 Hello everybody. Well, I kind of went around and had a few chats with people, and I'm glad to see there are some people here that have jackpad. They've already jackpad, but they're here. Do you understand what I'm saying? They're earning dollars right here, sitting here. And I've actually talked to a few. I've talked to two that are earning at least $3,000 a month right now. Yeah, seriously. I'm not going to tell you who it is. They said I should keep it secret, so I'll keep it secret. Well, how do they do it? How do they do it? And wouldn't you like to do that? Anybody like to do that here? Okay, please don't jackpad, stay here, and earn that money, because where we live, we live in a cloud-based world, isn't it? So you don't need to leave. I mean, there's no Pandey de Amélie in America. It's not the same Pandey de Amélie. And I have to eat Pandey de Amélie. Any time I travel, I look for a Nigerian restaurant after one week. I can't survive with potatoes. Anyway, on the lighter note, I'm going to talk about data culture and teams. Let me see, hopefully this works. Okay, so data culture and teams. So for me, I've been a lover of data for a very, very, very long time, long before computers. My project, I wrote it by, it was on the internet when I was writing my project. I needed a lot of data. So I remember going to one store, one store like that. I had old economists. Who reads the Economist's newspaper? Economist's magazine. Every week, the Economist. If you really want to improve your critical thinking skills, be reading the Economist every week, or do the audio every week. So I bought a pile of old Economist's newspapers, about 100 of them, reading through them. And that was my research from my project. Obviously, now it's just a click, right? So everybody's holding a powerful tool. Can you put it up? Extremely powerful tool. Right? How many of you, everybody has a smartphone, right? I recently got a Nokia, that old one for 5K, because someone unfortunately decided to transfer my phone. They transferred it to themselves. So I got that old stuff, and I kind of remember, like, we're so attached to this thing. We're so attached to it. Why is that? Anybody know? We love data. It's all about every single time you look at this data you're looking at. You're looking at data, you're looking at data. Who said this in chat? Instagram, you laugh. It's data. So we love data, and this tool is our tickets to everything. So we're going to use that tool. I'm going to gather data from you. Let me just go through. That's me, and these are all the places I've worked. I've worked everywhere. There's data, data, data, data. Anderson, big problem they had was they kind of did something bad with data. Who knows Enron? They kind of messed up that data there, and that kind of brought their downfall. Should I say our downfall? 70,000 people lost their jobs worldwide in one day. Just like that. Because they shredded data. Although, at the end of the day, they said it wasn't their fault, but we had already lost our jobs. So data is everywhere. It's not now that there's the internet revolution that data just started. Data has been everywhere ever since. So this is where I work. I work at D Brown Consulting. What we do is train and consulting services. We build data analytics solutions with clients, not for clients, with clients. And the nice thing is the technology today is so powerful that everybody is a citizen developer. Everybody in the organization can become a citizen developer. And then what I'm going to talk about in data teams is how can you formalize that? How can you leverage that process of everyone working with data and then structure it in a way that can leverage your organization to do better things? So that's what we do. We do training and consulting. At the end of the day, I'm going to give you a gift. There's a gift at the end. So make sure your phones are ready. You need your data device to get that gift. So data culture and teams. Can you scan this code? If you don't have... Some people probably don't have that QR... Well, go to pollev.com. If you don't have the ability to scan, go to the website, polev.com. Type D Brown as the room. And then go into the room. Alright? Once you scan that, there's a question there. Please answer the question. Let me see. What's the question? What experience do you have working with corporate data? Alright. I'll give you a few seconds to log on. So let's assume there are 1,000 people here and everybody enters this data. I've just collected 1,000 data points. Do you know how difficult that was to do before? I'll take a form. Can you pass out the forms to those forms? Of course, there's no forms, right? Fill it out and stuff. Then you gather all the forms, right? And some poor guy sitting at the corner, one week, please type everything out. He will type everything out. And then another guy will now come and analyze that data. And then another guy will come and interpret that data. All of that can be done in 5 seconds now. The world is getting faster and faster and faster with data. But that doesn't mean you shouldn't use your own insights, please. Some people just rely on data and don't think... Stop thinking because everything is done. Please don't stop thinking. You need to think. I can give you a story where I stopped thinking. I was in the US going to somewhere that was supposed to be close. Google Maps kind of took me basically on a 2-hour journey to another city. And because I stopped thinking, I mean this place was supposed to be 20 minutes, 20 hours, and I went to another city and it was one dead end. I'm