Garbage In, Garbage Out

— Janine Herman

Or in other words – Why Clean Data Matters

Messy data is like a bad recipe, wrong ingredients can lead to disaster. Dirty data can cause:

Wrong Insights – A small mistake can turn €100 into €10,000.
Wasted Time – Fixing errors repeatedly instead of automating them.
Bad Decisions – Faulty data leads to poor business choices.

Common Data Mess-Ups & Fixes

• Typos & Inconsistencies – “New York” vs. “NY” vs. “new york” → Fix with UPPER(), TRIM().
• Duplicates – John Doe appears twice, counted as one customer → Use DISTINCT (SQL) or “Remove Duplicates” (Excel).
• Missing Data – Some orders have no delivery date, breaking time-based reports → Fill gaps with COALESCE(), Power Query “Fill Down”.
• Date Format Chaos – 01/05/2024 (Jan 5) vs. 05/01/2024 (May 1) → Convert to YYYY-MM-DD (ISO standard).
• Hidden Spaces & Characters – “Apple ” ≠ “Apple” → Clean with TRIM(), CLEAN().

Golden Rule: Validate Before Analyzing

Quick checks can save hours of fixing reports later so don’t skip that step.