Data Migration Validation
Data Migration Validation
Data Migration Validation
Validate 95%+ of migrated data automatically
In commercial and digital banking transformations, migrating data from legacy systems to modern platforms is not just a technical step—it’s a business-critical milestone.
GLF’s automated validation methodology ensures that every data element counts, every defect is caught, and every migration is smooth.
Validate 95%+ of migrated data automatically, intelligently and confidently
In commercial and digital banking transformations, migrating data from legacy systems to modern platforms is not just a technical step—it’s a business-critical milestone.
Without rigorous validation, financial institutions risk operational disruption, regulatory non-compliance, and customer dissatisfaction.
GLF’s automated validation framework ensures that every record counts, every defect is caught, and every go-live is smooth.
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Error Detection
Data migration challenges and our solution
Data migration challenges and our solution
Data migration challenges and our solution
Without structured testing, banks risk regulatory gaps, usability issues, and production defects. GLF’s Testing COE model ensures repeatable, scalable, and business-aligned testing across releases, vendors, and platforms
Without structured testing, banks risk regulatory gaps, usability issues, and production defects. GLF’s Testing COE model ensures repeatable, scalable, and business-aligned testing across releases, vendors, and platforms
4 unique steps for data validation
4 unique steps for data validation
Seamlessly migrate legacy data with AI-powered accuracy, real-time validation, and audit-ready security—launch your systems confidently, on schedule.
Seamlessly migrate legacy data with AI-powered accuracy, real-time validation, and audit-ready security—launch your systems confidently, on schedule.
Seamlessly migrate legacy data with AI-powered accuracy, real-time validation, and audit-ready security—launch your systems confidently, on schedule.
Data Organization
01.
Product based identification of entities and data-sets, profiling by grouping, sub-grouping and clustering, checksum based reconciliation
Data Organization
01.
Product based identification of entities and data-sets, profiling by grouping, sub-grouping and clustering, checksum based reconciliation
Data Organization
01.
Product based identification of entities and data-sets, profiling by grouping, sub-grouping and clustering, checksum based reconciliation
Automation and ML
02.
Complex condition induced code for efficient comparison, accurate results using recursive learning scripts, automated data-loading and sifting
Automation and ML
02.
Complex condition induced code for efficient comparison, accurate results using recursive learning scripts, automated data-loading and sifting
Automation and ML
02.
Complex condition induced code for efficient comparison, accurate results using recursive learning scripts, automated data-loading and sifting
Supervised Feedback
03.
Knowledge-based data sampling, client impact based defect analysis, supervised review to remove false-negatives
Supervised Feedback
03.
Knowledge-based data sampling, client impact based defect analysis, supervised review to remove false-negatives
Supervised Feedback
03.
Knowledge-based data sampling, client impact based defect analysis, supervised review to remove false-negatives
Experience based testing
04.
Use historical data, domain expertise, and prior implementation learnings to identify high-risk areas early and ensure testing reflects real business scenarios, not just scripts.
Experience based testing
04.
Use historical data, domain expertise, and prior implementation learnings to identify high-risk areas early and ensure testing reflects real business scenarios, not just scripts.
Experience based testing
04.
Use historical data, domain expertise, and prior implementation learnings to identify high-risk areas early and ensure testing reflects real business scenarios, not just scripts.