Chapter 3. Control data quality

Table of Contents

3.1. About data quality checks
3.2. Validation rule analysis
3.2.1. About validation rule analysis
3.2.2. Workflow
3.2.3. Schedule a validation rule analysis to run automatically
3.2.4. Run a validation rule analysis manually
3.2.5. See also
3.3. Standard deviation outlier analysis
3.3.1. About standard deviation outlier analysis
3.3.2. Run a standard deviation outlier analysis
3.3.3. Modify a standard deviation outlier value
3.4. Minimum maximum outlier analysis
3.4.1. About minimum maximum value based outlier analysis
3.4.2. Workflow
3.4.3. Configure a minimum maximum outlier analysis
3.4.4. Run a minimum maximum outlier analysis
3.5. Follow-up analysis
3.5.1. About follow-up analysis
3.5.2. Create list of data values marked for follow-up

3.1. About data quality checks

The Data Quality app contains tools to validate the accuracy and reliability of the data in the system. You can verify the data quality with the help of validation rules and various statistical checks. Data quality has different dimensions including:




Data should be within the normal range for data collected at that facility. There should be no gross discrepancies when compared with data from related data elements.


Data for all data elements for all reporting organisation units should have been submitted.


Data should be consistent with data entered during earlier months and years while allowing for changes with reorganization, increased work load, etc. and consistent with other similar facilities.


All data from all reporting organisation units should be submitted at the appointed time.

You can verify data quality in different ways, for example:

  • At point of data entry, DHIS2 can check the data entered to see if it falls within the minimum maximum value ranges of that data element (based on all previous data registered).

  • By defining validation rules, which can be run once the user has finished data entry. The user can also check the entered data for a particular period and organization unit(s) against the validation rules, and display the violations for these validation rules.

  • By analysing data sets, that is, examine gaps in the data.

  • By data triangulation, that is, comparing the same data or indicator from different sources.