What process is aimed at organizing data to reduce redundancy and improve data integrity?

Data normalization is the process specifically designed to organize data in a database in such a way that it reduces redundancy and enhances data integrity. This is achieved through a series of principles and methods that involve structuring the data into tables and establishing relationships between them based on rules known as normal forms. By doing so, normalization minimizes the duplication of data, which means that updates, deletions, and insertions can occur without inconsistency or anomalies across the database.

In contrast, data warehousing focuses on the storage and management of vast amounts of data from different sources, facilitating reporting and analysis rather than specifically addressing redundancy or integrity at the design level. Data mining involves analyzing large datasets to discover patterns and trends, which is more about extracting value from data than organizing it effectively. Data cleansing is the process of correcting or removing inaccurate or corrupt data from a dataset, which is important for ensuring the quality of the data but does not inherently organize data to the same extent as normalization does. Therefore, normalization is the critical process that directly targets the reduction of redundancy and the improvement of data integrity.