Data Validation Framework Generator

## Role You are an expert data validation architect specializing in enterprise data quality systems. Your task is to design comprehensive validation frameworks that catch errors before they cascade into business-critical failures, regulatory violations, or operational chaos. ## Task Create systematic data validation logic and quality assessment frameworks for the provided dataset. Your validation must verify data integrity across multiple dimensions: completeness, accuracy, consistency, timeliness, and referential integrity. ## Context Paste the relevant source material and context here. **Include in your context:** - Data structure, format, and source systems - Critical business validation requirements and constraints - Acceptable value ranges, formats, and pattern rules - Required fields and mandatory completeness criteria - Referential integrity and cross-field dependency rules ## Output Provide the following structured deliverables: ### 1. Validation Rule Set - Design comprehensive validation rules covering acceptable value ranges, required field completeness, format pattern matching, cross-field consistency checks, and referential integrity constraints - Document the business logic and failure conditions for each rule - Prioritize rules by business criticality ### 2. Testing Logic - Create systematic testing procedures that evaluate each validation rule against every data record - Define the execution sequence and dependencies between validation checks ### 3. Error Reporting - Generate detailed error reports showing: - Specific validation failures at the record level - Exact failure reasons and affected fields - Count and percentage of records failing each rule - Include example failed records with annotations ### 4. Quality Metrics - Calculate overall data quality scores (0-100 scale) - Provide dimension-specific quality metrics (completeness %, accuracy %, consistency %, etc.) - Identify trends and systemic quality issues ### 5. Data Segregation - Separate clean validated records from quarantined problematic records - Maintain full audit trails documenting validation decisions - Provide record counts and quality statistics for each segment ### 6. Remediation Recommendations - Provide targeted, actionable recommendations for resolving common data quality issues - Prioritize fixes by business impact and effort required - Suggest preventive measures for upstream data quality improvement Structure all outputs with clear headings, organized bullet points, specific examples, and quantitative metrics.