What should be done if multiple members need to be selected for each dimension?

Using member filters is the correct approach when multiple members need to be selected for each dimension. Member filters allow users to efficiently specify and apply criteria to include or exclude certain members based on specific attributes or conditions. This functionality streamlines the selection process, particularly when dealing with large datasets or multiple members that meet certain criteria.

By utilizing member filters, the user can quickly focus on the members that are relevant to their analysis without needing to select each member individually. This also reduces the risk of errors that might occur when manually selecting numerous members one by one. Furthermore, member filters enhance overall data management and reporting efficiency, making it easier to analyze trends and generate insights.

The other strategies suggested do not effectively address the requirement to select multiple members across dimensions. For instance, selecting each member individually could be time-consuming and impractical, especially in complex datasets. Consolidating members into a single parent or aggregating figures may obscure important details and prevent granular analysis, which is counterproductive when specific insights are sought from multiple members.