 On the login page, there's the username, and we're in. Before we proceed to discussing the WHO data quality tool, I want to pause for a couple of minutes to draw your attention to a special dashboard that has been designed on this instance of DHIS2. This is the data quality dashboard. We encourage you to also configure on your own DHIS2 something that looks at data quality in the same way. In fact, the standard applications of DHIS2 are well-designed to look at such aspects of data quality as the completeness here you see for multiple data sets, the trend over the last 12 months in the reporting rates, or a table that disaggregates the reporting rates by district. This is a chart that is looking at that second dimension of data quality, which is internal consistency in time. Is the value of, in this case, A and C first visits? Is it consistent in time? And we see at national level, a couple of examples where the monthly value was not consistent in time. So a chart like this showing the month-to-month trend can allow you to identify the very largest outliers, suspicious values, values that are very different from what was recorded in other months. We will see how the WHO data quality tool is even more sensitive at picking up these outlier values. Also on the dashboard, there's a table that presents for you the very largest extreme outlier values reported for various data elements. Then there's a chart that is looking at the consistency between related elements. Remember, this was one of the metrics, one of the things to assess with the data quality review, in this case, comparing the first doses of PENTA reported by a district to the third doses of PENTA. And these two districts have a negative dropout rate. So that is suspicious because they have reported higher numbers of third doses of PENTA than first doses. And just wrapping up our quick view of this standard data quality dashboard, here's a chart which is comparing estimates of coverage from two different sources. The blue bars show you the routine estimate of coverage based upon the data reported monthly by each health facility. And that routine estimate of coverage is compared to the estimate from a recent demographic health survey. So this is the assessment of that third dimension of data quality that we mentioned. This is external consistency of the data. And we see that for one region, there is not very good external consistency between the survey estimate and the routine estimate. And finally, the fourth dimension of the data quality review framework looks at the consistency of the denominator values. And in this case, we see that there is not consistent growth from year to year in the estimated value of the population under one year of age that denominator used to calculate coverage with most immunization data. We see that there was a drop in between 2015 and 2016. Whereas with other years, there was what we would like to see, which is a steady annual change, a steady annual growth in this denominator. So we encourage you to use the standard applications of DHIS to look at data quality. What we're going to do now is switch over to using the WHO data quality tool application. If you've got a lot of applications to choose from, one way to rapidly find the WHO data quality tool is to, in the search apps line, type in WHO, and then you'll find the only application that has WHO and its name. We launch that application. And right away, we're taken to this page, which, as you see from the highlighting up at the top, this is the dashboard of the data quality, the WHO data quality tool. Later, we will learn about some of the other functions of the WHO data quality tool, and specifically the analysis function. But for now, we're going to focus on the dashboard and specifically on this first page. You see that the dashboard of the WHO data quality tool has four pages. We will present on each of these four pages. For now, we're going to focus on the completeness page of the dashboard of the WHO data quality tool. The completeness dashboard shows results for the completeness of several different data sets. If we scroll down here, we see ANC data set completeness, HMIS data set, immunization, malaria, maternity. And for each of these data sets, there are two charts. The chart on the left is showing you the trend in the reporting rate and the completeness over a 12-month period. You see here on the axis, which 12 months, from September 2019 to August of 2020. Why is it going up to August of 2020? Well, over here is something called the settings icon. And if we click on that, a special window opens that allows us to set how this dashboard is configured. We'll say something later about setting data. But let's change how the period is set. Now it is set to August 2020. And notice the chart on the left is showing completeness over 12 months up to August 2020. The chart on the right is showing the completeness for a single month, which month, August of 2020. Notice what happens if we change the period. We've selected July as the period. And now the chart on the left is showing also a trend over 12 months. But it is the 12 months which end in July of 2020. And the chart on the right shows us the completeness for a single month, the month of July 2020. The histograms, the bars in the chart on the right are disaggregated by region. It shows completeness for each of the four regions. If we look at the settings for organization unit, notice it is set here to disaggregate by region. Let's see if we can change that. Let's change it to disaggregation by district. Aha. Now we see that the charts on the right are showing completeness for the ANC data set disaggregated to show completeness for each of the districts in the country. So we see how we can configure the dashboard. We can change the configuration by changing the period. We can change disaggregation. We could also, instead of looking at all of the data nationwide, we could decide to look at only the data for one specific region. Here we have completeness only for the districts within region A. Or even if we click on the plus sign, then we see a drop-down menu showing all of the districts in region A. And we could select one and automatically disaggregation is set to one level below the district. So it's set to facility. We will go back. Leave this on the month of July. Set the national as the boundary. District as the disaggregation. And let's see if what happens when we change data from core and core. Notice core shows you results for multiple different programs. Let's change data to one specific program, immunization. Now we are looking at completeness only for the immunization data set. So we see how by changing the settings, we can use the dashboard to look at a different set of programs. In this case, core means multiple programs. We can look at the completeness for a different period. Even we can change the year and look at the completeness for December of 2019. And we can change organization unit to disaggregate differently or look at a different boundary of data. We will continue in the next presentation to review consistency time. And that's when we will see how useful it is to change the data to a specific program like immunization. Thank you.