like, oh wow, don't stop thinking. All right, so I want to know my audience. Students, zero experience, 50%. One to three years experience, 50%. I know there are some gurus in the house. Some people are not entering data. They're using data for something else. They're probably already sending some emails and stuff like that. I want your data to concentrate on this presentation. Yes, everybody? Okay, let's enter some more data. 50-50 means not many people have entered. What's the challenge? Site is blank. Is the internet on? Site is blank. Okay, we'll come back to that. I wanted to ask you what language you speak. Just shout it out. Shout out the language you speak. Pigeon. Pigeon. Hausa. Very few Hausa people. Ibo, okay. No French. Japanese. Okay, I've heard Yoruba, Hausa, Ibo. Pigeon. You speak Lagos. Somebody say Lagos. You speak Lagos. We all speak Lagos. You know Lagos, right? Lagos is stress. All right. So you can see Yoruba at the top. Yoruba, French, English, Spanish. All right, okay. So what I'm going to talk about is we're going to talk about what data culture looks like in the real life and the trouble with data today, the makeup of the data team. What's a data team? What's the makeup of a data team? And then I'll talk about your careers because I knew there's going to be a lot of students here. What advice do I have for you? How can you earn that $3,000 a month that someone is earning? A couple of people are earning here. I've asked at least two people earning between $3,000 a month right now. And they're working freelance. Okay. So Yoruba is, of course, we are in Lagos, so that's understood. So culture. Culture is the ideas, customs, and social behavior of a particular people or society. Typically, culture and language are very close together, but they're not the same. So this is the idea, customs and social behavior. Do you agree with me that customs change? Yeah? Customs change. There are certain things that we're doing in the past that we're not doing now. And the thing about culture is that when, let's say there's a war or something, maybe I remember I read something about e-fair people fighting with equity people long, long time ago. And you know, and then once they win, they obviously, their culture kind of goes with them and then completely changes the culture of the area. And that's how it has been from time immemorial. Culture changes. So then what exactly is data culture? What I want to use is, I'm going to use an example of, this just came out two days ago. Just two days ago Walmart, there's a story about Walmart and how they use data. Who knows Walmart here? Just put your hand up. You know Walmart? You've heard of them, right? So they're the biggest employer of labor, one of the biggest employers of labor in the world. They have 2.3 million employees. 2.3 million, right? What's the population of equity states? Who's from equity here? Who's your population? Okay, I don't think it's up to 3.3 million, is it? So the whole of equity kind of employed. 10,500 stalls. So can you imagine the data that is generated here? Trillions of transactions. And they use Power BI, my favorite tool. No, not my favorite tool, my second most favorite tool. My favorite tool is Excel. I mean, I love Excel. Yes, thank you. Thank you for clapping for Excel. Can we clap for Excel? Because Excel has changed lives and Excel has also kind of made people cry. It has done everything. But Excel is my first love and Power BI basically grew out of Excel, SQL and MDX. If you want to know the genesis of Power BI, one thing I'll give you, anytime you want to learn something, go learn where it came from. Who are the people that created it? What were they thinking when they created this thing? So we want to know Power BI very well, understand how MDX works, understand how Excel works, and understand how SQL works. Those three languages is what made Power BI. So this is Walmart. This is their director of finance, data and analytics. He said, with Microsoft Power BI, we've established a semantic model library. Write that word down, semantic model. You will need it a lot. So some keywords. Semantic model, go check what that means. It's a lot to do with culture. It's a lot to do with understanding the meaning behind data. A semantic model library covering massive amounts of financial data. It gives associates easy access to the full breadth and depth of Walmart's data, all the way from executive leadership to individual analysts and enables them to powerfully analyze the data in a very quick manner. This to me is the summary of data culture. The organization has implanted a data culture in the organization and obviously they have data teams working to make this happen. 2.3 million people, each and every one of them can connect to the data they want at any time. This is huge, very huge. So this is the model that they created. Of course we send you the slides and stuff about how they did it. It looks very simple. Yes, very simple. Power BI is pretty simple to implement. So building a semantic model, what they did in summary is they centralized data. They centralized everything around data should be in one place. So there's one important thing. One version of the truth. Very important. One version of the truth. To have a true data culture, you cannot have this person seeing the same data in a different way and this other person seeing it in a different way. It just cannot work. So you need to have one version of the truth and the way to do that is to centralize it. From here, I wanna show you one tool that you can use to make that happen. They democratized data. So they