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Outlier Root-Cause Finder
Systematically identify data quality issues, measurement errors, or business events causing metric spikes.
Updated June 2026
The prompt
On {{anomaly_date}}, {{metric}} spiked to {{anomaly_value}} (normal range: {{normal_range}}). Investigate: 1. Data pipeline logs — any errors or re-processing? 2. Business events — launches, outages, marketing campaigns? 3. Measurement logic — code changes, data type conversions, filter logic? 4. User behavior — bot activity, seasonal event, external news? 5. Upstream dependencies — did any source table change schema or reload? Provide hypothesis ranked by likelihood with evidence.
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Author
AI Khazna
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