Implementation Insights – Data Quality Workbench

The DQ Workbench goes beyond identifying errors — it captures and stores results so you can monitor data quality trends directly within Dashboards and the Data Visualizer app.

• Outlier Monitoring — Aggregate and store outlier counts as data values, giving you a clear view of data consistency across reporting periods.
• Validation Rule Trends — Record the number of validation rule violations over time to assess whether identified data quality issues across your system are being improved over time.
• Metadata Integrity Tracking — Record and visualize metadata violations to maintain a robust, well-configured system. Monitor whether corrective actions are being taken and having an effect.
• Min/Max Generation — Generate min/max thresholds using a range of statistical methods tailored to different data distributions and use cases.

While this an external tool, it supports the functionality introduced within the broader DHIS2 data quality toolkit.

• Presentation
• Github repo
• Documentation