458.Data Cleaning Secrets Using Famous Food Dataset:Handling Duplicate Records in SAS
Global Plate Chaos to Clean Insights: Mastering Duplicate Handling in SAS with Real-World Food Data 1. Introduction Imagine you’re working as a clinical data analyst in a pharmaceutical company. You receive a patient dataset for a Phase III trial. Everything looks fine until your summary statistics show 120 patients enrolled, while the actual study had only 100. Panic. What went wrong? Duplicate records. Now shift that same scenario to a business dataset say, global food analytics. You’re analyzing “most popular foods worldwide” to guide a multinational restaurant expansion strategy. But duplicate rows inflate demand for Pizza in Italy or Sushi in Japan. Suddenly, your insights are misleading, and millions of dollars are at stake. Bad data doesn’t just “look messy” it destroys decision-making . This is where tools like SAS and R come in. They don’t just clean data they enforce discipline, reproducibility, and regulatory compliance (especially critical in clinical trials...