 I am going to now give a quick introductory PowerPoint just to walk us through what we're actually going to be doing this week. What are the objectives of this academy? Well, we really only have five main objectives. These are quite broad, but let's just go through these quickly. We want you to be able to understand all of the necessary components of data quality. Many of you are experts. Many of you could probably give a lot of these presentations and lectures yourself. We appreciate all the various skillsets that are coming in, and we hope that you're able to communicate and talk to each other throughout this week to be able to share your own insights. But there are some foundational components of data quality that everyone needs to appreciate. And if we collectively understand this, we can have conversations and build each other up around it. So that's a big part of today is understanding these necessary components of data quality. This exists outside of DHS2. This is everyday work data quality kind of concepts. The next objective is that we want you to be able to know how to use all of the key DHS2 functionalities for data quality. I promise that there are many functionalities that you're probably not using. There's a lot of incredible features in DHS2 that allow you to perform routine, annual, or even notifications and alerts automatic data quality checks. It's important that you know how to use all of these, know when to use them, know how to use them, and then you're able to apply them to your own projects and or country implementations. And I think that's the third bullet point is we really want you to be able to take away from this academy practical skills that you can immediately apply. We don't want this to just be absolutely theoretical. We want you to actually be able to say, hey, I can use that feature. I can use that tool that Scott or Bob or whoever just introduced to me, and I'm going to be able to immediately use this and start performing routine data quality checks, you know, next week, later this week. We want these things to be immediately implemented and we want to hear about it. We want to hear about the successes, the failures, your user stories that you have in using DHS2 for data quality checks. We're going to incorporate all of these, all of this feedback, as well as answer any of your questions into some guidance documentations and improve this academy going forward. The second to last bullet point here is we want to make sure that you're exposed to a variety of use cases and examples. There are many really wonderful examples and stories that exist out there of people using DHS2 for some, for data quality checks and really utilizing the features to the max. And that's what we want to expose you to. So it's one thing for me to kind of go through everything in theory. It's a very different and extremely useful way thing for you to be able to see people actually doing it. And so throughout the week, you're going to be seeing all of the very several different data quality user stories. And you can start to build up your own data quality strategies and plans around what other people have done. There's a whole world of DHS2 users out there, many, many countries doing different things. You don't have to reinvent the wheel. You can just build on top of what good practices other people have already done. The final point, and this is the one that I really want to kind of drive home as much as possible, is we want to build a DHS2 community of practice around data quality. Everyone is talking about data use, data use, lots of folks focus on individual disease programs, patient tracking, but there doesn't really exist a strong DHS2 community of practice that's principally concerned with data quality. And of course, we appreciate that without data quality, people aren't going to use data. All of the data that's coming in from these various disease programs and projects, it's not going to be useful. It's not going to be reliable. And so just as much as we focus on all the other aspects of the different types of data we get in, the different way to look at data and visualize data, it's equally as important, if not more so, to also have a group focused on data quality. And how do we address data quality in DHS2?