Experiment design for future bets

Problem

Future bets on the connector pack platform—community-contributed packs, AI-generated dashboards, self-serve pack customization—require significant investment but have uncertain payoff. Without designed validation experiments (lightweight prototypes, user interviews, A/B tests, or instrumented feature flags), the team will either over-invest in ideas that don't pan out or under-invest in high-value ideas because the risk feels too high. Pre-designed experiments with clear success criteria allow the team to test hypotheses cheaply before committing to full implementation, reducing the cost of wrong bets on the pack publishing workflow.

Context

• The larger future bets for connector-specific dashboard packs and KPI narratives for Fivetran sources should be validated with scoped experiments before they absorb major implementation effort or become roadmap commitments.
• This task should design the experiments, not run them: define hypotheses, success signals, cheap prototypes or evaluation methods, and the decision rule for what happens next.
• Expected touchpoints include dashboard pack YAML, dbt/example assets, connector fixtures, and quickstart docs, opportunity/prerequisite notes, eval or QA harnesses where relevant, and any external dependencies required to run the experiments.

Possible Solutions

• A - Rely on team intuition to pick which future bet to pursue: fast, but weak when the bets are expensive or high-risk.
• B - Recommended: design lightweight validation experiments for the strongest bets: specify hypothesis, method, scope, evidence, and the threshold for continuing or dropping the idea.
• C - Build full prototypes for every future direction immediately: rich signal, but far too expensive for early-stage uncertainty.

Plan

1. Choose the future bets for connector-specific dashboard packs and KPI narratives for Fivetran sources that are both strategically important and uncertain enough to justify explicit experiments.
2. Define the hypothesis, cheapest credible validation method, required inputs, and success/failure signals for each experiment.
3. Document the operational constraints, owners, and follow-up decisions so the experiment outputs can actually change roadmap choices.
4. Rank the experiments by cost versus decision value and sequence the first one or two instead of trying to validate everything at once.

Implementation Progress

Review Feedback

• [ ] Review cleared