The auditor likely omitted the data normalization step, which is crucial when integrating multiple datasets from different sources (e.g., human resources (HR) and payroll). Without normalization, inconsistencies in formatting, naming conventions, or unique identifiers (e.g., employee ID vs. full name) can result in incorrect mismatches.

Standardization of Data Formats:

Employee names or IDs may be stored differently across systems (e.g., "John A. Doe" in HR vs. "Doe, John" in payroll).

Normalization ensures uniform formatting to enable accurate comparisons.

Removal of Duplicates & Inconsistencies:

Employee records could have multiple variations due to typos, abbreviations, or missing fields.

Proper cleaning and transformation of data ensures better accuracy.

Use of Unique Identifiers:

Instead of matching by name, the auditor should have used a unique identifier (e.g., Employee ID), which remains constant across systems.

A. Data analysis (Incorrect)

Reason: The auditor did attempt data analysis (matching employee records) but without proper preparation (normalization), the results were flawed.

B. Data diagnostics (Incorrect)

Reason: Data diagnostics refers to evaluating data quality issues, but it does not involve transforming data to a common format, which was the missing step.

C. Data velocity (Incorrect)

Reason: Data velocity relates to the speed at which data is processed, which is not relevant to the issue of incorrect matching.

IIA Global Technology Audit Guide (GTAG) 16: Data Analysis Technologies – Covers data quality, normalization, and audit data preparation.

IIA GTAG 3: Continuous Auditing – Discusses the importance of accurate data extraction and transformation.

IIA Standard 2320 – Analysis and Evaluation – Ensures appropriate data validation before concluding audit findings.

Why is Data Normalization Important?Analysis of Incorrect Answers:IIA References:Thus, the correct answer is D. Data normalization.