Power Query Patterns for Messy Business Data

Messy source files are one of the biggest reasons analytics pipelines fail. A stable transformation layer in Power Query protects reporting from recurring source variation.

Stabilize Schema First

Validate column presence, data types, and naming patterns before doing any business logic. If the schema is unstable, every downstream calculation becomes fragile.

Build Reusable Cleanup Steps

Standardize trimming, null replacement, date parsing, and duplicate handling into reusable step sequences. This reduces maintenance and makes refresh failures easier to debug.

Design for Auditability

Keep transformation logic readable and grouped by intent so future changes are safe. Clean code in Power Query matters as much as clean code in application development.