Automated data quality monitoring

Problem

Bad data is discovered when a dashboard breaks or a model produces nonsense. By then, decisions have been made on incorrect information. Manual data checks don't scale.

Solution

The AI agent profiles your data tables, learns normal patterns, and monitors continuously. It detects anomalies (unexpected nulls, volume spikes/drops, distribution shifts), schema changes, and freshness issues—alerting the data team before downstream impact.

Benefits

• Catch data issues before they reach dashboards
• Automated anomaly detection without manual rules
• Schema change alerts across pipelines
• Data freshness monitoring

How to get started

1. 1

   Connect your data warehouse

   Integrate with Snowflake, BigQuery, Redshift, or similar. The agent profiles existing tables.

2. 2

   Configure monitoring

   Set which tables and columns to monitor. Define alert channels (Slack, email, PagerDuty).

3. 3

   Respond to alerts

   When anomalies are detected, the agent provides context: what changed, when, and potential root causes.

Recommended tools

Monte Carlo, Great Expectations, Anomalo. See the full list on the AI Data Analyst Agent pillar page.