AMIGO Wiki
AI Managed Implementation Governance Office - Documentation
Introduction
Key Relationship
Important Field Dependencies
Object | Usage Notes | Visibility | Filter & Search Criteria -- | -- | -- | -- Hyperlink to Portfolio | Parent | | Hyperlink to Project | Child | | | | |💼 Business Usage
PMO Application
Data Mapping Rules define how data transforms from source to target systems during migrations and integrations. They specify field-level transformations, validations, and business logic to ensure data integrity.
Common Use Cases
| Use Case | Description |
|---|---|
| Data Migration | Transform legacy data to new system |
| System Integration | Map fields between connected systems |
| Data Consolidation | Merge data from multiple sources |
| Format Standardization | Normalize data formats |
| Business Rule Application | Apply transformation logic |
Mapping Rule Types
- Direct Mapping: Source value copied as-is
- Transformation: Value converted using logic
- Lookup: Value translated via reference table
- Concatenation: Multiple fields combined
- Default: Static value when source is empty
- Derivation: Calculated from other fields
Mapping Specification
| Element | Description |
|---|---|
| Source Field | Origin field reference |
| Target Field | Destination field reference |
| Rule Type | Transformation category |
| Logic | Conversion formula or lookup |
| Validation | Pre/post-transformation checks |
📚 References
- Data - Data management overview
- Organization - Organizational context
- Data Fields - Field definitions
- Data Quality Rules - Quality specifications