Multi-Layer Validation Framework
). Each layer catches different types of errors, and skipping any layer leaves blind spots that surface as production defects.
- Layer 1 - Count validation: source record counts match target counts per data object with zero unexplained variance
- Layer 2 - Field validation: hash-based comparison of key fields between source and target for 100% of migrated records
- Layer 3 - Referential integrity: verify all foreign key relationships survived migration (e.g., every order has a valid customer)
- Layer 4 - Business rules: apply domain-specific validations (GL balances sum to zero, inventory quantities are non-negative)
- Layer 5 - Cross-system: validate data consistency between ERP and integrated systems (CRM, WMS, banking portals)
Automated Reconciliation Techniques
Manual data validation does not scale. For migrations involving millions of records across dozens of data objects, automated reconciliation is essential. SQL-based comparison scripts, ETL tool validation features, and purpose-built data testing tools like QuerySurge, iCEDQ, or custom Python scripts using pandas enable systematic comparison at the speed and scale required.
- SQL reconciliation scripts: write automated queries that compare source and target aggregates, counts, and checksums
- Hash-based matching: generate MD5 or SHA-256 hashes of key field combinations for rapid record-level comparison
- Statistical sampling: for high-volume transactional data, validate a statistically significant random sample (95% confidence, 2% margin)
- Exception reporting: automated generation of discrepancy reports showing exactly which records and fields differ
- Regression suite: save all validation scripts as a reusable suite for each migration iteration and final production load
Business User Acceptance Testing for Data
Technical validation catches structural errors, but only business users can confirm that data makes sense in its operational context. Data UAT sessions pair subject matter experts with migration analysts to walk through key business scenarios using migrated data. Common discoveries include historical data that was technically correct but operationally misleading, or valid records that should have been excluded from migration scope.
- Scenario-based testing: business users execute 20-30 real transaction scenarios using only migrated data in the new system
- Report comparison: run key business reports in both legacy and new system, comparing outputs line by line for critical reports
- Exception review: business users review all records flagged by automated validation as potential issues for disposition decisions
- Sign-off protocol: formal sign-off per data domain from the designated data steward before migration is considered complete
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Load Shedding Downtime Loss Log Worksheet
An editable outage log that turns grid downtime and generator hours into lost units, lost contribution and diesel cost per shift.
Deploy AI-powered data validation agents that automatically reconcile migrated data—see Netray's validation platform.