We’re excited to launch short tutorials extracted from the recent online Data Quality Academy recordings! These hands-on videos teach practical techniques to detect, investigate, and prevent data quality issues in DHIS2 aggregate data.
The first batch of tutorials covers the complementary metrics of dataset and data element completeness. You’ll understand what makes datasets complete, learn to mark them as complete in DHIS2, and visualize completeness plus timeliness using pivot tables in the Data Visualizer App, while also reviewing individual data element completeness by comparing received values against expected totals.
A new batch of tutorials from the Online Data Quality Academy is now available!
Explore how validation rules in DHIS2 create internal and external consistency checks for your aggregate data. Learn how they are checked during data entry to prevent quality issues, how to review them in bulk for multiple facilities, and how they can compare values against statistical thresholds to detect outliers.
This weeks tutorials from the Online Data Quality Academy focus on assessing data completeness and reporting consistency in DHIS2.
Learn how to evaluate data element completeness using different denominators, understand how to use the indicator “Facilities consistently reporting”, and identify when low performance reflects real data quality issues versus other factors and how to spot facilities that require follow-up to improve reporting consistency.
The next set of tutorials is now available – this time focused on consistency of related data including using scatterplots, dropout rates and outlier analysis to identify data quality issues!
What you’ll learn:
• How to use scatter plots to examine consistency of related data and identify values that fall outside expected patterns
• How to analyze dropout rates in programs with follow-up services (e.g., immunization, antenatal care) to detect data quality issues
• What outliers are and why they matter for data quality
• How DHIS2 calculates outliers using statistical methods (mean, standard deviation, Z-scores)
• How to identify outliers across your organization units
• How to drill down and pinpoint the source of extreme values
• How to use built-in outlier analysis tools and dashboards
