See also input validation

Data validation is the process of ensuring that data is accurate, complete, and within acceptable limits before it is entered into a database or used in any kind of processing. This step is crucial to ensure the integrity of the information used in analytics, business decision-making, and any computational models. By applying rules and constraints to the data, organizations can catch potential errors early in the data processing pipeline, ensuring that only reliable information is used downstream.

https://en.wikipedia.org/wiki/Data_validation

There are several methods of performing data validation, including syntax validation, range checking, and consistency checks. Syntax validation ensures that the data conforms to a specific format, such as verifying that a date follows the YYYY-MM-DD format or that a postal code is the correct length. Range checking involves ensuring that data values fall within predefined limits, for example, checking that the value for age is between 0 and 120. Consistency checks examine the relationship between different data values to ensure they align correctly, such as verifying that a customer's birthdate is before the current date.

https://en.wikipedia.org/wiki/Data_validation

Data validation is an essential process in fields like data science, machine learning, and any other domain where data accuracy is critical. Invalid or erroneous data can lead to incorrect conclusions, poor decision-making, and biased models. Automating data validation as much as possible, through scripts or data validation software, can help organizations maintain high-quality datasets. Furthermore, validating data at multiple stages in the pipeline—such as at the time of entry, during transformation, and before analysis—helps prevent issues from snowballing.

https://en.wikipedia.org/wiki/Data_validation

Data Validation is the process of ensuring that data input into a system is accurate, complete, and within acceptable ranges or formats. It is a fundamental aspect of data quality management and plays a critical role in preventing errors, maintaining data integrity, and ensuring that systems operate as intended.

• Accuracy: Ensuring data is accurate prevents incorrect or misleading information from being processed or stored. This is essential for maintaining the reliability of reports, calculations, and analytics.
• Data Integrity: Validation helps maintain the consistency and correctness of data by enforcing rules and constraints, which prevents invalid or corrupt data from entering the system.

• Format Validation: Checks that data conforms to a specified format, such as date formats (e.g., YYYY-MM-DD) or email addresses. This ensures that data entries meet predefined standards and are usable by the system.
• Range Validation: Ensures that numerical data falls within an acceptable range or limits. For example, a system might validate that a user’s age is between 0 and 120 years.
• Consistency Validation: Checks that data is logically consistent across related fields or records. For instance, if an employee’s start date is before their end date, this consistency check ensures the data is logical.

• Rule Definition: Clearly define validation rules for different types of data to ensure uniformity and accuracy. This includes specifying acceptable formats, ranges, and consistency checks.
• Validation Layer: Implement validation both at the input level (e.g., user interfaces) and at the data processing level (e.g., database constraints) to catch errors early and ensure data integrity.
• Feedback and Error Handling: Provide meaningful feedback to users when validation errors occur. This helps users correct mistakes and improves the overall data entry experience.

• https://en.wikipedia.org/wiki/Data_validation
• https://www.techopedia.com/definition/26502/data-validation
• https://owasp.org/www-community/controls/Input_Validation

Snippet from Wikipedia: Data validation

In computing, data validation or input validation is the process of ensuring data has undergone data cleansing to confirm it has data quality, that is, that it is both correct and useful. It uses routines, often called "validation rules", "validation constraints", or "check routines", that check for correctness, meaningfulness, and security of data that are input to the system. The rules may be implemented through the automated facilities of a data dictionary, or by the inclusion of explicit application program validation logic of the computer and its application.

This is distinct from formal verification, which attempts to prove or disprove the correctness of algorithms for implementing a specification or property.

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