regeldaten

Regeldaten are structured data that define rules, regulations, or decision logic used by organizations and software systems. They encode when certain conditions apply and what actions should follow, enabling automated decision making, compliance monitoring, and governance processes.

Key components of regeldaten include a unique rule identifier, the conditions or premises (if-then logic), the consequent actions, and metadata such as author, version, validity period, and provenance. Relationships between rules, such as priorities, dependencies, and exceptions, are also modeled to support coherent behavior and conflict resolution. Regeldaten are often maintained in rule engines, decision tables, or rule-based ontologies, and may be expressed in formats such as BRMS configurations, RuleML, LegalRuleML, or JSON/YAML configurations.

In practice, regeldaten serve various domains. Regulatory compliance and reporting in finance, environmental and data protection areas rely on up-to-date rule sets. Tax calculations, insurance underwriting, healthcare clinical decision support, and supply chain governance are other common use cases. Regeldaten interfaces with master data, policy documents, and regulatory sources, requiring mappings, data quality checks, and traceability for audits.

Challenges include keeping rules current with evolving laws, localization and multilingual terminology, handling rule conflicts, performance considerations, and ensuring explainability of automated decisions. Good governance practices—versioning, audit trails, change management, and provenance tracking—are essential to maintain reliability and traceability of regeldaten over time. In industry contexts, regeldaten are a key element of regulatory technology (RegTech) and business rules management systems.