PDI Synthetic Data Generator

AI-Driven Data Simulation for Testing and Compliance 


The Synthetic Data Generator provides realistic, anonymized datasets to support testing, analytics, and compliance initiatives. 

PDI Synthetic Data Generator

Why PDI Synthetic Data Generator

Key Features


  • Secure Data Masking: Protects sensitive information while maintaining realistic patterns. 
  • Customizable Data Sets: Generates domain-specific datasets for training models and testing applications.  

  • Regulatory Compliance: Ensures adherence to GDPR, HIPAA, and other data privacy regulations. 

  • Scalability:  Generates large-scale datasets for AI model training and performance benchmarking. 

Benefits


  • Enhanced ML Training: Enhances machine learning and AI model training with realistic data. 
  • Data Protection: Protects sensitive customer data during testing. 

  • Compliant Testing: Reduces reliance on production data for testing, ensuring compliance. 
  • Accelerated Test Cycles: Accelerates product testing cycles with readily available synthetic datasets.  
PDI Synthetic Data Generator
3386851-Jan-07-2022-07-38-46-74-PM

 

How it Works

Use Cases for PDI Synthetic Data Generator

The PDI Synthetic Data Generator is an AI-driven solution that creates realistic, anonymized datasets to support testing, analytics, and compliance initiatives. Below are key use cases demonstrating its impact in Application Testing, Machine Learning Model Training, and Regulatory Compliance. 

Sample Use Case 1: Application Testing Use Case 

Scenario: Generating Test Data for Software Development


Business Challenge:

A global banking institution developing a new online banking application faced challenges in acquiring real customer data for testing due to security and privacy concerns. 

Solution with PDI Synthetic Data Generator:

  • Realistic Transaction Data:  Creates realistic customer transaction data without exposing sensitive information. 
     
  • Preserved Schema Integrity: Maintains database schema and relationships, ensuring the test environment mirrors production. 
  • Edge-Case Scenarios: Generates edge-case scenarios, helping developers test different transaction conditions.
     

Benefits:

  • Accelerated Testing: Speeds up application testing by eliminating dependencies on real user data. 

  • Enhanced Security: Enhances security by avoiding the use of actual customer records. 

  • Scalable Testing: Supports scalable testing with dynamically generated datasets. 

Synthetic Data Generation Process

Sample Use Case 2: Machine Learning Model Training Use Case

Scenario: Providing Large-Scale Data for AI/ML Model Development


Business Challenge:

A healthcare research company needed large volumes of medical records to train its AI-driven diagnostic model but was restricted by HIPAA compliance and data privacy regulations. 

Solution with PDI Synthetic Data Generator: 

  • Realistic Synthetic Data: Generates synthetic patient data that mimics real-world cases while ensuring anonymity. 
  • Balanced Class Distribution: Balances class distributions to create diverse datasets for machine learning models. 
  • Preserved Statistical Integrity: Preserves statistical properties, allowing accurate model training without violating privacy laws. 

Benefits:

  • Regulatory Compliance: Ensures compliance with data privacy regulations such as HIPAA and GDPR.
  • Accelerated AI Development: Accelerates AI model development by providing instant access to large datasets. 
  • Enhanced Model Accuracy: Improves model accuracy by offering diverse and high-quality synthetic data. 
PDI Machine Learning Model Training

Sample Use Case 3: Regulatory Compliance Use Case 

Scenario: Creating Privacy-Compliant Datasets for Auditing and Analysis 


Business Challenge:

A fintech company needed to share customer transaction data with third-party auditors while complying with GDPR and other privacy laws. 

Solution with PDI Synthetic Data Generator: 

  • Multi-Region Compliance: Supports multi-region compliance, ensuring data usage aligns with different regulatory frameworks. 
     
  • Anonymized Financial Data: Generates anonymized financial datasets while preserving transaction patterns.
  • Consistent Audit Insights: Maintain data consistency and integrity, ensuring auditors receive meaningful insights. 

Benefits:

  • Synthetic Data Privacy: Eliminates privacy risks by replacing real customer data with synthetic alternatives. 
  • Automated Anonymization: Reduces compliance overhead by automating data anonymization and generation. 

  • Secure Data Sharing: Facilitates secure data sharing across partners and auditors without legal concerns.
PDI Regulatory Compliance

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