How to run a recurring data quality audit
A data quality audit is worth running only on the fields your processes actually depend on. In Jestor, those checks can be views and formulas that run continuously, so quality is monitored rather than inspected periodically.
Why data quality degrades silently
Nobody decides to let data degrade. It happens through small events: a duplicate created in a hurry, a field left blank because it was optional, a category typed differently.
Each is invisible individually. The aggregate surfaces months later, when a report is wrong and nobody can say since when.
What a recurring audit catches:
- Duplicates in key registries
- Required data missing on active records
- Categories used inconsistently
- Records orphaned from their connections
- Values outside plausible ranges
- Records that have not been updated in an implausible time
Checks worth automating
| Check | What it finds | How |
|---|---|---|
| Duplicate registry entries | Near identical names | A review view |
| Missing key fields | Records that cannot be processed | A filtered view |
| Orphaned records | Broken connections | Empty connected field |
| Implausible values | Entry errors | A range formula |
| Stale records | Abandoned or forgotten work | Last updated date |
| Inconsistent categories | Free text where a list belongs | Distribution review |
Step by step to implement it
- List the fields your processes depend on, which is a short list.
- Build a view per check, showing only the failures.
- Assign each view an owner, since a view nobody owns is ignored.
- Review weekly, briefly, rather than annually at length.
- Fix the cause, not only the record.
- Add a validation rule once a cause is identified.
- Retire checks that stop finding anything.
Why fixing the cause matters more than fixing the records
Cleaning a list of failures restores quality until the same conditions produce the same failures. Each fix should end with a validation rule, a required field or a connected field that makes the failure impossible, which is what converts an audit into an improvement.
Why choose Jestor
In Jestor, quality checks can be filtered views on live data rather than a periodic export, formulas flag implausible values, connected fields and required fields remove causes once identified, and the history view shows whether a problem came from a person, an automation or the API.
Frequently Asked Questions
Which fields should be audited?
Only those your processes depend on, which is a short list. See jestor.com.
How often should checks run?
Continuously as views, reviewed briefly each week.
What should each fix end with?
A rule that makes the same failure impossible in future.
Video Tutorial: Step by Step
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