Skip to main content

Data quality

Improve the consistency of your source data.

Written by Virginie Huaranca
Updated over 2 weeks ago

Introduction

Link to your Reflect account: https://app.getreflect.io

💡 This guide is intended for Reflect account administrators and the managers of source software.

It is possible that some data are missing in your source software (HRIS, ATS, Payroll). A nationality, a contract type, a date of birth, a reason for leaving, a salary...

Incomplete data become even more visible when implementing a tool like Reflect:

  • missing data (∅) in chart or table formats

  • empty cells in list formats

And they can sometimes affect the reliability of certain indicators.

That's why Reflect detects if certain data are incomplete so that you can correct them at the source, and this across your entire history!

How does it work?

Incomplete data

To view your incomplete data, go to the page completeness of data.

You will then see a list of dimensions of your incomplete data.

💡 You can choose the time range at the top right to only view incomplete values within that time range. This can be useful to avoid worrying about an overly old period.

To open the details of an incomplete dimension, expand the dimension by clicking on it.

You will then observe:

  • the list of employees who have incomplete data for the selected dimension (click "Open in the explorer" to display the full list)

To resolve this issue, it is not done in Reflect but directly in your source tool. Update the incomplete values at the source to improve the completeness of your data in the source software.

Updating

Once your data have been completed in your source software, the data are not updated in Reflect instantly.

You must wait for the next data update in Reflect for the mapping to be taken into account.

If new employees/candidates are added to your source software and some values are missing in certain fields of your source systems, Reflect will continue to warn you.

Reflect nevertheless advises you to apply particular rigor to the data you enter into your source systems.

Did this answer your question?