Which term best describes the alterations made to data in order for it to support analytics?

The term "Data Transformation" is most accurately used to refer to the alterations made to data to make it suitable for analysis. Data transformation encompasses a range of processes including normalization, scaling, encoding, and other modifications that structure the raw data into a format that facilitates accurate and efficient analysis. By transforming data, it becomes coherent and standardized, allowing for improved clarity and utility in analytical models.

Data wrangling, while related, more broadly encompasses the entire process of cleaning and preparing data, which includes but is not limited to transformation. It often refers to the various activities involved in getting raw data ready for analysis.

Data aggregation involves combining multiple data points into a summary form, which can provide insights at a higher level but does not necessarily imply altering the existing flat structure of the data.

Data filtering pertains to reducing data by removing certain elements based on specified criteria, which can be essential for analysis but does not encompass the full range of changes and restructuring associated with data transformation.

Thus, the concept of data transformation specifically addresses the actions taken to manipulate data for analytical purposes, making it the most appropriate choice in this context.