IS 감사원은 조직의 비즈니스 인텔리전스 인프라를 검토하고 있습니다. 조직이 합리적인 수준의 데이터 품질을 달성하도록 돕기 위한 최상의 권장 사항은 다음과 같습니다.
정답: D
This is because analyzing the data against predefined specifications is a method of data quality assessment that can help the organization achieve a reasonable level of data quality. Data quality assessment is the process of measuring and evaluating the accuracy, completeness, consistency, timeliness, validity, and usability of the data. Predefined specifications are the criteria or standards that define the expected or desired quality of the data. By comparing the actual data with the predefined specifications, the organization can identify and quantify any gaps, errors, or deviations in the data quality, and take corrective actions accordingly12.
Reviewing data against data classification standards (A) is not the best answer, because it is not a method of data quality assessment, but rather a method of data security management. Data classification standards are the rules or guidelines that define the level of sensitivity and confidentiality of the data, and determine the appropriate security and access controls for the data. For example, data can be classified into public, internal, confidential, or restricted categories. Reviewing data against data classification standards can help the organization protect the data from unauthorized or inappropriate use or disclosure, but it does not directly improve the data quality3.
Outsourcing data cleansing to skilled service providers (B) is not the best answer, because it is not a recommendation to help the organization achieve a reasonable level of data quality, but rather a decision to delegate or transfer the responsibility of data quality management to external parties. Data cleansing is the process of detecting and correcting any errors, inconsistencies, or anomalies in the data. Skilled service providers are third-party vendors or contractors that have the expertise and resources to perform data cleansing tasks. Outsourcing data cleansing to skilled service providers may have some benefits, such as cost savings, efficiency, or scalability, but it also has some risks, such as loss of control, dependency, or liability4.
Consolidating data stored across separate databases into a warehouse is not the best answer, because it is not a method of data quality assessment, but rather a method of data integration and storage. Data integration is the process of combining and transforming data from different sources and formats into a unified and consistent view. Data warehouse is a centralized repository that stores integrated and historical data for analytical purposes. Consolidating data stored across separate databases into a warehouse can help the organization improve the availability and accessibility of the data, but it does not necessarily improve the data quality.
