What should be done to ensure that the data model is designed for optimal visualization performance?

To achieve optimal visualization performance in a data model, utilizing a star schema through denormalization of tables is a preferred approach. A star schema organizes data into fact tables and dimension tables, simplifying the relationships between them. The central fact table contains quantitative data for analysis, while the surrounding dimension tables hold descriptive attributes related to the facts.

Denormalizing tables reduces the complexity of queries, as fewer joins are needed to access related data. This design enhances performance during data retrieval, which is crucial when creating visualizations, as it allows for quicker response times and smoother interaction with the reports.

In contrast, raw data tables can lead to inefficient processing and complexities in relationships. Keeping dimensions normalized may provide some advantages in certain scenarios but can complicate the data model and lead to slower performance when multiple joins are required to access relevant data for visualizations. Utilizing many small tables could also result in excessive joins, further degrading performance.

Therefore, adopting a star schema is an effective strategy that balances the need for data integrity with the performance requirements of data retrieval for visualization.