Webinar: Implementation Insights – Data Quality Workbench (Part 3)

Note: This is Part 3 of a three part webinar series. You can watch a recording of Part 1 and Part 2 on YouTube.

For the next webinar in our implementation insights series, we are going to walk you through the data quality (DQ) workbench. 

The DQ workbench is an external, python based tool that allows you to supplement the built in data quality features within DHIS2. 

The DQ Workbench doesn’t just find errors—it tracks them, allowing you to visualize progress and trends over time directly within Dashboards and the Data Visualizer app.

• Outlier Monitoring: Summarize and store outlier counts as data values to track data consistency over time.
• Validation Rule Trends: Don’t just run analyses—capture the number of violations over time to see if your data quality is improving or declining.
• Metadata Integrity Tracking: Maintain a robust system by recording and visualizing metadata violations. Track changes over time to see if action is being taken to remedy these violations. 
• Min/Max generation: Generate min/max thresholds using a variety of statistical methods depending on your needs.

By receiving these counts, we are also able to turn them into indicators that provide further insight into our systems data quality. For example, we can track the percentage of submitted datasets within a period that do not have any validation violations!

We look forward to reviewing these concepts with you further during our webinar. Register now to join us!

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