AICOE Synthetic Data Generation

What problem does it solve?

Generating representative synthetic datasets is essential for designing and testing UI mockups and prototypes without relying on real or sensitive data.

Core Features & Use Cases

• Data Pattern Recognition: Identifies required data structures and relationships from use cases.
• Volume & Diversity Enforcement: Ensures datasets meet minimum standards such as 8-15 master records and 15-30 transactional records, including edge cases.
• Use Case Application: Facilitates the creation of mock data for dashboards, forms, and analytics, ensuring data realism, variety, and temporal spread over 12 months.
• Pipeline Dependency Management: Validates prerequisite files like use cases XMLs before generating data.
• Quality Compliance: Applies standards like varied statuses, edge cases, relationship validity, and appropriate categorical distributions.

Quick Start

Count existing use case files in the project directory, then generate mock datasets adhering to volume and quality requirements, verifying realism and diversity before finalizing.