And then we'll build a data model around that. Last month, we took a proxy for what? A proxy for luxury. I want you to understand where the luxurious countries in the world. This country has the most luxury. So we needed a proxy. What do you mean by a proxy? Something that can be a place of luxury. So we just thought out one thing could work. One metric, one data that could work. Luxury. Sorry? Casper. Casper, yes. Casper, yes. Do you know what the knaps in this country divided by a total number of people in the country. Perfect metric for luxury or maybe even higher luxury conduct between two people. The one in the city more, everybody is from their land prospering. Okay? If you divide it by population I guess you're not going to do the same. So we used flights, the total number of flights per year, how many flights this country by population, so flights per factor. And that's what it's going to be today. Today, what we're going to do is we're going to do wealth and health. So we need a proxy for wealth, a proxy for health. And then we're going to add it to the data model. Which is why, as nobody is here from last week, so we're going to build a fresh thing from start. Someone's here. Someone's here from last week. Oh, nice. Well done. Well done. Thank you very much. It doesn't mean that I'm going to be a mommy. Yeah, well done. Okay, so we're going to build something, a proxy for wealth and a proxy for health. And so I'm going to shout out what you think would be useful for health and what would be useful for wealth. We have all the nations. I want to analyze them from 1800 to date. Who lives for health? Yes, for health. Yes, for health. People living above the ages. Good. People living above the ages. How do you know that? Perfect. So life expectancy is excellent. And guess what? We also came to the same conclusion. The data is life expectancy. So we're using it as a proxy for health. Thank you very much. Nice, nice. Welcome. And then wealth. Who can give us a proxy for wealth? GDP per capita. Yeah. So again, that's what we're going to do. So GDP per capita. So we're going to get data on GDP per capita. I'll tell you the source. We will clean the data so that it follows the seven golden rules. What's it called? Life expectancy. We clean it to follow the seven golden rules. And then we now bring them together. We'll try and merge them together. So we kind of create a column that's exactly the same size. And then we even tell the world what we would need. What we need is we need to know all this life expectancy for all the countries in the world. For all the dates from maybe 1800 to date. So think about it. All the countries in the world. For all the time in the world that we're in. Kind of them. Yeah. So the columns we will need before we go for GDP. Let's agree together. What are the columns we need in this data? Because that's how you should think. Please don't jump into a software before you think. Seriously. Especially in Papua. People just jump into Papua and look at Papua. Okay. Don't do that. Get a piece of paper. Put your brain down. Yes. And then after you plan who is. So we're planning now. Before we go for T-Brake and Papa. Let's think. Life expectancy for all the countries in the world. All the dates in the world or whatever. And health. Which is GDP per capita. So what would the columns look like? What would the dates look like? For life expectancy? For you. Column one we have more. Column two we have more. Column three we have more. Column one dates. Column one dates. Column two. Column three perfect. Life expectancy. Okay. Please. Life expectancy. Column three we have more. Life expectancy. What do you think? Life expectancy. Okay. That's the table. That's the table. Yeah. In the right spots. The name of the column. Everything we have in life is in life expectancy. Life expectancy. Yes. Life expectancy. Yes. Life expectancy. So let me rewind. We have the column for dates. So we have the year 1800, 1800 and 1892. Or after this. First of January 1800. Because dates has to be dates. First of January 1990. First of January 1991. Good dates. The next column we arrive. Country. We need to know what country it is. Isn't it? Nigeria, Nigeria, Nigeria will happen maybe 200 times. Because 1800 to this. How many days is that? How many years is that? Anyway. So you have Nigeria for 1800. Nigeria for 1800 and 1892. Yeah. So excellent. Bates. Country. Very good. Very good. Very good. Confidential. Confidential. Very good. Wait a minute on how you are presenting a date. Yes. So you can be in any form. But I think if you are following this view. So I think it has to follow that. So the third column. Because remember what we said we're going to be at the end. This table for life expectancy. We're going to have another table for wealth. So you add it under it. So one one table that has everything. Eventually by six months from now. We'll have all sorts of metrics on the wall. Well held, this, this, that, that, that. And then we're going to do AI. So maybe in month four. There's an AI entity. We're going to tell the AI entity to try to explain what's going on with it. The other side. Think about it. There's AI about there. So once we get as much data as possible. And let AI tell us why we are where we are. Especially in Nigeria. Right? That's the idea. Yeah. So we have data on health and wealth. I'm going to put it on. So he's right. You have dates. Country. And that's going to call it indicator. Maybe indicator ID. So this indicator. What is indicator? Oh, it's life expectancy. Life expectancy. Life expectancy. So yes. If you repeat yourself. It's explaining what we're looking at. And then what's the last thing? The actual value itself. Because that 1975. Nigeria. Life expectancy was 22 years. No. Hopefully not. But you understand what I mean? So the