 So, now we're going to look at some additional best practices for data quality monitoring. One of the most important best practices is to use automatic data quality alerts and notifications. You need to be able to send the data quality alerts or when a data quality issue is detected directly to the user or users who are able to address that data quality issue. Oftentimes we see that data quality is poor because people don't trust the data. And one of the main reasons they don't trust the data is because they cannot see the data quality issues. Maybe they're looking at a high level national level and they're not able to see the individual data qualities because they're not going down to individual facility level. So when they do see a data quality issue, then they just make the assumption that all health facilities or all places that are capturing data also have data quality issues and that minimizes the amount of trust that a user may have in the system. One way to make sure that we're improving trust is to make the data quality issues as obvious as possible when they are detected and that's best done through alerts and notifications. So data quality issues are not really a failure of the system. They're a feature of any information system and the failure is not being able to detect them and to do something about them. We have to appreciate that every information system DHIS2 or otherwise is going to have data quality issues and using DHIS2 when there are issues, they can be automatically detected and they can be sent in alerts or notifications to people who can do something about them. When we send the alerts and notifications, we need to make sure that we're sending them to where people spend their time. We often see that users of DHIS2 are not spending a lot of time looking at their DHIS2 dashboards. Most people nowadays are spending much more time looking at their email, checking SMS or WhatsApp messages and we can send the alerts and notifications directly to where the users are spending their time, their email or as an SMS. And you see an example of that here on the screen. This is an automatic email generated by DHIS2 sent to a users and it's to giving you all of the notifications, validation notifications that have been automatically detected. And so you can see quite a lot there being sent in this single email. Again, sending it to someone's email, whether they're checking their email several times a day, maybe that's the first thing they do when they get into the office or maybe even before they get to the office or checking their email. One of the emails that they're going to see is this alert from DHIS2 saying there are some validation notifications, there's some data quality issues here. You need to go into DHIS2 and investigate them or do something about them. It's also very important that the notifications that are being automatically sent to someone's email or via SMS, give the user specific instructions on what needs to be done when that notification has been received by them. Don't just send a notification that's really generic plain text. Give the user a specific action point to follow up on from that message. So in this message that you see in this example email, you see that if you look at the very first notification, a validation of PIN2-3 outlier validation rule was detected at this health facility on this date. And the specific instruction is please confirm the value is correct for PIN2-3. So we're telling the user then they need to go to this health facility for this date and double check and make sure that value is correct. That's a very specific action to take. And these alerts notifications need to be that specific or even more specific. Now saving the best for last. The most important best practice to addressing data quality issues are having clearly defined data quality standard operating procedures. We've seen that in many countries that lack clearly defined data quality processes. They struggle with addressing data quality issues. Again, we have to appreciate that there will always be data quality issue. And we should not measure ourselves on just can we stop it at the source of the data quality issue at the point that is entered. But even more importantly, can we find it in a timely way and fix it before it starts to be factored into our national statistics. That is where clearly defined standard operating procedures come in. These define the process for each person along the data flow from data entry to analysis to summarizing, prioritizing to making actions, you know, all the way from health facility districts, regions, national levels. Every touch point of the data, you define the process for each one of these on how they should identify and respond to data quality issues. Every single person along the process should be looking out for data quality issues. Ensuring data quality is everyone's job. And if a data quality issue is found at any of these levels, that person should have a very clear idea of what they need to do about it in order to address it. A lot of the standard operating procedures can be aided by DHS2 or the HMIS. For example, DHS2 can produce automated messages to each person. They can send alerts and notifications, even reminders to individuals to follow up or do their part of the standard operating procedure for data quality. But it's important to appreciate that this goes all the way down national level down to the facility level. And there are some really clever ways at which standard operating procedures have been developed and even made very clear. So, for example, in South Africa, they have a very robust standard operating procedure on ensuring data quality. And that standard operating procedure is actually printed out onto a large piece of paper, like a poster, and it is put on the wall of every health facility in the office where they're actually entering data. So it's very clear that anyone who sits down at that computer, what is their job to ensure data quality? If they find this is how they check for data quality. This is what happens if they identify a problem for data quality, the entire process is printed out. It's on a poster, it's on the wall. You need to make these standard operating procedures very clear and easy to understand and very available. You shouldn't have to make people look around for instructions on what to do when they find a data quality. It should be very obvious just like in the case of what South Africa has done. Finally, a quick summary. So in this presentation, we've gone over the principles of data quality. We've looked at frequency of data quality checks, different methods of data quality checks, metrics for data quality, as well as some of the core indicators to monitor for data quality. We've also reviewed various data quality tools in DHIS-2. So we've looked at the WHO data quality tool. We've looked at the DHIS-2 data quality app. We've seen how data quality can be visualized on the dashboard and in other analytics. We've also had a quick look at the standard reports and the various data quality functionalities it has. We have also touched on standard operating procedures and how standard operating procedures are critical for routine data quality processing, staying on top of the data quality issues as they inevitably come in. We've seen some examples of some routine data quality monitoring dashboards, as well as touched on how important and useful automated notifications can be from DHIS-2. So that concludes this presentation. Thank you very much.