What does data normalization in Power BI aim to achieve?

Data normalization in Power BI is primarily focused on improving data integrity. This process involves organizing the data within the database to reduce redundancy and dependency, thereby ensuring that data is stored in an efficient and structured manner. By normalizing the data, you minimize the chances of data anomalies, ensuring that updates, deletions, and insertions maintain a consistent state across related datasets.

Improving data integrity is crucial for accurate reporting and analysis. It allows analysts to rely on the quality of the data they are working with, as normalization helps maintain the correctness and consistency of data throughout the various tables and datasets involved. This means that when you create reports or visualizations from normalized data, the results are more trustworthy and can lead to more informed decision-making.

While the other options relate to various aspects of data handling and reporting, they do not accurately capture the main objective of data normalization, which is fundamentally about ensuring the reliability and quality of the data itself. Therefore, focusing on data integrity is essential for effective data analysis and visualization in Power BI.