What Is Data Validation?

Data validation involves verifying data before using it for analysis, decision-making and other functions. It is an important step that ensures data integrity and quality before use. Quality data can enhance numerous areas of your business operations.

The Importance of Quality Data Validation

Data validation involves various checks and verification methods built into your data collection procedures. This can include dedicated technologies, staff and users who process and use data. Your chosen data validation techniques ensure that the data you collect is accurate, dependable and high-quality.

By verifying this, you ascertain that your team and organization do not utilize imprecise and partial information, preventing errors and challenges down the line. Implementing early data validation as you import data costs less than data cleansing later in your data life cycle.

Implementing Effective Data Validation

There are several data validation types or approaches. These are the common verification factors you can include in your validation:

• Data type: This step regulates the type of data input to ensure it matches the required type or format. If a column or row should include numerical values, this validation step will signal if there is incorrect data, such as text.
• Range or constraint: Verifies whether a field input meets range constraints like character counts and rejects or identifies invalid entries.
• Format or structure: Ensures data types meet formatting requirements set by your organization, such as date formatting.
• Code: Checks that inputs follow code consistencies, as in the case of location or country codes.
• Consistency: Similar to the code check, consistency validation ensures inputs meet cohesive and logical formats. It also aims to create consistency across entries in that field, such as capitalizing all first letters or using consistent punctuation.
• Uniqueness or duplication: This validation type identifies duplicate inputs for fields that should have unique data, such as email addresses or contact details from different users.

You can apply these basic validation techniques through various technologies, systems and processes.

Data Validation Tools and Best Practices

Introducing benchmark systems and solutions for validation can help you maintain and enhance your institutional data validation. Implementation best practices for tool and policy use include:

1. Optimize your data entry processes with manual structural formats or automated entry systems.
2. Introduce data cleansing at all phases of your data’s life to eliminate duplicates, ensure cohesive formatting and minimize errors.
3. Create standard processing adherence criteria or rules for data, including range, format and other elements.
4. Maintain validation rules.
5. Introduce automated and advanced data management and governance technologies that automate validation steps like anomaly detection via AI.
6. Establish methods for handling invalid data, such as manual and automated corrections, user notification or immediate rejection.