What is data quality for?
Data quality helps you identify missing information or certain unusual values that may affect your HR analyses.
💡 The data completeness and coherence pages are available to Reflect administrators. The checks depend on the modules and data available in your account.
Data completeness flags missing values for certain fields, such as date of birth. Data coherence flags certain unusual values to check; an alert does not necessarily mean the value is wrong.
Check data quality from an analysis
On a dashboard, hover over a card and click the health-cross icon. In the data query builder, the icon appears above the result. Administrators see the button when the analysis has relevant checks or fields. A red dot signals a check to review; an orange dot, a normalization to confirm.
Depending on the analysis, the Data Quality window can show completeness, coherence, normalization or mapping. Click Open to view the available details.
The completeness checks do not cover every field. A complete result applies only to the checked field, selected period and filters.
Identify incomplete data
Review the checks
In Reflect, open Data management → Data completeness.
Choose the period in the top right. To focus on a population, use the + button and select available filters, such as entity or department. If filters are incompatible with a check, remove them to view that check.
💡 Changing the period or filters targets your review; it does not correct source data or remove its effects from other analyses.
Open an “Incomplete values” row to see the number of people affected, a preview and the metrics that may be impacted.
View and export the list
Click Open in explorer to view the complete list. Check the period and filters, add useful columns such as entity or department, then click Export and XLSX (Excel) to download the list and share it with the person responsible for corrections.
Correct and check the result
Check the field in the source system. If it is empty, complete it there; for a file-based source, also correct the file sent to Reflect. Wait for the next source update or send the corrected file according to your import method.
See Data update for more information.
Return to the check with the same period and filters to verify the result after the update. If the field is filled in the source but remains empty in Reflect, check the source mapping or contact your Reflect team.
A field missing in the source and a source value that has not been normalized are different situations. If the value exists in the source but a category needed for your analyses is not set in Reflect, check Data normalization; do not change the source solely to correct its categorization in Reflect.