SC 016 — Data Repair Work

Summary

Identifies eight distinct ways data projects break and argues that understanding failure modes enables better maintenance strategies. Key solutions: deprecation processes for metric definition changes (old and new coexist temporarily), alerts without automatic syncing for schema changes (avoids cost surprises), and surrogate keys with uniqueness/null tests for granularity issues.

Core thesis: stability builds trust, and trust creates opportunities for value creation.

Key Arguments

• Data maintenance is undervalued but foundational — you can't create value on an unstable platform
• Metric definition changes need deprecation windows, not hard cuts
• Schema change alerts should inform, not auto-remediate (cost control)
• Surrogate keys with proactive testing prevent granularity issues before they cascade
• Stability → trust → opportunity is the progression

Writing Style Notes

Published during Hurricane Idalia evacuation — the personal stakes contrast with the methodical technical content. Practical and specific. The "repair work" framing elevates maintenance from chore to craft.

Connections

• [[01-projects/newsletter/index]] — part of the Sanity Check body of work
• [[06-reference/2026-04-03-data-maturity-processes-tools]] — maintenance practices as maturity indicator
• [[06-reference/2026-04-03-downfall-of-data-engineer]] — pipeline constipation as a failure mode this article addresses
• [[06-reference/2026-04-03-embrace-the-grind]] — data repair work IS the grind; stability as competitive moat
• [[06-reference/concepts/analytics-as-craft]] — elevating maintenance from chore to craft