Adatlékkelésellenrzések

Adatlékkelésellenrzések, a Hungarian term, translates to data integrity checks or data validation in English. These are processes and techniques employed to ensure the accuracy, completeness, consistency, and reliability of data throughout its lifecycle. The primary goal is to prevent, detect, and correct errors in data that could lead to flawed analysis, incorrect decisions, or system malfunctions.

There are various types of data integrity checks. These can include structural checks, which verify that data conforms to predefined formats and schemas, such as checking data types, lengths, and relationships between tables. Content checks focus on the accuracy of the data itself, for instance, verifying if values fall within acceptable ranges, if dates are valid, or if codes correspond to established lists. Referential integrity checks ensure that relationships between different data elements or tables are maintained correctly, preventing orphaned records. Finally, uniqueness checks confirm that specific data fields contain distinct values where required, such as primary keys.

Implementing data integrity checks is crucial in many fields, including database management, software development, scientific research, and financial reporting. Automated checks are often integrated into data entry forms, database constraints, ETL (Extract, Transform, Load) processes, and application logic. Manual reviews and audits can also play a role, especially for complex or critical data sets. By rigorously applying data integrity checks, organizations can build trust in their data assets and ensure the integrity of their operations and outputs.