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

Related pages:

Connect external data sources (HRIS, ATS, etc.)
Data Health 101

Overview

Each People Analytics metric has it’s own Data Health. Data Health indicator shows right next to the metric’s title in the header.

It can be:

Good health

This means the metric health is between 81% and 100%. Good data represents all the accurate, complete, and reliable employee data records coming from your HRIS or ATS systems.

Medium health

This means the metric health is between 61% and 80%. It indicates some data for this metric needs to be improved.

Low health

This means the metric health is between 1% and 60%. It indicates a larger number of data points need to be improved.

Missing

This means the metric health is 0%, or that all of data points used for calculating the metric are unhealthy.

How to access the Data Health details?

On the Metric Details page, click on the Health indicator in the metric’s header to open details.

If your data health is Good and 100% healthy

If the top of the Data Health page reports that your data is 100% healthy, it means that your HRIS and ATS contain all the data People Analytics can use, and that there’s not even a single data point for a single employee that’s missing.

If that’s the case, you can move on without looking at the remainder of the page.

If your data health is less than 100% healthy

Data Health tab 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
- 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

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