A Practical Approach to Data Quality in Commerce

Bad data is a quiet tax on every commerce project. It shows up as wrong prices, failed orders and frustrated buyers.

Measure what matters

Pick a few indicators, such as the share of products with complete attributes, orders that fail validation and addresses rejected by carriers. Track them over time and review them in the same meeting as sales numbers.

Fix at the source

Patching data downstream hides the problem and guarantees it returns. Feed issues back to the owning team with examples, and automate the checks that catch the same mistake twice.

Key takeaways

• Track a small set of data quality metrics

• Report issues to the system that owns the data

• Automate recurring checks