Governata lets you define validation rules, link them to data sources, run the checks, and calculate the results, with passing and failing record counts shown for each rule.
Enforceable rules
Linked to sources
Automated checking
Passing and failing records
A policy stating a field is mandatory does nothing unless a check exists to surface the rows that broke it.
An enforced rule is the difference between knowing what your data should be and knowing what it is.
Writing a clear validation rule that can be run against the data, not a general description of what it should be.
Tying each rule to the table and database it is applied to.
Executing the rule against the data and checking the records within its scope.
Showing total records checked and how many passed and failed, making the size of the violation clear.
Calculating the compliance rate automatically, so quality becomes comparable over time.
Reviewing and modifying a rule as requirements change or where results prove imprecise.
Writing the validation rule and setting the conditions it checks against the data.
Tying the rule to the table and database it will be applied to.
Executing the rule against the records and calculating how many passed and failed.
Analyzing the result and adjusting the rule where needed to improve its precision.
Rules running continuously against the data, instead of manual checking that repeats and never ends.
Written quality policies turned into enforced checks whose compliance can be measured.
Rules defined inside the software instead of scattered queries written and maintained by hand.
Knowing how far the data complies with its rules before relying on it in any analysis.
The name identifying the rule and indicating what it validates.
The table the rule is applied to within the data source.
The data source the checked table belongs to.
Whether the rule is active and running against the data or currently stopped.
It is a defined condition applied to data to verify its validity, such as a field being mandatory or a value conforming to a format or range.
A specification describes what the data should be. A rule actually checks the data and produces a figure showing how far it conforms.
It is the share of records that satisfied the rule out of all records checked, and it is the most direct indicator of the rule’s effect.
Yes. Rules are adjusted as requirements change or where results prove imprecise and need tuning.
Because a rule without a source remains text. Linking it to a table and database is what makes it executable and measurable.
They are logged as a quality issue and assigned to an owner for remediation, then rechecked to verify the fix.
Validation rules linked to their sources, executed with results and compliance rates calculated automatically.