## Advanced Analytics

Explore complex analytical transformations and their impact on business metrics

#### Summary

The marketing team wanted to understand customer promotion behavior, specifically, which customers had ever used promotional offers. This meant adding a new 'is_promotion' column to the 'stg_payment' model, to flag transactions where the payment method was ‘coupon’.

Adding columns to foundational models like 'stg_payment' makes teams nervous. Even though it seemed like a simple addition, the teams worried: "Will this break existing dashboards? Are downstream models still working? Will our current reports show different numbers?"

The data team had to prove that adding 'is_promotion' was truly non-breaking change, that all the old stuff would still work, and the new metrics would just be a bonus. Instead of deploying and hoping nothing broke, they demonstrated that the change was additive-only, with zero impact on existing models and metrics.

The result? New promotional insights delivered with confidence, and a template for safely extending data models without stakeholder anxiety.

#### Problems

**Prove schema additions won't break existing business logic**

- **Addition anxiety**: Adding columns to foundational models trigger fears that downstream logic might suddenly break.
- **Downstream uncertainty**: Teams can't predict which models, dashboards, or reports might be affected by seemingly simple schema changes.
- **Safety verification challenge**: Proving that new columns are truly "non-breaking" requires validating every dependent model and calculation.

#### Solutions

- **Visibility: Make schema changes crystal clear**
  
Lineage diffs highlighted the new 'is_promotion' column addition, while confirming no existing columns were modified or removed.

- **Verifiability: Demonstrate zero downstream impact**  
  
Breaking change analysis proved that downstream models like orders remained completely unaffected by the new promotional tracking column.

- **Velocity: Build confidence in safe extensibility**  
  
By documenting non-breaking evidence, the team established a pattern for safely adding analytical capabilities without messing up what already works.

## Trust, Verify, Ship

Cut dbt review time by 90% and ship accurate data fast.