 Once we have data entered into the platform, how can we check the data's accuracy and reliability? In this video, we will explore several tools in the DHIS2 data quality app that will help us to review and improve the quality of our data. First, in the data quality app, you can run a validation rule analysis which tests validation rules against the data registered in the system. After running the check, you will get a report with a list of data that need to be checked. For example, if we look at the first row, our validation rule says that the ventilators occupied should be less than or equal to the total number of ventilators. But the value registered for ventilators occupied is 71, while the value of number of available is 20. We can follow up with this location to determine the specific source of the error. Note that you can also run the validation rule analysis during the data entry process. The Outlier Analysis is another tool we can use to check data quality. This analysis can be performed looking at potential outlier values. It can identify values that are potential outliers when compared to the standard normal distribution of the data under review, or it can check the data against a predefined minimum and maximum value range. If the data fall outside that range, it will be identified as a potential outlier. Take in the validation rule analysis. When we run an outlier analysis, we get a report with all data that need to be checked. For example, in this row, the data indicate that 767 anti-natal clients had their blood pressure checked at this facility. This value of 767 falls outside of the normal distribution of these data and has been identified as a potential source of error. The third and final tool available in the data quality app is the follow-up analysis tool. Follow-up analysis creates a list of all data values marked for follow-up. We can mark a data value for follow-up in the data entry app and the data quality app via the reports from the outlier analysis tool. This is an example of the report obtained after running a follow-up analysis in DHIS-2. In coordination with WHO, a data quality tool for DHIS-2 has been created. This app generates findings on data quality following WHO's data quality review framework. This includes completeness, that is, if all the expected data are recorded, timeliness, or if the data were received on time, internal consistency, which compares internally submitted data with one another, and external consistency, which compares the data with other sources, such as surveys. As an example, we can review internal consistency. The WHO data quality tool allows us to identify outliers within our data. We can see an example of this in District C1, where the number of measles vaccines given in January 2020 is a much higher in comparison to the other values reported within the same district in the same year. This helps us easily visualize what data need to be double-checked before running any analysis. In summary, in DHIS-2, there are several tools that help us to check the quality of the data entered, such as validation rules, outlier analysis, and follow-up analysis. DHIS-2 and WHO have also collaborated to create a data quality tool that runs checks to validate completeness, timeliness, internal consistency, and external consistency.