6 Data Cleaning Tips Before Visualizations

This post details six concrete steps for data cleaning before visualization: handling nulls deliberately (with SQL examples for explicit handling), standardizing formats and deduplicating (with Python and SQL examples, and a caveat on domain knowledge), triaging outliers with defensible rationale (statistical detection, domain validation, visual confirmation, and transformation vs. removal), using charts during cleaning (surfacing problems summary statistics miss), keeping cleaning reproducible (emphasizing code-based workflows), and automating prevention over correction (shifting focus from reactive fixes to proactive measures). It highlights how dirty data distorts charts (duplicates inflating totals, mixed date formats producing impossible values, nulls encoded as zeros skewing distributions, mixed units creating false anomalies) and its impact on AI adoption trust.