didn't put any padlocks on data. Before you get this data, you can make friends with the guy in IT to take them out for lunch and to a movie. The data was asking. So what typically happens with our clients, it is for good reason they lock data. They lock it and everything. about five or six different codes. Sometimes they even forget the code, they can't open it, they have to remember. So they lock it up. Now you have to free data. You must free data. And one of the best ways to free data now in this day and age is cloud, isn't it? We're in the cloud so it's easier to free data. And lots of people are getting more used to the cloud. I think five years ago, many organizations know cloud security, security, security. Why? They still needed to be able to lock the data because you're intellectual property, right? Who knows Elon Musk? OK, you don't know him personally. Even knows him personally, I would like to see you. So Elon Musk, when he did the driverless car and everything, the entire data and technology that they use for that is free, is online, is open source. That's why I ask him, why would he put it as open source? Why exactly would he put it as open source? He said, well, by putting open source, we don't claim to have all the knowledge about how this thing works. Yes, we've done a lot of work. But by putting open source, the entire brains of the world and whoever is interested in it can come and work on this. And then they will develop it to a level that we can't as individuals as a company. And guess what? That means we can share that data. I don't mean share all your data. Please don't share all your data. You can lock some data privately, yes? But data needs to be freed. It needs to be democratized, especially in a corporate environment. So have a process where, yes, there's security, but I can connect to this data and get exactly what I want to do my work. Then, of course, optimize. That's where the teams, the data teams, they are meant to ensure that this optimization works. So then the background ensuring massaging, kind of correcting, and making sure that the data is optimized in a way that instantly are able to get insights. See this data? You should switch off data. Stuff like this. This is a call from the UK. Who knows? Should I pick it up? Maybe there's data related to money. No, of course not. Sorry about that. So please mute phones. OK, so centralized, democratized, optimized. That's what Walmart did. That's the summary of what they did. And I think this is an excellent summary of that data culture. So trouble with data. What have we discovered in our 16, 17 years of Dibran consulting? What have we seen with data? Because we're working with data right from day one. And what did we see? What are the things we saw? So we've summarized it into these problems. We think data is too siloed. And I've talked about that. We lock it up. We silo it, not only locking it up, every department has their own data. Everybody's using their own data in a siloed environment. So we came from, OK, we've locked the whole data so we're locking it department by department. Not really good. So then governance. Because of this siloed data, there's complex governance structures everywhere. We don't really know which data is where. OK, for example, this department calls sales, sales. Another department calls it revenue. Another department calls it production, recovery. I don't know. There are all sorts of names everywhere. You need to centralize that. There's something called the, there's CDS. I don't know, CDM. What's CDM? I don't know the technical term. Who knows CDM? Common data model. Yes, the common data model is free for everyone to use that Microsoft has developed that tries to standardize what these things are called. And that's part of you building a data culture. You need to understand the governance, know how to call it the same thing everywhere. So everybody's speaking the same language. And then inconsistencies, right? This generates inconsistencies because data is everywhere and stuff is inconsistent. Then there is duplication. Because of inconsistencies and siloed data, there's duplication of data. As a finance person, I need to do an analysis of revenue by sales teams. Analysis of revenue by sales teams. Okay. I need revenue data. I have that in finance. I need to know the sales team. I talk to HR. HR has everybody in the organization, right? I get the data from HR as CSV, put it down, and then connect to it. But guess what? People are employed every day, isn't it? Every single day people are being employed. People are living every day. So the moment you get that data from HR, it's already obsolete. Do you agree with me? So why don't you kind of work in a way that we can connect to a data source that everybody connects to? Just one. When it comes to people data, just connect to one. When it comes to revenue or financial data, connect to one data source, not siloed data sources. So because duplications happen. And then in a board meeting, you're presenting the numbers and you're saying, hey, this is 200 million per staff is what we made. But how many staff do you have? We have 250 staff. How many staff do you have? We have 252. HR, how many staff do you have? 248. As in the MDs like, okay, you guys, can you go and talk to each other? Then they do what? They do reconciliation meetings. And then they do duplicated data sets and everything. How do we manage this? It's all from the beginning. That's silo. And then minimum insight. So instead of spending time thinking of what is data telling us, you're spending time reconciling. How many