What is the Data Outlier Analysis tab in Data Quality Lab (DQL)?

This tab aims to help you identify potential data quality issues based on outlier data points.

The score is calculated based on the proportion of data points that are moderate (2-3 standard deviations from the mean) and extreme outliers (3+ standard deviations from the mean). The higher proportion of outliers, the worse the score will be.

Below this score explanation is a box plot which you can adjust using the geography filters, aggregation and time filters.

The box plot surfaces facilities based on the proportion of their data points that are outliers so you can quickly identify the most problematic facilities.

Clicking on a dot in the box plot will surface the reported values from that facility in a time series visualization for you to assess the outlier data points to see if there’s an issue.

You have the option to switch the box plot to a table visualization where the rows are facilities and columns are the % outliers - this way you can see all facilities and isolate specific ones of interest (vs. the box plot doing a good job bubbling up outliers but not allowing you to do a comprehensive or specific check)

*Note all data displayed in this manual is publicly available and is not confidential in any way or form

Updated on: 24/09/2025