PDI Data Validation Framework

Ensuring Data Integrity in Migrations


PDI’s Data Validation Framework automates the verification of migrated datasets, ensuring accuracy and consistency between legacy and new systems. 

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PDI Data Validation Framework

Key Features


  • Automated Data Reconciliation: Compares source and target environments for completensess. 
  • Integrity Checks: Detects discrepancies in data values, schema structures, and missing records. 

  • Audit-Ready Reports: Generates compliance-ready validation reports. 

  • Real-Time Alerts: Notifies stakeholders of validation errors.

Benefits


  • Ensures high data accuracy and consistency post-migration. 
  • Reduces manual effort in validation and reconciliation. 
  • Helps meet regulatory compliance requirements. 

  • Minimizes business disruptions caused by incorrect data migrations. 
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How it Works

Use Cases for PDI Data Validation Framework

The PDI Data Validation Framework is an AI-driven solution that automates the verification of migrated datasets, ensuring accuracy and consistency between legacy and modern systems. Below are key use cases demonstrating its impact in ETL Validation, Data Warehouse Integrity, and BI Report Accuracy. 

Sample Use Case 1: ETL Validation Use Case

Scenario: Ensuring Data Accuracy During ETL Migration


Business Challenge:

A global insurance company migrating its ETL pipelines from Informatica PowerCenter to AWS Glue faced challenges in validating data transformations, ensuring completeness, and detecting anomalies in the new system. 

Solution with PDI Data Validation Framework:

  • Automates comparison between source and target datasets to detect inconsistencies. 
  • Performs transformation integrity checks, ensuring ETL logic has not altered the data. 
  • Generates real-time validation alerts for missing or duplicate records. 

Benefits:

  • 30% reduction in validation efforts through automation. 
  • Ensures data integrity during ETL migration, preventing errors. 

  • Accelerates go-live readiness by identifying and resolving discrepancies early. 
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Sample Use Case 2: Data Warehouse Integrity Use Case 

Scenario: Verifying Data Consistency During Cloud Data Warehouse Migration 


Business Challenge:

A healthcare provider transitioning from an on-premises Oracle Data Warehouse to Google BigQuery needed to validate billions of records across complex schemas while maintaining regulatory compliance. 

Solution with PDI Data Validation Framework: 

  • Performs schema comparison between the legacy and cloud environments. 
  • Validates large-scale data sets for completeness, duplication, and format accuracy. 
  • Generates compliance-ready reports ensuring adherence to HIPAA and GDPR regulations. 

Benefits:

  • 50% faster validation with AI-powered reconciliation. 
  • Regulatory compliance ensured with audit-ready validation reports. 
  • Minimal business disruption by proactively resolving discrepancies before production deployment. 
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Sample Use Case 3: BI Report Accuracy Use Case

Scenario: Maintaining Data Consistency in BI Report Migration


Business Challenge:

A retail company migrating from SAP BusinessObjects to Power BI needed to ensure reports reflected the same KPIs, calculations, and data integrity as in the legacy system. 

Solution with PDI Data Validation Framework: 

  • Compares BI reports across platforms, validating aggregation logic and business metrics. 
  • Identifies missing, mismatched, or altered report values that could impact decision-making. 
  • Automates validation workflows, reducing reliance on manual data checks. 

Benefits:

  • 40% reduction in report validation time, ensuring consistency across BI platforms. 
  • Improved trust in analytics, reducing discrepancies in decision-making dashboards. 
  • Seamless BI transition with real-time validation alerts and reconciliation tools. 

 

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