Catch bad data before your CEO does
Automated profiling, quality checks, and anomaly alerts across your connected data — so dashboards stay trustworthy and surprises stay out of board meetings.
- Automated profiling on every dataset
- Freshness, completeness, and accuracy checks
- Alerts before bad data reaches a dashboard
What you can do with Data Quality
Data profiling
Automatic column-level statistics: distributions, null rates, uniqueness, and type conformance.
Quality checks
Declare rules — not null, in range, matches pattern, references exist — and run them on every refresh.
Freshness monitoring
Know when a source stops updating before stale numbers mislead a decision.
Anomaly detection
Statistical checks flag sudden volume drops, spikes, and schema drift automatically.
Quality scores
A single score per dataset — freshness, completeness, accuracy — trending over time.
Alert routing
Route failures to the owning team by email or webhook, with the failing rows attached.
How it works
Profile your data
Baseline statistics computed automatically
Declare checks
Rules for what good data looks like
Run on refresh
Every sync validates before publishing
Alert & fix
Owners get notified with failing samples
Frequently asked questions
Checks run inside DataSquares as part of refresh and pipeline runs — no separate infrastructure to deploy.