Which approach is beneficial for enhancing the quality of data during merging?

Using a common variable for combination is beneficial for enhancing the quality of data during merging because it ensures that the datasets being integrated align correctly with one another. When datasets share a common variable—such as an ID field, customer name, or timestamp—it facilitates accurate matching of records from each dataset, reducing the likelihood of errors that can arise when unrelated data is combined.

This approach allows for retaining relevant information from each dataset while ensuring that the data aligns meaningfully. Properly leveraging a common variable also aids in maintaining data integrity and consistency, which are crucial components in creating high-quality datasets for analysis.

In contrast, deleting duplicate rows, while a valid data cleaning process, does not directly enhance the merging process itself. Flattening all datasets into one may lead to a loss of structure and context, making the data harder to analyze or interpret. Avoiding the use of parameters might also lead to poor merging practices, as it could create ambiguity about how datasets relate to each other.