Numbers Don’t Lie—Until Dates Do: Rebuilding Accuracy in Financial Data
Global Loans, Broken Dates & Hidden Risks: Mastering SAS Data Cleaning for Reliable Analytics 1. Introduction – When Data Lies, Decisions Collapse Imagine you’re working on a global financial dataset tracking loans issued by top countries. Everything looks fine until you discover that some loan dates are “2023-15-45” , others are “NULL” , and a few are even in future centuries . Now picture this in a clinical trial instead of finance. A patient’s treatment start date is recorded incorrectly. That single error can misalign treatment timelines, skew efficacy analysis, and potentially mislead regulatory submissions. This is the silent danger of bad data . In the real world, datasets are messy: Missing values creep in Dates get corrupted Duplicate records inflate metrics Text fields become inconsistent And the worst part? These issues don’t throw errors they quietly distort your insights. This is where SAS (especially in regulated domains like clini...