An analyst wants to inform other users of changes made to a data set during the data cleansing process. Which action should this analyst perform?

Data manipulation refers to the process of adjusting, altering, or transforming data to meet specific requirements. While it is a part of the data cleansing process, it does not inherently inform users of the changes made. Without documentation, users may remain unaware of what data manipulations were performed, leading to potential misunderstandings or misuse of the data.

Automating the cleansing process improves efficiency and accuracy in handling data. However, automation alone does not provide insights into the specific changes made to the data set. Users still require documentation to understand the modifications and the context behind them, as automation focuses primarily on the execution of tasks rather than communication of results.

Verification involves checking the accuracy and quality of the cleaned data, ensuring it meets the required standards. While this step is important, it does not communicate the changes made during the cleansing process to users. Verification focuses on the correctness of the data rather than documenting the modifications, which is crucial for user awareness and understanding.