PayAnalytics - June 2026 feature release notes
This article lists the updates made available to users in the PayAnalytics application in June 2026.
Local privacy thresholds for EU Pay Transparency reporting
When sharing the category of worker average pay data, small groups may risk disclosing individual pay, creating a tension between pay transparency and data privacy. The EUPTD does not mandate a hard, universal minimum group size for internal disclosures; it leaves the specific threshold to individual Member State transposition. Organizations therefore need the ability to set a threshold that reflects their local legal requirements.
Admins who are configuring system-wide privacy settings as well as global/local Rewards users who generate EUPTD reports can now configure the privacy threshold that controls when category-of-worker average pay figures are shown or masked in Pay Transparency reports. When a group falls below the configured threshold, averages are suppressed to protect individual privacy. This setting is configurable per tenant and replaces the previous hardcoded default.
To use the feature, proceed as follows:
Go to Settings > System Parameters > Privacy threshold configuration.
Set the minimum group size threshold that reflects your organization's local legal requirements under your Member State's transposition of the EUPTD.
Within dataset configuration ensure, to select the Country value, to ensure the employees within the dataset are mapped to the configured privacy thresholds.
Save your changes. The threshold will apply to all subsequent Pay Transparency report generation. Groups below this size will have average pay figures masked.
Threshold configuration in PayAnalytics
Global dataset partition for local data management
Organizations that manage data imports centrally at group level but need local business units or countries to work with their own datasets (in order to run analyses and generate reports independently), previously had to split data manually or maintain separate upload processes.
Admins and global/local Rewards users who manage multi-entity or multi-country datasets can now, when duplicating a dataset, choose to partition (split) it into smaller segments based on a field such as country or business unit. Each partition becomes its own dataset with restricted, label-based access and make sure that local teams only see their relevant data. This is available both through the platform UI and via the dataset clone API for automated pipelines.
To use the feature, proceed as follows:
Go to Employee Datasets and select the dataset you want to split.
Open Dataset configuration and click Duplicate dataset.
Select Break into partitions by field and choose the field to split on (e.g. Country).
Review the partitions that will be created and confirm. Each partition will become a separate dataset with label-based access restrictions applied.
Dataset partitioning
Standardized estimated adjusted pay gap calculation method
The estimated adjusted pay gap is the figure PayAnalytics reports for the pay gap that remains once legitimate, explainable factors have been accounted for. We have updated the method behind it, moving from percentage residuals to log residuals, which gives a more statistically consistent basis for the adjusted gap and keeps it aligned with the way raise suggestions are calculated.
For each employee, PayAnalytics compares actual pay to the pay that the regression model predicts. Previously, the application was returning an outlier expressed as a percentage above or below the predicted pay. The percentage residual method built the adjusted gap directly from those percentage outliers. The log residual method first places each outlier on a logarithmic scale (the log residual is the outlier expressed logarithmically). It then averages those values within each group, and converts the difference between groups back into a percentage. The log residual method lets individual differences be averaged cleanly and expressed as a single percentage gap. It treats an increase and an equally sized decrease symmetrically, and reduces the pull of extreme salaries. The result is an adjusted gap that is steadier and more comparable from one analysis to the next.
The change now applies across the platform. Every analysis now displays the estimated adjusted pay gap using the log residual method, including analyses that were originally run under the previous method, so HR analysts and Compensation teams compare like with like, rather than a mix of two calculations. Raise suggestions are computed on the same basis, so the adjustments PayAnalytics proposes match the gap it reports.
We introduced the new method first as an opt-in setting for organizations that wanted it early, and it is now the standard calculation for everyone.
No migration steps are required; existing and new analyses will automatically display estimated adjusted pay gap figures using the log-residual method.
Multi-currency selection in the Compensation Assistant
Multi-currency selection in the Compensation Assistant result page allows users to reflect the compensation range and suggestions in their local currency or the local currency of the (prospective) employee/ role.
Targeted at global/local rewards users and/or local HRBPs/managers, this feature introduces a currency drop-down to the Compensation Assistant result page, so users can switch the display currency for suggested compensation and ranges without re-running the assistant, keeping consistent with currency switching elsewhere in the platform.
To use the feature, make sure that you have imported exchange rates, and that the underlying dataset is marked as multi-currency and the currency value is selected during configuration. When this is done, you can select the relevant analysis and run the compensation assistant. You will then see the currency selection drop-down at the top of the page, as illustrated in the following figure:
Currency selection in the Compensation Assistant