perfect data is going to be four columns. Because dates must be what? In a single column. Your report can have dates this way. But data must never do that. Your dates must be this way. That's key. So when we come back. We're going to connect to some raw data. Bring it in. Make it look like that. That's our first thing. After that, we're going to build a data model. We're going to build a report. Right? Let's go through it. Okay. So based on our plan. If you check what we created in your last time. How many columns do you say would be? Four. Four. Four. The first column is what? Eight. The second column is what? Hundred. Country. Okay. The top column is what? The indicator. The indicator. The indicator. The indicator. And the fourth column is what? The indicator. So we're looking at this data now. Look exactly like our plan. That means we need to keep this data in two pieces. This data is set to tomorrow. Do you remember when we said something about seven columns? We look at this data very well now. It's breaking some rules. Which one is that? All the data. All the data. So according to the rule. All dates must be in what? In the same column. But you look at this data very well. You can see that all dates are across the rules. Can you see there? These are dates. These are dates. 1800, 1801, 1803. So we need to look for a way to bring this date inside one column. Then all these values inside one column. That way we have achieved our many columns. So we need one more. The last one is what? Anybody? No. So we have to say hard and molecule on column. This is in command. So we fill it with income. Okay. So if I open this second data right inside. Which is live expectancy. So let me open it. Okay. Can you open yours to another view of the data? If you can see the second data. This is a same structure. So we need to clean the income and the live expectancy. So which one we have this column? Okay. So can we go to Pavia? Or Excel? No. So those are just in Excel. So please close these files first. Because what you're going to do now is. What tool do we use to get data, please? Data query. Data query. Connect to this and clean it up. So it has to be closed where you're connecting to it. So close those Excel files to just open them. Close everything. For those that are using Excel. We need Excel. And those that are using Pavia. We need Pavia. Call Excel guys postcode. Then Pavia. Excel. Okay Excel. Excel is not. Excel is not. Excel is not. Okay that's fine. So we have Excel and Pavia open. Everybody. This side. This side. Okay for those that are using code school. It is Excel. Excel that's it. It's not like Excel. Can you go to Data tab? Data tab. Data tab. It's not one for all the Excel. I saw some people's Excel would not work. So don't bother. If the Excel is some 2013. No don't bother. 2016 of all should be able to do this. So we have 2016. Who have 2016? 2016. 2016. So for 2016 guys this is something like new query. Can you see it? 2016. Can you see new query? This isn't new query here. For 2016 by Upwork. Can you see this? Get and transform data. Get and transform data. So you click on get data. For 2016 you click on new query. Right? So the fact that we are connected to this world. This here is the Excel. Excel is Excel. What is Excel? What is the Excel? Which one are we going to show? I think we are on Excel. The reason why the Excel is on Excel is that they have that one. There is no Excel here. Is that it? There is no Excel here. Can we think that it? So this is Excel. This is a best. Let's see if they can pick it themselves. So for Excel guys, go to get data, $2019, 2016, then do from file and from work. For Power BI, in your home tab here, can you see get data? You can see the home tab. Get data. Can you see the get data? Click the drop down. Can you see various connections that you can connect to? Right? You can connect to Excel, Power BI data set, SQL, analysis, text file, web. Click on more, click on more. For Power BI, click on more. How can you connect to so many things like giving a split? But for now, we are just going to do Excel. Right? So click on Excel, Excel. And click connect. Excel, and click connect. So now we get to where we have that file in your system. So for me, I have it from my desktop. Excel. Excel people too. Now we get to where you have that file in your system. You can have it from your desktop. It's called September. I can't see it. I can't see it, right? So that's why it's called Excel. So can we go back? Just close it. Close it, right? And we close it. Close it. Now we close the folder. Can we close the folder? That's the CSV now. Can we close it? Okay, so let's get the data again. Get data. Get data. Excel. Now this is not ready, right? Then can we see text plus CSV? Text plus CSV. Can we click on that? For CSV. Now you get to where you have the exercise file. Mine is in desktop. I can see them now. Can you see the file? Can you see this file? Can you see the file? Can you see the file? So we thought I would pick it first. Now we have an online experience. Link up, right? Okay, so link up, link up. Link up. Then click open. Click open. Can we all see this? Excel and Fabia. Both Excel and Fabia. Can we all see this? This route. Can we see this? So now I have three options here. I have load. I have transform data and I have counting. Which one are we using? Everybody have any difference? Apart from load, transform data. What different thing do you see? So load is here. Every time they are of this, they like changing things. Okay? The other side. So it's the same. So I know some of you have differences. Yes, yes. So we have three options. We have load. We have edit or transform data. Which one are we using? Transform data. We are transforming all entities, right? Because if you don't know, you can click there to Fabia. But we