During the preparation phase of a data analysis engagement, which step involves processing the data?

The step that involves processing the data is cleansing and normalizing the data. This is a critical part of the preparation phase in a data analysis engagement, as it ensures that the data is accurate and consistent, which is essential for effective analysis. Cleansing involves identifying and correcting inaccuracies or inconsistencies in the data, such as removing duplicates or filling in missing values. Normalizing refers to standardizing data formats and structures, making it easier to analyze and compare across different datasets.

This processing step is foundational because the integrity of the data directly influences the quality of the analysis and the insights that can be drawn from it. Without proper cleansing and normalization, subsequent analyses could lead to misleading conclusions, which is why it is vital to address this early in the engagement.

In contrast, determining whether predication exists focuses on establishing sufficient reason to proceed with an investigation, building a profile of potential frauds entails identifying characteristics that may indicate fraudulent behavior, and defining examination objectives sets the goals and scope for the analysis. While all these steps are important in their own right, they do not specifically involve processing the data in the way that cleansing and normalizing do.