Data Quality Testing: Catching Bad Data Before It Reaches Your Dashboard

A dashboard built on bad data is worse than no dashboard at all — it gives people false confidence to make wrong decisions. Data quality testing is your safety net.

Types of Data Tests

Schema tests verify that columns exist and have the right types. Uniqueness tests ensure primary keys don’t have duplicates. Not-null tests catch missing values in critical fields. Referential integrity tests confirm that foreign keys point to valid records. Range tests flag values outside expected bounds.

Where to Test

Test at every stage: ingestion (did the source deliver what we expected?), transformation (did our logic produce correct results?), and serving (is the final dataset complete and fresh?). Early detection prevents cascading failures.

Tools for Data Testing

dbt has built-in testing capabilities for warehouse transformations. Great Expectations provides a comprehensive framework for validating data at any stage. Soda offers a simple SQL-based approach that non-engineers can also use.

The goal isn’t to catch every possible anomaly — it’s to catch the ones that would cause bad business decisions. Start with tests on your most critical metrics and expand from there.