Data & Analytics
Data & Analytics
Document the business logic behind your data transformations. Create maintainable data pipelines with clear lineage.
What you can capture
ETL Transformation Logic
Capture the business rules behind data transformations. Document why data is transformed, not just how, making pipelines maintainable long-term.
Data Quality Rule Formalization
Define what makes data valid, complete, and accurate. Create executable quality rules from business definitions.
Warehouse Documentation
Document fact vs dimension decisions, slowly changing dimension strategies, and aggregation rules in business terms.
Real example: Revenue Recognition Logic
Before SpecBridge
"The revenue calculation is in a stored procedure from 2019. Nobody knows exactly why it does what it does."
After SpecBridge
Documented business rules: 7 revenue recognition scenarios, timing rules, adjustment triggers, and edge case handling with test data.
Key benefits
Reduce data pipeline debugging time
Create self-documenting data transformations
Improve data quality through clear rules
Enable business users to understand data lineage