When setting up a data map, which options are available for handling unmapped dimensions?

When setting up a data map in the context of Enterprise Planning and Budgeting Cloud, the option that allows for the handling of unmapped dimensions effectively is to map multiple unmapped source members to multiple target members. This strategy provides flexibility when integrating data from various sources, ensuring that all relevant data points are accounted for in the target system.

Mapping multiple source members to multiple target members is particularly beneficial when dealing with complex data sets that may not have a one-to-one correspondence. It allows for the consolidation or distribution of data across different dimensions, accommodating various scenarios like budget allocations, hierarchical structures, or different organizational units. This approach ensures that no data is left behind during the mapping process, thereby enhancing the accuracy and completeness of the consolidated data.

In contrast, the other options either limit the mapping capabilities or create potential gaps in data representation. Mapping a single source member to multiple target members can lead to ambiguity in data attribution. This would complicate reporting and analysis since it wouldn't be clear which source member relates to which target member in context. Likewise, mapping multiple source members to a single target member could result in data overwriting and loss of detail, as the specificity of individual source members is lost. Finally, mapping a single source member to a single target member