SQL Data Cleaning: Techniques Every Analyst Should Master

Why Clean Data Matters in SQL

Dirty data wastes time, misleads decisions, and breaks systems. Cleaning it is not optional.

Here’s why clean data matters:

• Accuracy
  Reliable data powers better decisions. Inaccurate data leads to wrong conclusions.
• Efficiency
  Clean data means faster queries, less processing, and quicker analysis.
• Compliance
  Many sectors require clean, standardized, and validated data for audits and legal reasons.

Top SQL Data Cleaning Techniques

1. Handle NULLs and Zeroes
   • Use IS NULL, COALESCE(), or CASE to detect and fix missing values.
2. Remove Duplicates
   • Use DISTINCT to eliminate simple repeats.
   • Use ROW_NUMBER() with PARTITION BY to delete duplicates based on specific columns.
3. Standardize Formats
   • Normalize text with LOWER(), UPPER(), TRIM()
   • Convert inconsistent date/time formats using CAST() or CONVERT().
4. Detect Outliers
   • Apply rules or use statistics to find values that don’t fit expected ranges.
5. Validate Integrity
   • Enforce NOT NULL, CHECK, and foreign keys to ensure structure and logic.

Best Practices

• Always backup your database before cleaning.
• Use transactions so you can safely rollback changes.
• Create or use indexes to speed up data processing.
• Keep detailed documentation of every change.

Are you applying these techniques in your projects?
What’s the most common issue you face with dirty data?