want to transform that data. Because data is more clear, right? So let's think of edit or transform. Transform here. We call edit or transform. We call edit or transform data. Then you can see this interface. This pathway. Before we continue, I want to be sure we all have this. You can see this interface now? Yes. Okay, so if you look at this interface, right? You want to achieve this. This portfolio, right? And the first option too. The first column, yes. You already have a point in here. But we have yes, yes. I need to look for a way to move this page to the top. Right? Because presently now, my head is showing column one, column two, column three, column four, right? Yes. And it's edit on my top. I cannot move this head up top. How can we do that? Actually, it's very, very easy. It's just for you to know what you're doing. What you're going to do. Right? If I go to transform tab. Go to transform tab. These two tabs are very, very important in Parkway. Transform and add column. Transform and add column. This transform tab. You join up the cleaning that you've been doing in Parkway based on this transform tab and add column. So for now, you want to shift the head up top, right? Can you do for a command? Can you use this first row as header? Is there a command here? Can you see that? Can we see use first row as headers? Okay. Up here. Look at it. Up here. Use first row as headers. Can we see what it is? Can we see what it is? You can use first row as headers. Okay. Forward. Okay. Okay, so now, we have our first row as headers. The next thing we want to do now is to print all these years into where? One single column. Then all the variables into one single column. How do you think we can achieve that? Copy and paste. Let me just make a copy and paste. Copy and paste. If you look at this, it says very well. This is just your data preview. You can't end it here. If I click on this place, I can't type anything. Parkway will just give you your data preview. So you'll be able to see how you're going to look like. So I can't edit, right? And you said transpose, right? You don't actually want to transpose everything because the country is mine and transpose work is different, right? So here we transpose everything, just everything like this. So we want to do something like transpose but transpose starts from in Xenon metaphor. So what do you think we should do? Select. Select? Select? Select? Select? Select? Select? Select? Select? Select? Select? Let's use the case. Don't let that start. Because later we have to be able to If people take data from Anisl, like we've gone through it, I have to be able to search For people. Once I reguify all the tips I don't know if most of us use 5.1 table here. For those that use 5.1 table, let me explain something. There's a difference between the data and the report. So most of the time, people use 5.1 table for their report. So they pre-ported this data. This is just like a report. Because you have to hear across through. They are reporting all these values based on all these years. So we need to off people this year to bring them back inside one column. So there's a command in your transform tab that can do that. But the principal will not select all. Not somebody said we should select all the affected columns, right? But we will not do that. Because if you select all the affected columns and you compile them. What if you have a new data? A new years column. So it's not a pipeline type. It's not a pipeline type. It's not a pipeline type. So for country now, country is fine. But all other columns, they are not fine, right? So to do that, come to country. Just right click on country like this. Right click on country. See a command called umpivot other columns. Umpivot other columns. Any other column that you see, umpivot it. That's your tip of mind. Can we click on it? Umpivot other columns. See this? So umpivot just before you. If you click on the areas in two columns. And all the values. So now we have achieved three of them. Now, come. Do you allow this next problem? Yes. Do you allow this next problem? Yes. Do you allow this next problem? Yes. We have to mention, after the show, we have this feeling called the best. Do you have this color? Everybody get it? Yes. Do you have this feeling? Okay, so click on our plan. The best color we are planning is this, right? So the second one is what? Country. So we have, yeah, right? Can you click on this area? And the name is as here. Just don't forget that. Yeah. Do you allow this next problem? Yes. So we need the fourth color. Which is indicator of what? I. I. So since we can't type in power query. You know what I said? Power query is just for your data piggy. You can't type. Right? Since we can't type in power query. How can we bring in the indicator to I. I. Well. So we need the indicator to I. So what indicator is this? This is an income, right? Yeah. This is income. So we need to look for a way to bring in a column. And we have income. All the way from the top to the last row. So I can really achieve that. Okay. I should have the column. Okay. I should have the column. I should have to add columns, right? Okay. So we add columns to I. What should we do? We can't write columns to I. We need to write this. It should be the one. It should be the one. Okay. So can we go to add columns to I. Do you want to add columns? Can we go to add columns? I need to add columns. When he was explaining his pathway, he said his pathway is to pick one language. What language is that? M, right? M language. Right? So to write any M language in halfway, you need this custom color. Look at it. You see this custom color. Right? We want to write a language. You see that custom color? Custom color. Custom color. Are you there? Yeah. So you look at this custom color very well. Okay, we have name column