Data cleaning and normalization are essential steps in the data analytics process to ensure that data is accurate, complete, and useful for analysis. The primary purpose of these steps is to identify and correct anomalies, inconsistencies, and errors, making the data usable for decision-making.

(A) The auditor eliminated duplicate information. ❌

Incorrect. Removing duplicates is one part of data cleaning, but it does not encompass the full process of making data usable.

(B) The auditor organized data to minimize useless information. ❌

Incorrect. While organizing data helps improve efficiency, it does not necessarily involve error detection and correction, which is key to data cleaning.

(C) The auditor made data usable for a specific purpose by ensuring that anomalies were identified and corrected. ✅

Correct. The primary goal of cleaning and normalizing data is to detect and fix anomalies (e.g., missing values, inconsistencies, formatting errors), ensuring that data is reliable for analysis.

IIA GTAG " Data Analytics: Elevating Internal Audit Performance " highlights that correcting data anomalies is a critical step in preparing data for effective use.

(D) The auditor ensured data fields were consistent and that data could be used for a specific purpose. ❌

Incorrect. While consistency in data fields is part of normalization, it does not fully address the broader purpose of identifying and fixing errors.

IIA GTAG – " Data Analytics: Elevating Internal Audit Performance "

IIA Standard 2320 – Analysis and Evaluation

NIST Data Quality Framework – Data Cleaning and Normalization

Analysis of Answer Choices:IIA References:Thus, the correct answer is C, as data cleaning and normalization ensure that anomalies are detected and corrected, making the data usable for a specific purpose