Dashboards you'd stake your quarter on.
Every pipeline ships with a built-in quality engine that scores every column on five dimensions. If a number on your dashboard moves, you can trace it back to the exact row, the exact test, the exact timestamp — and know the moment a number goes stale. No mystery numbers in the board deck.
Data Quality
A traffic light you can read in a meeting.
Every score rolls up into one of three colours. No dashboards full of knobs. No "what does 73 mean?"
Good
Ship it. The numbers on this column can be trusted for decisions.
Warning
Drill in. Something's off — investigate before the next board pack.
Critical
Stop the line. Don't publish dashboards against this column until it's fixed.
How the score is computed
Each column is graded on five dimensions, then rolled up into one number.
- Completeness. Of all the cells that should have a value, how many do?
- Uniqueness. Primary keys that are genuinely unique. No silent duplicates.
- Validity. Audit pass rate across the automatically generated test suite.
- Freshness. Age of the latest timestamp vs. your sync cadence.
- Referential Integrity. Foreign keys that actually resolve to parent rows.
Every column. Every test. One screen.
When something breaks, you know where. Expand any source to see the exact column, the exact failing test, and the score that moved the needle.
Data Quality
Column-level quality assessment across your data sources.
Every red light comes with a way to fix it.
Detecting a gap is the easy half. The quality engine ships with three remediation paths so the same person who sees a red badge can close it — no tickets, no engineering backlog, no waiting for the next sprint. Fix, re-run, watch the score recover.
Upload a spreadsheet
Patch historical rows with an ad-hoc CSV or Excel export. Merge on a primary key, no engineer required.
Connect a database
Link a legacy MySQL, Postgres, or SQL Server as a side source. We join the missing columns straight into staging.
Override the mapping
When two sources disagree, pick which one the engine should trust. One dropdown, no schema migration.
Scoring runs wherever your data lives.
Whether your warehouse is the integrated data lake, Snowflake, Databricks, BigQuery, or Microsoft Fabric, the quality engine targets it natively.
Integrated SQL Data Lake 
Tests that write themselves.
You map a connector. We generate the tests. Every primary key gets a not-null and a uniqueness check. Every required field gets a not-null. Every timestamp gets a sanity bound. Every foreign key gets a join audit. No YAML, no hand-rolled SQL, no test-writing Jira tickets.
The test suite scales with your data model — thousands of checks per tenant, generated and re-generated automatically every time you add a source.
The test suite scales with your data model — thousands of checks per tenant, generated and re-generated automatically every time you add a source.
Fresh or it doesn't count.
Every other quality tool grades your data on what's in the warehouse. We also grade when it got there. A "green" revenue number from Tuesday's batch is worse than a missing one — one tells you nothing, the other tells you a lie.
For every staging table, the engine stores the sync cadence, computes the gap between the latest timestamp and now, and fails the freshness test the moment the gap exceeds the SLA. Failed freshness flips the whole source to Warning or Critical — regardless of how clean the rows are.
Ready to stop second-guessing your dashboards?
Book a demo and we'll score your own data in under a day.