B. What's the name of our name column? It's okay. You get to ID. Can you type that? You get to ID. Can you type that? Yes. Okay, then down here you see custom column formula. Right? But actually I don't really want to perform any formula. I just want to bring in a test as a color to have it to my data. Right? So here I don't really need to perform any formula. Right? So whenever you want to use the text inside formula in Excel, what do you do? For those that use Excel, that type of formula. So you import that code, right? Yeah. So can we open that and import that too? And maybe that. Double code. Double code. Double code. Double code. Double code. Double code. Double code. Double code. Double code. Double code. Double code. So it has very close input for you. Right? Inside that code, it's just type income. Income. Income. Income. Income. Income. Income. Income. Have you heard that? Yes. Okay. Then let's import it. Importing. Can you see a new color now? Yes. So can we have this color as well? Like OBR in our plan. Right? So what's the first color of our plan? This. Yes. What we have here. We need here which we're about to get to get. Let's see. Okay. So let's drag here to the first color. Just click on the color header like this and drag it to the first color. Click on the header and drag it to the first color. The second one is country, right? Yes. So click. The country is finally ready. Yes. The indicator. Drag indicator to the third color. Drag indicator to the third color. Drag indicator to the third color. Do we have this now? Yes. So now our income data is fine. If you remember, this is all we want to do today. Instead we're analyzing health and wealth. And wealth. So this is about wealth, right? So we need wealth. We need health better. We have this app for health. We have this app for health. Life expectancy. So can we go and bring in that data? So we do that. Since you are in Parkway already, you don't need to do get the time gain when in Parkway. Right? Just go to your old tab. Go to your old tab. Old tab. Let me show you your old tab. I need old tab. Stop. So in your old tab, you need new source. New source. I'm bringing in another tab. New source. Right? So click the drop down for this new source. New source. Do we have new source? You can accept. New old tab. First check. Subscribe. So we are bringing in the data from text or CSG, right? So click on that CSG, text plus CSG. CSG. Are you there? Yes. So now I want to bring in life expectancy. Can you click on it? Click on it. I'm in another place. Open. Open. Okay, so this time we are not bringing in edit or transform data because we are already in Parkway. Right? Okay. Click okay. Do you have this new data? Do you have this new data? Do you have life expectancy now? Everybody? Yeah. Do you have this data? Do you have this data? Okay. Everybody has a data? Yeah. Okay. So these are three. This group is our next commercial. Okay. We have four groups. This, this, and three and four. Now, what we are going to do is, what we are going to go through the set, but here I know what we are going to show you now. Exactly what we did before. That's what we are going to do here. That's what we are testing recomb. So, the sets have to come from the group one first. The next step will come from this group. Anybody can show you now this group. Then this group, then this group. Just continue around. Right? Yeah. Okay, so I'll start from here. Then we'll come for seven. You don't have to look to the next group. Okay. Five. Five, six, five. You go to the next group. Okay, so I have a live experience now. What's the test that you do here? What's the test that you do here? What's the test that you do here? What's the test that you do here? I can't do that. You should follow. How? You should follow. Go to transform tab. Can we go to transform tab? Are you there? Then click on use page pro and type area. Okay, so group two. What's the next step? Where? Everybody knows step one. That's nature. You're all done in first. You're all done. Okay, so what's the next step? What's the next step? What's the next step? What's the next step? How can you do that? You're actually fine. How can you do that? Okay, I'm going to right click on country. And you go down to on people. Right click on country. And you go to pie board from that color. From pie board from that color. We all have this. Yes. Good thing you're the last step. How can we add color? It's not so hard. It's not so hard. What's the next step? How can we add color? You are good to add color to add shape. Let's become transform color. Let's become transform color. And then what? What's the name? Okay. So remember, purple is very, very case sensitive. Why do you use to name your color yellow time? That should be exactly the same. Right. So can you type the same indicator in the entire yellow time? Indicator I. Indicator I. Indicator I. Okay, so next thing. Okay. So you have to write that from one. If I let go to there, what should we type? I will do that. My expectance. Then we click okay, right? Yeah. You just want to make sure I name my identity type. This is same. The same. So can we name the year? Can we name the year? Yeah. So don't take this attribute and name year. That's called just year. Yes. We name the attribute, it's called it year. It's called it year. Okay, so we know what to do from here. So we need to, we need to arrange this color begin to write. So year should be in what color? Let's go. Let's go. Let's go. Why? It's black year to the best color. Let's black year to the best color. Country should be the second color. So why should we indicate that we do the third color? And we black year to the best color. So we should have this. Continue. Do we have this data? Yeah. Check the first one and then the same structure. Your color meta is the same