January 2026 Release Notes

You can now use the PQL editor when creating and editing the following PQL-based Knowledge entities: Auto-generated attributes, calculated attributes, filters, and KPIs.

These improvements make working with PQL queries faster, clearer, and more powerful across the platform. They also bring the following benefits:

To learn more about creating Knowledge Models, see Knowledge Models.

And for an overview of the PQL editor, see PQL editor.

We've added a dedicated Data Labels panel that gives you flexible and granular control of data labels in charts. It also provides feature parity with the data label customization options available in Studio Analysis (maintenance mode only).

Previously, data labels could make charts in Views look cluttered and the data labels themselves could be difficult to read. With the flexibility and greater control introduced with the new Data Labels panel, you can now:

For detailed information about the settings available, see Chart data labels.

With this Private Preview release, you can now migrate bookmarked filters from Analyses to Views.

Previously, bookmarked filters used in Analyses had to be manually recreated from scratch in Views, resulting in additional work for users.

Admin and analyst users with edit permissions can now migrate bookmarked filters as required during the Analysis to View migration and select whether to migrate all bookmarked filters or shared bookmarked filters only.

Once migrated, the bookmarked filter type and owner are respected in the View and shared with the same users in the View as in the original Analysis.

For more information, see Migrating bookmarked filters to Views.

And for more information about our private preview releases, see Feature release types.

You can now define multiple dependency columns within Extractor Builder custom configurations, which enables more sophisticated data extractions from your source systems. By setting multiple columns in your dependency configuration, the Extractor Builder automatically generates placeholders for each that you can use in your endpoint configuration to reference its value. This allows you to inject multiple values into a single request for precise data retrieval tailored to more complex use cases.

Additionally, this enhancement provides the architectural flexibility required from advanced source systems and APIs that require multi-layered requests. By being able to handle multiple dependency values, platforms that require composite keys or multiple filtering parameters to access data can also now be supported. This helps ensure a more robust and scalable integration process that keeps pace with your data landscape.

For more information on custom Extractor Building configurations, see Extractor Builder.

You can now migrate your legacy Views using the new migration wizard. In addition to migrating individual View components, you can choose between a fast-track migration or a custom migration:

Legacy Views are views created using the pre–May 2024 Studio feature set (previously known as the general availability feature set). While you can still use Legacy Views in Studio, they're no longer being developed or enhanced. We therefore recommend migrating your Legacy Views to the current Views feature to take advantage of the latest updates and improvements.

To learn how to migrate your legacy Views to Views, see Migrating Legacy Views to Views.

And to learn more about our private and public preview releases, see Feature release types.

We've made some changes to filters in Studio Views to help simplify and enhance their use.

Built-in (attribute and process) filters are now available in edit mode in addition to view and interactive modes. This lets you create and apply complex filters without writing PQL statements or switching between Studio modes. For more information, see Filtering Studio Views.

You can also now view the underlying PQL statements used to implement View filters in Edit mode. Copying and editing these PQL statements means you don't need to create your own filters from scratch. You can also save your filters to the Knowledge Model as global filters. To find out more, see Viewing and reusing filter PQL statements.

We've also made attribute filters more user friendly. You can select which attributes can be selected by business users, helping ensure only relevant options and data are displayed. For more information, see Attribute filters.

We've also improved syncing between process filters and View components. You'll get a notification if an event log is:

You can then review and edit your process filter settings if required. For more information, see Process filters.

We've also aligned container components with other components so they can be now excluded from filtering. For more information, see Containers / Conditional layouts.