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A.1.9 Explain the Role of Data Validation and Data Verification
Data validation and data verification are essential processes in database management to ensure the accuracy, consistency, and reliability of stored data. While the two terms are often used interchangeably, they serve distinct purposes in maintaining data quality.
1. Data Validation
Definition:
Data validation is the process of checking whether the data entered into a database is accurate, complete, and conforms to predefined rules or constraints. It is performed when data is first collected or entered into the system.
Role and Importance:
-
Ensures Data Accuracy:
- Validates that the data entered is correct and within expected limits.
- Example: A user’s age must be a number between 0 and 120.
-
Prevents Errors at Entry:
- Stops invalid data from being stored in the database.
- Example: Rejecting an email address that does not contain an “@” symbol.
-
Enhances System Reliability:
- Ensures only high-quality, meaningful data is stored, improving the database’s effectiveness for decision-making.
Validation Methods:
- Data Type Checks:
- Ensures the data is of the correct type (e.g., numeric, string, date).
- Example: Ensuring a phone number contains only digits.
- Range Checks:
- Confirms data falls within acceptable ranges.
- Example: Validating that the quantity of items purchased is non-negative.
- Format Checks:
- Verifies data matches a specified format.
- Example: Ensuring dates follow the
YYYY-MM-DDformat.
- Mandatory Fields:
- Ensures required fields are not left blank.
- Example: Rejecting a form submission without a name.
2. Data Verification
Definition:
Data verification is the process of ensuring that the data stored in the database matches the original source or expected values. It often takes place after data entry or during data migration.
Role and Importance:
-
Confirms Data Accuracy:
- Ensures that stored data is a true and correct representation of the source data.
- Example: Verifying that a customer’s phone number matches the one provided on their application form.
-
Ensures Data Integrity During Migration:
- Validates that no errors occurred while transferring data between systems.
- Example: Comparing old system records to ensure the same values exist in a new database.
-
Improves Confidence in Data Quality:
- Regular verification builds trust in the data, making it reliable for reporting and decision-making.
Verification Methods:
- Manual Verification:
- A human compares stored data with the source document.
- Example: Cross-checking printed forms with database entries.
- Automated Tools:
- Software compares source and destination data to find discrepancies.
- Example: Using a script to ensure all product IDs in a new system match those in the legacy system.
- Checksums or Hashing:
- Mathematical techniques ensure data integrity during transfers.
- Example: Verifying file integrity after download.
Key Differences Between Validation and Verification
| Aspect | Data Validation | Data Verification |
|---|---|---|
| Purpose | Checks if the data is accurate and conforms to rules. | Ensures data matches its original source or expected values. |
| When Performed | During data entry or collection. | After data is stored or migrated. |
| Focus | Prevents incorrect data from entering the database. | Confirms data accuracy in the database. |
| Example | Validating an email address format before submission. | Verifying that customer records match printed forms. |
Example Scenario: Online Registration System
-
Data Validation:
- During registration, the system checks:
- Email contains an “@” symbol.
- Date of birth is in the correct format (
YYYY-MM-DD). - Password meets strength requirements.
- During registration, the system checks:
-
Data Verification:
- After the user submits the form, the backend verifies:
- The email provided matches the user’s verified email.
- Data migrated between servers during maintenance remains consistent.
- After the user submits the form, the backend verifies:
