Data comparability can be assessed by asking whether:

Data comparability is essentially about determining whether different datasets can be legitimately used together in a meaningful way. When assessing comparability, it's vital to consider whether the datasets in question can be effectively integrated for analysis. This involves checking for consistency in data formats, structures, and definitions across the datasets that may be combined.

Choosing to investigate whether data should be integrated aligns directly with this assessment, as it emphasizes ensuring that the distinct datasets are compatible enough for analysis. This could involve looking at factors like common categorical variables, consistent units of measurement, and similar data collection methods. Ensuring datasets can be integrated safely is paramount for obtaining reliable insights.

The other options pertain to aspects of data utility but do not directly capture the essence of comparability. For example, predicting modeling and exploratory analyses focus on the capacity for analysis rather than how well datasets can be combined. Similarly, asking whether all databases can be combined safely overlooks the specific criteria and checks necessary to assess their compatibility, moving the focus away from the concept of comparability itself.