Quality and performance improvements
Problem
Post-launch usage data will reveal quality and performance issues in connector dashboard packs that matter to users: slow-loading dashboards caused by unoptimized queries against large connector tables, KPI definitions that produce confusing or inaccurate results for certain data shapes, and narrative flow problems where dashboards don't tell a coherent story. These issues directly affect user retention and trust. Without tying quality and performance improvements to measurable user-facing outcomes (load time, engagement rate, support ticket volume), the team risks optimizing things that don't matter while ignoring the pain points that drive users away.
Context
- Once connector-specific dashboard packs and KPI narratives for Fivetran sources is in regular use, quality and performance work needs to target the actual slow, flaky, or costly paths rather than generic optimization ideas.
- The right scope here is evidence-driven: identify bottlenecks, remove the highest-friction issues, and make sure the fixes are measurable and regression-resistant.
- Expected touchpoints include dashboard pack YAML, dbt/example assets, connector fixtures, and quickstart docs, telemetry or QA evidence, and any heavy workflows where users are paying the cost today.
Possible Solutions
- A - Tune isolated hotspots as they are reported: useful for emergencies, but it rarely produces a coherent quality/performance program.
- B - Recommended: prioritize measurable bottlenecks and quality gaps: couple performance work with correctness and UX validation so improvements are both faster and safer.
- C - Rewrite broad subsystems for theoretical speedups: tempting, but usually too risky and poorly grounded for this milestone.
Plan
- Identify the biggest quality and performance pain points in connector-specific dashboard packs and KPI narratives for Fivetran sources using real usage data, QA findings, and support feedback.
- Choose a small set of improvements with clear before/after measures and explicit user-facing benefit.
- Implement the fixes together with regression checks, docs, or operator notes wherever the change affects behavior or expectations.
- Review the measured outcome and turn any remaining hotspots into sequenced follow-up tasks instead of leaving them as vague future work.
Implementation Progress
Review Feedback
- [ ] Review cleared