understand that? Anybody here that can feel what I'm saying? Yeah, that is inefficient. And that's not how to build a data culture. So these are also examples of other problems. I'll send the slides to you. These same kind of similar problems we see in most of a lot of our clients. What you should get to is stop your data cleaning, data reconciliation, aggregation. Just you should just act on insights. Eliminate that process, right? So building a semantic model. You need to always understand who did what, where, and when. There are four Ws. Who did what, where, and when. Every conversation you're having, understand that. And then understand who owns the who data, who owns the what data, who owns the where data, and who owns the when data. Who are the ones that own it? And that is the only places you connect to. One source. There's another W there that you now need to do. That's the work you need to do. A W that's missing here. If you do these four Ws well, the last W will make sense. Who knows that last W? Thank you, why? So the last W is why, and that wouldn't happen if you don't have these four Ws sorted. Yes? Great. So this is a simple solution, and Parabia has the solution. What you need to do is create a workspace and ensure you have centralized shared data sets. Understand the who, the what, the where, and the when, and whoever is the who person, the who person probably is HR if it's internal who, and there's another who person which is sales or marketing that understands your customers. Those are the two who's of your business, the who internal and the who external, right? So get a shared data set. Anyone wanting to connect to employee data should connect to that shared data set, wherever they are in the organization. That'll mean you have endorsement. Somebody, maybe the data team or IT team, we've endorsed, we put a stamp. This is the correct data set, fully endorsed. So you endorse the data set, discoverability, make it easily accessible. If you want, we may say there are certain teams that shouldn't have access to this data, that's fine, but make it discoverable, right? Lineage, lineage basically means where did that data come from? What's the connection between the data you have now and where it came from? That's called data lineage. Understand data lineage, and then contact. Just basically ensure you have understanding of who should have access to what. And this is your solution to building that data culture and working with the team. So who will manage this? These are tools from Microsoft, Power Platform. You're gonna hear a lot about it, so I don't need to talk about it, right? Now who is going to manage this? Who are the teams that are going to manage this? A data-driven culture is one that utilizes data to enhance their team's instinct and not replace them. What I mean here is do not just rely on data and say, hey, like me, I went two hours driving, like a complete, I don't know what the heck was in my, I don't know, maybe I was listening to a good music or something. Yes, I didn't eat Pande, no, because of the US, I didn't eat Pande damn, that's the reason. See, I couldn't have done that in Lagos. Imagine traffic, if enter Third Main Land Bridge, and I'm like, oh crap, I'm supposed to be at the airport. How is that? You're heading to Leckie, geez. Anyway, so data-driven culture is one that utilizes data, but please, it shouldn't replace your critical thinking, shouldn't replace it. Now, the data teams, what are their responsibilities? I've broken it into three. There's the groundwork, questions, and actions. And our wonderful last speaker talked about actions and insights, that's where the actions come. So your groundwork is your collecting, your accessing, your reporting of data. Questions are those critical questions you ask, which you now do experiments on, and then you now understand your data even more, because your data is gold. If you can mine that data and get it to talk to you, you can change the way you do things and make more money. Because at the end of the day, a company needs to make money to pay bills and increase salaries, right? So, and then action, apply the learnings from your data across the organization. So the data team, this is what they're supposed to do. Once they understand, they do experiments on the data and stuff, and then they say, hey, we've discovered a better process. You know that process you're doing for the client? That takes five steps. We know how to take it in two steps. We've done the experiments, everything works, okay, let's implement. That's what a data team is supposed to do. So who are in this data team? Who are the members of this data team? Who is here? Okay, so we have the data scientists. Now there are many names. I've just summarized into four. Data scientists, data engineer, data analysts, data managers, right? So who is the data scientist? So this guy is an expert in statistics and computer science. Data science is basically the intersection between statistics and computer science. That's what data science is. You need to understand statistics. And a lot of us in organizations don't understand statistics. I was talking to a client once and I asked them a question. I said, you have sales center reps. How do you evaluate sales center reps? Your sales center reps, because they had to do some downsizing. They didn't have up to a thousand, using an example. They have a thousand sales center representatives. How do you assess who to keep? Because unfortunately you need to downsize. What metric should you use to measure performance? One metric, just one, who knows? Let's say average, maybe