Fix Data Validation Errors During Migration
Automatically detect and fix data validation errors with AI-powered validation. 98% error resolution in 15-40 minutes vs 4-10 days manual work.
Common Validation Errors
AI-powered detection and resolution for all types of validation errors
NOT NULL Constraint Violation
97% SuccessColumn "email" cannot be null, but source has 1,247 null values
Required fields missing in source data
Migration fails, data rejected
AI infers missing values from related records, applies business rules, or generates default values based on data patterns
CHECK Constraint Violation
95% SuccessAge must be between 0-120, but source has values like -5, 999
Data outside allowed ranges or invalid values
Records rejected, data quality issues
AI detects outliers, corrects obvious errors (typos, extra digits), and flags ambiguous cases for review
UNIQUE Constraint Violation
99% SuccessEmail must be unique, but 342 duplicate emails found
Duplicate values in columns requiring uniqueness
Migration fails, duplicate data
AI deduplicates records, merges duplicates intelligently, or appends suffixes to maintain uniqueness while preserving data
FOREIGN KEY Constraint Violation
98% SuccessOrder references customer_id=12345 which does not exist
Orphaned records or missing parent records
Referential integrity broken, migration fails
AI identifies orphaned records, creates missing parent records, or establishes correct relationships through entity resolution
Data Type Mismatch
96% SuccessColumn expects INTEGER but source has "N/A", "Unknown"
Incompatible data types or invalid format
Type conversion fails, data loss
AI converts compatible types, maps special values to appropriate representations, and handles edge cases automatically
Length/Size Constraint Violation
94% SuccessVARCHAR(50) but source has 200-character values
Data exceeds target column size limits
Data truncation or rejection
AI intelligently truncates preserving meaning, abbreviates content, or recommends schema adjustments for critical data
Format Validation Failure
98% SuccessEmail format invalid: "john.smith@", phone has letters
Data does not match expected format patterns
Validation fails, data quality issues
AI corrects common format errors, standardizes formats (phone, email, dates), and validates against regex patterns
Business Rule Violation
92% SuccessOrder date is after ship date, negative prices
Data violates domain-specific business logic
Logical inconsistencies, data integrity issues
AI learns business rules from data patterns, detects logical inconsistencies, and applies corrections based on domain knowledge
4-Phase Automated Fix Process
Complete validation error detection and resolution in 15-40 minutes
Phase 1: Detection
5-12 minutes
- Scan all source data against target schema
- Identify all constraint violations
- Classify error types and severity
- Generate comprehensive error report
Phase 2: Analysis
4-10 minutes
- Analyze error patterns and root causes
- Determine fixable vs. unfixable errors
- Prioritize errors by impact and frequency
- Generate fix recommendations
Phase 3: Resolution
4-12 minutes
- Apply automated fixes for common errors
- Infer missing values from context
- Correct format and type issues
- Flag ambiguous cases for review
Phase 4: Verification
2-6 minutes
- Re-validate all fixed records
- Verify constraint compliance
- Generate validation report
- Document all corrections made
Validation Strategies
Comprehensive validation at every stage of migration
| Strategy | Checks | Coverage | Timing |
|---|---|---|---|
| Schema Validation | Data types, nullability, constraints | 100% | Pre-migration |
| Constraint Validation | NOT NULL, UNIQUE, CHECK, FK | 100% | During migration |
| Format Validation | Email, phone, date, regex patterns | 100% | During migration |
| Business Rule Validation | Domain-specific logic | 95% | Post-validation |
| Referential Integrity | Foreign key relationships | 100% | Post-migration |
| Data Quality Checks | Completeness, accuracy, consistency | 100% | Continuous |
People Also Ask
What causes data validation errors during migration?
Validation errors occur when source data does not meet target schema requirements: missing required values (NOT NULL violations), duplicate values in unique columns, data outside allowed ranges (CHECK constraints), orphaned records (foreign key violations), incompatible data types, values exceeding size limits, invalid formats (email, phone), and business rule violations. DataMigration.AI detects all error types with 100% coverage and resolves 98% automatically.
How does AI fix validation errors automatically?
AI uses multiple techniques: infers missing values from related records and data patterns, corrects format errors (standardizes dates, phones, emails), deduplicates records intelligently, creates missing parent records for orphaned data, converts incompatible types safely, truncates oversized values preserving meaning, and applies business rules learned from data. The AI achieves 98% automated resolution with full audit trail of all corrections.
Can validation errors be fixed mid-migration?
Yes. DataMigration.AI performs real-time validation during migration, detecting and fixing errors before data reaches the target. The AI validates each record against schema constraints, applies automated fixes immediately, and only migrates validated data. This prevents invalid data from entering the target system and eliminates post-migration cleanup. Errors are resolved in 15-40 minutes for typical datasets.
What happens to unfixable validation errors?
For the 2% of errors that cannot be automatically resolved (ambiguous cases requiring business decisions), DataMigration.AI flags them for human review with detailed context: the specific error, affected records, suggested fixes, and business impact. The AI prioritizes errors by severity and frequency, provides fix recommendations, and allows batch approval of similar cases. All decisions are logged for audit purposes.
How long does validation error fixing take?
DataMigration.AI completes validation error detection and resolution in 15-40 minutes for typical datasets, compared to 4-10 days for manual fixing. The 4-phase process includes detection (5-12 min), analysis (4-10 min), resolution (4-12 min), and verification (2-6 min). Speed depends on dataset size, error complexity, and fix strategies used. 100x faster than manual approaches with 98% automated resolution.
Ready to Fix Validation Errors?
Get 98% automated error resolution in 15-40 minutes with AI-powered validation.