Poor-quality data drains $12.9 million a year from the average company, according to Gartner. When errors slip through, they slow decisions, raise compliance risk and erode customer trust. Continuous, rule-based checks catch issues early, keep scores trending upward and let every team work with numbers they can defend.

Most tools fix data after it breaks. Our approach is different: rules run at the very start of the pipeline, scores track over time, and alerts fire before bad records reach reports. You stop chasing errors and start preventing them.

The module follows a clear, four-step process to ensure data integrity:

1. Direct from the source: Data arrives exactly as it is—whether from a database, a cloud app or a flat file.
2. Early-stage testing: Freshly-landed records are checked in the Raw Vault, before any business logic can hide an error.
3. Actionable insights: Test results flow into an Information Mart and standard reports, so teams see the health of their data at a glance.
4. Continuous improvement: A feedback loop turns every finding into action: users fix issues, enforce new rules or launch automated corrections, raising the score release after release.