Back to blog

Tips

How to spot data quality issues in seconds

Quick visual checks that reveal problems before they cause harm.

Dec 20, 20244 min read
Bad data looks different from good data. Train your eye to spot the patterns and you'll catch issues fast.

Sort and scan extremes

Sort each key column. The top and bottom values reveal outliers, typos, and impossible values that hide in the middle.

  • Sort ascending to see minimums
  • Sort descending to see maximums
  • Outliers cluster at extremes

Look for blanks

Empty cells often indicate problems. Sort or filter to surface them. Some blank patterns are expected; unexpected ones need investigation.

  • Sort to push blanks to top or bottom
  • Check if blanks are random or patterned
  • Verify required fields aren't empty

Quick CTA

Sort and search instantly

Readable CSV makes data quality checks fast with instant sorting and search.

Try it

Check for consistency

Scan text columns for variations. 'California', 'CA', and 'Calif' are probably the same place but will group differently.

  • Sort text columns to cluster similar values
  • Look for variant spellings
  • Check case consistency

Verify counts

Compare your row count to the expected number. Missing or extra rows indicate problems that need investigation.

  • Row count should match source
  • Significant differences need explanation
  • Header rows can throw off counts

Key takeaway

A two-minute visual scan catches most data quality issues. Make it a habit before any analysis.