What is one of the key outcomes expected from the Data Preparation stage?

The Data Preparation stage is crucial in the analytics process, as it ensures that the data used for analysis is clean, relevant, and ready for modeling. One of the key outcomes expected from this stage is the removal of irrelevant or duplicate observations. This action helps improve the quality of the dataset by ensuring that the analysis is based on pertinent information, which can significantly affect the accuracy and reliability of the results.

Removing irrelevant observations helps to focus the analysis on the most significant data points, which are important for deriving meaningful insights. Eliminating duplicates also prevents skewed results that may arise from counting the same information multiple times. By addressing these issues during the Data Preparation stage, analysts can create more robust models and conclusions from their datasets.

The other options, while important in various contexts, do not specifically address this foundational outcome of removing unnecessary data points, which is critical for effective data analysis.