thing. But here if you just take the first one and take the second one. Check the first and second one and then the same thing. So what we are doing now, we need to look for a way to manage the data together because they are actually the same thing. They are the same structure. We need to look for a way to put them together. We don't need to report on this data separately. We can put all our data in the same page. Just like what we did in Excel. In Excel, you only need ammunition. Ammunition. The first one is what? Data. The second one is what? The third one is what? Report. Report. So you look into this sheet. Data, control, concept, data, data, report. Right? If I look at this data now, that means I have how many data? I have family. Super. I have income. And I have wealth. So because there is a general rule that all your data should be in the same place for your analysis to be very, very easy. So I need to look for a way to manage this data together to be worth, to be one. Right? And it's very easy to handle. In Excel, what you do is you copy, you move it down, you paste it on there, in part where you don't need that. So what we have to do here is something called can't. We want to have can't. This is the second data with the first one. Right? Okay. So take on the first data. Take on the first one. Take on the first data. We want to be top to the initial. We got income. I'm using a income. Everyone. In income. So now go to hometown. Go to hometown. hometown. Go to hometown. I mean hometown. So to your top right, there is something called a pen quiz. Yes. A pen quiz. Yes. To your top right. Okay. So please drop down for a pen quiz. Please drop down for a pen quiz. Are you there? So you can see two options for that. We have a pen quiz and we have a pen quiz as the name. Right? We have pen as the name. Okay. So that's a pen as the name. A pen quiz as the name. That's the difference between the two. So the difference between the two is that the first one, when you have pen, is going to have the second table with your first table. Or if you do a pen quiz as the name, you create a second table for it. That will make it the name. So we can make it the name. So anything a pen quiz as the name. A pen quiz as the name. So can you see these numbers? Yes. Okay. Can you see this? Can you see this? Can you see this? Yes. Okay, so you have a pen. You have two tables. Since you are using only two tables. You have how many tables? You have how many tables to attend to? How many tables? So in your how many tables do you have in top or pen? Then the second table is what? The second table. And if you drop down to the second one and you have the two. Then the two. Income of everybody at the second table. Have you done that? Yes. Can we click? Okay. Okay. Can we click? Okay. You should have another table now. You have a pen one. Do we have a pen one? Yes. Okay, so can we read it? What should we call it? What should we call it? Jesus. What should we call it? Everytime. What should we go to have anuti with you next time? Income type. Income type. Income, wealth, and transport. Which do you call it? Income type. Okay, let's just call it data. That's called data. Data. That's called data. Okay. Yeah. Call it data. Enter your keyboard, call your data, and enter your keyboard. Have you entered? So anytime you are working in Paris, you are cleaning your data. Always make sure that before you load your data, always check your time. There is data time. Because if you look at this column right now, this year column, beside the year, it has something like a, b, c. Can you see that ABC? It's one of them. It means that this column is a test column, right? If you click this ABC, can you see that ABC? The ABC you are looking at. If you click on ABC, are you looking at? So what do you see when you click? Different data time, right? A year should always be a test, because it's like a code. And you can't solve it. Your phone number, can you solve your phone number? Zero to basis. My phone number is zero to basis. So it's a number, but I can't solve it, because it's a code. If I can't solve it, can I solve it? No. I can't, because it's a code, right? So a year is just like a code. So you don't need to change it to number. Because if this year is a number, where do you get to path in your hand? Or Excel. So solve everything. And that's all we need, right? So you have to make sure a year is worth. A year is worth. Okay, so let's go to the next one. Sorry, can I introduce you? Can I go? What if you made it there at the end of the time? It's fine, but a year should always be a test. A year should be a test. So look at the data type. You have December, you have date, you have date and time, you have date, right? So date is always a date. A year is a test. What is a test? Okay, so country is worth. It's not a test. Indicator ID is worth. It should be a test. But the president is not ensuring any. Because you have KBC-120. Is that what you are? Think DIABC and change it to test. Then our value should be what? December or December? No, no, no, no. December. December. Decimal number. Decimal, right? So, and if you check your data type right now. So right now, December. Okay, so now go back to your own tab. We have term mix. Case of date and time. We have term. So we are sending this data to party guys. Right? Party is done with this one. Okay. So can you go to own tab now? Pick on your own tab. Pick on your own tab. Pick on your own tab. Okay, so pick on your own tab. Pick on your own tab. So now, two codes on supply. Have you seen codes on supply? Yes. Pick on codes on supply. You see how some codes are there? Why itself? They have codes on supply. Why do you think they have codes on supply? To be clear sir. To be clear sir. Pick on codes on code. Or codes on supply. Why itself codes on code. For