DatumFuse
Run a free data quality check
Get an instant health score and a list of issues to fix, straight from your pasted data.
Open AI Data Auditor →Bad data is expensive precisely because it is invisible until something downstream breaks — a dashboard with wrong totals, an import that fails, a model trained on garbage. By then the cost of tracing it back is high. A quick quality check up front turns those silent problems into a visible checklist.
The DatumFuse Auditor scores your dataset for completeness and uniqueness and surfaces missing values, duplicate rows, format inconsistencies, and schema issues — each with a recommended fix. Paste a sample to get an instant report with no signup, then send the issues it finds straight to the Cleaner or other tools to resolve them.
How it works
- 1
Paste your data
Add a CSV or JSON sample to check.
- 2
AI profiles it
It scores completeness and uniqueness and scans for issues per column.
- 3
Read the report
Get a health score and a prioritized list of what to fix.
- 4
Fix and re-check
Resolve issues with the matching tool, then re-audit to confirm.
Use cases
Before you analyze
Verify a dataset is sound before building a report on top of it.
Inbound file QA
Score every file a partner or system sends you to catch broken exports.
Pipeline monitoring
Re-check a recurring extract to catch upstream regressions early.
Frequently asked questions
- What is a data quality check?
- It measures completeness and uniqueness and flags missing values, duplicates, and schema mismatches so you know what to fix.
- Is it free and is signup required?
- You can run a quality check on pasted data for free with no signup.
- What does the score mean?
- It is a combined measure of how complete and how unique your data is, weighted by the severity of the issues found.
- What do I do with the issues it finds?
- Each issue points to the right next step — for example, send messy fields to the Cleaner or duplicates to be deduped — then re-audit to confirm.
Powered by the AI Data Auditor
Run a free AI data quality audit. Detect missing values, duplicates, schema mismatches, and integrity issues with clear scores and recommended fixes.
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