Which type of filter is generally better for improving dashboard performance?

The global filter is generally considered the most effective type for improving dashboard performance because it applies to the entire dashboard rather than specific elements or widgets. By filtering data at a global level, it minimizes the amount of data that needs to be loaded for every individual element, resulting in faster rendering times and smoother interactions. This is particularly useful in scenarios where multiple visualizations need to respond to the same filter criteria, as it ensures consistency across the dashboard without the overhead of processing the filter multiple times for each element.

In contrast, element-level filters and individual widget filters operate on a more granular scale, which can lead to repeated data processing and inefficiencies, especially when the same data needs to be filtered multiple times for different components. Dynamic filters, while useful for providing interactivity, may not always lead to improved performance because they depend on user actions to change the filter context, potentially resulting in a delay as the dashboard recalculates for each change. Therefore, the global filter stands out as the most effective choice for enhancing overall dashboard performance.