analyze-my-data

To get a ship or kill decision for A/B testing, you provide your warehouse query, hypothesis, primary metric, and guardrails. The analysis computes significance, confidence intervals, and guardrail deltas to return a clear ship, kill, iterate, or inconclusive-extend call.

Anomaly detection against rolling baselines works by sweeping daily metrics to identify deviations past per-metric thresholds. It displays the baseline used and proposes likely causes tied to recent operational context, helping you address metric shifts quickly.

A data-quality audit for operational warehouse tables is a reliability check that examines null rates, duplicates, freshness expectations, and referential integrity. It flags which tables or joins are likely breaking downstream analysis.

Yes, you can run SQL data quality checks with read-only data access. The process validates baselines and threshold rules while outputting dated analysis reports and structured DQ artifacts without requiring write permissions.

The best way to evaluate experiment lift and statistical significance is to input variant aggregates and guardrail metrics. This produces an evidence-based readout with transparent caveats and explicit decision calls rather than vague impressions.

Downstream analysis breaks after warehouse table updates when data-quality issues exist. Running a DQ audit checks for high null rates, duplicate records, stale freshness, and broken referential integrity to identify the failing joins.