Define data quality engineering

Trigger: A data governance initiative, new data source integration, or systemic data error triggers the need to formalize data quality standards.

1. Identify critical data elements and sources across enterprise and vehicle systems
2. Establish data quality dimensions including accuracy, completeness, and timeliness
3. Define validation rules, cleansing logic, and error handling procedures
4. Design automated data quality checks and pipeline architectures
5. Document data quality engineering standards and protocols
6. Review and approve specifications for engineering handoff

Outcome: Standardized data validation rules, cleansing logic, and engineering pipeline architectures are fully defined and ready for implementation.