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.

0%

0%

Faster UAT

0k+

0k+

Test Case Accelerators

0%+

0%+

Error Detection

0%

0%

Faster UAT

0k+

0k+

Test Case Accelerators

0%+

0%+

Error Detection

0%

0%

Faster UAT

0k+

0k+

Test Case Accelerators

0%+

0%+

Error Detection

0%

0%

Faster UAT

0k+

0k+

Test Case Accelerators

0%+

0%+

Error Detection

0%

0%

Faster UAT

0k+

0k+

Test Case Accelerators

0%+

0%+

Error Detection

0%

0%

Faster UAT

0k+

0k+

Test Case Accelerators

0%+

0%+

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

Fragmented Migration Efforts

We unify migration across systems and vendors and all 3rd parties

Fragmented Migration Efforts

We unify migration across systems and vendors and all 3rd parties

GLF’s full lifecycle framework spans discovery to post-go-live, aligning migration with implementation roadmaps for seamless execution

Manual Reconciliation Risks

We automate validation for 95%+ of migrated records.

Manual Reconciliation Risks

We automate validation for 95%+ of migrated records.

Our proprietary AI-powered tools replace manual sampling with machine-driven reconciliation, reducing errors and accelerating readiness

Inconsistent Mapping & Data Loss

GLF’s ready-to-use templates enable product-prescribed mappings to be quickly ingested and validated for gaps.

Inconsistent Mapping & Data Loss

GLF’s ready-to-use templates enable product-prescribed mappings to be quickly ingested and validated for gaps.

GLF accelerators support checksum validation, metadata alignment, and defect analytics to ensure integrity across platforms

Delayed Go-Live due to data transformation defects

Ensuring accuracy through automated data validation

Delayed Go-Live due to data transformation defects

Ensuring accuracy through automated data validation

Identify and eliminate data mismatches across legacy and new systems using AI-driven mapping, validation logs, and reconciliation frameworks to ensure smooth, error-free go-live.

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.

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

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.