Digital Doppelgängers, Missing Values & Validation Nightmares: Engineering Production-Ready Lookalike Analytics Using SAS DATA Step, PROC SQL and R
Global Lookalike Persons Dataset into Analysis-Ready Enterprise Analytics Using SAS (PROC SQL vs DATA Step) and Modern R Pipelines Introduction In enterprise analytics, bad data is never “just a small issue.” A single corrupted value can quietly destroy an entire reporting ecosystem. Imagine a global intelligence organization studying “lookalike persons” individuals across countries who resemble celebrities, politicians, athletes, or public figures. Now imagine duplicate identities entering the system, impossible ages being assigned, malformed timestamps corrupting event histories, and inconsistent region codes confusing downstream dashboards. Suddenly, AI facial-recognition systems begin matching the wrong people. Fraud-detection algorithms fail. Clinical enrollment systems incorrectly identify duplicate patients. Executive dashboards show misleading counts. Regulatory submissions fail QC validation. This is not theoretical. As experienced Clinical SAS Programmers and D...