Inputting Validation Options

Input validation is essential for preventing data entry errors and ensuring that your data conforms to business rules and constraints. The primary types of input validation we discuss include:

• Validation Against Master Data
• Range Validation
• Format Validation
• Relation-Driven Validation
• Erroneous Data Tolerance

Validation Against Master Data ensures that the data entered aligns with existing master data records. This type of validation is crucial for maintaining data accuracy and consistency.

• Master Data Association: Ensures that entered data corresponds to predefined master data values.
• Consistency Check: Validates against existing records to prevent discrepancies.
• Practical Example: When entering a customer ID, the system checks if the ID exists in the master customer data to ensure it's valid before accepting the entry.

Data validation through master data is one of the most important and most powerful data validation methods. In this video you will learn how to validate your date set based on the predefined master data values.

Range Validation restricts input data to fall within a specific numerical range. It ensures that values are within acceptable limits.​

• Numerical Limits: Define a range of acceptable values for fields.
• Error Prevention: Prevents entry of outlier values that could disrupt data accuracy.
• Practical Example: For an amount field, you can specify a range from -5,000 to 5,000. It ensures that any entered amount falls within this predefined range, preventing entry of unrealistic or erroneous amounts.

If you want to make sure that certain key figure field can’t contains values grater then 1000, this video will teach you how to set-up this condition in the Universal Model.

Format Validation ensures that the data entered adheres to a specified pattern or format. It is instrumental in enforcing consistency across similar data types.​

• Pattern Enforcement: Defines a format that data must follow.
• Consistent Data Entry: Ensures uniformity in data representation.
• Practical Example: For company codes, you might enforce a format where the code must start with CO followed by a number between 001 and 999. It ensures that all company codes are uniformly formatted and easily recognizable.

Universal Model can also read different assert formats codes in order to validate certain data.

Relation-Driven Validation employs relationships between fields to validate data entries. This type of validation ensures logical consistency between related fields.

• Logical Consistency: Validates data based on relationships between fields.
• Rule Enforcement: Enforces business rules and logical constraints.
• Practical Example: When entering order details, the delivery date must be validated to ensure it is after the order date. This logical rule ensures that deliveries cannot occur before orders are placed, maintaining data integrity.

Universal Model can also read different assert formats codes in order to validate certain data.

Input validation via relation enables us to validate our input data set based on the reference values stored in another table. This video will explain how to achieve this in the Universal Model.

Erroneous Data Tolerance allows for handling data entry errors by defining a tolerance level. It is useful when uploading large datasets where some errors are permissible for later correction.

• Error Management: Defines a tolerance level for acceptable data entry errors.
• Batch Upload Facilitation: Facilitates large dataset uploads by allowing some erroneous records to be uploaded for later correction.