Three Quick Checks Every Data Analyst Should Run Before Trusting a Dataset

A data practitioner has outlined a five-minute validation routine to catch hidden data quality issues before any analysis begins. The first step involves scanning for non-standard null representations—such as dashes, blank strings, or placeholder numbers like 9999—that standard null checks would miss entirely. Second, examining value distributions rather than relying on summary statistics like mean or median can reveal skewed or placeholder-heavy columns that aggregates obscure. Third, timestamp columns should be scrutinized for timezone inconsistencies, ingestion gaps, and logical contradictions such as creation dates appearing after update dates. The author argues that most costly analytical errors stem not from flawed models but from misunderstood input data, making early sanity checks far cheaper than correcting downstream mistakes.