Power-Bi-Model-Design-Review

Star Schema Compliance:
□ Clear separation of fact and dimension tables
□ Proper grain consistency within fact tables  
□ Dimension tables contain descriptive attributes
□ Minimal snowflaking (justified when present)
□ Appropriate use of bridge tables for many-to-many

Table Design Quality:
□ Meaningful table and column names
□ Appropriate data types for all columns
□ Proper primary and foreign key relationships
□ Consistent naming conventions
□ Adequate documentation and descriptions

Relationship Quality Assessment:
□ Correct cardinality settings (1:*, *:*, 1:1)
□ Appropriate filter directions (single vs. bidirectional)
□ Referential integrity settings optimized
□ Hidden foreign key columns from report view
□ Minimal circular relationship paths

Performance Considerations:
□ Integer keys preferred over text keys
□ Low-cardinality relationship columns
□ Proper handling of missing/orphaned records
□ Efficient cross-filtering design
□ Minimal many-to-many relationships

Storage Mode Optimization:
□ Import mode used appropriately for small-medium datasets
□ DirectQuery implemented properly for large/real-time data
□ Composite models designed with clear strategy
□ Dual storage mode used effectively for dimensions
□ Hybrid mode applied appropriately for fact tables

Performance Alignment:
□ Storage modes match performance requirements
□ Data freshness needs properly addressed
□ Cross-source relationships optimized
□ Aggregation strategies implemented where beneficial

Evaluate Model Structure:

Fact Table Analysis:
- Grain definition and consistency
- Appropriate measure columns
- Foreign key completeness
- Size and growth projections
- Historical data management

Dimension Table Analysis:  
- Attribute completeness and quality
- Hierarchy design and implementation
- Slowly changing dimension handling
- Surrogate vs. natural key usage
- Reference data management

Relationship Network Analysis:
- Star vs. snowflake patterns
- Relationship complexity assessment
- Filter propagation paths
- Cross-filtering impact evaluation

Data Quality Assessment:

Completeness:
□ All required business entities represented
□ No missing critical relationships
□ Comprehensive attribute coverage
□ Proper handling of NULL values

Consistency:
□ Consistent data types across related columns
□ Standardized naming conventions
□ Uniform formatting and encoding
□ Consistent grain across fact tables

Accuracy:
□ Business rule implementation validation
□ Referential integrity verification
□ Data transformation accuracy
□ Calculated field correctness

Size Optimization Assessment:

Data Reduction Opportunities:
- Unnecessary columns identification
- Redundant data elimination
- Historical data archiving needs
- Pre-aggregation possibilities

Compression Efficiency:
- Data type optimization opportunities
- High-cardinality column assessment
- Calculated column vs. measure usage
- Storage mode selection validation

Scalability Considerations:
- Growth projection accommodation
- Refresh performance requirements
- Query performance expectations
- Concurrent user capacity planning