Metrics

Track statistical properties of your data and detect anomalies

Metrics are statistical measurements that track properties of your data over time. AnomalyArmor captures metrics on a schedule, builds baselines, and alerts you when values fall outside expected ranges.

What Metrics Track

Metric Type	What It Measures	Example Use Case
row_count	Total rows in a table	Detect data loss or unexpected growth
null_percent	Percentage of null values	Catch ETL issues leaving nulls
distinct_count	Unique values in a column	Detect cardinality changes
duplicate_count	Duplicate values	Find unexpected duplicates
min_value	Minimum numeric value	Catch invalid data (negative prices)
max_value	Maximum numeric value	Detect outliers
avg_value	Average numeric value	Monitor central tendency

How Anomaly Detection Works

1. Baseline building: Historical values establish what’s “normal”
2. Z-score calculation: Each new value is compared to the baseline
3. Sensitivity threshold: Values exceeding the threshold trigger alerts

A sensitivity of 2.0 means values more than 2 standard deviations from the mean are flagged as anomalies.

Next Steps

Create a Metric

Set up your first data quality metric

Set Up Alerts

Get notified when anomalies are detected