🚇 On this page:

Learn how to understand quality and integrity of your data and ways to improve it.

Related pages:

Overview

The Data health page will show you clean, incomplete, ambiguous or missing employee data records as well as ways to fix those issues.

Frequent visits to this page should be a priority because People Analytics heavily relies on data from your HRIS and ATS to construct its various metrics, insights and filters.

Hence, to make your People Analytics metrics reliable and complete, you must first ensure that the data it collects is reliable and complete.

How to access the Data Health page?

You can access the Data Health page through the link in the global navigation bar on the top of every page.

What’s “good”, “ambiguous” and “missing data”?

Before we get into the details, let’s quickly get familiar with our data terminology.

Good data

Good data represents all the accurate, complete, and reliable employee data records coming from your HRIS or ATS systems.

Ambiguous data

Ambiguous data represents all the data records coming from your HRIS or ATS systems that are either of questionable reliability or not quite applicable for the specific data attribute.

Here are a few examples:

✨ Example 1:

An employee named Mark Johnson has an entry for their age, but the entry shows that they are 14 years old. This could indicate an error in data entry.

✨ Example 2:

Emily Johnson has an entry for her salary, but it indicates her salary is negative in its value.

✨ Example 3:

Emily Johnson has a couple of entries related to their job changes. However, all these entries have the same date of change, which indicates these were filled out in a batch fashion. Even though Emily has data on job changes, all these entries have the same date, which makes it hard to arrange her job changes by date.

Missing data

Missing data represents the incomplete or missing employee data records coming from your HRIS or ATS systems. You should work towards collecting and completing this data.

On the road to 100% healthy data

Data Health page consists of two key sections:

Overview
Employee table

Overview

In the overview section of Data Health page you’ll find

Filter controls the data displayed on the page, whether it pertains to the scope of the data or the timeframe.
Your overall data health score which shows the sum of your good, ambiguous and missing data
Breakdown of data attributes sorted by best (most of good data) to worst (least good data present) by default
- Data attributes could also be sorted by “worst to best”, “by the impact score” and “by the metrics affected”
- On the right hand side you’ll find two columns “Impact score” and “Metrics affected”
  - Impact score measures by which percentage a certain data attribute is affecting your overall data. It takes into account both the number of metrics a certain attribute affects, as well as the number of employees who have incomplete or missing data for that attribute. This score provides a measure of the attribute's significance and potential data quality issues.
  - "Metrics affects” refers to the number of metrics impacted by a specific data attribute. Alternatively, it can be interpreted as the frequency with which a particular data attribute is utilized in calculations across all available metrics."
- Clicking a certain data attribute will filter out the employee table below

What are data attributes?

Data attributes are standard bits of information you are already tracking in your ATS and HRIS. Those are the fields like employment status, employee’s age, employee’s ethnicity, etc.

To assemble data correctly and present it as a metric, we map all the data attributes and define their functionalities. In that way, we can know which elements of data are contributing to which precise metrics. Moreover, we can understand if specific metrics do not show proper values due to inadequate or missing input data.

Employee table

Employee table contains only the employees with unhealthy data. The employees with 100% healthy data won’t be displayed here. The purpose of this table is to show you which employee or group of employees you should focus first.

You can search for a certain employee using the search field on filter out by a data attribute from above.

Each row contains

Employee name
Columns such as role, division and location (this may vary depending on your data)
Missing data column – this column sums up all the data attributes missing for a certain employee
- Number next to data attribute title represents how many times is this attribute missing for this certain employee

💬 Need help? Just reply with "Ask a Person" in a Support Conversation to speak with a Product Support Specialist.

Using the Data Health tools in Retention Insights