party guys, codes on supply. For which one? How it goes, right? See how it goes? Go to own tab. And pick on codes on supply. Your own tab here. Can you see codes on supply? Up here. Sorry. Can you see codes on supply? Up here, your own tab. So pick on codes on supply. Then for itself, you see codes on code. So you should be in Fabian. Are you in Fabian? And are you in Excel? But for those that are in Excel, Excel will bring your data as you want. As they say. Do you see a table for Excel? Yes, but that's fine. Are you in Fabian? So that is our table. It's not ready. How about in Excel? Close and load and close and load. So basically, in Excel, you know the limitation of Excel in terms of rooms, right? Which is 1 million rows. If your data is more than 1 million, so many people are bringing that data to Excel. They just connect to that data. Right? So if you can close and load too, you can actually select a connection only so that you won't take that table to Excel. And it can also load to you. Data delivery. Right? So for us, I don't think... So we're kind of running the data. So let's build a small report at least. Okay, so I'll do the report. So typically, everything you've done now is automated. You don't need to do it again, it's done. If you have new data, you get new data, go and dump it in that same place with the same name and refresh to come in. Yeah? So that's how we're going to start this job. The key here is once you bring the data, a new data comes. You don't need to keep on doing some stuff. It's done. So now we're going to build a quick report so we can analyze health as well. But remember guys, we are using data, not income and life expectancy, right? But maybe it's just data. So we're not receiving it in our report anymore. Are you going on? Look up, look up, look up, look up, look up. Go to your relationship view. Up here, down here. Up here, you have to be here, right? So go to this your relationship view. So see all your table. Okay, look up, look up. In half an hour, you have to be here. You have to report data and relationship of model view. So click on the third one, which is model view or relationship view. Are you there? Yeah, I think so. For itself, it's top right. It's top right. Okay, so for part of the idea, this is what you have to do. Just right click on think of, right click on this think of, right click on the think of, income table. Can you see? I in the portfolio. I in the portfolio. I see it. Click on it. So sorry for the excel guys, you don't have the path query. I think you just need to watch now because obviously the visualizations are completely different. So at least you've got the path query in excel. That's the limit for now excel and you need to do completely different things to not do reporting. So just watch. Okay, so for part of the idea, right click on your think of and do either report here. I do report here. Have you heard that? Three, three. Yes, get it. Right click here like this. You see, I do report here. You. Your initials. Yes, so like I said, I'm telling you. Because I do too. So if you don't use it, you have to do it because you see it wrong. So for example, how is this table, I can hide that and it's not my fault. So if you can make a mistake, you can copy your value from the management and the model will be for you. So guys, another question, so someone asked a question which is valid. So when you finish your path query, you can actually disable it in loading because currently it's loaded. But this is how you now hide, this is like a programmer. You know in the beginning I said in Excel, you turn it to Excel again, a programmer. So you are now a programmer. The users, what the users will use is that one at the top called reporter. So that report here, that's what they will use. But you don't want them to see what you don't want them to touch. So we are hiding this so that they don't see it. But if you are in power query, please don't load this, only load this one. You have to start, and then it will be loaded in the first place. But you have loaded this one way of doing it, hiding it so that they don't see it. So that's just how we are trying to achieve it. Right. Okay, so coming back to your report, I'm just trying to create a report. We have a different approach. Can you show one small report at least? So how does this organization work? Okay, so you copy? How can you copy here? All like Excel. Can you see visualization? So in Excel, you have a list of things. Right? You go to your menu in charge, you pick your information, you pick the charge. But for part here, you just have all your visual options. Right? So I can pick any charge, for example. For example, if I pick, let me pick this line chart. Right? If I pick this line chart, for example, can you see it's showing empty? So if I click on this chart, to your top, right here, you can see axis, region, values, and Qtips. Right? So axis is what you want to have as your axis. Then the value is what you want to have as your value. Right? So for this one, I'm going to pick a line chart. Let's just do this together. Pick a line chart. Pick a line chart. Pick a line chart. Yeah? I'm going to pick a line chart. So this is a line chart, right? So can we draw here, to our axis, just drawing, just like you're piloting. Right? Draw here to your axis, or you can just pick. Right? Or you can just pick. Have you done that? No. Okay. Look up. Look up now. I have a line chart now, right? Yeah. I want to put something inside this line chart. So you have to click on the line chart first. Right? Okay, let's use a line chart. I'm going to use a table, for example. If you click on the line chart first, I want to plot something in this chart. I want to see something, right? And what I want to see is here and van. Right? So go to your data, and take here the team