Description: Alteration in the context of ETL (Extract, Transform, Load) and data anonymization refers to the process of modifying existing data structures or data within a database. This process is crucial for ensuring that sensitive information is not exposed, thus allowing compliance with privacy and data protection regulations. Alteration can include techniques such as data masking, generalization, and perturbation, which transform the original data into formats that preserve analytical utility without compromising privacy. Additionally, alteration can be used to improve data quality by eliminating inconsistencies or errors, facilitating integration into information systems. In an environment where the amount of data generated is immense, alteration becomes an essential tool for organizations seeking to leverage data intelligence while protecting sensitive information about their users. This process is not only relevant for data security but also plays an important role in data analytics, allowing companies to perform analyses without risking violations of individual privacy.
