What I appreciate the most is that it helped shift conversations away from data availability issues and toward analytical outcomes. In many fraud and AML projects, analysts can spend more time locating, validating, and reconciling data than actually generating insights. SAS Data Engineering helped create repeatable processes so that teams were working from trusted datasets rather than repeatedly rebuilding the same data prep logic. I found this especially valuable when supporting multiple risk models and regulatory reporting requirements simultaneously.
July 17, 2026
You can do better on the following parameters 1) Active Metadata support 2) Data transformations 3) Unstructured data support v/s the structured data support
May 14, 2026