they're expected to call, make like 200 calls a day, right? So average number of calls per day over the last six months, is that good enough? You know it's a trick question, right? So you're obviously being quiet. Okay, I understand. But anyway, that's what people use, average calls. So say, okay, over the last six months, what's your average number of calls? And you expect them to do 200. Somebody did only 100. Another person did 300. Okay, those high ones, you keep them. Now unfortunately, that's the wrong metric. So if you're a data scientist, you know that the metric you're supposed to use and what customers really want is one word. If you want to know what customers want, write this word down. Every customer in the world wants consistency. That is the number one word for customer service. Consistency. Not excellent service. Yes, excellent service is nice, but consistent service is better. If this light was consistently off throughout today, we would know that there's no light, and we'll do something about it. But if it's on, off, hopefully they won't switch it off because I said this. Okay, please, please don't. Okay, so if it's on, off, on, off, that is inconsistent, that drives us nuts. So who knows the measure, the statistical measure for consistency? Average, I'm not asking you. I have some MVPs here, I'm not asking them, no. Variance, who knows? Mean, that sounds very mean. Okay, no. Okay, so, nope. It is standard deviation. So when you're measuring whether or not people are performing in your organization and you have data, it's supposed to use standard deviation. Now, unfortunately, the way they taught me in school was like this, a standard deviation, you calculate the mean variance of the mean variance, calculation sum sum variance, and then you do the mean and then you do the variance, you do the square root, and then you do this, and then you do the square root. That's the wrongest way. All you should say is standard deviation is a deviation from a standard. That's all. How has this person consistently deviated from the standard? That's all, that's what standard deviation is. And it's a wonderful measure that gives you one number. And so your standard deviation, okay, this 200 employees, right? Your standard deviation is 20. What does that mean? That means plus or minus 20, that's the range. 200 is our standard, yeah, between 180 and, thank you. And the acceptable standard deviation in our organization is 10, plus or minus 10. That's how you should do it. But is the data scientist, that kind of knows that, isn't it? That's what the data scientist's job is, to understand statistics and then use that with data and computer science to understand the data of the organization well. So a lot of us think, oh, it's coding, coding, coding, coding, coding. Yes, that's good, but you also understand statistics and computer science. That's an excellent data scientist. All right, so then the next person in the team is data engineer. The data engineer, what they do is they build data sets. That data is what I was talking about. They build data models, they do the DAX and everything. If you're a DevOps guy, this is the backend guy. This is the backend engineer. If you're a DevOps person, this is the backend engineer, data engineer, right? Building those structures. Then you have the data analyst. I like this guy, because I think I have two hats. But data analyst is the one that does the data visualization, data storytelling, understanding reporting analytics, when he answers, when he's basically answering business questions from people he becomes known as the business analyst. That's the difference between a data analyst and a business analyst. The business analyst is like a data analyst that answers business questions. So there's so many names out there. And then you have the manager, the data manager. So this is basically a project manager. Why are you laughing at this old lady? Okay. This is the person that kind of puts everybody together and shows that everyone's working as a team and makes sure everything works pretty well. So that's that. I had a question for you, but I'm going to rush through this. What dominant role are you playing right now? It's just one person. Data scientist, yes. This scientist is very, very kind of cool. Everybody loves science. Yeah. Well, please, you need a lot of statistics. Yeah. So typically we have loads of data analysts. That's what companies employ a lot of data analysts. But when you want to create a data team, the key thing there is you need these roles. And in Nigeria, I'm sure we play not one role. I don't think I've seen anyone that just plays one role. Right? We are multitaskers. And that's because we don't have huge organizations. Abroad, when you have a very huge organization, people play individual roles and work in a team. So my next question for you was, how many roles do you play? I know you do more than two. So I'll leave that out. Now, this is basically what I'm going back to Walmart as I end. This is what Walmart did. This is kind of a summary of what they did to centralize, to build a data culture and data team within the organization. This is a summary of what they did, speed of insight, no wasted time, no duplicated efforts and the rest. Right? So I have this up for questions and I will leave this there and then see if you have any questions for me. You answer that, but I have a gift for you. So I'm going to go to the gift first. All these slides are about jobs. The current jobs out there is mostly data, data, data, data jobs. That's what everybody wants in the world. So you don't