What does data normalization typically help with?

Data normalization is a critical process in data management and analysis, primarily aimed at organizing data to minimize redundancy and improve data integrity. By structuring the data in a way that eliminates duplicate entries and ensures that each piece of data is stored in only one place, normalization helps maintain a clean and efficient database.

When data is normalized, it reduces the risk of inconsistencies, where the same data may be entered differently in multiple places. This consistency is essential for accurate analysis and reporting. Normalized data also tends to be more reliable, as it prevents anomalies and discrepancies that might arise from having duplicate or overlapping records.

In the context of the other options, while they touch on different aspects of data management, they do not directly relate to the primary goals of normalization. Making data sets larger pertains to data expansion rather than organization. Enhancing the privacy of data focuses on protecting sensitive information, which is typically achieved through different methods such as encryption or access controls. Improving data visualization techniques involves the representation of data but does not address the issues of redundancy and integrity that normalization tackles. Thus, the core reason why normalization is vital is its role in reducing redundancy and enhancing the overall integrity of the data.