Canonical Data Model
A canonical data model describes a standardized data structure for the integration and exchange of information between different systems.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityAdvanced
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Mismatch Between Systems
- Resistance to Change
- Increased Training Requirements
- Document Architecture
- Offer User Training
- Gather User Feedback
I/O & resources
- Existing Systems
- Data Analysis Tools
- Requirements Documents
- Integrated Database
- System Documentation
- Data Quality Analyses
Description
The canonical data model promotes interoperability between systems and facilitates data integration. By providing a uniform structure, data exchange is optimized and misunderstandings are reduced.
✔Benefits
- Facilitated Data Integration
- Fewer Data Conflicts
- Improved System Communication
✖Limitations
- May Require Adjustments
- Difficulties with Legacy Systems
- Implementation Costs May Vary
Trade-offs
Metrics
- Integration Time
Time taken to integrate data between systems.
- Integration Costs
Total costs for implementing the canonical data model.
- Error Rate
Frequency of errors during data integration.
Examples & implementations
Data Integration at a Large Retailer
A company implemented a canonical data model to optimize its system landscape.
Cloud Data Migration in a Financial Institution
A financial institution successfully migrated its data to the cloud using a canonical model for data structure.
API Development for an E-Commerce Company
An e-commerce company developed a new API using a canonical data model.
Implementation steps
Analyze Requirements
Create Data Model
Conduct Implementation
⚠️ Technical debt & bottlenecks
Technical debt
- Insufficient Documentation
- Old Systems Without Updates
- Lack of Resources for Maintenance
Known bottlenecks
Misuse examples
- Ignoring Legal Requirements
- Ignoring Data Inconsistencies
- Insufficient Resources for Implementation
Typical traps
- Rushed Implementation Without Planning
- Lack of Communication Between Teams
- Underestimating Training Needs
Required skills
Architectural drivers
Constraints
- • Legal Requirements for Data Processing
- • Data Protection Regulations
- • Technical Limitations of Sourcing