Coforge's Data Cosmos Auto Data Mapping

Auto Data Mapping is Coforge's AI-powered intelligent accelerator, part of the Coforge Data Cosmos™ toolkit, engineered to simplify and accelerate complex data modeling and mapping across legacy and modern data landscapes. In today's data environments, mapping between source and target systems is often manual, error-prone, and time-consuming. Auto Data Mapping addresses this by combining AI-driven mapping, visual design, and end-to-end lineage visibility into a unified platform — enabling faster, accurate, and governed data transformations.

The platform features a 4-Stage Intelligent Pipeline that drives end-to-end automation: • AI-Powered Auto Mapping – Leverages AI to suggest accurate source-to-target data mappings automatically. Analyzes schema metadata, column names, data types, and business context to generate high-confidence mapping recommendations — eliminating manual mapping effort. • Visual Mapping Canvas – Features an intuitive side-by-side canvas for schema mapping and transformation design. Enables data engineers and architects to visually review, refine, and approve AI-suggested mappings with drag-and-drop simplicity. • End-to-End Data Lineage – Provides clear lineage graphs to ensure full traceability across all connected systems. Maps data flow from source to target including transformations, joins, and business rules — supporting audit readiness and governance compliance. • Schema Management Layer – Supports flexible modeling via schema ingestion through CSV or manual input. Enables teams to onboard new sources and targets rapidly, manage schema versions, and maintain a governed catalog of all mapped entities.

Key Use Cases: • Banking – Automate source-to-target mapping for core banking data migrations (Oracle/Teradata → Snowflake on Azure/Azure Synapse). Lineage graphs ensure audit compliance for regulatory reporting transformations. • Insurance – Accelerate claims and policy data mapping across legacy mainframe systems to modern cloud warehouses. AI-driven validation ensures actuarial logic fidelity across all mapped transformations. • Travel – Map complex reservation, crew, and operations data across fragmented airline/hospitality systems. Visual canvas enables ops teams to validate domain-specific mapping logic. • Healthcare – Map patient and clinical data across EHR systems with full lineage traceability. Schema management ensures HIPAA-compliant data handling across all mapped entities.

The 8-week implementation engagement covers: discovery and source profiling, platform deployment on Azure, configuration of AI-powered mapping pipelines for priority data sources, visual canvas setup, lineage graph activation, schema management configuration, monitoring dashboard setup, and knowledge transfer.

Target Audience: Enterprise Data Engineering & Platform Teams, Data Architects, Cloud Migration Leads, Data Quality & Governance Teams, and Analytics Leaders.