The Perfect Data Rule Library provides a curated starting point for data quality monitoring across industries, regulatory regimes, and reporting domains—so teams don’t have to start from a blank page.
Most data quality programs stall because teams must invent rules, definitions, and expectations from scratch. The Rule Library accelerates adoption by capturing common patterns seen across real-world programs.
The library is organized around common data quality failure modes rather than one-off checks. This makes it easier to apply rules across different systems and architectures.
The Rule Library reflects monitoring needs seen across regulated and data-intensive industries.
The library is designed to support different stages of program maturity.
Teams select a subset of relevant rules to quickly establish baseline monitoring on critical reports and datasets.
Rules are customized to reflect local definitions, thresholds, data models, and operational tolerance for defects.
As programs mature, teams extend the library with organization-specific rules while maintaining consistent patterns and naming.
We’ll walk through relevant industry patterns, recommend a starter set, and show how teams adapt rules to their environment.