van. Take here, take van. Okay. Take here, take van. Finish chart. End finish chart. Also, you can pick any of these visual, can you even pick a table? There's a table visual there. So just lay around with any visual that you can see there, and create a spot for like 5 minutes. Five minutes. Yeah, I'll start with the chart. Yes, sir. I'll start with your hand. Excuse me. Excuse me. Yes? Yeah, and later on we talked about this in my mind. So what you put life expectancy on that, once you're done with the clock together. Yeah, exactly. So you add another visual. So maybe you can add in shima. Okay, shima. Yeah, there's a separate. So click somewhere outside your line chart. No, no. So click outside your line chart. So the white, anywhere white. Outside your line chart. Click, and they're going to bring another visual. Click anywhere, there's something called shima. Can you look for shima? It's okay. And then do the same thing. Can you see shima? Can you see shima? It's beside the mark. It's beside one, one of, like that. So two different charts. So we go to an empty spot and look for shima. So I want you to select value and indicator. Value and indicator. Right? So value and indicator, you select those two. You'll see that it's one of them. It's overpowering the other. Isn't it? So value and indicator. So let's just select value instead of value. It's a do value and country. For that new shima. Value and country. Yeah, for now. On the shima, we'll get to somewhere. Don't worry. So we're now going to pick another visual that's going to select either life expectancy or what? So call it like a slice item. So then pick a slice item. So slice which one you want to see. So you've got two visuals. One is a line chart. One is a what? What's it called? Shima from all the countries in the world. Right, so click another white space. And this time we're selecting the third visual. Click another white space. So what is the third visual? So there's something called a slice item. So click on the white space. You can show the white space. And then even look up. You see what the slice item is? It's somewhere down there. The little one. What? It's like a font item. That's the font item. What's the font item? It's a font item, right? Can you select it? Select that font item. So make sure you click the white space. Select font item and select indicator. Then select indicator. Select font item and select indicator. Are we there? So it can make it bigger. You know sometimes very tiny. So just like Excel. Right click format cells. And then we increase the font size. There is something like format there. I don't know if you can just show. Just increase the indicator size. Okay, so look up. If you find out that the slice item is everywhere again. So your format should be by a field here. Inside your field. Under visualization. Right in the middle. Click it. You see it's searchable. But it's just like size. Size. Just like size in the search. Okay. And you can select size. Sliced item and item. So what you want to increase is the item. So you put the size of the item. Just increase the size. Don't increase it until you're happy with the size. Thank you. Thank you size and item. Thank you. Thank you. So increase the size. Can we see the size? Great. So pick income or life expectancy. Any one of them. Just select one. Life expectancy. Or income. Right. So obviously you have many countries there. Even though it's kind of up. I would like you to bring in continent. You know countries are too many in the world. If you can bring in continent. You can do analysis by continent. Don't do it. Do you think they are up for that? Just 5 minutes. No, we're going to worship by 5 minutes. Because when you have plenty things, you can't really see much. When you bring continent, you can see Africa. See how Africa is doing. Yes. So let's bring in continent. So let's bring in continent. And then what we're going to do is pull out continent to our data. Because right now we have a four column data. So if you bring in continent, what is related to continent in that four columns? Sorry? In that four columns. Countries related to continent. So basically we already understood relationships. So we need another table that has country and continent. Then we said countries was related, isn't it? All we need to do is connect them. Once we connect them now, we can do analysis by continent. So I'm going to bring in a table with country, continent, connect them, and we'll help you in new context. Right? So can we do that? Okay, so now if the country is very smart. So in your whole top here, let's just bring in get data. Get data. Okay. So we're going to get the data from text. From text file. From text. From text. From CSE. Get data from text for CSE. Then we can see countries and continent. Please open it. Okay. So do you have this? We don't have the data. We don't have the data. Come to the point. Okay. You can have some data. You can have the data. Bua-ra-ve. You have the data. Bua-ra-ve. Bua-ra-ve. So if you don't have it, just watch, take notes, so you can meet yourself. There's one other thing I would tell you online how to do, right there, on the forum. I'm going to show you another thing because I'm going to do a correlation analysis. Want to correlate life expectancy. Want to see life expectancy can explain our wealth and explain our health. Do that, but I'm going to do that online, okay, it's good time. So that will interest you because you have to handle that well. So keep your health there, keep your wealth there, keep your wealth there, keep your health there. But let's do this. So those that don't have, just watch, just watch. Okay, so if you have a note, transfer data, can't see me or can't see me, right? Click on transfer data because we need to check that people are showing that it is clear. Click