adatellenrzésben

Adatellenőrzésben, often translated as data validation or data verification in English, refers to the process of ensuring that data is accurate, complete, and consistent. This is a crucial step in data management and analysis, as flawed data can lead to incorrect conclusions and poor decision-making.

The core of adatellenőrzésben involves setting up rules and checks to scrutinize incoming or existing data. These checks can range from simple format validations, such as ensuring a date field contains a valid date, to complex business logic checks, like verifying that a customer's age falls within a reasonable range or that a product code exists in the master database.

Common types of data validation include:

* **Format validation:** Checks if data conforms to a predefined format (e.g., email addresses, phone numbers).

* **Type validation:** Ensures data is of the correct data type (e.g., numeric, text, boolean).

* **Range validation:** Verifies if numeric data falls within an acceptable minimum and maximum value.

* **Uniqueness validation:** Checks for duplicate entries where records should be distinct.

* **Completeness validation:** Ensures that all mandatory fields are populated.

* **Consistency validation:** Verifies that related data points do not contradict each other.

Adatellenőrzésben can be implemented at various stages, from data entry points to batch processing and data warehousing. Automated tools and scripts are widely used to perform these checks efficiently, flagging any discrepancies for review and correction. The ultimate goal is to maintain data integrity, which is fundamental for reliable reporting, analysis, and operational processes.