Field aliases are used to what type of data?

Field aliases are specifically used to normalize data within Splunk. Normalization refers to the practice of ensuring that data is presented in a consistent manner, which facilitates easier analysis and comparison. By creating field aliases, users can refer to the same underlying data using different names or labels. This is particularly useful in situations where different data sources might have their own naming conventions for similar fields, allowing users to unify those field names for reporting and analysis purposes.

For instance, if one dataset uses "src" to denote source IP addresses while another uses "source_ip," creating a field alias allows a user to refer to both fields using a single name for queries and reports. This capability improves data integration from various sources and enhances the overall usability of the Splunk platform for analysis.

In the context of the other options, transforming data typically involves changing its format or structure rather than merely assigning an alternative name, which is the role of aliases. Cleaning data generally refers to the process of addressing inaccuracies or irrelevant data points, while calculating data involves performing mathematical operations on data values. Normalization is specifically aligned with the function of field aliases, making it the most suitable answer.