need to jack path physically. Jack path, virtually please. Stay here. Right? So resources from Microsoft. Quickly scan this. There's a particular page on the Microsoft. Microsoft has so much documentation. This one basically tells you about how to collaborate, share and integrate data across multiple products. We'll make sure you get all these slides. All right? So, but you can scan that. But the big one I want you to scan is this. Some people here are going to win a million Naira worth of training. One million. Debran Consultant, can you give it up to our guys? Debran Consultant team there, there. Yeah. So, they've put together a bootcamp. We do this every year, mostly every year. So you're going to basically be a member of this bootcamp where we're going to drill you to become either a data analyst or a financial modeler. You kind of choose depending on what you want. As a millionaire, I'm not going to pay anything. And then we're going to work with our partners to ensure that you get interviews for jobs. How many people want to job here? So make sure you scan this later. I'll put this up. I'm going to tweet this. I'll tweet this time up, I know. I'll tweet it so I'm a Debran analyst so you can always get the link there later, okay? Because I know I have to jump off. And so that is that. These are questions coming up. I'll see which one goes to the top. But that is me for today. I hope you've enjoyed my session. And if you need to speak with me later, this is stuff on Nigeria. So this is me, all right? Thank you very much, everybody. Thank you so much, Mr. David B. It's always a pleasure to have you. And on behalf of Data First Africa 22 planning team and everybody here, we want to present you this. So thank you for coming. Thank you, thank you, thank you. All right, thanks everybody. Okay, does anybody have, we can take one question for him. Okay. So data, you can vote for the question. Typically you can all type, I'll leave that up. You can all type, I'll answer those questions online. If you keep on asking the question throughout the conference, right? So the one at the top, if you just put, I'll just answer the one at the top. Or anyone that has a question, you can just ask. Okay, question. Okay, so the training you need to apply. Once you apply, there's a form, the link will take you to a webpage. You fill it out. We're going to go through the whole thing and then we'll choose, I think 200 or 300 people that we're going to train or something, or 500 people. I'm not sure how many, the team. So you just apply and then you're going to do a test. So there's going to be a test and based on the test, we're going to pick the people, right? So best of luck. You can share it to anybody and everybody, but they must be in Nigeria. If you're not physically in Nigeria, you won't qualify. All right, so my question. My question is on influencing the data culture in an organization. So I recently worked with the team. It's, they're kind of scattered everywhere. There's the IT team, then there are other teams. Now the IT team is also divided into several teams. So there's the analysis team, also the analysis team. Now, we want to work. We have to retrieve data from several places. And at the time we were working, data has changed. So I tried to kind of find a way to do it, but we're like company policy and all that. How do you influence data culture in that kind of situation? Excellent question. So if you remember that, this is siloed. Can I remember the top siloed? So this is siloed data. There's the mentality of locking. Unfortunately, a lot of organizations still have that locking mentality. So what I would like you to do is, the thing about you changing things is do you have influence? Where are you? You're probably a junior person there. You don't have influence to the top, but you can make that influence happen. There are certain things you can do. The case studies, go to Microsoft sites, look for case studies around your industry. You know, they do those short videos. Find a way, I don't know if you have open places where you can talk to the CEO. Can you talk to the CEO at any time? Okay, no. So that's another bottleneck. But you need to find an influencer. Find an influencer in your organization. There will be somebody that can influence the CEO. Have a discussion. Show them this, what people are doing. Now what I showed you about Walmart, 2.3 million people. A company with 10 people can still do exactly what they did. It doesn't cost anything. It's not expensive anymore, right? So you need first, find an influencer. So maybe senior manager and manager stuff you can talk to, understand to buy your idea. Then see how you can ship that to the CEO. Because if it doesn't change from the top, there's nothing I will tell you that will happen. It won't change, right? So that locking culture, unfortunately, is there. Gradually needs to kind of stop. So maybe you can find a project that doesn't need locked data. Maybe it's a project that doesn't need data to be locked. And then just let them see how wonderfully that project just works and everything updates. Maybe you solve it with clients or something. And every time you're solving clients, the data is entering and updating. And make sure you present that. They're like, how does this work? Can't we have our reports like this? When a senior person says that, that's your entry room. Yes, I have a question. Don't be afraid to ask questions. When you're in meetings, just put your hand up and ask questions directly. Don't be scared, ask, right? Doesn't matter who the person is, just ask. All right, thanks.