Previously, when connecting your data to Faraday, individuals we didn’t recognize (those without a match in the Faraday Identity Graph, or FIG) were simply dropped from your data. Now, data about these unrecognized identities are ingested and stored normally. You can now review how your data is being enriched with FIG traits via the new enrichment column in Datasets. The higher the fill rate, the more identities we were able to enrich with our built-in consumer data.


These changes may result in larger cohort counts due to Faraday being better able to handle partially-matched identities. On the other hand, your pipelines that target the everyone cohort as a population to include may be smaller due to identity resolution improvements, where previously individuals may have been incorrectly counted twice. Overall, these changes should increase prediction accuracy.