Schema Changes Impact
Explore complex analytical transformations and their impact on business metrics
Summary
The 'customers' model had grown too complex with multiple CTEs buried inside, making it hard to debug, test, and reuse logic across other models. The data team wanted to refactor.
But here's the challenge: stakeholders don't care about code architecture. They just care that their trusted metrics don't change. When you refactor, you want to make sure there's zero-impact.Instead of asking stakeholders to blindly trust that "nothing changed," the data team demonstrated it with data.
The result? Increased stakeholder trust and code improvements that developers could confidently release, knowing the metrics remained rock-solid.
Problems
Trust breaks: Even "harmless" refactoring makes stakeholders nervous. They've seen too many "simple" changes break their dashboards.
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Complex models: CTEs buried inside large models make debugging difficult and prevent reuse across the data warehouse.
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Validation overhead: Proving refactoring didn't change results requires manual comparison of every affected metric and downstream model.
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Stakeholder anxiety: Business teams don't understand code architecture and worry that any change might break their trusted numbers.
Solutions
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Visibility: Make what changed instantly clear
DAG visualization showed new intermediate models without overwhelming stakeholders with code details. -
Verifiability: Prepare evidence and bring stakeholders into the loop
Profile diffs and automated comparisons showed identical outputs across all affected models, eliminating manual verification. -
Velocity: Build repeatable processes for sustainable speed
By proving zero business impact, the team established a pattern for future architectural improvements without stakeholder anxiety.
Trust, Verify, Ship
Cut dbt review time by 90% and ship accurate data fast
