Create an interactive data analysis tool that detects unusual observations and helps analysts investigate why they differ from expected patterns.
Allow users to upload CSV datasets and choose the relevant date, category, identifier, and measurement columns.
Build:
Configurable detection methods for outliers, sudden changes, missing periods, and unexpected category shifts.
A comparison of anomalies against relevant historical or peer-group baselines.
A visual timeline and distribution explorer.
An explanation panel showing which measurements triggered each alert.
A way to annotate observations as valid exceptions, data errors, or unresolved cases.
A detector for recurring anomaly patterns.
A review queue ranked by configurable severity and business impact.
An exportable investigation report with evidence and analyst notes.
Do not equate unusual data with incorrect data or malicious behavior. Explain the limitations of each detection method, account for small samples where possible, and avoid invented root causes. Include synthetic datasets containing seasonal changes, legitimate outliers, and genuine data-quality errors.
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