What are the stages involved in data transformation?

In the context of data transformation within Workday Prism, the stages involved often encompass various techniques that modify and prepare data for analysis. The correct choice highlights essential operations typically required in the data transformation process.

Filtering plays a crucial role as it allows users to remove unnecessary data points from a dataset, making subsequent steps more streamlined and efficient. Grouping data helps in organizing it into meaningful structures that facilitate analysis, typically by attributes or measures relevant for reporting and decision-making. Joining data involves combining multiple datasets to create a comprehensive view, ensuring that different pieces of information that correlate can be analyzed together. Finally, managing fields is critical for ensuring that data is in the correct format and structure for analysis, allowing users to customize the data as per their analytical needs.

These stages are fundamental in preparing data for effective analysis, ensuring that the resulting dataset is not only clean and relevant but also organized and insightful for further processes. This understanding is essential for anyone working within Workday Prism as it directly impacts the effectiveness of data-driven decision-making.