data-governance-and-quality

Data quality validation enforces required-field checks, format and type verification, and range constraints during data ingestion. Define validation rules in your configuration to automatically reject or flag records that fail to meet your data dictionary standards, ensuring consistent field integrity across vehicle insurance datasets.

Field mappings and data dictionaries are managed through structured definitions—typically documented in FIELD_MAPPING.md—that specify required fields, their types, valid ranges, and transformations. This centralized approach enables consistent field naming, derived attribute computation, and audit traceability across your data pipeline.

Establish governance by defining your data dictionary with core field definitions, creating validation rules for required fields and business constraints, then applying audit mechanisms to track data lineage and changes. This ensures reliable daily reports and enforces consistent data quality standards across ingestion, cleaning, and aggregation steps.

Audit mechanisms track data lineage and record changes across ingestion, cleaning, and mapping stages. These generate reports showing which records were modified, validated, or rejected, providing the data provenance and compliance visibility needed for insurance operations.

Apply format and type checks, required-field validation, range constraints on numeric fields, and business rule enforcement during cleaning. Derived field computation validates calculated attributes, while field-level mappings ensure consistent transformations—all configurable via validation rules tied to your data dictionary.

No prior expertise required. Start by defining core fields in your data dictionary, then enable validation rules for required fields during ingestion. The Skill handles validation, mapping, and audit generation automatically, making governance accessible for teams new to structured data quality practices.