Data Reconciliation After Migration: Complete Validation in 10-30 Minutes
Achieve 99.99% validation accuracy with AI-powered reconciliation. Automated row-by-row comparison, aggregate validation, business rule verification, and instant discrepancy resolution for complete data confidence.
AI-Powered Reconciliation Benefits
4-Phase Reconciliation Process
Phase 1: Record Count Validation
2-5 minutesVerify total record counts match between source and target systems
- Table-Level CountsCompare total rows for each table
- Partition-Level CountsValidate counts by date, region, or other partitions
- Relationship CountsVerify foreign key relationship counts
- Deleted RecordsAccount for soft-deleted or archived records
-- Source System SELECT COUNT(*) as source_count FROM customers; -- Target System SELECT COUNT(*) as target_count FROM customers; -- AI validates: source_count = target_count
Phase 2: Aggregate Validation
3-8 minutesCompare aggregate metrics to detect data quality issues
- Sum ValidationCompare SUM of numeric columns (revenue, quantities, balances)
- Average ValidationVerify AVG values match for key metrics
- Min/Max ValidationCheck MIN and MAX values for data ranges
- Distinct Count ValidationVerify COUNT(DISTINCT) for unique values
-- Source System SELECT SUM(order_total) as total_revenue, AVG(order_total) as avg_order, COUNT(DISTINCT customer_id) as unique_customers FROM orders; -- Target System (must match exactly) SELECT SUM(order_total) as total_revenue, AVG(order_total) as avg_order, COUNT(DISTINCT customer_id) as unique_customers FROM orders;
Phase 3: Row-by-Row Comparison
5-15 minutesField-level comparison of every record for 100% accuracy
- Checksum ComparisonGenerate MD5/SHA256 hash for each row, compare hashes
- Field-by-Field ComparisonCompare each column value for mismatches
- Data Type ValidationVerify data types match schema definitions
- NULL HandlingEnsure NULL values preserved correctly
Phase 4: Business Rule Validation
2-7 minutesVerify business logic and data relationships are intact
- Referential IntegrityAll foreign keys reference valid parent records
- Data ConstraintsCheck constraints, unique constraints validated
- Business LogicCustom rules (e.g., order total = sum of line items)
- Temporal ConsistencyDate sequences and timestamps logical
Automated Discrepancy Resolution
AI-Powered Fix Strategies
Comprehensive Reconciliation Report
Report Includes:
Traditional vs AI-Powered Reconciliation
| Factor | Traditional Manual | AI-Powered |
|---|---|---|
| Time to Complete | 2-4 weeks | 10-30 minutes |
| Coverage | 5-10% sample | 100% of records |
| Accuracy | 90-95% | 99.99% |
| Cost | $50K-200K | $5K-20K (90% savings) |
| Discrepancy Detection | Manual review | Automated with root cause |
| Resolution | Manual fixes | 95% auto-resolved |
| Documentation | Manual reports | Automated comprehensive reports |
People Also Ask
How long does data reconciliation take after migration?
AI-powered reconciliation completes in 10-30 minutes for most databases, validating 100% of records with 99.99% accuracy. Traditional manual reconciliation takes 2-4 weeks and only validates 5-10% samples. The AI approach provides complete validation in a fraction of the time with higher accuracy.
What is the difference between validation and reconciliation?
Validation checks if data meets quality rules (format, type, constraints), while reconciliation compares source and target data to ensure they match exactly. Reconciliation includes validation plus row-by-row comparison, aggregate checks, and business rule verification. Both are essential for migration success.
How do you reconcile large databases quickly?
AI-powered reconciliation uses parallel processing to validate 1M-10M records per minute, checksum comparison for fast row matching, intelligent sampling for massive datasets, and automated discrepancy categorization. This achieves 100% coverage in 10-30 minutes vs 2-4 weeks for manual approaches.
What happens if reconciliation finds discrepancies?
AI automatically categorizes discrepancies by type (missing records, field mismatches, type issues, referential integrity), identifies root causes, and applies intelligent fixes with 95% success rate. Missing records are re-migrated, field mismatches corrected based on transformation rules, and detailed reports generated for manual review of complex cases.
Is 100% data reconciliation necessary?
Yes, 100% reconciliation is critical for production systems, especially for financial, healthcare, and compliance-regulated data. Sample-based validation (5-10%) misses critical errors that can cause business disruption, compliance violations, and financial losses. AI makes 100% reconciliation practical and affordable in 10-30 minutes.