What does data normalization aim to achieve?

Data normalization is a key process in database design and management aimed primarily at minimizing redundancy and improving data integrity. By organizing data into well-structured tables and eliminating duplicate or irrelevant data entries, normalization ensures that the data maintains consistency across the database. This process involves establishing relationships between data entities and creating a schema that promotes efficient data storage and retrieval.

When data normalization is effectively applied, it reduces the likelihood of anomalies during data insertion, updates, or deletions, which means that the same piece of data appears in only one place within the database. This not only streamlines data management but also enhances the accuracy and reliability of the data, which is crucial for business intelligence applications that depend on quality data for analysis and reporting.

The other options do not align with the primary goals of data normalization. Increasing dataset size is generally not a focus of normalization; rather, it aims to optimize the dataset's structure. Creating artificial intelligence models is a separate domain that requires data preprocessing, which may include normalized data but is not the primary purpose of normalization itself. Lastly, converting all data into unstructured formats is contrary to the objectives of normalization, which emphasizes structured data organization to facilitate efficient querying and data integrity.