Which data tier is responsible for reformatting raw data into a usable format?

The correct choice focuses on the integration tier of data processing, specifically addressing the function of reformatting raw data into a usable format. In a data warehousing context, this tier is crucial as it transforms unprocessed data into structured and organized formats that can be easily understood and utilized by end-users or applications.

Standardization not only ensures that data is formatted uniformly but also involves applying specific rules and processes to achieve consistency across datasets. This might include cleaning the data, resolving discrepancies, and ensuring that the data adheres to predefined formats and standards, making it ready for analysis and reporting.

The other tiers mentioned do play significant roles in data handling; however, they focus on different aspects of data processing. The landing tier is primarily concerned with the initial storage of raw data as it enters the data warehouse, while the integration tier focuses on combining various data sources without necessarily transforming the raw data into a consumable format. Common keys refer to identifiers that help in merging datasets rather than the reformatting of data itself. Therefore, the focus on standardization makes it the most appropriate answer regarding the reformatting function.