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VET Information Standard Business Rules
This practical reference for RTOs explains how to interpret the VET Information Standard business rules, distinguishing between different categories of rules and ensuring that all reported data:
- Is structured and formatted in line with the standard.
- Meets required validation rules before submission.
- Is monitored for quality, completeness and consistency over time.
What are business rules?
Business rules in the VET Information Standard ensure that data reported to NCVER is accurate, consistent and adheres to the VET Information Standard before it is accepted into the national VET data system.
There are two types of VET Information Standard business rules:
1. Validation rules
These rules are applied at the point of submission ensuring data meets the format and structure required by the standard. If the submitted data does not meet a validation rule, either an error or warning will be triggered.
An error will require the RTO to correct the data before it can be successfully submitted. Information about how to correct an error by updating information in the VET Information standard is available.
A warning is an indicator that an RTO’s data needs attention to fix an issue prior to submission, but the data will not be prevented from being submitted.
Validation rules ensure the following:
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Mandatory data is provided.
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Data is in the correct length, field type and format*.
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Classification codes and values are correct.
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Related data is consistently reported in combination and makes sense.
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Records are in the right order.
*Additional information – formatting attributes
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Date format: The VET Information Standard uses a format of YYYY-MM-DD for all date specific fields.
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Alphanumeric type: The term alphanumeric as a data type allows for a combination of letters (A-Z), numbers (0-9) and extended 'ASCII characters'. This data type has been used as it allows flexibility of the text entered into a text field. For example - “ABC123-/.".
Some validation rules relate to the structure of the data and how it is provided to NCVER, while other validation rules relate to the accuracy and completeness of the data.
2. Data quality rules
Data quality rules assess an RTO’s data after submission to identify potential quality issues. Unlike validation rules, they:
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Monitor data over time.
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Highlight unusual change patterns from previous submissions, trends, and anomalies.
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Identify missing or incomplete data.
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Use thresholds to assess the level of impact.
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Must be resolved, or have an approved exception, before an RTO can submit its end-of-reporting-period declaration.
Some data quality rules assess individual records, while other rules compare data across a reporting period or historical submissions to identify broader trends. Where data provided exceeds an expected threshold for a particular data quality rule, an error will be triggered and require some correction to submitted data.
Addressing business rule errors
Business rule errors will be presented to RTOs in their student management system (SMS). To correct business rule errors, an RTO will need to update the appropriate data for each relevant record in their SMS and, when ready, validate and submit/re-submit that data.
Understanding business rule structures
Each business rule is presented in the standard with the following information:
Some examples of each business rule type are provided below:
Rule ID |
Business rules |
Corrective actions |
Jurisdiction |
Grouping |
|---|---|---|---|---|
BRDE00001 |
When reporting ABN for a third-party in a program enrolment, ABN must be in a valid format, as prescribed by the ABR. |
The ABN must be in a valid format, as prescribed by the ABR. |
National |
Program enrolment |
BRDG00001 |
Address line 1 description must be provided when training organisation identifier is not on Training.gov.au. |
The head office description (address line 1) was not provided for a training organisation that is not listed on Training.gov.au. |
National |
Training organisation |
BRDQ00061 |
Current calendar year training activity contains too many student records where the student is aged under 15 years old and the At school indicator is showing as a blank value. |
Data submitted in the current calendar year contains a high proportion of student records where the student is aged under 15 years and showing a blank secondary school value. Review all student records where the student is under 15 years of age, and where the At school indicator field is blank and update these accordingly. |
National |
Student |