on transfer data. Or edit. Transfer data or edit. Can we see this? Let's go, can we see this? Okay, so if you look at this thing very well, we have continent name, continent code, and country name. But if you look at this country name very well, there's something wrong. What is it? For country name, there's something wrong. What's that? That's too long. Yeah, after many countries there are other countries. There's common. There's something wrong. So you need to go by which way to remove that. And I'm going to remove that very fast. That's too long. Don't switch your transfer data. Go to your transfer tab. Are you there? Up there, can you see extract? Extract. So you can see less. First character, last character. What are we using? Let's just press before the digital. You can do that for us in the digital. So if you click on that before the digital. Thanks. So do you understand why I have any questions? So we're removing the digital, doing something, bringing things up. When you write your name on something, what breaks your name on something? It's a space, so space is the digital. Currently, the comma is the digital, isn't it? So we're using that, and then what's it? The left, the right. So click on that, click on text before the digital. And what's our digital? Can you type comma? Type comma. And click OK. Just type comma. And click OK. Have you typed comma and clicked OK? So you can see now that there's no excess obstruction. There are some that have no excess obstruction. Some in the bracket. Although they're in the bracket, I think that has an impact to cats. There's no exception. There's no exception at all. OK, so the company, anytime we reach on, right? We are handling something to our table, right? As in that nation. As in that table. So whenever we are handling something, like a table to another table, we have to reach on its own. Right? Because it has to be unique. So that this thing will talk to the other thing. So we have to make sure there's no duplicate in this country name. Right? So we can be able to plug it to the first name. So click on this country name. Country name. Right? Go to your home tab. Home tab. Are you home tab? Yes. So to your top right, at least remove rows. Remove rows. Click to drop down. Can you see remove duplicate? Yes. Let's click on it. Or you can also adjust my click. You still see remove duplicate. I feel like that. OK, so now let's... Can you rename it? So can you rename this table as... Country name. Country. Country. Country later. Country later. OK. Country later. Country later. Country later. OK, sir. Country later. OK. So click it. Do you remember this data? Yes. So click it here. And we need to write country later. Country later. OK. Have you heard that? Yes. Have you heard it? Yes. OK, so let's close and apply again. Close and apply. OK. OK. OK. Let's go to your house. Do you have all of them? They are text. So there's no value. Maybe any other... Have you closed and applied? Yes. Have you closed and applied? So this is the important step, guys. Final step. Final step. Have you closed and applied? Yes. So you have this. But if you look up, look up, look up. You have two data here now. I have country data and I have data itself. But this data, I want to be sure that maybe they are speaking to each other. Right? OK. Then you are happy again. This relationship that you went to the other time. Can you think of it? A modern view. A relationship view. A modern view. A relationship view. A modern view. A relationship view. A modern view. Are you there? Yes. Smile is creating the connection. Is it not creating any connection? Yes. For some people, it's not creating any connection, right? Yes. So you have to be sure. Maybe this happy has connected the right thing. Because most of the time, you are not able to control your technology. You don't have to trust them sometimes. Right? So for this one, we need to connect data to country data, right? So just click on this line to cross check. What is connecting to what? Click on the line to cross check what is connecting to what. So can you... So is country connected to country data? Yes. That means we are fine. But if you look at this thing very closely, connected to others that are eating things. The message is eating things, right? Should we remove it? The connection was just a bit. But remove it. Remove it. Okay? So to remove it, you have to click on the line. Right click. Let me see this. Right click. Right click. Right click. Did it? Yes. Did it? Let me see for the second one. Make sure you are only connected to the data. Okay. So do you have data on country data now? Yes. I cannot go back to my report view. I will click the slider again, the same slider you picked at that time. Then you will now see continent. Continent view. Continent view. So now I can filter by Africa. I can check by Asia, by Europe, by North America. So the report is now... So all you do is select your filter and select continent view. And now it's connected to your report. Can I see that? So now we can limit our reports by which continent you want to see. I can't go Africa. So if we had population data, we would have just given it, which we had last month. Okay? So everybody, what we are going to do is, we are going to do this. We are going to do this. So what we are going to do is, we are going to build out this solution. Are we together guys? We are going to add last month's data to this month's data. And in the next, maybe next week, you are going to see the tool last month and this month and this month. Okay, next month we are going to add more. And we add more. So eventually we are building like a tool that we can now eventually use AI. We now use AI to explain our world. Okay? So, I'd like us to give a hand to Wally and David. Our time is really up, taking ten more minutes of...