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
Trust the numbers
Catch bad data before your board does
Every refresh profiles your columns, runs your checks, and rolls four dimensions into one score — with anomalies surfaced automatically.
A closer look
Inside Data Quality
Column profiling on every scan
A quality scan reads each column and computes a full statistical portrait — completeness, distinct and duplicate counts, min/max, averages, and top-value distributions — powering the profile and overview tabs.
- Per-column missing, distinct, duplicate, and quality-percent metrics
- Min/max, averages, and top-10 value distributions
- Inferred semantic types (email, phone, URL, number, date)
14 built-in test types
Assert what should be true with rules that run as SQL against your source and return PASS, WARN, or FAIL with exact row counts. Run one, run all, schedule on a cron, or fire on every sync.
- Null, unique, value-range, row-count, email, regex, allowed-values
- Date-range, no-future-date, foreign-key, duplicate, and custom SQL
- Per-test severity from info to critical, with last-run status
AI scan and quality scores
An AI scan analyzes your models and surfaces anomalies and fixes you didn't think to test for, ranked by confidence. A weighted quality score rolls four dimensions into one number per table and workspace.
- Anomaly findings ranked high, medium, or low by confidence
- Score blends completeness, validity, uniqueness, and freshness
- Freshness monitoring flags tables that stopped updating
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.
14 test types
Null checks to custom SQL
4 dimensions
Completeness to freshness
AI scan
Confidence-ranked anomalies
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.