Google Data Analytics Certification Practice Test 2025 - Complete Exam Prep

The purpose of data transformation is primarily to change the data's format or structure to make it suitable for analysis. This process involves manipulating data into a more usable form, which may include tasks like changing data types, aggregating data, scaling values, or restructuring the organization of the data. This is crucial because raw data often comes from various sources and may not align with the requirements of the analytical tools or methods that will be applied. By transforming the data, analysts ensure that it is consistent, clean, and in the right format for further processing or analysis.

Other possible options, while related to the overall data handling process, do not capture the primary focus of data transformation. Combining datasets refers more to the integration of different data sources but does not necessarily imply changing the format. Visualizing the data relates to the graphical representation of data, which is a separate step that occurs after data has been processed and transformed. Collecting data is the initial phase of the data lifecycle, and it precedes any transformation, focusing on gathering raw information for analysis.