v1.0 stability and defect burn-down

Problem

After public launch, real user traffic will expose defects in connector dashboard packs that internal testing didn't catch—connector schema changes that break queries, edge cases in data volumes, timezone mismatches in KPI calculations, and rendering issues across different warehouse backends. Without a structured stability program that tracks defect rates, burns down the backlog on a recurring cadence, and monitors reliability trends per connector, the pack catalog will degrade over time. Users who encounter broken dashboards at launch and don't see rapid fixes will churn, and the team won't know which connectors are most problematic without systematic tracking.

Context

• After launch, recurring defects in connector-specific dashboard packs and KPI narratives for Fivetran sources will damage trust faster than new features can restore it, so this phase should prioritize stability over new scope.
• The goal is to identify the repeat offenders, remove the highest support burden, and make failure patterns measurable enough that the team knows whether quality is improving.
• Expected touchpoints include dashboard pack YAML, dbt/example assets, connector fixtures, and quickstart docs, bug history, support or incident notes, and any tests or QA gaps that let defects recur.

Possible Solutions

• A - Keep mixing bug fixes with feature work opportunistically: preserves flexibility, but lets long-tail reliability work stay perpetually unfinished.
• B - Recommended: run an explicit stability program: rank defect classes, burn down the highest-frequency issues, and pair fixes with validation so regressions stop recurring.
• C - Freeze all new work until zero known defects remain: simple in principle, but unrealistic and usually counterproductive.

Plan

1. Aggregate the recurring failures in connector-specific dashboard packs and KPI narratives for Fivetran sources from bugs, support notes, and recent releases, then rank them by user impact and repeat rate.
2. Turn the top defect classes into a concrete burn-down list with owners, acceptance criteria, and the validation needed to keep each fix from regressing.
3. Land or schedule the highest-leverage fixes first, including any docs or operator changes that reduce repeat incidents.
4. Review the remaining defect mix after the first burn-down pass and update the next tranche of work based on actual stability improvements.

Implementation Progress

Review Feedback

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