In the data analytics process, the first step after obtaining data is to ensure its completeness and accuracy. If data is incomplete or inaccurate, the entire analysis process is compromised, leading to unreliable results.

Let’s analyze each option:

Option A: Verify completeness and accuracy.

Correct.

Completeness ensures that all necessary data points are included, preventing missing or incomplete datasets.

Accuracy ensures that data values are correct and free from errors, ensuring reliability for analysis.

IIA Reference: Internal auditors use data validation techniques to confirm completeness and accuracy before analysis. (IIA GTAG: Auditing with Data Analytics)

Option B: Verify existence and accuracy.

Incorrect. While existence is important (ensuring data is valid and not fabricated), completeness is more critical in the initial step to avoid missing data.

Option C: Verify completeness and integrity.

Incorrect. Integrity refers to the reliability and consistency of data across systems, which is a later step after verifying completeness and accuracy.

Option D: Verify existence and completeness.

Incorrect. Existence is less relevant at the initial stage than accuracy, which is crucial for avoiding misinterpretation of results.

Thus, the verified answer is A. Verify completeness and accuracy.