Comprehensive and Detailed In-Depth Explanation:
In data analytics, data cleaning involves identifying and correcting errors, inconsistencies, and redundancies in the dataset to ensure accuracy and reliability. By eliminating duplicate or irrelevant data, the internal auditor enhances the quality of the dataset, which is crucial for accurate analysis and risk assessment. This process is a preparatory step before analyzing the data to identify high-risk areas. Normalization (option A) refers to organizing data to reduce redundancy but is more specific to database design. Analyzing data (option B) and reviewing data prior to defining the question (option D) are steps that occur before and after data cleaning, respectively.