Performance Optimization
Analyze query performance improvements and resource utilization changes
Summary
The data team discovered that currency amounts were stored as DOUBLE instead of DECIMAL, causing subtle but dangerous floating-point rounding errors in financial calculations. While individual differences seemed tiny, when accumulated across thousands of transactions they potentially distorted revenue reports and customer lifetime value metrics.
How much would existing numbers change? The data team wanted to make sure finance teams understood if historical comparisons would still be valid, whether dashboards would show different totals, and if the precision improvements were worth potential confusion.
Instead of making the change and hoping for the best, the data team validated every potentially affected calculation. They showed exactly where rounding differences occurred, quantified the impact on key metrics, and gave stakeholders clear evidence that the new precision was worth any minor adjustments to existing reports.
The result? Improved data accuracy and prevent future errors that finance teams could trust and verify.
Problems
Master the art of validating column type changes without breaking historical trust
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Unknown impact anxiety: Without validation, teams face the terrifying question: "How much will our trusted historical data change?"
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Historical continuity risk: Type changes risk making months of historical reports incomparable to current data, breaking trend analysis and forecasting.
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Stakeholder conviction challenge: When changes do impact historical numbers, convincing finance teams that short-term disruption prevents long-term silent errors becomes a trust-building test.
Solutions
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Visibility: Make what changed instantly clear
Schema diff showed the data type change from DOUBLE to DECIMAL(10,2) across all affected amount columns. -
Verifiability: Prepare evidence and bring stakeholders into the loop
Profile diffs and value comparisons quantified exactly how much precision improved and where rounding has no differences. -
Velocity: Build repeatable processes for sustainable speed
By documenting precision impacts upfront, the team avoided post-deployment questions about "why numbers look different" from finance stakeholders.
Trust, Verify, Ship
Cut dbt review time by 90% and ship accurate data fast
