The difference between verification and validation data testing

Verification and validation are two distinct processes in software and data testing, each with its own purpose and methods. Let’s explore the differences between verification and validation in the context of data testing, and I’ll provide examples as well.

1. Verification

Verification is the process of ensuring that a system or component complies with specified requirements. In data testing, this typically involves checking whether the data transformation and processing logic adheres to the defined data model, schema, and business rules. Verification ensures that the data is correctly built according to the intended design and requirements.

Example of Data Verification

Suppose you have a DBT model that transforms raw sales data into a format suitable for reporting. You can perform verification by checking whether the transformed data adheres to the specified schema and constraints. Here’s an example using SQL in DBT:

In this example, you’re verifying that the transformed sales data follows the specified schema, doesn’t have missing values, and adheres to a business rule regarding the sales date.

2. Validation

Validation is the process of evaluating a system or component during or at the end of the development process to determine whether it satisfies the intended use or purpose. In data testing, validation focuses on ensuring that the data produced is accurate and fit for its intended purpose or application. It verifies whether the data meets the user’s needs and expectations.

Example of Data Validation

Continuing with the sales data example, after verifying that the transformed data adheres to the schema and business rules, you can perform validation by checking if it aligns with the expectations of the end-users or stakeholders. Here’s an example:

In this case, you’re validating the transformed data by calculating the total sales for a specific period and confirming that it aligns with what the stakeholders expect. In essence, verification ensures that the data conforms to predefined requirements and standards, while validation focuses on whether the data meets the needs and expectations of the end-users or stakeholders. Both processes are e