Question 54

Data auditing is the appropriate tool for determining the level of completeness of information system data.

Data auditing involves systematically reviewing and verifying the accuracy and completeness of data within an information system. This process helps identify gaps or discrepancies in the data, ensuring that it meets the required standards for reliability and consistency.

A) Data analytics

Data analytics focuses on analyzing data to derive insights, trends, and patterns. While it can highlight issues related to data quality, it does not specifically assess the completeness of data itself, making it less effective for this purpose.

B) Data cleansing

Data cleansing is the process of correcting or removing inaccurate, incomplete, or irrelevant data from a dataset. Although it improves data quality, it is a corrective measure rather than a tool for assessing completeness, which is the primary concern in this scenario.

C) Data governance

Data governance refers to the overall management of data availability, usability, integrity, and security. While it establishes policies and standards for data management, it does not directly evaluate the completeness of data, thus making it unsuitable for the specific need outlined in the question.

D) Data auditing

Data auditing is specifically designed to evaluate the completeness and accuracy of data. This tool systematically checks the data against defined standards and metrics, making it the most appropriate choice for assessing the level of completeness in information system data.

Conclusion

Data auditing stands out as the definitive tool for determining the completeness of information system data due to its focused methodology in verifying data accuracy and comprehensiveness. In contrast, data analytics, cleansing, and governance each serve different functions related to data management but do not provide the specific assessment required for completeness. Thus, data auditing is the optimal choice for this assessment task.