Changelog

Identified and referenced deployments will now include an enrichment precision column. This is broken down into individual-level and residence-level.

• Individual enrichment precision means that Faraday is able to assign person-level traits to an identity, like age, consumer habits, or marital status.
• Residence enrichment precision means that Faraday is able to assign address- or property-level traits to an identity, like home value or number of bedrooms.
• Region enrichment precision means that Faraday is able to assign postcode-level traits to an identity, like average commute time or average property tax.

The Faraday Identity Graph received a significant update, with over 800 brand new traits and updates to hundreds of existing traits. These new and updates traits are available by default for users when building predictive models–no additional action is required. Check out our blog for more info.

Faraday now allows users to export the traits available to their accounts as a CSV. When cross-referenced with FIG, this export creates an up-to-date data dictionary, providing an overview of trait metadata such as category, coverage, and class.

View our documentation to learn more.

The events index in the Dashboard UI now displays the number of events in each event stream, when the most recent event was added, and the status of the event stream (e.g. ready, error). Additionally, each event stream now includes a section for analysis that shows the event stream's change over time.

Faraday's Lookup API now supports the use of identity sets, which enable you to include multiple identifiers in a single request, rather than having to create a separate request for each identifier. If the root identifier isn't found, Faraday searches for a match through each subsequent identifier. For more info, check out our Lookup docs.

Users can now automatically declare all columns in a Dataset as Traits, eliminating the need to manually label each one. This is achieved using the output_all_columns_as_traits property, which includes options to fine-tune which columns are included or excluded.

View our documentation to learn more.

Added three new date columns to resource index views. Created date is the date that the resource was created. Modified refers to the date that the resource’s configuration was last modified (e.g. a trait was added to a cohort’s definition). Lastly, processed is the last time the output was processed by Faraday (i.e. the output changed). Additionally, columns can now be sorted by ascending or descending values.

Added the ability to archive resources via the options menu on any resource. Attempting to archive a resource with downstream dependencies will result in a message stating that archiving this resource will also archive its dependencies. Resources that have been archived can be viewed via the archived filter on the list view of that resource type, and they can be unarchived via the same options menu.

Added a resource dependency visualization to each resource, which reveals the overall layout of resources in your Faraday account, and highlights which individual resources are connected to the one you’re currently viewing. This visualization can be helpful for understanding the impact that editing, deleting, or archiving a resource can have on your account.

Faraday now provides clients with explicit control over their models' use of 1st-party and 3rd-party data in their Pipelines. This allows clients to compare scores based exclusively on 1st-party data to those with a combined data set, featuring both 1st and 3rd-party data.

This update ensures more transparency and flexibility in data management, reducing manual intervention and increasing trust in the results.