Levels

Maturity Level 1¶

Data quality business rules (conditions) have been defined, documented and, verified by subject matter experts (SMEs) (process for evaluation and acceptance defined)
Business rules are aligned with in-scope use cases and specific user stories
Business rules are standardized and registered into a repository with a defined mechanism for logging additions and performing updates

A defined architecture exists to translate business rules into machine-executable code (some rules will be OWL expressions, some will be SHACL shapes and SHACL constraints, some will be translated into workflow logic)
Business provenance and lineage are traceable across the data supply chain and evaluated against defined business rules (all business rules must be traceable and understandable in context---must understand the purpose and importance of the rule)
Business rules for in-scope use cases are implemented in the EKG

Metrics---The measurement criteria are defined for data quality business rules (which rules are executed, how often, improvement)
Performance---The value of business rules are related to business concepts (products, financial performance, organizational objectives)---able to trace the core relationship between the business objectives and the data quality business rules (correlation between rules and outcomes are known, able to be queried and traceable within the EKG)

Business rules are combined with AI capability for compliance (dynamic optimization of business rules)
Model-driven (senior management can begin to optimize business objectives using business rules in the EKG---i.e. alignment of business rules with “what if” scenarios)

All business rules are driven by business objectives (objectives are in the EKG with appropriate scorecards)