Experiment design for future bets

Problem

Some of the most impactful potential profiler features — AI-generated column descriptions, automated data quality scoring, anomaly detection on profile drift — are high-risk bets where the value proposition is plausible but unproven. Building any of these to completion before validating the core hypothesis (e.g., "AI-generated descriptions are accurate enough that analysts trust and use them") would be a significant investment with uncertain return. Without pre-designed validation experiments — minimal prototypes, defined success criteria, and concrete evaluation protocols — the team has no way to cheaply test whether a future bet is worth pursuing, and decisions default to opinion rather than evidence.

Context

• The larger future bets for warehouse profiling, semantic inference, and analyst-facing inspect/context artifacts 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 dataface/core/inspect/, schema-context consumers, inspect docs, and core tests, 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 warehouse profiling, semantic inference, and analyst-facing inspect/context artifacts 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

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